Learning Disabilities Comorbid with Behavioral, Developmental Disorders and Autism

**93**

**Chapter 7**

**Abstract**

**1. Introduction**

of learning difficulties.

ADHD as a Specific Cause for

In the spectrum of possible causes for discrepancy between the capacity to learn

and the level of school achievement, Attention Deficit Hyperactivity Disorder (ADHD) has an important place. The aim of this chapter is to present obtained own results for a group of 200 pupils, mean age 10.5 ± 2.35 years, and both genders, diagnosed as ADHD following DSM-5 criteria. As psychometric tests, Kohs Block Design Test, Achenbach CBCL, ACTeRS, Stroop Color Word Task (SCWT), and Wisconsin Card Sorting Test (WCST) are used. Additionally, Q-EEG recording using Mitsar 19-channel Q-EEG 201 system was performed. Obtained results confirmed the diagnosis of ADHD as well as the presence of serious difficulties in executive system functioning through ERP's component extracted from Q-EEG analysis. In the chapter, results for Q-EEG will be discussed more extensively including subtypes. As a used nonpharmacological therapeutic approach, very posi-

tive outcome of neurofeedback treatment of these children is accentuated.

**Keywords:** learning problems, ADHD, psychometric tests, Q-EEG, neurofeedback

Being nonattentive, nonpatient, and enable to follow the teacher instructions during classes, children with Attention Deficit Hyperactivity Disorder (ADHD) represent a huge problem in the educational process. They have additionally variety

ADHD is a clinically heterogeneous neurobehavioral disorder associated with tremendous financial costs, stress to families, adverse academic, and occupational outcomes. In adult period, this condition is not totally overcome and stay as a huge risk for addiction, dangerous behavior, unsuccessful occupation, high rate of divorces, etc. The diagnostics of this condition change in different periods of time. As "a minimal brain damage" or as "minimal brain dysfunction," the condition was named till the 1994, where for the first time, it was renamed as Hyperkinetic Disorder. Three main symptoms, inattention, impulsivity, and hyperactivity, are listed in both manuals, the International Classification of Diseases (ICD-10) where the disorder is named as "Hyperkinetic Disorder" (HKD) and the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) where it is named as Attention Deficit Hyperactivity Disorder (ADHD). In DSM-IV, the diagnostic includes three different groups of children: the predominantly Hyperactive-Impulsive Type, the predominantly Inattentive Type, and the Combined Type of ADHD. It was approved that this disorder is more frequently found in boys [1] with

the ratio of boys to girls being approximately 4:1 for all three subgroups [2].

Learning Disability

*Nada Pop-Jordanova*

#### **Chapter 7**

## ADHD as a Specific Cause for Learning Disability

*Nada Pop-Jordanova*

#### **Abstract**

In the spectrum of possible causes for discrepancy between the capacity to learn and the level of school achievement, Attention Deficit Hyperactivity Disorder (ADHD) has an important place. The aim of this chapter is to present obtained own results for a group of 200 pupils, mean age 10.5 ± 2.35 years, and both genders, diagnosed as ADHD following DSM-5 criteria. As psychometric tests, Kohs Block Design Test, Achenbach CBCL, ACTeRS, Stroop Color Word Task (SCWT), and Wisconsin Card Sorting Test (WCST) are used. Additionally, Q-EEG recording using Mitsar 19-channel Q-EEG 201 system was performed. Obtained results confirmed the diagnosis of ADHD as well as the presence of serious difficulties in executive system functioning through ERP's component extracted from Q-EEG analysis. In the chapter, results for Q-EEG will be discussed more extensively including subtypes. As a used nonpharmacological therapeutic approach, very positive outcome of neurofeedback treatment of these children is accentuated.

**Keywords:** learning problems, ADHD, psychometric tests, Q-EEG, neurofeedback

#### **1. Introduction**

Being nonattentive, nonpatient, and enable to follow the teacher instructions during classes, children with Attention Deficit Hyperactivity Disorder (ADHD) represent a huge problem in the educational process. They have additionally variety of learning difficulties.

ADHD is a clinically heterogeneous neurobehavioral disorder associated with tremendous financial costs, stress to families, adverse academic, and occupational outcomes. In adult period, this condition is not totally overcome and stay as a huge risk for addiction, dangerous behavior, unsuccessful occupation, high rate of divorces, etc.

The diagnostics of this condition change in different periods of time. As "a minimal brain damage" or as "minimal brain dysfunction," the condition was named till the 1994, where for the first time, it was renamed as Hyperkinetic Disorder. Three main symptoms, inattention, impulsivity, and hyperactivity, are listed in both manuals, the International Classification of Diseases (ICD-10) where the disorder is named as "Hyperkinetic Disorder" (HKD) and the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) where it is named as Attention Deficit Hyperactivity Disorder (ADHD). In DSM-IV, the diagnostic includes three different groups of children: the predominantly Hyperactive-Impulsive Type, the predominantly Inattentive Type, and the Combined Type of ADHD. It was approved that this disorder is more frequently found in boys [1] with the ratio of boys to girls being approximately 4:1 for all three subgroups [2].

In May 2012, American Psychological Association was revising the Fourth Edition of the Diagnostic and Statistical Manual of Mental Disorders, which included some changes in the section on specific learning disabilities. Consequently, DSM-5 considers Specific Learning Disability (SLD) as a type of Neurodevelopmental Disorder that delays the ability to learn or use specific academic skills (e.g., reading, writing, or arithmetic). In this context, SLD is a clinical diagnosis that is not necessarily synonymous with "learning disabilities" used mainly within the education system. SLD characterizes the specific manifestations of learning difficulties at the time of assessment in three major academic domains, namely reading, writing, and mathematics. The group of entities named as "Other Neurodevelopmental Disorders" includes Intellectual Disability, Autism Spectrum Disorder, Attention-Deficit/Hyperactivity Disorder, and Communication Disorders and Motor Disorders. Consequently, whatever criteria for diagnostics are followed, ADHD stays as a huge cause for learning problems.

The differences in diagnostic criteria following different manuals have influenced to the different prevalence rates. As a result, HKD is estimated to be present in approximately 0.5% of children, whereas ADHD has been reported in between 5 and 11.4% of the population [3]. The differences in diagnostics have important implications for both, diagnosis and treatment, because depending on which criteria are used, a child may or may not be considered to have a clinical disorder, which subsequently will influence on the decision about his/her involvement in the school process as well as the need of some treatment.

Many researchers mentioned that ADHD in reality represents a continuum from normal to abnormal behavior. Especially, behavioral studies of children with a predominance of inattentive type have found these children to have some specific problems. For example, inattentive children are less impulsive and less manifest conduct problems than hyperactive children. By contrast, they are more anxious, socially withdrawn, and shy and have more internalizing symptoms. Additionally, they present more frequently academic underachievement and learning problems. Inattentive children are easily confused, stare frequently, often daydream, and they are lethargic, hypoactive, and passive, which are not common in hyperactive children. More specifically, in inattentive children, it was approved deficits in speed of information processing and in focused or selective attention, whereas in the combined type of ADHD, the problem of sustained attention (persistence) and distractibility is more characteristic. These findings suggested that maybe inattentive children should be treated as special group of disorder and not be considered only as a form of ADHD [4].

Although genetic markers in the identification of children with ADHD were not jet found, it was proven that dopamine-related genes are involved in the pathogenesis (such as D1, D2, and D4) [5, 6]. Some form of heredity is additionally confirmed with the fact that this condition could be present in the same family members, especially in twins [7]. In a few recent findings, it was showed that attention deficit hyperactivity disorder (ADHD) shares similar genetic roots and brain structure with autism and obsessive-compulsive disorder (OCD). The impulsivity is characteristic behavior in all three conditions. Additionally, the brain architecture in these conditions presents abnormal findings especially in the structure of the corpus callosum, together with widespread disruptions in white matter. However, children with OCD present fewer structural alterations in comparison with those with autism or ADHD. It is the possible reason that children with autism as well as ADHD manifest earlier specific symptoms in comparison with OCD, which could have a start even in adolescence. Some rare genetic variants associated with autism and schizophrenia also increase a person's chance of having attention deficit hyperactivity disorder (ADHD) [8, 9].

**95**

frontal lobes [14].

*ADHD as a Specific Cause for Learning Disability DOI: http://dx.doi.org/10.5772/intechopen.91272*

somal regions that need to be further investigated.

biological marker of genetic risk for ADHD [11].

inhibition, and action monitoring.

Performing genetic analysis, eight copy number variants (CNVs) are identified, which are more common in people with ADHD than in those without this problem. These same CNVs are also implicated in autism and schizophrenia. In this context, the new hypothesis arises that autism, schizophrenia, and ADHD could have similar biological underpinnings [10]. However, findings do not approve susceptibility genes of larger effect for ADHD, but they can identify genes of smaller effect. Whole genome linkage studies have provided some interesting results for chromo-

The complexity of the ADHD phenotype combined with some genetic findings suggests that identifying endophenotype may be a useful strategy for exact diagnosis. An endophenotype, i.e., intermediate phenotype, is defined as a quantitative biological trait, which is heritable, is reliable in reflecting the function of a discrete biological system, and is presumed to be more closely related to the genetic cause of the disease than the clinical phenotype. The integration of these two approaches (endophenotype and genetic variants) will possibly yield to more definitive results. In this context, increased theta power in EEG record is supposed to be a candidate

In order to find possible neurologic basis for ADHD, many imaging techniques

As was mentioned before, endophenotype is becoming an important concept in the study of ADHD. The endophenotype in psychiatry can be categorized as anatomical, developmental, electrophysiological, metabolic, sensory, or psychological/cognitive. In this way, endophenotype represents simpler indicator for genetic mechanism than the visible behavioral symptoms. It helps to define subtypes of a particular disorder and can be used as a quantitative trait in genetic analysis of proband and families. In this way, Q-EEG spectrum classification of ADHD population has been developed, defining four main endophenotypes: I subtype where abnormal increase of delta-theta frequency range centrally or centrally frontally is dominant; II subtype where abnormal increase of frontal midline theta rhythm is present; III subtype with an abnormal increase of beta activity frontally; and IV subtype characterized with an excess of alpha activities at posterior, central, or

Still, the complexity of ADHD influences on the underdiagnoses or misdiagnosis of this condition in many school children. Contrary, some hyper diagnostics are also possible. For example, in my research, many gifted children obtained the diagnosis as ADHD because the usual school program for them has been boring, and they manifested hyperactive behavior. The misdiagnose could be also the result of many comorbid disorders, which accompanied ADHD such as conduct problems, high general anxiety, depression, speech problems, autism spectrum disorder, or epilepsy. In this situation, the true ADHD could be overlapped by other similar

are used. Positron Emission Tomography confirmed that brain metabolism in children with ADHD is lower in the areas responsible for the attention, social judgment, and movement. It is confirmed also with fMRI, SPECT, or BOLD techniques. However, Q-EEG recording appeared to be more available, inexpensive, and useful indicator of brain metabolic activity. It is confirmed that low metabolic activity in the area that generates the corresponding EEG signals is characterized by increasing the slow activities (delta and theta waves) and decreasing the fast beta activities. Strong evidence for the usefulness of the Q-EEG in the diagnostic assessment of ADHD comes from a study performed by Monastra and his team [12, 13]. Many studies confirmed that the main brain system, which is impaired in ADHD, is the executive system. Two parameters are specific for the executive system: (1) arousal, as a generalized activation of the system and (2) attention/focused activation of the system, associated with working memory, action selection, action

#### *ADHD as a Specific Cause for Learning Disability DOI: http://dx.doi.org/10.5772/intechopen.91272*

*Learning Disabilities - Neurological Bases, Clinical Features and Strategies of Intervention*

In May 2012, American Psychological Association was revising the Fourth Edition of the Diagnostic and Statistical Manual of Mental Disorders, which included some changes in the section on specific learning disabilities. Consequently, DSM-5 considers Specific Learning Disability (SLD) as a type of Neurodevelopmental Disorder that delays the ability to learn or use specific academic skills (e.g., reading, writing, or arithmetic). In this context, SLD is a clinical diagnosis that is not necessarily synonymous with "learning disabilities" used mainly within the education system. SLD characterizes the specific manifestations of learning difficulties at the time of assessment in three major academic domains, namely reading, writing, and mathematics. The group of entities named as "Other Neurodevelopmental Disorders" includes Intellectual Disability, Autism Spectrum Disorder, Attention-Deficit/Hyperactivity Disorder, and Communication Disorders and Motor Disorders. Consequently, whatever criteria for diagnostics are followed,

The differences in diagnostic criteria following different manuals have influenced to the different prevalence rates. As a result, HKD is estimated to be present in approximately 0.5% of children, whereas ADHD has been reported in between 5 and 11.4% of the population [3]. The differences in diagnostics have important implications for both, diagnosis and treatment, because depending on which criteria are used, a child may or may not be considered to have a clinical disorder, which subsequently will influence on the decision about his/her involvement in the school

Many researchers mentioned that ADHD in reality represents a continuum from normal to abnormal behavior. Especially, behavioral studies of children with a predominance of inattentive type have found these children to have some specific problems. For example, inattentive children are less impulsive and less manifest conduct problems than hyperactive children. By contrast, they are more anxious, socially withdrawn, and shy and have more internalizing symptoms. Additionally, they present more frequently academic underachievement and learning problems. Inattentive children are easily confused, stare frequently, often daydream, and they are lethargic, hypoactive, and passive, which are not common in hyperactive children. More specifically, in inattentive children, it was approved deficits in speed of information processing and in focused or selective attention, whereas in the combined type of ADHD, the problem of sustained attention (persistence) and distractibility is more characteristic. These findings suggested that maybe inattentive children should be treated as special group of disorder and not be considered

Although genetic markers in the identification of children with ADHD were not jet found, it was proven that dopamine-related genes are involved in the pathogenesis (such as D1, D2, and D4) [5, 6]. Some form of heredity is additionally confirmed with the fact that this condition could be present in the same family members, especially in twins [7]. In a few recent findings, it was showed that attention deficit hyperactivity disorder (ADHD) shares similar genetic roots and brain structure with autism and obsessive-compulsive disorder (OCD). The impulsivity is characteristic behavior in all three conditions. Additionally, the brain architecture in these conditions presents abnormal findings especially in the structure of the corpus callosum, together with widespread disruptions in white matter. However, children with OCD present fewer structural alterations in comparison with those with autism or ADHD. It is the possible reason that children with autism as well as ADHD manifest earlier specific symptoms in comparison with OCD, which could have a start even in adolescence. Some rare genetic variants associated with autism and schizophrenia also increase a person's chance of having attention deficit

ADHD stays as a huge cause for learning problems.

process as well as the need of some treatment.

only as a form of ADHD [4].

hyperactivity disorder (ADHD) [8, 9].

**94**

Performing genetic analysis, eight copy number variants (CNVs) are identified, which are more common in people with ADHD than in those without this problem. These same CNVs are also implicated in autism and schizophrenia. In this context, the new hypothesis arises that autism, schizophrenia, and ADHD could have similar biological underpinnings [10]. However, findings do not approve susceptibility genes of larger effect for ADHD, but they can identify genes of smaller effect. Whole genome linkage studies have provided some interesting results for chromosomal regions that need to be further investigated.

The complexity of the ADHD phenotype combined with some genetic findings suggests that identifying endophenotype may be a useful strategy for exact diagnosis. An endophenotype, i.e., intermediate phenotype, is defined as a quantitative biological trait, which is heritable, is reliable in reflecting the function of a discrete biological system, and is presumed to be more closely related to the genetic cause of the disease than the clinical phenotype. The integration of these two approaches (endophenotype and genetic variants) will possibly yield to more definitive results. In this context, increased theta power in EEG record is supposed to be a candidate biological marker of genetic risk for ADHD [11].

In order to find possible neurologic basis for ADHD, many imaging techniques are used. Positron Emission Tomography confirmed that brain metabolism in children with ADHD is lower in the areas responsible for the attention, social judgment, and movement. It is confirmed also with fMRI, SPECT, or BOLD techniques. However, Q-EEG recording appeared to be more available, inexpensive, and useful indicator of brain metabolic activity. It is confirmed that low metabolic activity in the area that generates the corresponding EEG signals is characterized by increasing the slow activities (delta and theta waves) and decreasing the fast beta activities. Strong evidence for the usefulness of the Q-EEG in the diagnostic assessment of ADHD comes from a study performed by Monastra and his team [12, 13].

