EEG complexity as a biomarker for autism spectrum disorder risk

The human brain exhibits a remarkable network organization. Although sparsely connected, each neuron is within a few synaptic connections of any other neuron [1]. This remarkable connectivity is achieved by a kind of hierarchical organization that is not fully understood in the brain, but is ubiquitous in nature and is called a scale-free network [2‐4] that changes with development. Complex networks are characterized by dense local connectivity and sparser long-range connectivity [2] that are fractal or self-similar at all scales. Modules or clusters can be identified on multiple scales. A comparison of network properties using functional magnetic resonance imaging showed that children and young adults' brains had similar "small-world" or scale-free organization at the global level, but differed significantly in hierarchical organization and interregional connectivity [5]. White matter fiber tracking has revealed that brain development in children involves changes in both short-range and long-range wiring, with synaptogenesis and pruning occurring at both the local (neuronal) level and the systems level [5]. Abnormal network connectivity may be a key to understanding developmental disabilities.

Autism is a complex and heterogeneous developmental disorder that affects the developmental trajectory in several key behavioral domains, including social, cognitive and language abilities. The underlying brain dysfunction that results in the behavioral characteristics is not well understood. Complex mental disorders such as autism cannot easily be described as being associated with underconnectivity or overconnectivity, but may involve some form of abnormal connectivity that varies between different brain regions [6]. Normal and abnormal connectivity may also change during development, so that, for example, a condition may not exist at age 3 months but may emerge by age 24 months. A key to understanding neurodevelopmental disorders is the relationship between functional brain connectivity and cognitive development [7]. Measuring functional brain development is difficult both because the brain is a complex, hierarchical system and because few methods are available for noninvasive measurements of brain function in infants. New nonlinear methods for analyzing brain electrical activity measured using scalp electrodes may enable differences in infant brain connectivity to be detected. For example, coarse-grained entropy synchronization between electroencephalography (EEG) electrodes revealed that synchronization was significantly lower in children with autism than in a group of typically developing children [8], supporting the theory that autistic brains exhibit low functional connectivity. In the autistic brain, high local connectivity and low long-range connectivity may develop concurrently because of problems with synapse pruning or formation [9, 10]. Estimation of changes in neural connectivity might be an effective diagnostic marker for atypical connectivity development.

EEG signals are believed to derive from pyramidal cells aligned in parallel in the cerebral cortex and the hippocampus [11], which act as many interacting nonlinear oscillators [12]. As a consequence of the scale-free network organization of neurons, EEG signals carry nonlinear, complex system information reflecting the underlying network topology, including transient synchronization between frequencies, short- and long-range correlations and cross-modulation of amplitudes and frequencies [13]. The mathematical relationship between network structure and time series is a subject of current research and may eventually shed further light on the relationship between neural networks and EEG signals.

A great deal of information about interrelationships in the nervous system likely remains undiscovered because the linear analysis techniques currently in use fail even to detect them [14]. If brain function and behavior are mirrors of each other as is commonly accepted [15‐18], then biomarkers of complex developmental disorders may be hidden in complex, nonlinear patterns of EEG data. The dynamics of the brain are inherently nonlinear, exhibiting emergent dynamics such as chaotic and transiently synchronized behavior that may be central to understanding the mind-brain relationship [19] or the "dynamic core" [20]. Methods for chaotic signal analysis originally arose from a need to rigorously describe physical phenomena that exhibited what was formerly thought to be purely stochastic behavior, but was then discovered to represent complex, aperiodic yet organized behavior, referred to as self-organized dynamics [21]. The analysis of signal complexity on multiple scales may reveal information about neural connectivity that is diagnostically useful [1, 19, 22].

One interpretation of biological complexity is that it reflects a system's ability to adapt quickly and function in a changing environment [23]. 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 controls and patients diagnosed with schizophrenia [24]. Patients with schizophrenia were found to have lower complexity than controls in some EEG channels and significantly higher interhemispheric and intrahemispheric cross-mutual information values than controls [25]. A study of the correlation dimension (another measure of signal complexity) of EEG signals in healthy individuals showed an increase with aging, interpreted as an increase in the number of independent synchronous networks in the brain [22].

Several different methods for computing complex or nonlinear time series features have been defined and used successfully to analyze biological signals [26, 27]. Sample entropy, a measure of time series complexity, was significantly higher in certain regions of the right hemisphere in preterm neonates who received skin-to-skin contact than in those who did not, indicating faster brain maturation [28]. Sample entropy has also been used as a marker of brain maturation in neonates [29] and was found to increase prenatally until maturation at about 42 weeks, then decreased after newborns reached full term [30].

