**4.1 Infant brain development and autism spectrum disorders**

Autism spectrum disorder (ASD) constitutes a heterogeneous developmental syndrome that is characterized by a triad of impairments that affect social interaction, communication skills, and a restricted range of interests and activities (APA, 2000), with highly variable outcomes. Studies have consistently shown that early intervention leads to better long-term outcomes. But early intervention is predicated on early detection. Behavioral measures have thus far proven ineffective in diagnosing autism before about 18 months of age, in part because the behavioral repertoire of infants is so limited. Neural development may precede overt behavioral observations and thus provide an earlier marker for emerging autistic behaviors. Yet, measuring functional brain development is difficult because few noninvasive methods are available for infants and it is not clear what features to measure that are biomarkers of normal development. As discussed above, multiscale entropy computed from EEG time series has been shown to be a particularly informative analysis tool for physiological signals.

Complex mental disorders such as autism are associated with abnormal brain connectivity that may vary between different regions and different scales (Noonan et al., 2009). Estimation of changes in neural connectivity might be an effective diagnostic biomarker for abnormal connectivity development that leads to ASD behaviors. The electrical signals produced by neural networks are believed to contain information about the network structure (Raghavendra et al., 2009; Stam, 2005; Zavaglia et al., 2008). The physics of complex networks suggests that system invariants will be encoded in the time series produced and measured by EEG sensors (Fuchs et al., 2007; Gao and Jin, 2009). Multiscale entropy (MSE) discussed above is one invariant measure of system dynamics that has been shown to particularly sensitive to changes in physiological systems (Costa et al., 2005b; Hu et al., 2009b; Takahashi et al., 2010), including mental disorders such as Alzheimer's Disease (Abasolo et al., 2006), schizophrenia (Takahashi et al., 2010), the effect of antipsychotic drugs (Takahashi et al., 2010) and normal aging (Bruce et al., 2009). Preliminary results suggest that MSE may be a biomarker for autism endophenotypes (Bosl et al., 2011) and may provide an earlier diagnosis than behavioral assessments.

MSE values were computed for 79 different infants: 46 at high risk for ASD (hereafter referred to as HRA) based on having an older sibling with a confirmed diagnosis of ASD and 33 controls, defined on the basis of a typically developing older sibling and no family history of neurodevelopmental disorders. The study participants were part of an on-going longitudinal study and for this analysis visits were evaluated at regular intervals. Infants were seated on their mothers' laps in a dimly lit room while a research assistant engaged their attention by blowing bubbles. This procedure was followed to limit the amount of head movement made by the infant that would interfere with the recording process. Continuous EEG was recorded with a 64-channel Sensor Net System. The data were amplified, filtered (band pass 0.1-100.0 Hz), and sampled at a frequency of 250 Hz. Data were manually reviewed to remove sections with obvious artifacts and continuous, clean 20 second segments were identified to compute alert, resting state MSE values.

Results shown in figure 2 were computing by averaging all channels in a given region, including left/right hemispheres, left/right frontal and total scalp MSE values. The plots are derived by averaging regional MSE values for all infants at a given age. These reveal a significant difference between high risk and typically developing infants (Bosl et al., 2011). The data suggest that there are not only significant differences between the high risk group and typically developing controls, but MSE follows characteristic trajectories during development. To our knowledge, these preliminary results are the first demonstration of a complex systems analysis of EEG data for biomarkers in infants at risk for a complex neurodevelopmental disorder. More details about this study and the use of machine learning to discover diagnostically significant patterns in MSE data can be found in (Bosl et al., 2011).

Fig. 2. The change in mean MSE over all channels is shown for each age. Averaging over all channels reveals that in general MSE is higher than the normal controls than in the high risk group, but regional differences cannot be seen.

The trajectory of the curves between 6 and 12 months in figure 2 appear to be as informative as information at any specific age. This is a period of important changes in brain function that are foundational for the emergence of higher level social and communicative skills that are at the heart of ASD. A key characteristic of autism is lack of social perception; autistic spectral disorders are also associated with abnormal brain connectivity (Belmonte et al., 2004; Kleinhans et al., 2008). A number of major cognitive milestones typically occur beginning at around the age 9 months and perhaps earlier in girls. These include joint perception (Behne et al., 2005) and loss of the ability to discriminate certain categories of faces (Pascalis et al., 2002). The latter developments are especially significant because they reveal how *socially-grounded experiences* influence changes in the neurocognitive mechanisms that underlie speech and face processing. In a prospective study, (Ozonoff et al., 2010) found that social communicative behaviors in infants that later developed ASD declined dramatically between 6 and 18 months when compared to typically developing infants. The results shown here may indicate that MSE is sensitive to neural correlates underlying the observed social and cognitive developments.

were manually reviewed to remove sections with obvious artifacts and continuous, clean 20