Many studies confirmed that the main brain system, which is impaired in ADHD, is the executive system. Two parameters are specific for the executive system: (1) arousal, as a generalized activation of the system and (2) attention/focused activation of the system, associated with working memory, action selection, action inhibition, and action monitoring.

As was mentioned before, endophenotype is becoming an important concept in the study of ADHD. The endophenotype in psychiatry can be categorized as anatomical, developmental, electrophysiological, metabolic, sensory, or psychological/cognitive. In this way, endophenotype represents simpler indicator for genetic mechanism than the visible behavioral symptoms. It helps to define subtypes of a particular disorder and can be used as a quantitative trait in genetic analysis of proband and families. In this way, Q-EEG spectrum classification of ADHD population has been developed, defining four main endophenotypes: I subtype where abnormal increase of delta-theta frequency range centrally or centrally frontally is dominant; II subtype where abnormal increase of frontal midline theta rhythm is present; III subtype with an abnormal increase of beta activity frontally; and IV subtype characterized with an excess of alpha activities at posterior, central, or frontal lobes [14].

Still, the complexity of ADHD influences on the underdiagnoses or misdiagnosis of this condition in many school children. Contrary, some hyper diagnostics are also possible. For example, in my research, many gifted children obtained the diagnosis as ADHD because the usual school program for them has been boring, and they manifested hyperactive behavior. The misdiagnose could be also the result of many comorbid disorders, which accompanied ADHD such as conduct problems, high general anxiety, depression, speech problems, autism spectrum disorder, or epilepsy. In this situation, the true ADHD could be overlapped by other similar

conditions. From a neuropsychological perspective, comorbidity is considered to be the result of the same brain and cognitive mechanisms involved in attentional and behavioral regulations.

#### **2. Sample and methods**

The aim of this chapter is to present own results for a group of 200 pupils, mean age 10.5 ± 2.35 years, and both genders, diagnosed as ADHD. The majority of examinees are boys (85%) manifesting deficit of attention and concentration together with hyperactivity. In girls, the inattention was the main problem. In all of them, school achievement was less than it was been expected by parents and teachers.

Beside interview and clinical examination, the diagnosis is made by multidisciplinary team (pediatrician, neurologist, and psychologist), according criteria noted in DSM-5 manual. All children were tested with Kohs Block Design Test, the Stroop Color Word Task (SCWT), and Wisconsin Card Sorting Test (WCST) and recorded with Q-EEG. Mothers fulfilled Child Behavior Checklist (CBCL) and ADD-H: Comprehensive Parent Rating Scale (ACTeRS). Obtained results are compared with the results for control group, which is consisted of 50 healthy children matched by age and gender.

The Child Behavior Checklist (CBCL) [15], fulfilled by mothers, contains 113 questions related to depression, social communication or withdrawal, somatic complaints, some schizoid traits, hyperactivity, problems in the psychosexual development, problems in the conduct, problems with the judgment, and level of anxiety. Several forms of this instrument are available depending on the age and gender of the examinees. Symptoms are grouped as internalized and externalized. They reflect a distinction between fearful, inhibited, over controlled behavior and aggressive, antisocial, under controlled behavior. The profile can contribute to a formal diagnosis by showing the degree of child's deviance in behaviors that parents could observe better than clinicians, as well as to help to organize effective therapeutic approach.

ACTeRS [16] is composed of 24 items that measure four separate entities: attention, hyperactivity, social skills, and oppositional behavior. This instrument was developed by researchers at the University of Illinois Institute for Child Behavior and Development. In our research, ACTeRS is fulfilled also by mothers. The instrument shows the level of attention, hyperactivity, social skills, and oppositional behavior presented on percentile scale.

The Kohs Block Design Test [17] is performance test standardized to measure intelligence level for mental ages 3–19. The test is easy and understandable without the need of many verbal explanations. In this context, it is especially valuable for testing those with language and hearing difficulties. The test consists of 16 colored cubes and 17 cards with colored designs, which the subject is invited to replicate. Kohs cubes are used to assess the analytic, synthetic, and logic thinking. Block design test possesses a high degree of correlation and reliability with Binet-Simon IQ test and WISC.

The Stroop Color and Word Test (SCWT) [18] was designed to discover possible organic cause of disorder. It assesses cognitive function and provides diagnostic information on possible brain dysfunction due to organic lesions. The test is quick and easy for administration, and it is based on the facts that reading words are faster than the identification of the presented color. The validity and reliability make it a highly useful instrument.

The Wisconsin Card Sorting Test (WCST) [19] is a neuropsychological test for evaluating the mental flexibility ("set shifting") when the stimulus is changed, e.g., the attention, the working memory, and visual processing.

**97**

girls (**Figure 1**).

*ADHD as a Specific Cause for Learning Disability DOI: http://dx.doi.org/10.5772/intechopen.91272*

mance and measuring the response trial.

quantitative data were obtained using WinEEG software.

the statistical program STATISTICA 10.0 was used.

of them are published in Macedonian journals).

deficit of beta activity (16–20 Hz) (**Figure 3**).

**3. Results and discussion**

2-h duration.

WCST and SCWT were performed using software named Computer Assisted Neuropsychological Diagnostics and Therapy (CANDIT) developed by the Institute of Neuropsychology, Zurich, Switzerland. Each evaluation of the child takes about

The electrophysiological assessment was performed with system Mitsar 19-channel QEEG 201 (Mitsar Ltd). Quantitative EEG (Q-EEG) is a collection of quantitative methods designed to process EEG signals. The Q-EEG includes spectral and wavelet analyses of the EEG signals. The recording is made up of two conditions, eyes closed and eyes open, lasting 5 min each. In the following, data were recorded, while subjects were performing a visual continuous performance task (VCPT) from Psytask program designed by the Human Brain Institute in Saint Petersburg, Russia. This program comprises the Go/No Go task, which performance is associated with a group of psychological operations named as executive, such as detection and recognition of the stimulus, refreshing the working memory, initiation, and/or inhibition of the behavior and monitoring of the action results. The duration of the tasks was approximately 22 min. Separate channels for recording a signal from the button were used for monitoring the accuracy of the test perfor-

Electrodes were placed according to the International 10–20 system using an electrode cap with tin electrodes (Electrocap International Inc.). The input signals referenced to the linked ears were filtered between 0.5 and 50 Hz and digitized at a sampling rate of 250 Hz. The impedance was kept below 5 kΩ for all electrodes. The

The results obtained from the psychometric measuring are presented in a form of scores and compared with test norms, adopted by the age and gender of the examinees, and presented in figures and tables. The results are considered to be statistically significant at a significance level of 0.05. The data from the electrophysiological assessment were transformed with Fourier analysis and compared with a normative database, grouped by their age. For calculations in this research,

(At the beginning of this part, I must confirm that presented results are a compilation of different groups of examinees evaluated at different times, and some

follows: mathematics 3; language 4.5; and nature and society 3 (range 1–5).

drawals, hyperactive, and manifest delinquent behavior (**Figure 2**).

As a start in the evaluation of children with learning problems, testing the intelligence level is of primary importance. Obtained results are very useful for further evaluation especially for exclusion of the intelligence as a factor for presented problems. Evaluation with Kohs Block Design Test showed that ADHD children have intellectual capacities in the norm (IQ = 96 ± 13.15). Mean school notes were as

Profile obtained for ACTeRS, fulfilled by mothers, confirmed abnormal scores in the scales for attention, social adaptation, and oppositional behavior (between 10 and 23 percentiles), which corresponds with the core symptoms of the disorder. Boys and girls presented similar results, although boys are more hyperactive than

CBCL fulfilled also by mothers showed for boy's accentuated anxiety, depression, social withdrawals, and aggressive behavior. Girls are also with social with-

The Q-EEG assessment generally showed dominant theta activity (4–8 Hz) and

*Learning Disabilities - Neurological Bases, Clinical Features and Strategies of Intervention*

behavioral regulations.

age and gender.

behavior presented on percentile scale.

make it a highly useful instrument.

the attention, the working memory, and visual processing.

**2. Sample and methods**

conditions. From a neuropsychological perspective, comorbidity is considered to be the result of the same brain and cognitive mechanisms involved in attentional and

The aim of this chapter is to present own results for a group of 200 pupils, mean age 10.5 ± 2.35 years, and both genders, diagnosed as ADHD. The majority of examinees are boys (85%) manifesting deficit of attention and concentration together with hyperactivity. In girls, the inattention was the main problem. In all of them, school achievement was less than it was been expected by parents and teachers. Beside interview and clinical examination, the diagnosis is made by multidisciplinary team (pediatrician, neurologist, and psychologist), according criteria noted in DSM-5 manual. All children were tested with Kohs Block Design Test, the Stroop Color Word Task (SCWT), and Wisconsin Card Sorting Test (WCST) and recorded with Q-EEG. Mothers fulfilled Child Behavior Checklist (CBCL) and ADD-H: Comprehensive Parent Rating Scale (ACTeRS). Obtained results are compared with the results for control group, which is consisted of 50 healthy children matched by

The Child Behavior Checklist (CBCL) [15], fulfilled by mothers, contains 113 questions related to depression, social communication or withdrawal, somatic complaints, some schizoid traits, hyperactivity, problems in the psychosexual development, problems in the conduct, problems with the judgment, and level of anxiety. Several forms of this instrument are available depending on the age and gender of the examinees. Symptoms are grouped as internalized and externalized. They reflect a distinction between fearful, inhibited, over controlled behavior and aggressive, antisocial, under controlled behavior. The profile can contribute to a formal diagnosis by showing the degree of child's deviance in behaviors that parents could observe better than clinicians, as well as to help to organize effective therapeutic approach. ACTeRS [16] is composed of 24 items that measure four separate entities: attention, hyperactivity, social skills, and oppositional behavior. This instrument was developed by researchers at the University of Illinois Institute for Child Behavior and Development. In our research, ACTeRS is fulfilled also by mothers. The instrument shows the level of attention, hyperactivity, social skills, and oppositional

The Kohs Block Design Test [17] is performance test standardized to measure intelligence level for mental ages 3–19. The test is easy and understandable without the need of many verbal explanations. In this context, it is especially valuable for testing those with language and hearing difficulties. The test consists of 16 colored cubes and 17 cards with colored designs, which the subject is invited to replicate. Kohs cubes are used to assess the analytic, synthetic, and logic thinking. Block design test possesses a high degree of correlation and reliability with Binet-Simon

The Stroop Color and Word Test (SCWT) [18] was designed to discover possible organic cause of disorder. It assesses cognitive function and provides diagnostic information on possible brain dysfunction due to organic lesions. The test is quick and easy for administration, and it is based on the facts that reading words are faster than the identification of the presented color. The validity and reliability

The Wisconsin Card Sorting Test (WCST) [19] is a neuropsychological test for evaluating the mental flexibility ("set shifting") when the stimulus is changed, e.g.,

**96**

IQ test and WISC.

WCST and SCWT were performed using software named Computer Assisted Neuropsychological Diagnostics and Therapy (CANDIT) developed by the Institute of Neuropsychology, Zurich, Switzerland. Each evaluation of the child takes about 2-h duration.

The electrophysiological assessment was performed with system Mitsar 19-channel QEEG 201 (Mitsar Ltd). Quantitative EEG (Q-EEG) is a collection of quantitative methods designed to process EEG signals. The Q-EEG includes spectral and wavelet analyses of the EEG signals. The recording is made up of two conditions, eyes closed and eyes open, lasting 5 min each. In the following, data were recorded, while subjects were performing a visual continuous performance task (VCPT) from Psytask program designed by the Human Brain Institute in Saint Petersburg, Russia. This program comprises the Go/No Go task, which performance is associated with a group of psychological operations named as executive, such as detection and recognition of the stimulus, refreshing the working memory, initiation, and/or inhibition of the behavior and monitoring of the action results. The duration of the tasks was approximately 22 min. Separate channels for recording a signal from the button were used for monitoring the accuracy of the test performance and measuring the response trial.

Electrodes were placed according to the International 10–20 system using an electrode cap with tin electrodes (Electrocap International Inc.). The input signals referenced to the linked ears were filtered between 0.5 and 50 Hz and digitized at a sampling rate of 250 Hz. The impedance was kept below 5 kΩ for all electrodes. The quantitative data were obtained using WinEEG software.

The results obtained from the psychometric measuring are presented in a form of scores and compared with test norms, adopted by the age and gender of the examinees, and presented in figures and tables. The results are considered to be statistically significant at a significance level of 0.05. The data from the electrophysiological assessment were transformed with Fourier analysis and compared with a normative database, grouped by their age. For calculations in this research, the statistical program STATISTICA 10.0 was used.

#### **3. Results and discussion**

(At the beginning of this part, I must confirm that presented results are a compilation of different groups of examinees evaluated at different times, and some of them are published in Macedonian journals).

As a start in the evaluation of children with learning problems, testing the intelligence level is of primary importance. Obtained results are very useful for further evaluation especially for exclusion of the intelligence as a factor for presented problems. Evaluation with Kohs Block Design Test showed that ADHD children have intellectual capacities in the norm (IQ = 96 ± 13.15). Mean school notes were as follows: mathematics 3; language 4.5; and nature and society 3 (range 1–5).

Profile obtained for ACTeRS, fulfilled by mothers, confirmed abnormal scores in the scales for attention, social adaptation, and oppositional behavior (between 10 and 23 percentiles), which corresponds with the core symptoms of the disorder. Boys and girls presented similar results, although boys are more hyperactive than girls (**Figure 1**).

CBCL fulfilled also by mothers showed for boy's accentuated anxiety, depression, social withdrawals, and aggressive behavior. Girls are also with social withdrawals, hyperactive, and manifest delinquent behavior (**Figure 2**).

The Q-EEG assessment generally showed dominant theta activity (4–8 Hz) and deficit of beta activity (16–20 Hz) (**Figure 3**).

**99**

**Figure 2.**

*CBCL profiles for boys and girls.*

*ADHD as a Specific Cause for Learning Disability DOI: http://dx.doi.org/10.5772/intechopen.91272*

Theta/beta ratio is presented in **Figure 4**.

found in our sample (under 2%).

statistically lower (**Table 2**).

The endophenotype presented in Q-EEG records is evaluated according to Kropotov's typology [20]. In Macedonian ADHD children (*N* = 200), majority (48%) belongs to the combined 1 and 2 subtypes. The other 25% of children showed very slow alpha excess (subtype 4), which corresponds to inattentive form of ADHD mainly found in girls. In another 25%, we found high theta/beta ratio in frontal-central cortex (subtype 1). The subtype 3 with overactive cortex is rarely

VCPT, as a part of Q-EEG analysis, showed that hyperactive children performed significantly much omission and commission errors, longer reaction time (RT), and high variation of the reaction time (var RT) compared with test norms (**Table 1**). The analysis of P3Go component (activation processes) did not showed significant differences concerning the latency and amplitude, while for P3NoGo component (inhibition processes), the latency is not disturbed, but the amplitude is

Generally, psychometric and psychophysiological evaluation of the examinees confirmed the hyperactivity, average intellectual capacities, and significant number of perseverative and nonperseverative mistakes. Results for VCPT showed significantly higher number of omission and commission errors related to the inattention, shorter reaction time (RT), and higher variation in reaction time (var RT) than test

**Figure 1.** *Profiles obtained for ACTeRS.*

*ADHD as a Specific Cause for Learning Disability DOI: http://dx.doi.org/10.5772/intechopen.91272*

*Learning Disabilities - Neurological Bases, Clinical Features and Strategies of Intervention*

**98**

**Figure 1.**

*Profiles obtained for ACTeRS.*

**Figure 2.** *CBCL profiles for boys and girls.*

Theta/beta ratio is presented in **Figure 4**.

The endophenotype presented in Q-EEG records is evaluated according to Kropotov's typology [20]. In Macedonian ADHD children (*N* = 200), majority (48%) belongs to the combined 1 and 2 subtypes. The other 25% of children showed very slow alpha excess (subtype 4), which corresponds to inattentive form of ADHD mainly found in girls. In another 25%, we found high theta/beta ratio in frontal-central cortex (subtype 1). The subtype 3 with overactive cortex is rarely found in our sample (under 2%).