Living systems exhibit a fundamental propensity to move forward in time. This property also describes physical systems that are far from an equilibrium state. For example, heat moves in only one direction, from hot to cold areas. In thermodynamics, this property is related to the requirement that all systems must move in the direction of higher entropy. Time irreversibility is a common characteristic of living biosignals. It was found to be a characteristic of healthy human heart electrocardiographic (ECG) recordings and was shown to be a reliable way to distinguish between actual ECG recordings and model ECG simulations [31]. ECG signals from patients with congestive heart disease were found to have lower time irreversibility indices than healthy patients [32]. Interestingly, the time irreversibility of EEG signals has been associated with epileptic regions of the brain, and this measure has been proposed as a biomarker for seizure foci [33]. Time irreversibility may be used as a practical test for nonlinearity in a time series.

This study is a preliminary investigation of the difference in multiscale entropy between two groups of infants between 6 and 24 months of age. The groups include typically developing infants and infants who have an older sibling with a confirmed diagnosis of autism spectrum disorder (ASD) and who are thus at higher risk for developing autism. ASD is a developmental disorder in which symptoms emerge during the second year of life. Behavioral indicators are not evident at 6 months of age [34‐36]; however, on the basis of the use of a novel observational scale to assess ASD characteristics in infants, distinguishing characteristics were seen at 12 months [35]. Another study compared behavioral measures such as frequency of gaze at faces and shared smiles in infants. Again, group differences between those who later developed an ASD and typically developing controls were apparent at age 12 months, but not at age 6 months [34]. Only one study has investigated behavioral differences at age 9 months: infants at risk for ASD showed distinct differences in visual orientation from those with no family history of autism [37]. These behavioral observations suggest that important developmental differences are occurring in the brains of typically developing infants and those who will later develop an ASD. Although there have been no other published studies on brain development during the first year of life, one of the most replicated findings, based on a retrospective review of medical records, is accelerated growth in head circumference (a valid and reliable proxy for brain growth), which begins at around 6 to 9 months of age [38‐40]. If multiscale entropy is a measure of functional brain complexity, then it may be a useful marker for distinguishing differences in brain activity between at-risk and typical infants.

The multiscale entropy and time irreversibility characteristics of five different time series are shown in Figure 1. The example time series amplitudes are shown in the first column. The second column displays plots of the multiscale entropy, where the horizontal axis is the coarse-grained scale from 1 to 20. White noise shows a characteristic decline in entropy with temporal scale, indicating loss of correlation between longer time intervals. Note that the deterministic but chaotic logistic equation has an entropy profile similar to white noise, suggesting that signal characteristics that appear as noise may in fact contain significant dynamic information about the system. The physiological (EEG) time series has a unique entropy curve that increases with temporal scale, similar to the cardiac signals observed in ECG readings [31, 45].

The third column of Figure 1 is the multiscale time asymmetry value. The value of a in the lower right corner of the time asymmetry plot is the value of the time asymmetry index summed over scales 1 to 5. A nonzero time asymmetry value is a sufficient condition for nonlinearity of a time series. Although white noise and the logistic curve have similar entropy profiles, the time asymmetry index distinguishes the nonlinear chaotic signal from noise. The EEG signal shown here clearly contains nonlinear characteristics on the basis of the nonlinear time asymmetry index.

Using all of the EEG data, we first calculated time asymmetry to determine the degree of nonlinearity present in the signals. Figure 2 shows the time asymmetry index for all 64 channels of the resting state EEG for control and high-risk groups by age. The value of the time asymmetry index in the scalp plot was determined by averaging the index value over all members of that age and risk group. Since the value may take on positive or negative values and will be near zero for time-reversible signal, the persistence of the nonzero values in this plot is an indicator of signal nonlinearity. The multiscale entropy and t _rev values have independent physiological meanings [31]. Since apparent differences exist between controls and the high-risk group at all ages for both mMSE and t _rev, these two quantities together may provide a more sensitive biomarker for developmental age and atypical development. However, in this study, only the multiscale complexity was used to classify the high-risk group.