Results shown in figure 2 were computing by averaging all channels in a given region, including left/right hemispheres, left/right frontal and total scalp MSE values. The plots are derived by averaging regional MSE values for all infants at a given age. These reveal a significant difference between high risk and typically developing infants (Bosl et al., 2011). The data suggest that there are not only significant differences between the high risk group and typically developing controls, but MSE follows characteristic trajectories during development. To our knowledge, these preliminary results are the first demonstration of a complex systems analysis of EEG data for biomarkers in infants at risk for a complex neurodevelopmental disorder. More details about this study and the use of machine learning to discover diagnostically significant patterns in MSE data can be found in (Bosl et

Fig. 2. The change in mean MSE over all channels is shown for each age. Averaging over all channels reveals that in general MSE is higher than the normal controls than in the

The trajectory of the curves between 6 and 12 months in figure 2 appear to be as informative as information at any specific age. This is a period of important changes in brain function that are foundational for the emergence of higher level social and communicative skills that are at the heart of ASD. A key characteristic of autism is lack of social perception; autistic spectral disorders are also associated with abnormal brain connectivity (Belmonte et al., 2004; Kleinhans et al., 2008). A number of major cognitive milestones typically occur beginning at around the age 9 months and perhaps earlier in girls. These include joint perception (Behne et al., 2005) and loss of the ability to discriminate certain categories of faces (Pascalis et al., 2002). The latter developments are especially significant because they reveal how *socially-grounded experiences* influence changes in the neurocognitive mechanisms that underlie speech and face processing. In a prospective study, (Ozonoff et al., 2010) found that social communicative behaviors in infants that later developed ASD declined dramatically between 6 and 18 months when compared to typically developing infants. The results shown here may indicate that MSE is sensitive to neural correlates underlying the

high risk group, but regional differences cannot be seen.

observed social and cognitive developments.

second segments were identified to compute alert, resting state MSE values.

al., 2011).

Currently there are no objective medical tests for diagnosing autism. According to the American Academy of Pediatrics (AAP), data strongly suggest better outcomes in children whose conditions are diagnosed early and participate in early intervention programs. Children who are diagnosed early and given intensive behavioral intervention can often be mainstreamed and live normal, productive lives. The AAP recommends that *all* children be screened for autism at 18 and again at 24 months. Unfortunately, behavioral assessment is time consuming and requires specialized training, both of which pose a problem for routine screening. Because atypical brain development is likely to precede abnormal behavior by months or even years, a critical developmental window for early intervention may be missed if diagnosis is based entirely on a behavioral phenotype. If reliable methods can be developed to lower the age of diagnosis, and if insight is gained into the biological mechanisms that underlie the disorder, it may be possible to develop intervention strategies that can be implemented during the first year of life. Complex systems analysis of EEG may enable a relatively inexpensive and reliable way to estimate the risk of developmental disorders within the first year of life.

### **4.2 Schizophrenia**

Early studies of signal synchronization in patients diagnosed with schizophrenia used a *linear* measure of complexity based on global correlation of all channels (Wackermann et al., 1993) and synchronization based on linear correlation between amplitudes (Saito et al., 1998). Although these methods will necessarily miss nonlinear signal characteristics, they found significant differences between patients and normal controls that were interpreted to reflect decreased coordination between different brain regions, a characteristic of schizophrenia (Uhlhaas et al., 2008; Whittington, 2008).

One interpretation of biological complexity is that it reflects a systems' ability to quickly adapt and function in a changing environment (Costa et al., 2005b). The complexity of EEG signals was found in one study to be associated with the ability to attend to a task and adapt to new cognitive tasks; a significant difference in complexity was found between normal subjects and those with diagnosed schizophrenia (Li et al., 2008). Schizophrenic patients were found to have lower complexity than normal controls in some EEG channels and significantly higher interhemispheric and intrahemispheric cross mutual information values (another measure of signal complexity) than the normal controls (Na et al., 2002).

While signal complexity is a property of a single time series or EEG channel, transient synchronized activity is a measure of the interaction between different channels and an indication of communication and coordination between different brain regions. Synchronization may be used as a marker for diagnosing underlying mental disorders such as schizophrenia and may also reveal causal mechanisms (Whittington, 2008). The complexity of synchronization patterns changes during network development and reflects different neural wiring schemes and levels of cluster organization (Fuchs et al., 2007).

Abnormalities in phase synchronization between multiple bands have been found to be sensitive biomarkers for mental dysfunction in schizophrenic patients (Uhlhaas et al., 2008; Whittington, 2008). Unfortunately, similar abnormalities in synchronous activity have been found associated with a number of other mental disorders (Uhlhaas et al., 2008), so further research is required to discover if more refined patterns of synchrony exist for discriminating different disorders or subtypes. A developmental perspective may be useful here. For example, while many attempts to correlate cortical thickness with intelligence have failed, recent research demonstrated that specific characteristic growth *trajectories* of cortical thickness from infancy to early teen years were highly correlated with above or below average intelligence (Shaw et al., 2006), suggesting that growth curves of brain function may contain more information than any combination of measurements at one specific age. This would require routine brain measurements become part of the medical record and algorithms that recognize abnormal trends would need to be used to interpret data after regular checkups.