VCPT, as a part of Q-EEG analysis, showed that hyperactive children performed significantly much omission and commission errors, longer reaction time (RT), and high variation of the reaction time (var RT) compared with test norms (**Table 1**).

The analysis of P3Go component (activation processes) did not showed significant differences concerning the latency and amplitude, while for P3NoGo component (inhibition processes), the latency is not disturbed, but the amplitude is statistically lower (**Table 2**).

Generally, psychometric and psychophysiological evaluation of the examinees confirmed the hyperactivity, average intellectual capacities, and significant number of perseverative and nonperseverative mistakes. Results for VCPT showed significantly higher number of omission and commission errors related to the inattention, shorter reaction time (RT), and higher variation in reaction time (var RT) than test

**101**

**Figure 5.**

*Executive ERP components.*

*ADHD as a Specific Cause for Learning Disability DOI: http://dx.doi.org/10.5772/intechopen.91272*

*\*p < 0.01.*

**Table 1.**

*\*p < 0.01.*

**Table 2.**

*Results for VCPT in ADHD children.*

*Components P3Go and P3NoGo in ADHD group.*

norms. Values of P3Go component in latency and amplitude are different from the norm, while P3NoGo component showed significant difference in the amplitude. For better understanding obtained results of analysis, a schematic presentation of components included in executive functions of the brain is shown in **Figure 5**. The components are associated with distinct psychological operations, such as engagement operations (P3bP component), comparison (vcomTL and vcomTR), motor inhibition (P3supF), and monitoring (P4monCC) operations. The ERP results in our evaluated children showed significantly lower amplitude and longer latency for the engagement (P3bP), motor inhibition (P3supF), and monitoring

**VCPT ADHD Norm** *t* **test** *p* **value** Omission errors (Go) 32.25 4 15.65 0.00001\* Commission errors (NoGo) 4.75 1 7.58 0.00000\* RT(ms) Go 456.89 486 −9.17 0.0001\* var RT 18.97 11.7 8.78 0.0000\*

**ERP ADHD Norm** *t* **test** *p* **value** P3Go (ms) 327.15 327.89 −0.12 0.9 P3Go (mV) 9.73 8.55 0.77 0.44 P3NoGo (ms) 402.05 415.78 −0.69 0.49 P3NoGo (mV) 4.67 6.23 −2.89 0.006\*

In the treatment of our clients, we applied behavior therapy, and especially some biofeedback modalities. Any stimulant medication is not allowed in our country.

(P4monCC) components, which confirm the executive dysfunction.

**Figure 4.** *Theta/beta ratio.*



#### **Table 1.**

*Learning Disabilities - Neurological Bases, Clinical Features and Strategies of Intervention*

*Average maps of relation of EEG power spectra in ADHD children (EO upper, EC below).*

**100**

**Figure 4.** *Theta/beta ratio.*

**Figure 3.**

*Results for VCPT in ADHD children.*


#### **Table 2.**

*Components P3Go and P3NoGo in ADHD group.*

norms. Values of P3Go component in latency and amplitude are different from the norm, while P3NoGo component showed significant difference in the amplitude.

For better understanding obtained results of analysis, a schematic presentation of components included in executive functions of the brain is shown in **Figure 5**. The components are associated with distinct psychological operations, such as engagement operations (P3bP component), comparison (vcomTL and vcomTR), motor inhibition (P3supF), and monitoring (P4monCC) operations. The ERP results in our evaluated children showed significantly lower amplitude and longer latency for the engagement (P3bP), motor inhibition (P3supF), and monitoring (P4monCC) components, which confirm the executive dysfunction.

In the treatment of our clients, we applied behavior therapy, and especially some biofeedback modalities. Any stimulant medication is not allowed in our country.

**Figure 5.** *Executive ERP components.*

We introduced biofeedback methodology in 1996 as the first team in our region. Biofeedback is a technique, which helps to learn the control of unaware body's functions (heart rate, dermal activity, muscle tension, peripheral temperature, breathing frequency, brain waves, etc.). Biofeedback could be peripheral or central – neurofeedback. Neurofeedback is a specific behavioral therapy technique used to teach or improve self-regulation of brain activity. The goal of frequency band neurofeedback is to activate a specific brain network.

Common protocol for neurofeedback in many studies comprised diminishing theta activity and optimizing beta brain activity in specific skull points depending on the Q-EEG subtype [21–24]. For our group with ADHD, we used personalized biofeedback protocols depending on the Q-EEG subtype. Generally, we started with 3–5 sessions of electro dermal biofeedback for diminishing anxiety and stress level, and in the following, we used the neurofeedback, two times per week, in the duration of 50 min for each session. **Table 3** shows obtained results before and after biofeedback application in our patients. It is clear that with this kind of therapy, we achieved diminishing of theta, higher power of beta brain waves, changes of theta/ beta ratio, and change of brain rate parameter.

In the assessment of ADHD, patient's theta/beta ratio is a parameter used in many studies [24, 25]. The brain rate parameter is indicator introduced by Pop-Jordanova N. and Pop-Jordanov J. for the evaluation of general mental arousal [26, 27]. The values of this parameter are approved in other studies performed in our country [28–30].

The most commonly reported finding in electrophysiological studies of children with ADHD is increased low frequency activity (predominantly theta) compared with age-matched normal controls. Our results are similar and correspond to the previous research examining electrophysiological measures in children and adolescents with ADHD compared with normal controls, which generally reported an increase in theta activity [31, 32] and a decrease in beta activity [33].

Having in mind that ADHD is a complex syndrome, the diagnosis must include large neuropsychological assessment to evaluate mainly the executive system because the symptoms could be different from child to child. In this context, the analysis of ERP's component extracted from Q-EEG records is a modern approach in the diagnosis of ADHD showing the difference in amplitude or latencies. Van der Meere [34] supposed that the smaller amplitude of P3 component is the result of smaller ability for the engagement of the child in the task performance. Additionally, Keage et al. [35] obtained shorter latencies of P3 component in ADHD patients. It must be mentioned that the executive system is changeable through the developmental process, which suggests that ADHD could be the result of slower developmental of some neurological parts of the brain. It is the reason why some children overcome hyperactivity and impulsivity with maturation.

In a multicenter study [24], the theta/beta ratio was found to discriminate ADHD patients and normal controls with high sensitivity and specificity. In this context, Snyder and Hall [36] based on meta-analysis concluded that


**103**

*ADHD as a Specific Cause for Learning Disability DOI: http://dx.doi.org/10.5772/intechopen.91272*

subtypes of ADHD can be used as biomarkers of disorder.

German group at the University of Tubingen [39].

mance training in school children and in sport [40, 41].

functioning in easy and cheap way.

**4. Conclusions**

diagnostics.

developmental delay.

the theta/beta ratio has much higher predictive power than rating scales do, for separating ADHD for healthy children. However, the absolute and relative power of theta is higher in young children than in adolescents and adults [20, 37]. High theta/beta ratios and high theta values in ADHD can be interpreted as a result of a

The electrophysiological characteristics of ADHD obtained with Q-EEG recording and recent machine-learning methods promise easy-to-use approaches that can be complementary to the existing diagnostic tools, especially when sufficiently large samples are used. To separate ADHD group from healthy people, neuroalgorithms are used as model for multidimensional brain networks. For this reason,

For our own experience, we can conclude that quantitative EEG is a promising approach in diagnostics of this complex disorder. In other words, for diagnostics, it is not enough to listen parents and teachers, but it is imperative to apply a large psychophysiological evaluation of suspected pupils. Q-EEG results can also be helpful in predicting response to stimulant medication and in selecting protocols for neurofeedback. So, we are facing today a renaissance of EEG. On the one hand, the renaissance is associated with obtaining new knowledge regarding neuronal mechanisms of generation of electric neuronal oscillations in spontaneous EEG as well as regarding functional meaning of different waves in event-related potentials [38, 39]. Based on extensive research during the last decade, we now recognize the existence of Q-EEG subtypes in ADHD patients and understand the need of different neurofeedback protocols to correct these abnormalities. However, some of the protocols at the first year of neurofeedback era were obtained empirically without Q-EEG analysis. Most of the protocols use the conventional EEG in the frequency range higher than 0.1 Hz, while EEG at lower frequencies was used in studies of a

We can conclude that a Q-EEG allows to the psychologist looking for the brain

In the therapeutic approach, neurofeedback is confirmed as an excellent tool for training certain brain networks and thus improving the behavior, but the therapist is still an indispensable component in the treatment. The support, the instructions, and the presence of the professional in vicinity to the child are a guaranty for success. In some countries (i.e., Israel), different modalities of biofeedback are used in school settings for stress management as well as for training abilities for better achievement. In this context, our team have good experience with peak perfor-

• In the vide spectrum of learning disabilities, ADHD takes a large part.

• Different psychometric tests can be used, but they are not sufficient for

• Evaluation of brain dynamics, especially executive functions are inevitable.

• Endophenotype represents simpler indicator to genetic mechanism than the

behavioral symptoms and is very important for treatment plan.

trician, clinical psychologist, and child neuropsychiatrist.

• The diagnosis must be done with the collaboration of teachers, parents, pedia-

**Table 3.**

*Main parameters before and after neurofeedback training.*

#### *ADHD as a Specific Cause for Learning Disability DOI: http://dx.doi.org/10.5772/intechopen.91272*

*Learning Disabilities - Neurological Bases, Clinical Features and Strategies of Intervention*

is to activate a specific brain network.

beta ratio, and change of brain rate parameter.

our country [28–30].

We introduced biofeedback methodology in 1996 as the first team in our region. Biofeedback is a technique, which helps to learn the control of unaware body's functions (heart rate, dermal activity, muscle tension, peripheral temperature, breathing frequency, brain waves, etc.). Biofeedback could be peripheral or central – neurofeedback. Neurofeedback is a specific behavioral therapy technique used to teach or improve self-regulation of brain activity. The goal of frequency band neurofeedback

Common protocol for neurofeedback in many studies comprised diminishing theta activity and optimizing beta brain activity in specific skull points depending on the Q-EEG subtype [21–24]. For our group with ADHD, we used personalized biofeedback protocols depending on the Q-EEG subtype. Generally, we started with 3–5 sessions of electro dermal biofeedback for diminishing anxiety and stress level, and in the following, we used the neurofeedback, two times per week, in the duration of 50 min for each session. **Table 3** shows obtained results before and after biofeedback application in our patients. It is clear that with this kind of therapy, we achieved diminishing of theta, higher power of beta brain waves, changes of theta/

In the assessment of ADHD, patient's theta/beta ratio is a parameter used in many studies [24, 25]. The brain rate parameter is indicator introduced by Pop-Jordanova N. and Pop-Jordanov J. for the evaluation of general mental arousal [26, 27]. The values of this parameter are approved in other studies performed in

The most commonly reported finding in electrophysiological studies of children with ADHD is increased low frequency activity (predominantly theta) compared with age-matched normal controls. Our results are similar and correspond to the previous research examining electrophysiological measures in children and adolescents with ADHD compared with normal controls, which generally reported an

Having in mind that ADHD is a complex syndrome, the diagnosis must include

large neuropsychological assessment to evaluate mainly the executive system because the symptoms could be different from child to child. In this context, the analysis of ERP's component extracted from Q-EEG records is a modern approach in the diagnosis of ADHD showing the difference in amplitude or latencies. Van der Meere [34] supposed that the smaller amplitude of P3 component is the result of smaller ability for the engagement of the child in the task performance. Additionally, Keage et al. [35] obtained shorter latencies of P3 component in ADHD patients. It must be mentioned that the executive system is changeable through the developmental process, which suggests that ADHD could be the result of slower developmental of some neurological parts of the brain. It is the reason why some

increase in theta activity [31, 32] and a decrease in beta activity [33].

children overcome hyperactivity and impulsivity with maturation.

*Main parameters before and after neurofeedback training.*

In a multicenter study [24], the theta/beta ratio was found to discriminate ADHD patients and normal controls with high sensitivity and specificity. In this context, Snyder and Hall [36] based on meta-analysis concluded that

**Parameter Before NF (μV) After NF (μV)** *t* **test Significance** Beta brain waves 4.86 ± 1.6 8.0 ± 1.38 5.23 *p* < 0.01 Theta brain waves 20.95 ± 1.38 15.29 ± 1.38 8.47 *p* < 0.01 Theta/beta 4.7 ± 1.38 2.0 ± 1.6 4.5 *p* < 0.01 Brain rate 7.86 ± 0.56 8.22 ± 0.63 6.6 *p* < 0.01

**102**

**Table 3.**

the theta/beta ratio has much higher predictive power than rating scales do, for separating ADHD for healthy children. However, the absolute and relative power of theta is higher in young children than in adolescents and adults [20, 37]. High theta/beta ratios and high theta values in ADHD can be interpreted as a result of a developmental delay.

The electrophysiological characteristics of ADHD obtained with Q-EEG recording and recent machine-learning methods promise easy-to-use approaches that can be complementary to the existing diagnostic tools, especially when sufficiently large samples are used. To separate ADHD group from healthy people, neuroalgorithms are used as model for multidimensional brain networks. For this reason, subtypes of ADHD can be used as biomarkers of disorder.

For our own experience, we can conclude that quantitative EEG is a promising approach in diagnostics of this complex disorder. In other words, for diagnostics, it is not enough to listen parents and teachers, but it is imperative to apply a large psychophysiological evaluation of suspected pupils. Q-EEG results can also be helpful in predicting response to stimulant medication and in selecting protocols for neurofeedback. So, we are facing today a renaissance of EEG. On the one hand, the renaissance is associated with obtaining new knowledge regarding neuronal mechanisms of generation of electric neuronal oscillations in spontaneous EEG as well as regarding functional meaning of different waves in event-related potentials [38, 39].

Based on extensive research during the last decade, we now recognize the existence of Q-EEG subtypes in ADHD patients and understand the need of different neurofeedback protocols to correct these abnormalities. However, some of the protocols at the first year of neurofeedback era were obtained empirically without Q-EEG analysis. Most of the protocols use the conventional EEG in the frequency range higher than 0.1 Hz, while EEG at lower frequencies was used in studies of a German group at the University of Tubingen [39].

We can conclude that a Q-EEG allows to the psychologist looking for the brain functioning in easy and cheap way.

In the therapeutic approach, neurofeedback is confirmed as an excellent tool for training certain brain networks and thus improving the behavior, but the therapist is still an indispensable component in the treatment. The support, the instructions, and the presence of the professional in vicinity to the child are a guaranty for success. In some countries (i.e., Israel), different modalities of biofeedback are used in school settings for stress management as well as for training abilities for better achievement. In this context, our team have good experience with peak performance training in school children and in sport [40, 41].

#### **4. Conclusions**


• Neurofeedback confirmed its usefulness and cost-benefit as a nonpharmacological treatment. A brain-rate parameter, introduced by our team, appeared to be more realistic in the assessment and the follow up of the obtained results. In the future, we propose to include brain-rate-based neurofeedback training.

### **Acknowledgements**

Many thanks for my collaborators Silvana Markovska-Simoska, PhD, Macedonian Academy of Sciences and Arts, and Tatjana Zorcec, Clinical Psychologist PhD, University Children Hospital, Skopje. They recorded the Q-EEG and give the interpretations. No funding was obtained for this research.

### **Conflict of interest**

The author declares no conflict of interest.

#### **Author details**

Nada Pop-Jordanova Macedonian Academy of Sciences and Arts, Skopje, Republic of North Macedonia

\*Address all correspondence to: npopjordanova@ymail.com

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**105**

*ADHD as a Specific Cause for Learning Disability DOI: http://dx.doi.org/10.5772/intechopen.91272*

> and OCD: A data-driven, diagnosisagnostic approach. Translational Psychiatry. 2019;**9**:318. DOI: 10.1038/

> [9] Brem S, Grünblatt E, Drechsler R,

neurobiological link between OCD and ADHD. Attention Deficit and Hyperactivity Disorders. 2014;**6**(3):175- 202. DOI: 10.1007/s12402-014-0146-x

Garcia-Martínez I, Bosch R, Nogueira M, et al. Corrigendum to 'Genome-wide copy number variation analysis in adult Attention-Deficit and Hyperactivity Disorder'. Journal of Psychiatric

Research. 2014;**52**:60-67. DOI: 10.1016/j.