To make some general comparisons of EEG complexity between risk groups and different ages, mMSE curves were averaged over all members of subgroups by both age and risk group. Figure 3 shows that the HRA group had a consistently lower mean complexity over all channels, across all scales and at all ages. Figure 4 shows the group average mMSE value versus age for infants in each of the two risk groups. The bold black line in Figure 4 represents the mean mMSE value averaged over all 64 EEG channels. Left and right laterality were determined by averaging all left-side and all right-side channels separately. Similarly, mMSE values for four left frontal and four right frontal channels were averaged and plotted versus age. Note that the data in Figure 4 are treated as if drawn from a cross-sectional study as described previously. Mean values, standard deviations and statistical significance (P values from t-test) for the channel averages are given in Table 1. Differences between group averages are significant at age 18 months for overall mean mMSE, and the differences are significant for the left frontal region at all ages except 9 months. Of note is that significant differences were not found at age 9 months for any of the three MSE averages in Table 1, although head circumference was significantly different only at age 9 months. As discussed below, when all mMSE data were considered without averaging (that is, mMSE curves at each channel), machine learning algorithms found the greatest classification accuracy at age 9 months. Although it appears in Figure 4 that the most prominent difference between the control and HRA groups was the change in mMSE between ages 9 and 12 months, significance levels were not computed for changes in this study because measurements at each age were taken from different populations of infants.

Several features are immediately apparent. A general asymmetry in mMSE is seen in both control and high-risk groups, although this asymmetry appears to decline from ages 12 to 18 months as the left and right hemispheres and frontal curves come closer together at age 18 months. EEG complexity changes with age, but not uniformly. In the controls, the overall EEG complexity, shown by the solid black line in Figure 4, increases from ages 6 to 9 months then decreases slightly from ages 9 to 12 months before increasing again from ages 12 to 18 months. Left and right channels and the right frontal channels all follow this same pattern, though left and right hemisphere complexity is not symmetric. The left frontal channels follow a different pattern, increasing strongly until age 12 months and then declining after that. The complexity curves for the high-risk group follow a similar pattern, but the overall complexity is lower and the increases and decreases are much more exaggerated. Perhaps even more distinct is the left frontal curve in the high-risk group. It follows the same pattern as all other regions, unlike the left frontal curve in the controls.

Since the complexity changes seem to vary with EEG channel, a better picture of complexity development with age and between risk groups can be seen in a scalp plot. Figure 5 shows the mean mMSE value for all EEG channels by risk group and age. The complexity values here were computed by averaging the mMSE over all coarse-grained scales for that channel as in Figure 2. Complexity variation with age and between risk groups is immediately apparent. One or two channels of the left frontal region appear to increase in complexity continuously with age in the controls, as does the right parieto-occipital region. The overall complexity in the high-risk group was lower than in the control group. Although the pattern of complexity change from ages 6 to 9 months appears similar in both groups, the high-risk group shows a marked decline in overall complexity from ages 9 to 12 months.

Height, head circumference and exact age in days at the time of testing, as well as group means, standard deviations and significance levels, are included in Table 1. The only significant group difference among these variables was in head circumference at age 9 months: The infants in the HRA group had a larger mean head circumference than the typically developing controls.

The primary goal of this study was to explore whether measures of EEG complexity might reveal functional endophenotypes of ASD and thus identify them as potential biomarkers for risk of ASD at very early ages before the onset of clear behavioral symptoms. Our findings show significant promise for the specific measure of multiscale entropy that was used to compare high- and low-risk infants between the ages of 6 and 24 months. Differences in mean mMSE over the entire scalp and especially in the left frontal region were significant at most ages measured, except at age 9 months. The trajectory of the curves between ages 6 and 12 months in Figure 4 appears to be as informative as information at any specific age. This result makes the relatively high accuracy at age 9 months of the machine classification using all of the mMSE curves as feature vectors particularly notable. This early period of life is one 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 the difficulties associated with ASD. A number of major cognitive milestones typically occur beginning at around age 9 months and perhaps earlier in girls. These milestones include, for example, the development of the ability to perceive intentional actions by others [46], as well as loss of the ability to perceive speech sound distinctions in non-native languages [47] and loss of the ability to discriminate certain categories of faces [48]. These latter developments are especially significant because they reveal how socially grounded experiences influence changes in the neurocognitive mechanisms that underlie speech and face recognition processing. Thus, Marcus and Nelson [49] argued that infants mold their face-processing system on the basis of the visual experiences they encounter, just as their speech-processing skills are molded to their native language [50, 51]. This model assumes a narrowing of the social-perceptual window through which language and faces are processed, which in turn results in an increase in cortical specialization. In a prospective study, Ozonoff et al. [34] found that social communicative behaviors in infants who later developed ASD declined dramatically between ages 6 and 18 months compared to typically developing infants.

