**1. Introduction**

The utilization of adjunctive, alternative or complementary treatment methods (CAM) has been growing in recent decades, driven by demand. Based on the report published by the

© 2016 The Author(s). Licensee InTech. 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. © 2018 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.

National Institute of Mental Health (NIMH), there is a problem of burgeoning "off-label" medication prescription [1] and overrated effectiveness of pharmacological treatment for some conditions. Additionally, it has been found that best outcomes often depend on a combination of treatment strategies, including psychotherapy [2–6].

Spontaneous and evoked local field potentials were observed in various cortical and subcortical regions of patients in whom cortical and subcortical electrodes had been implanted for purposes of characterization, diagnosis, deep brain stimulation, or lesioning [25]. The cortical electrical potentials were found to be correlated with infra-slow metabolic oscillations such as fluctuations of local oxygen levels. It was also demonstrated that spectral characteristics of infra-slow oscillations of the human brain remained stable over days and weeks [25, 26]. Recently, ILF potentials have found increased interest in the international scientific community, especially with the growing scientific evidence for a significant role of infra-slow potential fluctuations in modulating the level of cortical excitability and thus regulating brain

Effect of Infra-Low Frequency Neurofeedback on Infra-Slow EEG Fluctuations

http://dx.doi.org/10.5772/intechopen.77154

77

Infra-low frequency training, developed by Susan and Siegfried Othmer, extended the conventional frequency-based training to the lower frequency range. Feedback is then a matter of observing the slowly undulating signal. The technique has been described in a paper titled Clinical Neurofeedback: Training Brain Behavior [33]. The first reported clinical application was to Post-Traumatic Stress Disorder among military veterans [34]. The second dealt with cases of epilepsy [13]. The method came to be applied broadly to mental health concerns, with a range of application that was even larger than that of EEG-based training. The method has demonstrated dramatically positive outcomes for a variety of mental conditions, including different forms of anxiety, depression, sleep disturbances, ADHD, the autism spectrum, developmental trauma, migraines and other headaches, and traumatic brain injury [35, 36]. A surprising observation with respect to ILF training is the rapidity with which results are sometimes achieved even with challenging clinical presentations. The tonic slow cortical potential appears to be an exquisite reflection of the dynamics of cortical excitability. When the signal is derived in bipolar montage, network relations are revealed, and thus the training impinges on functional connectivity. By operating in the ILF regime, the training preferentially accesses the functional connectivity of the intrinsic connectivity networks that were originally identified with fMRI [29]. These low frequencies also give preferential access to the

In consequence, the ILF training impinges on the ultradian cyclic fluctuation of physiological arousal and related autonomic nervous system regulation [26, 38]. For example, in anxiety disorders, the disruption of autonomic stress regulation system results in a range of symptoms [39]. Indeed, the data of Smith and colleagues support the hypothesis that ILF training preferentially influences autonomic nervous system regulation and thus improves the emotional equilibrium of patients, which in turn positively influences attention and working memory [36]. Further evidence along those lines was recently documented in a large-scale compilation of pre-post continuous performance test data on a clinical population [40]. Improvement in performance was consistently observed, irrespective of the conditions being targeted in the training. The previous studies showed that ILF patterns of both electrical and nonelectrical phenomena remained quite stable over time. The goal of the present study is to demonstrate that the ILF training procedure induces persistent changes in the amplitude distribution within the ILF

glial role in the regulation of the glial-neuronal system [37].

dynamical activity [27–32].

spectral range.

According to the definition proposed by the National Institute of Health (NIH), complementary medicine (CAM) constitutes a broad domain of healing resources that lie outside those intrinsic to the politically dominant health care system of a society [7]. Inevitably, CAM will include technologies that are in the preliminary stages of mainstream acceptance. Such a technology is neurofeedback, which typically utilizes specific frequencies of the EEG in feedback configuration in order to promote cerebral self-regulatory competence. Despite its origins in well-grounded animal research in the 1960s [8], and subsequent studies in application to epilepsy and attention deficit hyperactivity disorder (ADHD) in the 1970s and 1980s [9, 10], neurofeedback was not adopted into standard medical practice at the time.

Originally discovered in 1956 by Kamiya, what came to be called EEG biofeedback found its first clinical application to anxiety [11], but that finding was not welcomed by the mental health disciplines either. Nevertheless, neurofeedback has matured as a type of CAM over the last several decades, with some 2700 citations in PubMed for neurofeedback, EEG biofeedback, and neurotherapy. With refinements derived on the basis of "practice-based evidence," neurofeedback is now belatedly entering the mainstream.

Neurofeedback belongs in the class of brain computer interface technologies, in that it allows the user to react to his own brain electrophysiological signals in real time [12]. These are registered from surface electrodes, subjected to frequency-selective signal processing, and rendered observable in the form of visual, auditory and tactile feedback. In its dominant realization, the feedback is based on frequencies within the conventional EEG spectrum of 0.5–40 Hz.

In infra-low frequency (ILF) neurofeedback, the modulation target is the brain rhythmic activity that lies below 0.5 Hz [13, 14]. Despite a multidecadal history of research, the organization and functional role of this low-frequency rhythmic activity remains unspecified, and this topic is currently garnering renewed research interest after a considerable hiatus.

The term ILF was introduced by a Soviet Union neurophysiologist Aladjalova in 1956 in her paper "Infra-slow rhythmic changes of the brain electrical potential" [15]. In this paper, she described brain oscillations in the ILF region and suggested a possible physiological basis for these phenomena. Since that time, a vast amount of empirical knowledge has been obtained in studies by Russian scientists in animal research and in studies on human subjects, through reliance on nonpolarizable electrodes to achieve low drift characteristics [16, 17]. The authors found two types of infra-slow oscillations with periods of 10s and 30–90s, respectively. In the United States, similar work was pursued by Kamiya et al. [18]. The ILF domain has also been studied in Austria and Germany [19–24].