[11] Tye C, Rijsdijk F, McLoughlin G. Genetic overlap between ADHD symptoms and EEG theta power. Brain and Cognition. 2014;**87**:168-172. DOI: 10.1016/j.bandc.2014.03.010. [Epub: 19

[12] Monastra V, Lubar J, Linden M. The

hyperactivity disorder reliability and validity studies. Neuropsychology.

Linden M, et al. Assessing attention deficit/hyperactivity disorder via quantitative electroencephalography

Neuropsychology. 1999;**13**:424-433. DOI: 10.1037/0894-4105.13.3.424

[14] Kropotov J. Quantitative EEG, ERP's and neurotherapy. Amsterdam: Elsevier

[15] Achenbach M, Edelbrock C. Manual for the Child Behavior Check List

development of a quantitative electroencephalographic scanning process for attention deficit-

s41398-019-0631-2

Riederer P, Walitza S. The

[10] Ramos-Quiroga JA, Sánchez-Mora C, Casas M,

jpsychires.2014.01.008

April 2014]

2001;**15**:136-144

Publ; 2009

[13] Monastra V, Lubar J,

an initial validation study.

[1] Gaubb M, Caryn L, Carlsonph D. Gender differences in ADHD: A metaanalysis and critical review. Journal of the American Academy of Child & Adolescent Psychiatry. 1997;**36**(8):1036-1045. DOI:

10.1097/00004583-199708000-00011

[2] de Quirós GB, Kinsbourne M, Palmer RL, Rufo DT. Attention deficit disorder in children: Three clinical variants. Journal of Developmental

and Behavioral Pediatrics.

[3] Scahill L, Schwab-Stone M. **E**pidemiology of ADHD in schoolage children. Child and Adolescent Psychiatric Clinics of North America.

[4] Barkley R. The inattentive type of ADHD as a distinct disorder: What remains to be done. Clinical Psychology. 2001;**8**(4):489-493. DOI: 10.1093/

[5] Jaber M, Robinson SW, Missale C, Caron MG. Dopamine receptors and brain function. Neuropharmacology. 1996;**35**(11):1503-1519. DOI: 10.1016/

[6] Loo S, Hale S, Hanada G, Macion J, Shrestha A, McGough J, et al. Familial clustering and DRD4 effects on EEG measures in multiplex families with attention-deficit hyperactivity disorder. Journal of the American Academy of Child and Adolescent Psychiatry.

[7] Mick E, Faraone SV. Genetics of attention deficit hyperactivity disorder. Child and Adolescent Psychiatric Clinics of North America. 2008;**17**(2):261-284.

DOI: 10.1016/j.chc.2007.11.011

[8] Kushki A, Anagnostou E,

Hammill C, et al. Examining overlap and homogeneity in ASD, ADHD,

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*ADHD as a Specific Cause for Learning Disability DOI: http://dx.doi.org/10.5772/intechopen.91272*

#### **References**

*Learning Disabilities - Neurological Bases, Clinical Features and Strategies of Intervention*

Many thanks for my collaborators Silvana Markovska-Simoska, PhD, Macedonian Academy of Sciences and Arts, and Tatjana Zorcec, Clinical

and give the interpretations. No funding was obtained for this research.

The author declares no conflict of interest.

Psychologist PhD, University Children Hospital, Skopje. They recorded the Q-EEG

Macedonian Academy of Sciences and Arts, Skopje, Republic of North Macedonia

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

\*Address all correspondence to: npopjordanova@ymail.com

provided the original work is properly cited.

**Acknowledgements**

**Conflict of interest**

• Neurofeedback confirmed its usefulness and cost-benefit as a nonpharmacological treatment. A brain-rate parameter, introduced by our team, appeared to be more realistic in the assessment and the follow up of the obtained results. In the future, we propose to include brain-rate-based neurofeedback training.

**104**

**Author details**

Nada Pop-Jordanova

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[25] Monastra V, Monastra D, George S. The effects of stimulant therapy, EEG biofeedback, and parenting style on the primary symptoms of attention-deficit/ hyperactivity disorder. Applied Psychophysiology and Biofeedback.

2002;**27**(4):231-249. DOI: 10.1023/A:1021018700609

2005;**26**(2):35-42

2009;**10**(Suppl. 1):S1-S9

[29] Markovska-Simoska S, Pop-Jordanova N. Quantitative EEG spectrum-weighted frequency (brain rate) distribution in adults with ADHD. CNS Spectrums.

[30] Demerdzieva A, Pop-Jordanova N. Brain Rate Parameter in Children with General Anxiety Disorder. Pril (Makedon Akad Nauk Umet Odd Med

[31] Mann C, Lubar J, Zimmerman A, Miller C, Muenchen R. Quantitative analysis of EEG in boys with attention deficit hyperactivity disorder: Controlled

Pediatric Neurology. 1992;**8**:30-36. DOI:

study with clinical implications.

10.1016/0887-8994(92)90049-5

2011;**16**(5):111-119

Nauki). 2019;**40**(1):41-50

[26] Pop-Jordanova N, Pop-Jordanov J. Spectrum-weighted EEG frequency ("brain-rate") as a quantitative indicator of mental arousal. Prilozi.

[27] Pop-Jordanov J, Pop-Jordanova N. Neurophysical substrates of arousal and attention. Cognitive Processing.

[28] Pop-Jordanova N. Chapter 8: Brain Rate as an Indicator of the Level of Consciousness. In: Cvetkovic D, Cosic I, editors. States of Consciousness: Experimental Insights into Meditation, Waking, Sleep and Dreams. Berlin, Heidelberg: Springer; 2011. pp. 187-201

and Revised Child Behavior Profile. Burlington: University of Vermont; 1983

[16] Ullmann K, Sleator K, Sprague L. Introduction to the use of ACTeRS. Psychopharmacology Bulletin.

[17] Kohs' test. Zavod za produktivnost

[18] Stroop JR. Studies of interference in serial verbal reactions. Journal of Experimental Psychology.

dela. Ljubljana, Slovenia: 1967

[19] Anderson SW, Damasio H, Jones RD, Tranel D. Wisconsin card sorting test performance as a measure of frontal lobe damage. Journal of Clinical and Experimental Neuropsychology.

[20] Kropotov J, Ponomarev VA.

[21] Pop-Jordanova N. Biofeedback application for somatoform disorders and attention deficit hyperactivity disorder (ADHD) in children.

International Journal of Medicine and Medical Sciences. 2009;**1**(2):17-22

[22] Pop-Jordanova N, Markovska S, Zorcec T. Neurofeedback treatment of children with attention deficit hyperactivity disorder. Prilozi.

[23] Pop-Jordanova N. Chapter 13: QEEG characteristics and biofeedback modalities in children with ADHD. In: Norvilitis JM, editor. Current Directions in ADHD and Its Treatment. Rijeka, Croatia: InTech; 2012. pp. 249-268

[24] Lubar JF. Discourse on the development of EEG diagnostics and biofeedback for attention-deficit/

components. Neuroreport. 2009;**20**:1592-1596. DOI: 10.1097/

WNR.0b013e3283309cbd

Decomposing N2 NOGO wave of eventrelated potentials into independent

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[32] Matsuura M, Okubo Y, Toru M, et al. A cross-national EEG study of children with emotional and behavioral problems: A WHO collaborative study in the Western Pacific Region. Biological Psychiatry. 1993;**34**(1-2):59-65. DOI: 10.1016/0006-3223(93)90257-E

[33] Callaway E, Halliday R, Herning RI. A comparison of methods for measuring event-related potentials. Electroencephalography and Clinical Neurophysiology. 1983, 1983;**55**(2):227-232. DOI: 10.1016/0013-4694(83)90192-X

[34] Van der Meere J. The Role of Attention, Hyperactivity Disorders of Childhood. Cambridge University Press; 1996. pp. 111-148

[35] Keage H, Clark CR, Hermens D, Gordon E. Distractibility in AD/ HD predominantly inattentive and combined subtypes: The P3a ERP component, heart rate and performance. Journal of Integrative Neuroscience. 2006;**5**(1):139-158. DOI: 10.1142/S0219635206001070

[36] Snyder SM, Hall JR. A metaanalysis of quantitative EEG power associated with attention-deficit hyperactivity disorder. Journal of Clinical Neurophysiology. 2006;**23**(5):440-455. DOI: 10.1097/01. wnp.0000221363.12503.78

[37] Näätänen R. Attention and Brain Function. PsycINFO Database Record; 1992

[38] Luck S. An introduction to the event-related potential technique. In: A Bradford Book. 2005

[39] Strehl U, Aggensteiner P, Wachtlin D, Brandeis D, Albrecht B, Arana M, et al. Neurofeedback of slow cortical potentials in children with attention-deficit/hyperactivity disorder: A multicenter randomized trial controlling for unspecific effects.

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[41] Pop-Jordanova N, Demerdzieva A. Biofeedback training for peak performance in sport - Case report. Macedonian Journal of Medical Sciences. 2010;**3**(2):113-118

**109**

**Chapter 8**

**Abstract**

Underpinnings

processes, including working memory and language.

numerical cognition, learning

**1. Introduction**

arithmetic [6].

**Keywords:** dyscalculia, learning disabilities, dyscalculia diagnosis,

already observed in children in the first years of school [12].

Developmental Dyscalculia:

Nosological Status and Cognitive

*Ricardo Moura, Suzane Garcia and Júlia Beatriz Lopes-Silva*

Mathematics is one of the main challenges faced by students throughout school life, with long-lasting impact on social life, including employability and incomes. The development of the research on numerical cognition occurred together with the study of math learning and its related deficits, in special developmental dyscalculia (DD). The present chapter explores the literature on DD in two levels. First, we discuss about the nosological status of the disorder together with considerations about its diagnosis. Afterward we review the main research findings regarding the cognitive underpinnings of DD, from numerical representations to domain general

Living in today's society requires well-developed mathematical competencies. As more cliché as this statement may sound, there is a robust scientific literature indicating that higher mathematical competencies are associated with higher employability and incomes [1–3], profitable financial decisions [4], and even better health outcomes [5]. Despite this well-established body of evidence, many adults and children, even from developed countries, struggle to perform simple

The reasons for failing at math are diverse and include socioeconomic [7, 8], educational [9], and emotional factors [10, 11]. Math is a complex and abstract discipline and depends mostly of formal instruction at school. Moreover, mathematical knowledge is also largely cumulative, so that newer, more complex, and abstract concepts depend on previous knowledge, which can either be acquired intuitively, like reciting the sequence of number words, or also formally at school. Therefore, we can say that a great part of the difficulties faced by children when learning or performing math activities are due to the complexity of mathematics itself. It is known that, compared to other disciplines, difficulty in learning math is

Some children, nevertheless, show persistent and important difficulties in learning math, which cannot be explained by socioeconomic, emotional, educational, psychiatric, or intellectual factors. In these cases the label developmental

#### **Chapter 8**

## Developmental Dyscalculia: Nosological Status and Cognitive Underpinnings

*Ricardo Moura, Suzane Garcia and Júlia Beatriz Lopes-Silva*

#### **Abstract**

Mathematics is one of the main challenges faced by students throughout school life, with long-lasting impact on social life, including employability and incomes. The development of the research on numerical cognition occurred together with the study of math learning and its related deficits, in special developmental dyscalculia (DD). The present chapter explores the literature on DD in two levels. First, we discuss about the nosological status of the disorder together with considerations about its diagnosis. Afterward we review the main research findings regarding the cognitive underpinnings of DD, from numerical representations to domain general processes, including working memory and language.

**Keywords:** dyscalculia, learning disabilities, dyscalculia diagnosis, numerical cognition, learning

#### **1. Introduction**

Living in today's society requires well-developed mathematical competencies. As more cliché as this statement may sound, there is a robust scientific literature indicating that higher mathematical competencies are associated with higher employability and incomes [1–3], profitable financial decisions [4], and even better health outcomes [5]. Despite this well-established body of evidence, many adults and children, even from developed countries, struggle to perform simple arithmetic [6].

The reasons for failing at math are diverse and include socioeconomic [7, 8], educational [9], and emotional factors [10, 11]. Math is a complex and abstract discipline and depends mostly of formal instruction at school. Moreover, mathematical knowledge is also largely cumulative, so that newer, more complex, and abstract concepts depend on previous knowledge, which can either be acquired intuitively, like reciting the sequence of number words, or also formally at school. Therefore, we can say that a great part of the difficulties faced by children when learning or performing math activities are due to the complexity of mathematics itself. It is known that, compared to other disciplines, difficulty in learning math is already observed in children in the first years of school [12].

Some children, nevertheless, show persistent and important difficulties in learning math, which cannot be explained by socioeconomic, emotional, educational, psychiatric, or intellectual factors. In these cases the label developmental dyscalculia (DD) is often applied, and difficulties encompass a broad range of mathematical tasks, like reading and writing numbers in different formats, comparing numbers and quantities, and performing the basic arithmetical operations [7, 13–16]. Some authors also indicate deficits in abilities concerning magnitude representation and the comprehension and use of symbolic codes to represent numerical information [17–19]. The estimates for prevalence of DD vary from 3 to 6% of school-aged children [7, 20, 21].

Despite the relative consensus about what are the difficulties that characterize DD, there is still some debate concerning the diagnostic criteria, neuropsychological underpinnings, and rehabilitation strategies. In the following sections, we will discuss in detail each of these three topics.

#### **2. Nosological status**

#### **2.1 Diagnosis**

Two main questions concern the diagnosis of DD. The first question is about the diagnostic criteria, and in the literature on the epidemiology of learning disabilities, three approaches are commonly reported. The discrepancy criteria are probably the most common in research studies and define math learning disability from the discrepancy between an average of above- average performance on general cognitive capacity (often the IQ ) and the low performance on standardized math tests. The absolute threshold criteria is similar to the discrepancy criteria, but the disability is defined solely by the low performance in a standardized math test. The response to intervention criteria establish the diagnosis after investigating how the child responds to a set of psychopedagogical interventions. In this way, the persistency of the difficulty and not the discrepancy between capacity and performance is the main criteria for diagnosis.

The second main question concerns the definition on how low the performance in an achievement test must be in order to diagnose DD. The cutoff scores frequently used are 30th, 25th, 10th, and 5th percentiles. Higher cutoff scores (25th and 30th percentiles) are less conservative and, naturally, more prone to false positives. Lower cutoff score is more conservative when labeling children and less prone to false positive. Some authors argue that the sample of individuals labeled under higher cutoff scores is more heterogeneous, with their difficulties in math being more attributable to social, educational, and motivational factors and therefore are less stable over time [22]. On the other hand, the individuals whose performance falls into the more conservative cutoff scores are a more homogeneous group, and their difficulties are more probably associated to cognitive factors. Mazzocco [15] suggests that the individuals with performance under the fifth percentile must be identified as DD, and those with performance under the 30th percentile must be identified as "mathematics difficulties."

#### **2.2 Comorbidity and cognitive heterogeneity**

The investigation of DD nosology also involves studying its comorbidities with other syndromes and how the cognitive profile varies among individuals. It is estimated that only 30% of the DD children are free of comorbidities [23]. The main comorbidities of DD are with developmental dyslexia and ADHD, with comorbidity rates of 40% for the first [24] and between 25 and 42% for the second [23, 25].

According to Rubinsten and Henik [26], different cognitive deficits can be the cause of difficulties in learning math, with comorbidities being mostly due

**111**

[17, 38, 39].

these hypotheses, see Butterworth [14].