We hypothesize that the following developmental sequence may explain the data in Table 3. At age 6 months, no significant behavioral differences have been noted in prospective studies between typically developing infants and those who develop autism [34, 35]. Thus, few differences in electrophysiological data are expected at age 6 months, as shown in Figure 4 and Table 3. However, if girls are considered separately, differences in mMSE appear to be significant at age 6 months. If the multiscale entropy calculations from the EEG signals are indeed a biomarker for endophenotypes of autism familial traits, then by 9 months of age many infants in the high-risk group will display unique characteristics in their mMSE profiles that enable them to be distinguished from the controls. Those infants in the high-risk group who do not have multiple risk factors and later develop normally would not be expected to exhibit abnormalities in their mMSE profiles throughout the developmental period. These hypotheses might account for the HRA infants in our study who were classified similarly to our typical controls. This hypothesis will be tested when sufficient numbers of infants in the HRA group have reached 2 to 3 years of age and a diagnosis of ASD or typical development can be made.

Developmental abnormalities from ages 6 to 12 months are particularly distinct in the two groups (low and high risk for ASD), allowing the groups to be classified quite accurately, although some overlap between the HRA and control groups should be expected at all ages. From 12 to 24 months of age, the distinction between the two groups declines. This likely reflects the trend for some fraction of high-risk infants to develop more typical cognitive and behavioral function, even though they may carry endophenotypes that share common complexity profiles at an earlier age with other high-risk infants who will later be diagnosed with ASD.

Rather than analyzing entropy at single age points, using a trajectory of entropy values from ages 6 to 24 months might be more informative. Although EEG complexity has been shown in several studies to increase with age [30, 52, 53], the increase is neither monotonic nor uniform across different brain regions. The abnormalities in brain development that lead to autistic characteristics may not be immediately apparent by inspecting relevant brain activity, even if the data contain diagnostically significant information. For example, a recent study of the relationship between cortical thickness and intelligence found no correlation between absolute cortical thickness at any particular age and intelligence. However, a specific pattern of developmental changes in cortical thickness was highly correlated with intelligence [54].

One of the characteristics of the high-risk group is heterogeneity: This group includes infants who will go on to develop an ASD and those who are within the normal range genetically, developmentally and behaviorally, as well as those in between who exhibit mild autism-like traits. Further study of this cohort as they grow and develop will enable this hypothesis to be tested. Rather than binary classification into typical controls and heterogeneous high-risk groups, classification on the basis of actual behavioral assessments will allow a more accurate test of the efficacy of using the mMSE to measure brain function.

Abnormal brain connectivity, whether locally, regionally or both, may be a cause of a number of behavioral disorders, including ASD [9], and changes in local complexity are believed to be related to brain connectivity [55]. Local neural network connectivity undergoes rapid change during early development, and this may be reflected in the multiscale entropy of EEG signals, which is one measure of signal complexity that has been associated with health and disease [23]. A number of recent studies have demonstrated a link between brain connectivity and complexity, and EEG signal complexity may provide valuable information about the neural correlates of cognitive processes [56]. Early markers for neurological or mental disorders, particularly those with developmental etiologies, may be the growth trajectories of complexity as measured by multiscale entropy curves. The results described in this paper suggest that infants in families with a history of ASD have quite different EEG complexity patterns from 6 to 24 months of age that may be indicators of a functional endophenotype associated with ASD risk. Differences between mean mMSE averaged over all channels or in frontal regions in the two groups are significant at all ages except 9 months. Machine classification on the basis of mMSE curves in each channel as a feature set is able to determine group membership, particularly at 9 months of age. The classification accuracy decreases after age 12 months, possibly because of the influence of normal brain development and the development of normal characteristics in many of the high-risk infants. Classification accuracy for boys alone still appears to be significant and relatively high at age 18 months. More data about the future outcomes of the HRA infants and the computation of additional features, such as laterality of entropy, together with behavioral and cognitive assessments as the cohort of participants in this study grows, may enable the high-risk population to be subclassified more accurately. Future longitudinal analysis of data from this cohort will allow growth trajectories, as well as the future outcomes of the high-risk children, to be compared. Deeper understanding of the relationship between these neurophysiological processes and cognitive function may yield a new window to the mind and provide a clinically useful psychiatric biomarker using complexity analysis of EEG data.