Spontaneous and evoked local field potentials were observed in various cortical and subcortical regions of patients in whom cortical and subcortical electrodes had been implanted for purposes of characterization, diagnosis, deep brain stimulation, or lesioning [25]. The cortical electrical potentials were found to be correlated with infra-slow metabolic oscillations such as fluctuations of local oxygen levels. It was also demonstrated that spectral characteristics of infra-slow oscillations of the human brain remained stable over days and weeks [25, 26]. Recently, ILF potentials have found increased interest in the international scientific community, especially with the growing scientific evidence for a significant role of infra-slow potential fluctuations in modulating the level of cortical excitability and thus regulating brain dynamical activity [27–32].

National Institute of Mental Health (NIMH), there is a problem of burgeoning "off-label" medication prescription [1] and overrated effectiveness of pharmacological treatment for some conditions. Additionally, it has been found that best outcomes often depend on a com-

According to the definition proposed by the National Institute of Health (NIH), complementary medicine (CAM) constitutes a broad domain of healing resources that lie outside those intrinsic to the politically dominant health care system of a society [7]. Inevitably, CAM will include technologies that are in the preliminary stages of mainstream acceptance. Such a technology is neurofeedback, which typically utilizes specific frequencies of the EEG in feedback configuration in order to promote cerebral self-regulatory competence. Despite its origins in well-grounded animal research in the 1960s [8], and subsequent studies in application to epilepsy and attention deficit hyperactivity disorder (ADHD) in the 1970s and 1980s [9, 10], neurofeedback was not adopted into standard medical practice at

Originally discovered in 1956 by Kamiya, what came to be called EEG biofeedback found its first clinical application to anxiety [11], but that finding was not welcomed by the mental health disciplines either. Nevertheless, neurofeedback has matured as a type of CAM over the last several decades, with some 2700 citations in PubMed for neurofeedback, EEG biofeedback, and neurotherapy. With refinements derived on the basis of "practice-based evidence,"

Neurofeedback belongs in the class of brain computer interface technologies, in that it allows the user to react to his own brain electrophysiological signals in real time [12]. These are registered from surface electrodes, subjected to frequency-selective signal processing, and rendered observable in the form of visual, auditory and tactile feedback. In its dominant realization, the feedback is based on frequencies within the conventional EEG spectrum of

In infra-low frequency (ILF) neurofeedback, the modulation target is the brain rhythmic activity that lies below 0.5 Hz [13, 14]. Despite a multidecadal history of research, the organization and functional role of this low-frequency rhythmic activity remains unspecified, and this topic is currently garnering renewed research interest after a considerable

The term ILF was introduced by a Soviet Union neurophysiologist Aladjalova in 1956 in her paper "Infra-slow rhythmic changes of the brain electrical potential" [15]. In this paper, she described brain oscillations in the ILF region and suggested a possible physiological basis for these phenomena. Since that time, a vast amount of empirical knowledge has been obtained in studies by Russian scientists in animal research and in studies on human subjects, through reliance on nonpolarizable electrodes to achieve low drift characteristics [16, 17]. The authors found two types of infra-slow oscillations with periods of 10s and 30–90s, respectively. In the United States, similar work was pursued by Kamiya et al. [18]. The ILF domain has also been

bination of treatment strategies, including psychotherapy [2–6].

neurofeedback is now belatedly entering the mainstream.

studied in Austria and Germany [19–24].

the time.

76 Biofeedback

0.5–40 Hz.

hiatus.

Infra-low frequency training, developed by Susan and Siegfried Othmer, extended the conventional frequency-based training to the lower frequency range. Feedback is then a matter of observing the slowly undulating signal. The technique has been described in a paper titled Clinical Neurofeedback: Training Brain Behavior [33]. The first reported clinical application was to Post-Traumatic Stress Disorder among military veterans [34]. The second dealt with cases of epilepsy [13]. The method came to be applied broadly to mental health concerns, with a range of application that was even larger than that of EEG-based training. The method has demonstrated dramatically positive outcomes for a variety of mental conditions, including different forms of anxiety, depression, sleep disturbances, ADHD, the autism spectrum, developmental trauma, migraines and other headaches, and traumatic brain injury [35, 36].

A surprising observation with respect to ILF training is the rapidity with which results are sometimes achieved even with challenging clinical presentations. The tonic slow cortical potential appears to be an exquisite reflection of the dynamics of cortical excitability. When the signal is derived in bipolar montage, network relations are revealed, and thus the training impinges on functional connectivity. By operating in the ILF regime, the training preferentially accesses the functional connectivity of the intrinsic connectivity networks that were originally identified with fMRI [29]. These low frequencies also give preferential access to the glial role in the regulation of the glial-neuronal system [37].

In consequence, the ILF training impinges on the ultradian cyclic fluctuation of physiological arousal and related autonomic nervous system regulation [26, 38]. For example, in anxiety disorders, the disruption of autonomic stress regulation system results in a range of symptoms [39]. Indeed, the data of Smith and colleagues support the hypothesis that ILF training preferentially influences autonomic nervous system regulation and thus improves the emotional equilibrium of patients, which in turn positively influences attention and working memory [36]. Further evidence along those lines was recently documented in a large-scale compilation of pre-post continuous performance test data on a clinical population [40]. Improvement in performance was consistently observed, irrespective of the conditions being targeted in the training.

The previous studies showed that ILF patterns of both electrical and nonelectrical phenomena remained quite stable over time. The goal of the present study is to demonstrate that the ILF training procedure induces persistent changes in the amplitude distribution within the ILF spectral range.