**3.2 Symbolic representations**

*Developmental Dyscalculia: Nosological Status and Cognitive Underpinnings*

to a combination of deficits. For example, the pure cases (for which the label DD is applied) are due to a deficit in the abstract representation of number, in the cognitive level, and a deficit in the functioning of the intraparietal sulcus, in the neural level. The comorbidity of dyscalculia and ADHD would be explained by the co-occurrence of deficits in the processing of number and in attentional mechanisms. In turn, comorbidity with dyslexia is due to a single deficit in the angular gyrus that would cause a deficit in associating symbols (Arabic numbers, words) to a meaning. The cases of comorbidity would be referred as mathematics

Following the diversity of activities involved in math and the heterogeneity of manifestations observed in mathematics difficulties, the cognitive mechanisms are also diverse and related to basic numerical representations, working memory, visuospatial reasoning, and language. In the following, the literature on each of

Humans, like all other animals, are born with only a rudimentary, languageindependent, system dedicated to grasping quantities from the environment [27]. Naturally, this system is not able to process numerical symbols, which are, from a phylogenetic perspective, a very recent cultural invention that demands enculturation in order to be assimilated by the human brain [28]. This inherited preverbal number knowledge operates in two forms, which are considered independent subsystems: the object-tracking system (OTS) and the approximate number system (ANS; [27]). The OTS represents small numerosities up to four with high accuracy and reaches its developmental plateau early in development. The ANS, in turn, is responsible for the representation of larger numerosities analogically and, therefore, with increasingly imprecision. One largely accepted model suggests that the ANS represents numbers in an approximate and logarithmically compressed fashion, according to the classical psychophysical laws of Weber and Fechner [29]. Since the last decade, the relationship between basic numerical representations

and performance on mathematics has been in the spotlight for many research groups. A handful of evidence has indicated a positive relation between ANS accuracy and math performance [30–37]. Moreover, it has also been shown that children with DD are impaired even in simple tasks that tap ANS representations, such as estimating the numerical size of a set of dots and comparing two sets of dots

A very well-established theory is that DD is the result of a deficit in the foundational representations of numbers [14, 26]. For some researchers, this deficitary representation of numbers lies in the ANS [17]. Other researchers, in turn, propose that the deficitary numerical system in DD is the numerosity coding, which is responsible for processing precise, but not continuous, numerical quantities, and in which the whole arithmetical thinking is based on. For a detailed discussion about

Basic numerical representations are not restricted to nonsymbolic representations. Actually, learning symbolic systems for representing numbers is a landmark

*DOI: http://dx.doi.org/10.5772/intechopen.91003*

learning difficulties (MLD).

**3. Cognitive mechanisms**

**3.1 Nonsymbolic representations**

these mechanisms will be reviewed in more detail.

*Developmental Dyscalculia: Nosological Status and Cognitive Underpinnings DOI: http://dx.doi.org/10.5772/intechopen.91003*

to a combination of deficits. For example, the pure cases (for which the label DD is applied) are due to a deficit in the abstract representation of number, in the cognitive level, and a deficit in the functioning of the intraparietal sulcus, in the neural level. The comorbidity of dyscalculia and ADHD would be explained by the co-occurrence of deficits in the processing of number and in attentional mechanisms. In turn, comorbidity with dyslexia is due to a single deficit in the angular gyrus that would cause a deficit in associating symbols (Arabic numbers, words) to a meaning. The cases of comorbidity would be referred as mathematics learning difficulties (MLD).

#### **3. Cognitive mechanisms**

*Learning Disabilities - Neurological Bases, Clinical Features and Strategies of Intervention*

6% of school-aged children [7, 20, 21].

discuss in detail each of these three topics.

**2. Nosological status**

main criteria for diagnosis.

identified as "mathematics difficulties."

**2.2 Comorbidity and cognitive heterogeneity**

**2.1 Diagnosis**

dyscalculia (DD) is often applied, and difficulties encompass a broad range of mathematical tasks, like reading and writing numbers in different formats, comparing numbers and quantities, and performing the basic arithmetical operations [7, 13–16]. Some authors also indicate deficits in abilities concerning magnitude representation and the comprehension and use of symbolic codes to represent numerical information [17–19]. The estimates for prevalence of DD vary from 3 to

Despite the relative consensus about what are the difficulties that characterize DD, there is still some debate concerning the diagnostic criteria, neuropsychological underpinnings, and rehabilitation strategies. In the following sections, we will

Two main questions concern the diagnosis of DD. The first question is about the diagnostic criteria, and in the literature on the epidemiology of learning disabilities, three approaches are commonly reported. The discrepancy criteria are probably the most common in research studies and define math learning disability from the discrepancy between an average of above- average performance on general cognitive capacity (often the IQ ) and the low performance on standardized math tests. The absolute threshold criteria is similar to the discrepancy criteria, but the disability is defined solely by the low performance in a standardized math test. The response to intervention criteria establish the diagnosis after investigating how the child responds to a set of psychopedagogical interventions. In this way, the persistency of the difficulty and not the discrepancy between capacity and performance is the

The second main question concerns the definition on how low the performance

The investigation of DD nosology also involves studying its comorbidities with other syndromes and how the cognitive profile varies among individuals. It is estimated that only 30% of the DD children are free of comorbidities [23]. The main comorbidities of DD are with developmental dyslexia and ADHD, with comorbidity rates of 40% for the first [24] and between 25 and 42% for the second [23, 25]. According to Rubinsten and Henik [26], different cognitive deficits can be the cause of difficulties in learning math, with comorbidities being mostly due

in an achievement test must be in order to diagnose DD. The cutoff scores frequently used are 30th, 25th, 10th, and 5th percentiles. Higher cutoff scores (25th and 30th percentiles) are less conservative and, naturally, more prone to false positives. Lower cutoff score is more conservative when labeling children and less prone to false positive. Some authors argue that the sample of individuals labeled under higher cutoff scores is more heterogeneous, with their difficulties in math being more attributable to social, educational, and motivational factors and therefore are less stable over time [22]. On the other hand, the individuals whose performance falls into the more conservative cutoff scores are a more homogeneous group, and their difficulties are more probably associated to cognitive factors. Mazzocco [15] suggests that the individuals with performance under the fifth percentile must be identified as DD, and those with performance under the 30th percentile must be

**110**

Following the diversity of activities involved in math and the heterogeneity of manifestations observed in mathematics difficulties, the cognitive mechanisms are also diverse and related to basic numerical representations, working memory, visuospatial reasoning, and language. In the following, the literature on each of these mechanisms will be reviewed in more detail.

#### **3.1 Nonsymbolic representations**

Humans, like all other animals, are born with only a rudimentary, languageindependent, system dedicated to grasping quantities from the environment [27]. Naturally, this system is not able to process numerical symbols, which are, from a phylogenetic perspective, a very recent cultural invention that demands enculturation in order to be assimilated by the human brain [28]. This inherited preverbal number knowledge operates in two forms, which are considered independent subsystems: the object-tracking system (OTS) and the approximate number system (ANS; [27]). The OTS represents small numerosities up to four with high accuracy and reaches its developmental plateau early in development. The ANS, in turn, is responsible for the representation of larger numerosities analogically and, therefore, with increasingly imprecision. One largely accepted model suggests that the ANS represents numbers in an approximate and logarithmically compressed fashion, according to the classical psychophysical laws of Weber and Fechner [29].

Since the last decade, the relationship between basic numerical representations and performance on mathematics has been in the spotlight for many research groups. A handful of evidence has indicated a positive relation between ANS accuracy and math performance [30–37]. Moreover, it has also been shown that children with DD are impaired even in simple tasks that tap ANS representations, such as estimating the numerical size of a set of dots and comparing two sets of dots [17, 38, 39].

A very well-established theory is that DD is the result of a deficit in the foundational representations of numbers [14, 26]. For some researchers, this deficitary representation of numbers lies in the ANS [17]. Other researchers, in turn, propose that the deficitary numerical system in DD is the numerosity coding, which is responsible for processing precise, but not continuous, numerical quantities, and in which the whole arithmetical thinking is based on. For a detailed discussion about these hypotheses, see Butterworth [14].

#### **3.2 Symbolic representations**

Basic numerical representations are not restricted to nonsymbolic representations. Actually, learning symbolic systems for representing numbers is a landmark in the development of mathematical reasoning. As children learn to speak a sequence of numerical words, they are still devoid of any quantitative meaning [40]. Gradually, these number words are associated with nonsymbolic numerical representations [41, 42]. The mapping between a list of words and their respective numerical representations (meanings) will be established gradually as children become able to perform a range of new tasks. For example, they can use these numeric words to label a set of objects (say "six" when looking to six dolls at a glance). These activities only develop completely around the age of five, when children master the principle of cardinality [43].

Schneider and collaborators [44], in a meta-analysis study, found that the association with performance in arithmetic tests is stronger for symbolic comparison tasks than for the nonsymbolic ones. Furthermore, a find consistently reported by studies indicates that children with DD exhibit weaker performance than controls in tasks requiring comparison of symbolic numbers, like Arabic numbers and number words [18, 38, 45–47]. According to a model proposed by Rousselle and Noël [18], DD can also occur due to a deficit in accessing nonsymbolic representations from numerical symbols (access deficit hypothesis).

#### **3.3 Language**

Language influences mathematics in different ways. Many mathematical tasks rely on verbal processing, such as learning the multiplication table, writing and reading numbers, and learning the Arabic code. According to Simmons et al. [48], the relationship between phonological awareness (often measured by a rhyme detection or phoneme elision tasks) and math learning is independent of measures of vocabulary and nonverbal reasoning, thus indicating a genuine verbal-numerical relationship.

Language skills also characterize an important landmark in the development of mathematical abilities. A special case is the ability to convert between numerical notations, often measured by tasks of number writing and number reading, and called number transcoding. Number transcoding is especially important early in school life, since it demands the understanding of basic lexical and syntactic components of Arabic and verbal numerals. As suggested by previous studies, understanding the place-value syntax of Arabic numbers and matching it with number words constitutes a significant landmark that young children must reach in order to succeed in mathematical education [49].

Some scientific evidence suggests that children master the numerical codes after 3 or 4 years of schooling. During the first year of elementary school (around 7-yearold), children still struggle to write and read Arabic numerals [50, 51]. Shortly after, in third and fourth grades (8- and 9-year-old children), most of these difficulties with Arabic numerals are already overcome [38]. This issue was further investigated by Moura et al. [52] in a study using more complex number transcoding tasks and investigating children with and without MLD. Results revealed significant number transcoding difficulties in children with MLD. These difficulties were more prominent in Arabic number writing, but the magnitude of this difference decreased with age, indicating that children with MLD tend to reach the performance of their typical achievers peers. Importantly, from the first to fourth school grades, most of the errors observed in children, regardless of their achievement in mathematics, are well explained by the syntactic complexity of numerals, as most errors were observed in numbers with more digits, and more syntactically complex (like 1002, 4015). A detailed analysis of transcoding errors suggested that children with MLD struggle with the syntactic structure of Arabic numerals, mainly with 3- and 4-digit

**113**

cases [61, 74].

developing peers.

**3.5 Visuospatial abilities**

over procedures [62, 63].

*Developmental Dyscalculia: Nosological Status and Cognitive Underpinnings*

such as number transcoding and learning arithmetic facts [54].

numbers, until the fourth grade, while typical achievers seem to overcome these difficulties around the third grade. Moreover, the acquisition of lexical primitives seems to be well developed in typical achievers by the first year of elementary school, while children with MLD show a small though significant proportion of

Another important evidence for this interaction between numerical and verbal skills is the high comorbidity between DD and dyslexia. Epidemiological studies indicate that approximately 40% of dyslexic children also have deficits in arithmetic [24]. Some studies suggest comorbidity rates up to 70%, which may be overestimated because of diagnostic criteria and constructs evaluated by standardized arithmetic and reading tests [53]. Importantly, the comorbidity between DD and dyslexia is greater than would be expected by chance if the two entities were fully segregated independently. An influential hypothesis states that children with developmental dyslexia struggle with numerical activities that rely on verbal codes,

The association between mathematics skills and working memory and attention has been extensively reported in the literature. In fact, a high variety of numerical tasks including number transcoding, complex calculations, and problem solving require working memory resources and planning. According to Rubinsten and Henik [26], a relevant part of children with DD also present comorbid attention

Interestingly, a brain region that is considered crucial for numerical development, the intraparietal sulcus, is also involved in a range of nonnumerical activities, including attentional control and reasoning [56–59]. Recent studies propose that an important cognitive mark of DD is attentional control. Gilmore et al. [60] found that, due to strategies aiming to control for nonnumerical visual parameters, commonly used dot comparison tasks require inhibitory control mechanisms. Surprisingly, this executive function component is more strongly related to mathematics achievement than the numerical components of magnitude comparison tasks. Similarly, Szucs et al. [61] also proposed that children with DD have more difficulties in inhibiting irrelevant nonnumerical information than their typically

Together with working memory, visuospatial abilities are one of the most critical abilities related to mathematics achievement, being associated mainly with performance in multidigit calculation, mainly in those requiring borrowing and carry-

Despite the evidence for a role of visuospatial skills in calculation, a pure visuospatial deficit in children with DD is perhaps less clear than the other cognitive skills discussed above, as there is no well-established visuospatial subgroup of DD. The co-occurrence of mathematics and visuospatial deficits were widely discussed in the

If, on the one hand, there is no consensus about a visuospatial deficit in DD, on the other hand, many studies found that children with DD present deficits in the visuospatial component of working memory [61, 67–73]. Importantly, the verbal component of working memory is frequently reported as preserved in these

context of the so-called nonverbal learning disability [64–66].

*DOI: http://dx.doi.org/10.5772/intechopen.91003*

lexical errors (e.g., writing *twelve* as 20).

**3.4 Working memory and attention**

deficit hyperactivity disorder (ADHD) [21, 55].

#### *Developmental Dyscalculia: Nosological Status and Cognitive Underpinnings DOI: http://dx.doi.org/10.5772/intechopen.91003*

numbers, until the fourth grade, while typical achievers seem to overcome these difficulties around the third grade. Moreover, the acquisition of lexical primitives seems to be well developed in typical achievers by the first year of elementary school, while children with MLD show a small though significant proportion of lexical errors (e.g., writing *twelve* as 20).

Another important evidence for this interaction between numerical and verbal skills is the high comorbidity between DD and dyslexia. Epidemiological studies indicate that approximately 40% of dyslexic children also have deficits in arithmetic [24]. Some studies suggest comorbidity rates up to 70%, which may be overestimated because of diagnostic criteria and constructs evaluated by standardized arithmetic and reading tests [53]. Importantly, the comorbidity between DD and dyslexia is greater than would be expected by chance if the two entities were fully segregated independently. An influential hypothesis states that children with developmental dyslexia struggle with numerical activities that rely on verbal codes, such as number transcoding and learning arithmetic facts [54].

#### **3.4 Working memory and attention**

*Learning Disabilities - Neurological Bases, Clinical Features and Strategies of Intervention*

in the development of mathematical reasoning. As children learn to speak a sequence of numerical words, they are still devoid of any quantitative meaning [40]. Gradually, these number words are associated with nonsymbolic numerical representations [41, 42]. The mapping between a list of words and their respective numerical representations (meanings) will be established gradually as children become able to perform a range of new tasks. For example, they can use these numeric words to label a set of objects (say "six" when looking to six dolls at a glance). These activities only develop completely around the age of five, when

Schneider and collaborators [44], in a meta-analysis study, found that the association with performance in arithmetic tests is stronger for symbolic comparison tasks than for the nonsymbolic ones. Furthermore, a find consistently reported by studies indicates that children with DD exhibit weaker performance than controls in tasks requiring comparison of symbolic numbers, like Arabic numbers and number words [18, 38, 45–47]. According to a model proposed by Rousselle and Noël [18], DD can also occur due to a deficit in accessing nonsymbolic representations from

Language influences mathematics in different ways. Many mathematical tasks rely on verbal processing, such as learning the multiplication table, writing and reading numbers, and learning the Arabic code. According to Simmons et al. [48], the relationship between phonological awareness (often measured by a rhyme detection or phoneme elision tasks) and math learning is independent of measures of vocabulary and nonverbal reasoning, thus indicating a genuine verbal-numerical

Language skills also characterize an important landmark in the development of mathematical abilities. A special case is the ability to convert between numerical notations, often measured by tasks of number writing and number reading, and called number transcoding. Number transcoding is especially important early in school life, since it demands the understanding of basic lexical and syntactic components of Arabic and verbal numerals. As suggested by previous studies, understanding the place-value syntax of Arabic numbers and matching it with number words constitutes a significant landmark that young children must reach in

Some scientific evidence suggests that children master the numerical codes after 3 or 4 years of schooling. During the first year of elementary school (around 7-yearold), children still struggle to write and read Arabic numerals [50, 51]. Shortly after, in third and fourth grades (8- and 9-year-old children), most of these difficulties with Arabic numerals are already overcome [38]. This issue was further investigated by Moura et al. [52] in a study using more complex number transcoding tasks and investigating children with and without MLD. Results revealed significant number transcoding difficulties in children with MLD. These difficulties were more prominent in Arabic number writing, but the magnitude of this difference decreased with age, indicating that children with MLD tend to reach the performance of their typical achievers peers. Importantly, from the first to fourth school grades, most of the errors observed in children, regardless of their achievement in mathematics, are well explained by the syntactic complexity of numerals, as most errors were observed in numbers with more digits, and more syntactically complex (like 1002, 4015). A detailed analysis of transcoding errors suggested that children with MLD struggle with the syntactic structure of Arabic numerals, mainly with 3- and 4-digit

children master the principle of cardinality [43].

numerical symbols (access deficit hypothesis).

order to succeed in mathematical education [49].

**3.3 Language**

relationship.

**112**

The association between mathematics skills and working memory and attention has been extensively reported in the literature. In fact, a high variety of numerical tasks including number transcoding, complex calculations, and problem solving require working memory resources and planning. According to Rubinsten and Henik [26], a relevant part of children with DD also present comorbid attention deficit hyperactivity disorder (ADHD) [21, 55].

Interestingly, a brain region that is considered crucial for numerical development, the intraparietal sulcus, is also involved in a range of nonnumerical activities, including attentional control and reasoning [56–59]. Recent studies propose that an important cognitive mark of DD is attentional control. Gilmore et al. [60] found that, due to strategies aiming to control for nonnumerical visual parameters, commonly used dot comparison tasks require inhibitory control mechanisms. Surprisingly, this executive function component is more strongly related to mathematics achievement than the numerical components of magnitude comparison tasks. Similarly, Szucs et al. [61] also proposed that children with DD have more difficulties in inhibiting irrelevant nonnumerical information than their typically developing peers.

#### **3.5 Visuospatial abilities**

Together with working memory, visuospatial abilities are one of the most critical abilities related to mathematics achievement, being associated mainly with performance in multidigit calculation, mainly in those requiring borrowing and carryover procedures [62, 63].

Despite the evidence for a role of visuospatial skills in calculation, a pure visuospatial deficit in children with DD is perhaps less clear than the other cognitive skills discussed above, as there is no well-established visuospatial subgroup of DD. The co-occurrence of mathematics and visuospatial deficits were widely discussed in the context of the so-called nonverbal learning disability [64–66].

If, on the one hand, there is no consensus about a visuospatial deficit in DD, on the other hand, many studies found that children with DD present deficits in the visuospatial component of working memory [61, 67–73]. Importantly, the verbal component of working memory is frequently reported as preserved in these cases [61, 74].

### **4. Conclusion**

Even though the study of the cognitive basis of numerical representations and mathematical performance is relatively new, a consistent body of scientific evidence has already been gathered, allowing important advances in the comprehension of the development of mathematical abilities and in the identification and remediation of mathematical difficulties. Nevertheless, this is a broad field of study and there are still several open questions. Currently, longitudinal and replication studies are especially relevant [75].

#### **Author details**

Ricardo Moura1 \*, Suzane Garcia1 and Júlia Beatriz Lopes-Silva2

1 University of Brasília (UnB), Brasília, Brazil

2 Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil

\*Address all correspondence to: ricardoojm@gmail.com

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**115**

*Developmental Dyscalculia: Nosological Status and Cognitive Underpinnings*

learning difficulties in young children. Developmental Disabilities Research

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[11] Carey E, Hill F, Devine A, Szücs D. The chicken or the egg? The direction of the relationship between mathematics anxiety and mathematics performance. Frontiers in Psychology. 2016;**6**:1987. DOI: 10.3389/fpsyg.2015.01987

[12] Mazzocco MMM, Hanich LB, Noeder MM. Primary school age students' spontaneous comments about math reveal emerging

10.1155/2012/170310

2005. pp. 455-467

pp. 29-47

dispositions linked to later mathematics achievement. Child Development Research. 2012;**170**:310. DOI:

[13] Butterworth B. Developmental dyscalculia. In: Campbell JID, editor. Handbook of Mathematical Cognition. New York, NY, US: Psychology Press;

[14] Butterworth B. Foundational numerical capacities and the origins of dyscalculia. Trends in Cognitive Sciences. 2010;**14**(12):534-541. DOI:

10.1016/j.tics.2010.09.007

[15] Mazzocco MMM. Defining and differentiating mathematical learning disabilities and difficulties. In: Berch DB, Mazzocco MMM, editors. Why Is Math So Hard for some Children? The Nature and Origins of Mathematical Learning Difficulties and Disabilities. Baltimore: Brookes; 2007.

Reviews. 2009;**15**(1):60-68

Routledge; 2008

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*DOI: http://dx.doi.org/10.5772/intechopen.91003*

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Ricardo Moura1

\*, Suzane Garcia1

\*Address all correspondence to: ricardoojm@gmail.com

2 Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

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[63] Raghubar K, Cirino P, Barnes M, Ewing-Cobbs L, Fletcher J, Fuchs L. Errors in multi-digit arithmetic and behavioral inattention in children with math difficulties. Journal of Learning Disabilities. 2009;**42**:356-371. DOI:

10.1177/0022219409335211

[64] Cornoldi C, Mammarella I, Fine JG. Nonverbal Learning

[65] Drummond CR, Ahmad SA, Rourke BP. Rules for the classification of younger children with nonverbal learning disabilities and basic phonological processing disabilities. Archives of Clinical Neuropsychology. 2005;**20**(2):171-182. DOI: 10.1016/j.

Disabilities. Oxford: Oxford University

[66] Rourke BP, Conway JA. Disabilities of arithmetic and mathematical

reasoning: Perspectives from neurology and neuropsychology. Journal of Learning Disabilities. 1997;**30**:34-46

[67] Bull R, Espy KA, Wiebe SA. Shortterm memory, working memory,

ridd.2011.03.012

Press; 2016

acn.2004.05.001

achievement. PLoS One.

pone.0067374

*Developmental Dyscalculia: Nosological Status and Cognitive Underpinnings DOI: http://dx.doi.org/10.5772/intechopen.91003*

control, not non-verbal number acuity, correlate with mathematics achievement. PLoS One. 2013;**8**(6):e67374. DOI: 10.1371/journal. pone.0067374

*Learning Disabilities - Neurological Bases, Clinical Features and Strategies of Intervention*

and lexical competencies. Journal of Experimental Child Psychology. 2013;**116**(3):707-727. DOI: 10.1016/j.

[53] Landerl K, Moll K. Comorbidity of learning disorders: Prevalence and familial transmission. Journal of Child Psychology and Psychiatry.

[54] Simmons FR, Singleton C. Do weak phonological representations impact on arithmetic development? A review of research into arithmetic and dyslexia. Dyslexia. 2008;**14**(2):77-94. DOI:

[55] Currie J, Stabile M. Child mental health and human capital accumulation: The case of ADHD. Journal of Health Economics. 2006;**25**(6):1094-1118. DOI:

[56] Corbetta M, Shulman GL. Control of goal-directed and stimulus-driven attention in the brain. Nature Reviews Neuroscience. 2002;**3**(3):201-215. DOI:

Jakobson LS, Carey DP. A neurological dissociation between perceiving objects and grasping them. Nature. 1991;**349**(6305):154-156. DOI:

[58] Jung RE, Haier RJ. The parietofrontal integration theory (P-FIT) of intelligence: Converging neuroimaging

[59] Ungerleider LG, Haxby JV. "What" and "where" in the human brain. Current Opinion in Neurobiology. 1994;**4**(2):157-165. DOI: 10.1016/

[60] Gilmore C, Attridge N, Clayton S, Cragg L, Johnson S, Marlow N, et al. Individual differences in inhibitory

evidence. Behavioral and Brain Sciences. 2007;**30**(2):135-154. DOI: 10.1017/S0140525X07001185

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[57] Goodale MA, Milner AD,

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10.1038/349154a0

[46] De Smedt B, Noël MP, Gilmore C, Ansari D. How do symbolic and non-symbolic numerical magnitude processing skills relate to individual differences in children's mathematical skills? A review of evidence from brain and behavior. Trends in Neuroscience and Education. 2013;**2**:48-55. DOI:

10.1016/j.tine.2013.06.001

jecp.2008.12.006

s007870070004

2000;**77**(3):236-263

[47] Landerl K, Kölle C. Typical and atypical development of basic numerical skills in elementary school. Journal of Experimental Child Psychology. 2009;**103**(4):546-565. DOI: 10.1016/j.

[48] Simmons F, Singleton C, Horne J. Phonological awareness and visualspatial sketchpad functioning predict early arithmetic attainment: Evidence from a longitudinal study. European Journal of Cognitive Psychology. 2008;**20**:711-722. DOI: 10.1080/09541440701614922

[49] Geary DC. From infancy to adulthood: The development of numerical abilities. European Child Adolescent Psychiatry. 2000;**2**(22):II11-II16. DOI: 10.1007/

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[50] Geary DC, Hoard MK, Hamson CO. Numerical and arithmetical cognition: Patterns of functions and deficits in children at risk for a mathematical disability. Journal of Experimental Child

[51] Geary DC, Hamson CO, Hoard MK. Numerical and arithmetical cognition: A longitudinal study of process and concept deficits in children with learning disability. Journal of Experimental Child Psychology.

[52] Moura R, Wood G, Pinheiro-Chagas P, Lonnemann J, Krinzinger H, Willmes K, et al. Transcoding abilities in typical and atypical mathematics achievers: The role of working memory and procedural

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[62] Moeller K, Pixner S, Zuber J, Kaufmann L, Nuerk HC. Early placevalue understanding as a precursor for later arithmetic performance-A longitudinal study on numerical development. Research in Developmental Disabilities. 2011;**32**(5):1837-1851. DOI: 10.1016/j. ridd.2011.03.012

[63] Raghubar K, Cirino P, Barnes M, Ewing-Cobbs L, Fletcher J, Fuchs L. Errors in multi-digit arithmetic and behavioral inattention in children with math difficulties. Journal of Learning Disabilities. 2009;**42**:356-371. DOI: 10.1177/0022219409335211

[64] Cornoldi C, Mammarella I, Fine JG. Nonverbal Learning Disabilities. Oxford: Oxford University Press; 2016

[65] Drummond CR, Ahmad SA, Rourke BP. Rules for the classification of younger children with nonverbal learning disabilities and basic phonological processing disabilities. Archives of Clinical Neuropsychology. 2005;**20**(2):171-182. DOI: 10.1016/j. acn.2004.05.001

[66] Rourke BP, Conway JA. Disabilities of arithmetic and mathematical reasoning: Perspectives from neurology and neuropsychology. Journal of Learning Disabilities. 1997;**30**:34-46

[67] Bull R, Espy KA, Wiebe SA. Shortterm memory, working memory,

and executive functioning in preschoolers: Longitudinal predictors of mathematical achievement at age 7 years. Developmental Neuropsychology. 2008;**33**:205-228

[68] Geary DC. Mathematics and learning disabilities. Journal of Learning Disabilities. 2004;**37**:4-15

[69] Hitch GJ, McAuley E. Working memory in children with specific arithmetical learning difficulties. British Journal of Psychology. 1991;**82**(3):375- 386. DOI: 10.1111/j.2044-8295.1991. tb02406.x

[70] Keeler ML, Swanson HL. Does strategy knowledge influence working memory in children with mathematical disabilities? Journal of Learning Disabilities. 2001;**34**(5):418-434. DOI: 10.1177/002221940103400504

[71] Passolunghi MC, Siegel LS. Shortterm memory, working memory, and inhibitory control in children with difficulties in arithmetic problem solving. Journal of Experimental Child Psychology. 2001;**80**(1):44-57. DOI: 10.1006/jecp.2000.2626

[72] Passolunghi MC, Siegel LS. Working memory and access to numerical information in children with disability in mathematics. Journal of Experimental Child Psychology. 2004;**88**(4):348-367. DOI: 10.1016/j. jecp.2004.04.002

[73] Swanson HL. Cognitive processes that underlie mathematical precociousness in young children. Journal of Experimental Child Psychology. 2006;**93**:239-264

[74] Ashkenazi S, Rosenberg-Lee M, Tenison C, Menon V. Weak taskrelated modulation and stimulus representations during arithmetic problem solving in children with developmental dyscalculia. Developmental Cognitive Neuroscience.

**121**

stimuli.

**Chapter 9**

**Abstract**

**1. Introduction**

learning for transmitting teaching.

limit their social interaction.

with Autism

Learning Disabilities in Children

Children with autism spectrum disorders often present signs of cognitive strategies that are not within the expected developmental profile. Therefore, it should be expected that the learning process of children with this disorder should be the focus of several studies regarding schooling and literacy. Unfortunately, that is not the real situation. In this chapter, the authors propose to present an overview of the available literature about learning, reading, and literacy in children with the autism spectrum disorders and report results of studies about the association between executive functions and reading abilities in children with autism spectrum disor-

This chapter aims to gather and integrate studies on the development and neurocognitive processes involved in learning by children with autism spectrum disorder (ASD). It is believed that we need to know the neuropsychological foundations of

Fonseca [1] describes that, although learning capacity is inherent to several spe-

The literature reports that difficulties in learning conditional relationships between stimuli and concepts can lead to restrictions on an individual's life and

Communication plays an important role in integration of auditory and visual stimuli. This way, the understanding of the environment arises from the interaction between people, and learning is a result of the relationship created through sensory

**2. Learning by children with ASD: language, social, and cognitive factors**

It is known that language occurs mostly by meaningful experiences and situations. Although it depends on cognitive development, physiological integrity, and linguistic abilities, the environmental demands and support have an essential role in the child's learning process. The construction of a socially shared code that leads to the assignment of meaning to the world's various elements and experiences depends

cies, the human is the only species that transmits teaching intentionally.

*Ingrid Ya I Sun, Ana Carolina Martins Cortez* 

*and Fernanda Dreux Miranda Fernandes*

ders that attend to regular and special schools in Brazil.

**Keywords:** autism, children, language, learning, reading

2012;**2**(Suppl. 1):S152-S166. DOI: 10.1016/j.dcn.2011.09.006

[75] Noël MP, Turconi E. Assessing number transcoding in children. European Review of Applied Psychology. 1999;**49**(4):295-304

#### **Chapter 9**

*Learning Disabilities - Neurological Bases, Clinical Features and Strategies of Intervention*

2012;**2**(Suppl. 1):S152-S166. DOI:

[75] Noël MP, Turconi E. Assessing number transcoding in children. European Review of Applied Psychology. 1999;**49**(4):295-304

10.1016/j.dcn.2011.09.006

**120**

## Learning Disabilities in Children with Autism

*Ingrid Ya I Sun, Ana Carolina Martins Cortez and Fernanda Dreux Miranda Fernandes*

#### **Abstract**

Children with autism spectrum disorders often present signs of cognitive strategies that are not within the expected developmental profile. Therefore, it should be expected that the learning process of children with this disorder should be the focus of several studies regarding schooling and literacy. Unfortunately, that is not the real situation. In this chapter, the authors propose to present an overview of the available literature about learning, reading, and literacy in children with the autism spectrum disorders and report results of studies about the association between executive functions and reading abilities in children with autism spectrum disorders that attend to regular and special schools in Brazil.

**Keywords:** autism, children, language, learning, reading

#### **1. Introduction**

This chapter aims to gather and integrate studies on the development and neurocognitive processes involved in learning by children with autism spectrum disorder (ASD). It is believed that we need to know the neuropsychological foundations of learning for transmitting teaching.

Fonseca [1] describes that, although learning capacity is inherent to several species, the human is the only species that transmits teaching intentionally.

The literature reports that difficulties in learning conditional relationships between stimuli and concepts can lead to restrictions on an individual's life and limit their social interaction.

Communication plays an important role in integration of auditory and visual stimuli. This way, the understanding of the environment arises from the interaction between people, and learning is a result of the relationship created through sensory stimuli.

#### **2. Learning by children with ASD: language, social, and cognitive factors**

It is known that language occurs mostly by meaningful experiences and situations. Although it depends on cognitive development, physiological integrity, and linguistic abilities, the environmental demands and support have an essential role in the child's learning process. The construction of a socially shared code that leads to the assignment of meaning to the world's various elements and experiences depends

on the interaction with other significant persons. Language and memory are also dependent on meaningful situations and experiences. Abilities acquired through systematic training, despite frequently presenting fast results, are discarded as fast as they are acquired if they are not used or associated with meaningful contexts.

Based on these ideas, it seems reasonable to suppose that children with autism spectrum disorders present some disadvantage in the learning process because they have a social inability that is inherent to the ASD features, with varied degrees of impairment in social interactions. This way, it is accepted that language impairment of children with autism is not necessarily associated with linguistic structures, although they are affected in some children. Language impairments of children with ASD are essentially related to pragmatic abilities, also involving different levels of inabilities, from the lack of contact to subtle difficulties regarding interaction and conversation abilities. This is another reason why it is fundamental to understand the child's context and environment, to assess the impact of each child's inabilities and design intervention plans that address the most efficient and timely intervention.

Several recent studies show that including families in the therapeutic process of children with ASD increases better outcomes and prognosis than traditional oneon-one therapeutic approaches.

Authors like Winnicot [2] consider emotional health as the development's "back bone," allowing cognitive and linguistic development and therefore enabling successful learning processes. Regardless of the causal relation and of the hierarchy among these areas of development, the importance of emotional health to learning is unquestionable. Perceiving and processing sensorial information and positively assimilating and interpreting information in order to build and learn healthily and creatively—that is, so that cognitive processing really occurs—depend on emotional health.

Studies that focus on the importance of engaging parents and caregivers are increasing in number and impact, with results increasingly consistent showing that the quality of life of parents and caregivers as well as their involvement in the intervention processes with children with ASD has a positive influence in the outcomes of these processes.

The symptoms often found that ASD individuals also fit in the attention deficit hyperactivity disorder (ADHD) diagnosis, leading researches to compare learning performance between individuals with ASD and ADHD. Both diagnoses present significant impairments in cognitive performance, and it is important to make considerations from the neurocognitive perspective, raising questions and studies that involve tasks that require skills such as executive function (EF), theory of mind (ToM), language, and even correlations between them, seeking possible relations of causality.

EF is currently defined as a cognitive process necessary to define a goal and accomplish it, including the skills needed for it. Among them, working memory, inhibitory control, and cognitive flexibility are included. Working memory is the ability to rescue information previously stored to accomplish a task. Inhibitory control is the ability to suppress any actions or information that may interrupt or hinder the execution of the task or planning.

EF is closely linked to communicative skills, impacting learning, autonomy, and social life of the individual with ASD. This, in part, makes it difficult to understand the direct impact of EF impairment on children with ASD. Even the studies do not yet reach a consensus on impairments in EF in this population. Some studies indicate deficit and risk indicating the causal relationship between EF and other abilities, while others show that individuals with ASD do not present greater impairment than other groups with typical development (TD), developmental

**123**

*Learning Disabilities in Children with Autism DOI: http://dx.doi.org/10.5772/intechopen.89234*

impairment of the disorder.

with inhibition control.

expressive.

children with ASD.

**3. Learning to read**

their parents and their community.

language disorder (DLD), and ADHD, indicating that this may not be the central

Some researchers, including Kado and collaborators [3], report in their paper that the working memory performance of children with ASD and ADHD is similar, but their performance is below when compared with TD children, even when matched with IQ and school age. However, other researchers like Roleofs and collaborators did not find significant differences in working memory between adolescents and adults with ASD and intellectual disability when compared with individuals without ASD matched with IQ [4]. In an attempt to understand the interdependence of working memory with language, some studies separate the assessment of this cognitive ability between visual or spatial working memory and verbal working memory. A very interesting research that tries to understand the relation of working memory and language ability was Hill's paper in 2015 [5]. The working memory was evaluated and compared in 5- to 8-year-old children with ASD and DLD. In this study, children with ASD were separated into two groups: children with and without language impairment. Children with proper language had better performances than children with language impairment. In addition, children with ASD and impaired language performed similarly to children with DLD in most verbal working memory tasks, but none of these groups differed in visual working memory tasks, suggesting their interdependence. This also happens

The findings of inhibitory control studies in children with ASD are diverse. Some indicate significant losses, while others find no differences compared to ADHD and DT. A widely used test to verify this ability is Stroop, which requires a refined language skill. Corbett and his collaborators [6] performed several inhibitory control tests, with and without the need for verbal expressive language. In the test, requiring verbal ability, children with ASD and ADHD had worse performances than TD children. In the test where the verbal expressive ability was not required—children should heard or saw a certain number to answer or not—children with ASD performed worse than children with DT and ADHD. However, it is important to note that, even in the test of visual working memory, which supposed

to not requiring expressive language, the task required a linguistic ability.

And the same pattern happens in researches that attempt to assess cognitive flexibility [7] using tasks that require some level of language, comprehensive or

The fact that neuropsychological assessments are intended to assess language and are not sensitive to these skills has been a frequent problem in most proposed assessments. In general, these assessments are made by psychologists who don't have deep knowledge to determine language failures or even to distinguish or define the language structures required for that. Many misjudge language only as an expressive or verbal act, which is conceptually wrong, or disregard the cultural component of language, or even fail to evaluate language ability alone, often considering the cognitive strategies used by the child as language ability or otherwise. And as noted above, this knowledge is essential to clarify a possible causal relationship or to shed light on the possible association between cognitive and language areas, not only in

For children with typical development, learning to speak can naturally come out observing and participating in moments and situations of communication with *Learning Disabilities - Neurological Bases, Clinical Features and Strategies of Intervention*

on the interaction with other significant persons. Language and memory are also dependent on meaningful situations and experiences. Abilities acquired through systematic training, despite frequently presenting fast results, are discarded as fast as they are acquired if they are not used or associated with meaningful contexts. Based on these ideas, it seems reasonable to suppose that children with autism spectrum disorders present some disadvantage in the learning process because they have a social inability that is inherent to the ASD features, with varied degrees of impairment in social interactions. This way, it is accepted that language impairment of children with autism is not necessarily associated with linguistic structures, although they are affected in some children. Language impairments of children with ASD are essentially related to pragmatic abilities, also involving different levels of inabilities, from the lack of contact to subtle difficulties regarding interaction and conversation abilities. This is another reason why it is fundamental to understand the child's context and environment, to assess the impact of each child's inabilities and design intervention plans that address the most efficient and timely

Several recent studies show that including families in the therapeutic process of children with ASD increases better outcomes and prognosis than traditional one-

Authors like Winnicot [2] consider emotional health as the development's "back bone," allowing cognitive and linguistic development and therefore enabling successful learning processes. Regardless of the causal relation and of the hierarchy among these areas of development, the importance of emotional health to learning is unquestionable. Perceiving and processing sensorial information and positively assimilating and interpreting information in order to build and learn healthily and creatively—that is, so that cognitive processing really occurs—depend on emotional

Studies that focus on the importance of engaging parents and caregivers are increasing in number and impact, with results increasingly consistent showing that the quality of life of parents and caregivers as well as their involvement in the intervention processes with children with ASD has a positive influence in the outcomes

The symptoms often found that ASD individuals also fit in the attention deficit hyperactivity disorder (ADHD) diagnosis, leading researches to compare learning performance between individuals with ASD and ADHD. Both diagnoses present significant impairments in cognitive performance, and it is important to make considerations from the neurocognitive perspective, raising questions and studies that involve tasks that require skills such as executive function (EF), theory of mind (ToM), language, and even correlations between them, seeking possible relations of

EF is currently defined as a cognitive process necessary to define a goal and accomplish it, including the skills needed for it. Among them, working memory, inhibitory control, and cognitive flexibility are included. Working memory is the ability to rescue information previously stored to accomplish a task. Inhibitory control is the ability to suppress any actions or information that may interrupt or

EF is closely linked to communicative skills, impacting learning, autonomy, and social life of the individual with ASD. This, in part, makes it difficult to understand the direct impact of EF impairment on children with ASD. Even the studies do not yet reach a consensus on impairments in EF in this population. Some studies indicate deficit and risk indicating the causal relationship between EF and other abilities, while others show that individuals with ASD do not present greater impairment than other groups with typical development (TD), developmental

**122**

intervention.

health.

causality.

of these processes.

hinder the execution of the task or planning.

on-one therapeutic approaches.

language disorder (DLD), and ADHD, indicating that this may not be the central impairment of the disorder.

Some researchers, including Kado and collaborators [3], report in their paper that the working memory performance of children with ASD and ADHD is similar, but their performance is below when compared with TD children, even when matched with IQ and school age. However, other researchers like Roleofs and collaborators did not find significant differences in working memory between adolescents and adults with ASD and intellectual disability when compared with individuals without ASD matched with IQ [4]. In an attempt to understand the interdependence of working memory with language, some studies separate the assessment of this cognitive ability between visual or spatial working memory and verbal working memory. A very interesting research that tries to understand the relation of working memory and language ability was Hill's paper in 2015 [5]. The working memory was evaluated and compared in 5- to 8-year-old children with ASD and DLD. In this study, children with ASD were separated into two groups: children with and without language impairment. Children with proper language had better performances than children with language impairment. In addition, children with ASD and impaired language performed similarly to children with DLD in most verbal working memory tasks, but none of these groups differed in visual working memory tasks, suggesting their interdependence. This also happens with inhibition control.

The findings of inhibitory control studies in children with ASD are diverse. Some indicate significant losses, while others find no differences compared to ADHD and DT. A widely used test to verify this ability is Stroop, which requires a refined language skill. Corbett and his collaborators [6] performed several inhibitory control tests, with and without the need for verbal expressive language. In the test, requiring verbal ability, children with ASD and ADHD had worse performances than TD children. In the test where the verbal expressive ability was not required—children should heard or saw a certain number to answer or not—children with ASD performed worse than children with DT and ADHD. However, it is important to note that, even in the test of visual working memory, which supposed to not requiring expressive language, the task required a linguistic ability.

And the same pattern happens in researches that attempt to assess cognitive flexibility [7] using tasks that require some level of language, comprehensive or expressive.

The fact that neuropsychological assessments are intended to assess language and are not sensitive to these skills has been a frequent problem in most proposed assessments. In general, these assessments are made by psychologists who don't have deep knowledge to determine language failures or even to distinguish or define the language structures required for that. Many misjudge language only as an expressive or verbal act, which is conceptually wrong, or disregard the cultural component of language, or even fail to evaluate language ability alone, often considering the cognitive strategies used by the child as language ability or otherwise. And as noted above, this knowledge is essential to clarify a possible causal relationship or to shed light on the possible association between cognitive and language areas, not only in children with ASD.

#### **3. Learning to read**

For children with typical development, learning to speak can naturally come out observing and participating in moments and situations of communication with their parents and their community.

In contrast, the act of learning to read and write is a complex task, composed of multiple interdependent processes, including understanding how the visual symbols correspond to spoken language [8].

There is a range of articles that discuss the importance and interdependence of good oral language development for the success of written code acquisition, since writing is considered a representation of language.

The literature of clinical neuropsychology reports that an assessment of cognitive strengths and weaknesses is useful for children with any developmental or learning disorder [9]. Considering the heterogeneity of the clinical settings of children with ASD, assessing and understanding the child's individual strengths and weaknesses help better focus school plans and medical treatment and understand the possible areas of difficulty [9, 10].

Westerveld et al. [11] argue that learning to read is just another challenge for children with ASD. In their study, they found that approximately 30–60% of these children present some difficulty to develop literacy. It is important to highlight that even higher functioning children are also part of the statistics.

Jones et al. [12] described that the cognitive heterogeneity of children with ASD is an element that makes it difficult to characterize the academic difficulties of this population. In addition, they report that cognitive abilities may not be congruent with their writing operations.

In their paper, Fletcher and Miciak [9] argue the fact that some children have deficits in cognitive tests may not necessarily indicate causal direction in a child's learning difficulties. A cognitive deficit does not indicate "why" a child has a learning problem.

Another possible justification found in the literature for this variation in the development of reading and writing in children with ASD is the individual differences in language skills in the areas of phonology, semantics, and syntax [11, 12].

Davidson and Weismer [10] describe that reading disabilities can be classified based on problems that arise in decoding or comprehension abilities. It's important to know the history of reading instruction for children with exceptional educational needs to consider what is known about reading abilities in individuals with ASD [13].

Gabig [14] in her study with children with ASD, who reduced performance in areas such as vocabulary, may have negative influences on skills such as phonological processing. In addition, she found that some abilities related to decoding ability appear to be relatively intact.

Richardson and Heikki [8] discuss that the reasons for the phonological deficit in autism are still not clear but certainly interfere in the quality of mental representations and in the quality of the lexical, creating a poor link between the phonological awareness and reading skills.

Other authors question whether insufficient performance in reading skills are from specific verbal material defects or the consequence of perceptual, temporal, or long-term memory failure problems [15].

Overall, studies indicate that although the ability to recognize written words may be similar to that of typically developing learners, children with ASD tend to have deficits in integrating information. That is, they have difficulty retrieving and integrating meanings necessary for reading comprehension, including the ability to create connections between content read with prior knowledge and the ability to make inferences [16].

The literature describes that most children with autism show average ability to recognize words while reading and to accurately spell words for age and grade level. In contrast, what the literature cannot yet explain is whether phonological

**125**

abilities.

*Learning Disabilities in Children with Autism DOI: http://dx.doi.org/10.5772/intechopen.89234*

children with autism [14].

awareness accompanies the good performance of phonetic decoding presented by

There are several studies that speculate if children with ASD would perform poorer when decoding pseudowords than when reading sight words because of a rote memorization of the visual shape of words. Most of their results indicated that children with autism do not show preference for the visual recognition of sight words over the decoding of pseudowords. It suggests that ASD children are capable of using visual and phonological recognition process to identify written words. Thus, studies lead us to believe that children with autism can benefit from other

Hyperlexia is frequently one condition presented by children with ASD. It is characterized by a child's precocious ability to read (far above what would be expected at their age). As with all individuals, children with hyperlexia have a wide range of skills and deficits. The high abilities to decode do not exclude the possibility that children may have a cognitive, language learning and/or social disorder. What experts argue is that content that can be "formally" taught can be more easily learned by children with ASD. Already "intuitive" content such as phonologi-

Corso et al. [17] tested the correlation between reading tasks and different neuropsychological functions. They concluded that the strongest significant cor-

Pellicano [18] pointed out that there are no studies that explicitly investigate the nature of executive functions in autism, arguing that there are only researches with the fractionation of these functions, that is, as if just one of these components can

It is also often possible to find studies that compare the performance of children with ASD in theory of mind abilities (ToM). Some studies report that children with executive function deficits but with intact theory of mind abilities are hardly found. Since the use of theory of mind abilities is essential to the mental and behavioral functioning, understanding the nature of these skills cannot be discarded during

One of the reasons why individuals with ASD may have difficulties in representing situations involving theory of mind may be explained by the fact that they have difficulty integrating clues that are relevant to the context and self-representation. This would be a justification for the text comprehension difficulties so often observed in this population, especially the difficulties related to understanding

Deficits in the functioning of EF and literacy may differ between disturbances. Assessing them and identifying their deficits can provide information on which systems may be impaired and, most importantly, what can be done to stimulate them.

The intervention approach may consider all areas of oral or written language where the children have deficits. It's important to associate information about the student's facilitating routes, whether auditory, visual, or motor. This way, the therapist should investigate whether the influence of several processing modalities obtain a more comprehensive understanding of the child's potential perceptual

Bosseler and Massaro [20] describe that technology is also being used in educa-

**4. Important considerations for clinical intervention in SLP**

tional settings as an effective method of getting children engaged.

access channels to achieve good reading and writing performance.

cal awareness skills would be less understood by this population.

relations occurred during executive functions tasks.

the assessment of reading and writing skills [19].

pragmatic and nonliteral aspects of language.

be specifically affected in autism.

#### *Learning Disabilities in Children with Autism DOI: http://dx.doi.org/10.5772/intechopen.89234*

*Learning Disabilities - Neurological Bases, Clinical Features and Strategies of Intervention*

bols correspond to spoken language [8].

the possible areas of difficulty [9, 10].

with their writing operations.

ing problem.

with ASD [13].

appear to be relatively intact.

cal awareness and reading skills.

make inferences [16].

long-term memory failure problems [15].

writing is considered a representation of language.

even higher functioning children are also part of the statistics.

In contrast, the act of learning to read and write is a complex task, composed of multiple interdependent processes, including understanding how the visual sym-

There is a range of articles that discuss the importance and interdependence of good oral language development for the success of written code acquisition, since

The literature of clinical neuropsychology reports that an assessment of cognitive strengths and weaknesses is useful for children with any developmental or learning disorder [9]. Considering the heterogeneity of the clinical settings of children with ASD, assessing and understanding the child's individual strengths and weaknesses help better focus school plans and medical treatment and understand

Westerveld et al. [11] argue that learning to read is just another challenge for children with ASD. In their study, they found that approximately 30–60% of these children present some difficulty to develop literacy. It is important to highlight that

Jones et al. [12] described that the cognitive heterogeneity of children with ASD is an element that makes it difficult to characterize the academic difficulties of this population. In addition, they report that cognitive abilities may not be congruent

In their paper, Fletcher and Miciak [9] argue the fact that some children have deficits in cognitive tests may not necessarily indicate causal direction in a child's learning difficulties. A cognitive deficit does not indicate "why" a child has a learn-

Another possible justification found in the literature for this variation in the development of reading and writing in children with ASD is the individual differences in language skills in the areas of phonology, semantics, and syntax [11, 12]. Davidson and Weismer [10] describe that reading disabilities can be classified based on problems that arise in decoding or comprehension abilities. It's important to know the history of reading instruction for children with exceptional educational needs to consider what is known about reading abilities in individuals

Gabig [14] in her study with children with ASD, who reduced performance in areas such as vocabulary, may have negative influences on skills such as phonological processing. In addition, she found that some abilities related to decoding ability

Richardson and Heikki [8] discuss that the reasons for the phonological deficit in autism are still not clear but certainly interfere in the quality of mental representations and in the quality of the lexical, creating a poor link between the phonologi-

Other authors question whether insufficient performance in reading skills are from specific verbal material defects or the consequence of perceptual, temporal, or

Overall, studies indicate that although the ability to recognize written words may be similar to that of typically developing learners, children with ASD tend to have deficits in integrating information. That is, they have difficulty retrieving and integrating meanings necessary for reading comprehension, including the ability to create connections between content read with prior knowledge and the ability to

The literature describes that most children with autism show average ability to recognize words while reading and to accurately spell words for age and grade level. In contrast, what the literature cannot yet explain is whether phonological

**124**

awareness accompanies the good performance of phonetic decoding presented by children with autism [14].

There are several studies that speculate if children with ASD would perform poorer when decoding pseudowords than when reading sight words because of a rote memorization of the visual shape of words. Most of their results indicated that children with autism do not show preference for the visual recognition of sight words over the decoding of pseudowords. It suggests that ASD children are capable of using visual and phonological recognition process to identify written words. Thus, studies lead us to believe that children with autism can benefit from other access channels to achieve good reading and writing performance.

Hyperlexia is frequently one condition presented by children with ASD. It is characterized by a child's precocious ability to read (far above what would be expected at their age). As with all individuals, children with hyperlexia have a wide range of skills and deficits. The high abilities to decode do not exclude the possibility that children may have a cognitive, language learning and/or social disorder.

What experts argue is that content that can be "formally" taught can be more easily learned by children with ASD. Already "intuitive" content such as phonological awareness skills would be less understood by this population.

Corso et al. [17] tested the correlation between reading tasks and different neuropsychological functions. They concluded that the strongest significant correlations occurred during executive functions tasks.

Pellicano [18] pointed out that there are no studies that explicitly investigate the nature of executive functions in autism, arguing that there are only researches with the fractionation of these functions, that is, as if just one of these components can be specifically affected in autism.

It is also often possible to find studies that compare the performance of children with ASD in theory of mind abilities (ToM). Some studies report that children with executive function deficits but with intact theory of mind abilities are hardly found.

Since the use of theory of mind abilities is essential to the mental and behavioral functioning, understanding the nature of these skills cannot be discarded during the assessment of reading and writing skills [19].

One of the reasons why individuals with ASD may have difficulties in representing situations involving theory of mind may be explained by the fact that they have difficulty integrating clues that are relevant to the context and self-representation.

This would be a justification for the text comprehension difficulties so often observed in this population, especially the difficulties related to understanding pragmatic and nonliteral aspects of language.

Deficits in the functioning of EF and literacy may differ between disturbances. Assessing them and identifying their deficits can provide information on which systems may be impaired and, most importantly, what can be done to stimulate them.

#### **4. Important considerations for clinical intervention in SLP**

The intervention approach may consider all areas of oral or written language where the children have deficits. It's important to associate information about the student's facilitating routes, whether auditory, visual, or motor. This way, the therapist should investigate whether the influence of several processing modalities obtain a more comprehensive understanding of the child's potential perceptual abilities.

Bosseler and Massaro [20] describe that technology is also being used in educational settings as an effective method of getting children engaged.

Some authors argue that if we guarantee the use of materials that address the different routes, learning can occur simply due to multiple exposures without necessarily having feedback and formal interference from the therapist. Although Bosseler and Massaro observed that children profited from seeing and hearing, spoken language can better guide language learning than modality alone.

What we should expect is that stimulated content must be learned operatively, processed, stored, and related to a set of experience to apply functionality and use it in a meaningful way.

Currently, there are already some available therapeutic methods that can be developed by parents at home. However, there are not yet numerous clinical articles that allow a more accurate interpretation of the results. Thus, there are limitations in measuring the effectiveness of these approaches in treating autistic children, especially in the long-term.

There are authors who emphasize how important it is to encourage these types of family-based therapeutic approaches as key interveners; however, understand that caregiver training should be done very carefully so that such interventions are not inadequately developed and reinforce difficulties and changes in child development.

#### **5. Conclusion**

As we have seen, environmental support plays an essential role in the child's learning process. The findings suggest that children with autism spectrum disorders (ASD) have some disadvantage in the learning process due to their inherent social disability to ASD characteristics.

The literature describes that parental support and engagement in intervention processes with children with ASD positively influence the outcomes of these processes. Therefore, the intervention process should encompass all the possibilities and resources of oral and written language stimulation, associated with the information and collaboration presented by the caregivers.

The learning disabilities of children with autism exist, and our ultimate goal for these children is to create a connection between learning and functionality.

#### **Author details**

Ingrid Ya I Sun\*, Ana Carolina Martins Cortez and Fernanda Dreux Miranda Fernandes School of Medicine of University of Sao Paulo, São Paulo, Brazil

\*Address all correspondence to: ya.ingrid@gmail.com

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**127**

*Learning Disabilities in Children with Autism DOI: http://dx.doi.org/10.5772/intechopen.89234*

> the technology-enhanced learning environment for learning to read. Human Technology. 2014;**10**(1):39-60. DOI: 10.17011/ht/urn.201405281859

> Comprehensive cognitive assessments are not necessary for the identification and treatment of learning disabilities. Archives of Clinical Neuropsychology. 2017;**32**(1):2-7. DOI: 10.1093/arclin/

[10] Davidson MM, Weismer SL. Characterization and prediction of early reading abilities in children on the autism spectrum. Journal of Autism & Developmental Disorders. 2014;**44**(4):828-845. DOI: 10.1007/

[11] Westerveld MF et al. A systematic review of the literature on emergent literacy skills of preschool children with autism spectrum disorder. The Journal of Special Education. 2016;**50**(1):37-48.

DOI: 10.1177/0022466915613593

Golden H, Marsden AJS, Tregay J, Simonoff E, et al. Reading and arithmetic in adolescents with autism spectrum disorders: Peaks and dips in attainment. Neuropsychology. 2009;**23**(6):718-728. DOI: 10.1037/

[13] Brown HM, Oram-Cardy J, Johnson A. A meta-analysis of the reading comprehension skills of individuals on the autism spectrum. Journal of Autism and Developmental Disorders. 2013;**43**(4):932-955. DOI: 10.1007/s10803-012-1638-1

[14] Gabig CS. Phonological awareness and word recognition in reading by children with autism. Communication

Disorders Quarterly. 2010. DOI: 10.1177/1525740108328410

[12] Jones CRG, Happé F,

a0016360

s10803-013-1936-2

[9] Fletcher JM, Jeremy M.

acw103

[1] Fonseca V. Papel das funções cognitivas, conotativas e executivas na aprendizagem: uma abordagem neuropsicológica. Revista da Associação

Brasileira de Psicopedagogia.

[2] Winnicott DW. Playing and Reality.

[3] Kado Y, Sanada S, Yanagihara M, et al. Executive function in children with pervasive developmental disorder and attention-deficit/hyperactivity disorder assessed by the Keio version of the Wisconsin card sorting test. Brain & Development. 2012;**34**(05):354-359

[4] Roleofs RL, Visser EM, Berger HJC, Prins JB, Van Schrojenstein Lantman-De

Valk HMJ, Teunisse JP. Executive functioning in individuals with intellectual disabilities and autism spectrum disorders. Journal of Intellectual Disability Research.

[5] Hill AP, Van Santen J, Gorman K, Laghorst BH, Fombonne E. Memory in language-impaired children with and without autism. Journal of Neurodevelopmental Disorder.

[6] Corbett BA, Constantine LJ, Hendren R, Rocke D, Ozonoff S. Examining executive functioning in children with autism spectrum disorder, attention deficit hyperactivity disorder and typical development. Psychiatry Research.

[7] Leung RC, Zakanis KK. Brief report: Cognitive flexibility in autism spectrum disorders: A quantitative review. Journal of Autism & Developmental Disorders.

[8] Richardson U, Lyytinen H. The GraphoGame method: The theoretical and methodological background of

2015;**59**(02):125-137

2015;**7**(01):10

2009;**166**(2-3):210-222

2014;**44**(10):2628-2645

2014;**31**(96):236-253

London: Tavistock; 1971

**References**

*Learning Disabilities in Children with Autism DOI: http://dx.doi.org/10.5772/intechopen.89234*

#### **References**

*Learning Disabilities - Neurological Bases, Clinical Features and Strategies of Intervention*

in a meaningful way.

development.

**5. Conclusion**

**Author details**

especially in the long-term.

disability to ASD characteristics.

mation and collaboration presented by the caregivers.

Ingrid Ya I Sun\*, Ana Carolina Martins Cortez and Fernanda Dreux Miranda Fernandes

provided the original work is properly cited.

\*Address all correspondence to: ya.ingrid@gmail.com

School of Medicine of University of Sao Paulo, São Paulo, Brazil

Some authors argue that if we guarantee the use of materials that address the different routes, learning can occur simply due to multiple exposures without necessarily having feedback and formal interference from the therapist. Although Bosseler and Massaro observed that children profited from seeing and hearing, spoken language can better guide language learning than modality alone.

What we should expect is that stimulated content must be learned operatively, processed, stored, and related to a set of experience to apply functionality and use it

Currently, there are already some available therapeutic methods that can be developed by parents at home. However, there are not yet numerous clinical articles that allow a more accurate interpretation of the results. Thus, there are limitations in measuring the effectiveness of these approaches in treating autistic children,

There are authors who emphasize how important it is to encourage these types of family-based therapeutic approaches as key interveners; however, understand that caregiver training should be done very carefully so that such interventions are not inadequately developed and reinforce difficulties and changes in child

As we have seen, environmental support plays an essential role in the child's learning process. The findings suggest that children with autism spectrum disorders (ASD) have some disadvantage in the learning process due to their inherent social

The literature describes that parental support and engagement in intervention processes with children with ASD positively influence the outcomes of these processes. Therefore, the intervention process should encompass all the possibilities and resources of oral and written language stimulation, associated with the infor-

The learning disabilities of children with autism exist, and our ultimate goal for

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

these children is to create a connection between learning and functionality.

**126**

[1] Fonseca V. Papel das funções cognitivas, conotativas e executivas na aprendizagem: uma abordagem neuropsicológica. Revista da Associação Brasileira de Psicopedagogia. 2014;**31**(96):236-253

[2] Winnicott DW. Playing and Reality. London: Tavistock; 1971

[3] Kado Y, Sanada S, Yanagihara M, et al. Executive function in children with pervasive developmental disorder and attention-deficit/hyperactivity disorder assessed by the Keio version of the Wisconsin card sorting test. Brain & Development. 2012;**34**(05):354-359

[4] Roleofs RL, Visser EM, Berger HJC, Prins JB, Van Schrojenstein Lantman-De Valk HMJ, Teunisse JP. Executive functioning in individuals with intellectual disabilities and autism spectrum disorders. Journal of Intellectual Disability Research. 2015;**59**(02):125-137

[5] Hill AP, Van Santen J, Gorman K, Laghorst BH, Fombonne E. Memory in language-impaired children with and without autism. Journal of Neurodevelopmental Disorder. 2015;**7**(01):10

[6] Corbett BA, Constantine LJ, Hendren R, Rocke D, Ozonoff S. Examining executive functioning in children with autism spectrum disorder, attention deficit hyperactivity disorder and typical development. Psychiatry Research. 2009;**166**(2-3):210-222

[7] Leung RC, Zakanis KK. Brief report: Cognitive flexibility in autism spectrum disorders: A quantitative review. Journal of Autism & Developmental Disorders. 2014;**44**(10):2628-2645

[8] Richardson U, Lyytinen H. The GraphoGame method: The theoretical and methodological background of

the technology-enhanced learning environment for learning to read. Human Technology. 2014;**10**(1):39-60. DOI: 10.17011/ht/urn.201405281859

[9] Fletcher JM, Jeremy M. Comprehensive cognitive assessments are not necessary for the identification and treatment of learning disabilities. Archives of Clinical Neuropsychology. 2017;**32**(1):2-7. DOI: 10.1093/arclin/ acw103

[10] Davidson MM, Weismer SL. Characterization and prediction of early reading abilities in children on the autism spectrum. Journal of Autism & Developmental Disorders. 2014;**44**(4):828-845. DOI: 10.1007/ s10803-013-1936-2

[11] Westerveld MF et al. A systematic review of the literature on emergent literacy skills of preschool children with autism spectrum disorder. The Journal of Special Education. 2016;**50**(1):37-48. DOI: 10.1177/0022466915613593

[12] Jones CRG, Happé F, Golden H, Marsden AJS, Tregay J, Simonoff E, et al. Reading and arithmetic in adolescents with autism spectrum disorders: Peaks and dips in attainment. Neuropsychology. 2009;**23**(6):718-728. DOI: 10.1037/ a0016360

[13] Brown HM, Oram-Cardy J, Johnson A. A meta-analysis of the reading comprehension skills of individuals on the autism spectrum. Journal of Autism and Developmental Disorders. 2013;**43**(4):932-955. DOI: 10.1007/s10803-012-1638-1

[14] Gabig CS. Phonological awareness and word recognition in reading by children with autism. Communication Disorders Quarterly. 2010. DOI: 10.1177/1525740108328410

[15] Capovilla AGS, Joly MCRA, Ferracini F, Caparrotti NB, Carvalho MR, Raad AJ. Estratégias de leitura e desempenho em escrita no início da alfabetização. Psicologia Escolar e Educacional. 2004;**8**(2):189-197. DOI: 10.1590/S1413-85572004000200007

[16] Nunes DRP, Walter EC. Processos de leitura em educandos com autismo: um estudo de revisão. Revista Brasileira de Educação Especial, Marília;**22, 2016**(4):619-632

[17] Corso HV, Sperb TM, Jou GI, Salles JF. Metacognição e funções executivas: relações entre os conceitos e implicações para a aprendizagem. Psicologia: Teoria e Pesquisa. 2013;**29**(1):21-29. DOI: 10.1590/ S0102-37722013000100004

[18] Pellicano E. Links between theory of mind and executive function in young children with autism: Clues to developmental primacy. Developmental Psychology. 2007;**43**(4):974-990. DOI: 10.1037/0012 1649.43.4.974

[19] Meyer LKC. A Compreensão de leitura e a Teoria da Mente em crianças com autismo. Pouso Alegre. 2018:190f

[20] Massaro DW, Bosseler A. Read my lips: The importance of the face in a computer-animated tutor for vocabulary learning by children with autism. Autism. 2006;**10**(5):495-510. DOI: 10.1177/1362361306066599

**129**

Section 4

Assessment of Speech and

Language-Based Learning

Disabilities

### Section 4
