The aim of this work was to establish whether those with ASD show greater variability across single-trial evoked EEG compared with neuro-typical individuals. A second aim was to compare single-trial EEG variability when extracted from spatially filtered data and from raw-scalp EEG data in order to select the most appropriate variables for group comparison. All three measures of peak variability – P1 amplitude, P1 latency, and maximum α-band phase coherence – were smaller when analyzed from the spatially filtered data than from the scalp EEG data, highlighting the benefits of applying spatial filtering techniques to EEG. Having validated the use of CSD and ICA in this study, measures of single-trial variability were compared between the participants with and without ASD, with the finding that intra-participant variability was significantly greater in the participants with ASD than in the control group.
These data suggest that previous reports of increased response time variability in those with ASD (Geurts et al., 2008
) may be underpinned by variability within cortical dynamics associated with the ability to synchronize the activity of stimulus-related cell assembly(ies) consistently across trials. The experimental paradigm used here did not elicit a significant difference between response time variability in those with and without ASD. This may be because only a small number of trials (36) were available to ascertain variability, in contrast to Geurts et al. (2008
) who used nearly twice as many (64 trials), or it may be because of the small group sizes and consequently reduced power of the analyses performed here. Nevertheless, there was a significant relationship between response time variability and P1 amplitude variability. Note that P1 and the behavioral data were extracted from separate trials (the P1 was extracted from trials in which the eliciting stimulus was a Gabor patch with a spatial frequency content of 8
cycles/degree and the behavioral data were derived from trials in which the eliciting stimulus was a zebra), therefore it is not the case that specific trial-by-trial variations in ERP amplitude are driving the variability in response time, rather it appears as though a common mechanism may underpin both behavioral variability and ERP amplitude variability.
As described in the Introduction, neurocortical dynamics result from the activation of partially distinct and interacting cell assemblies; the mechanism of communication within these cell assemblies is synchronous oscillations. A number of authors have suggested that ASD may be characterized by reduced neural synchrony, especially of high-frequency (γ-band) oscillations (e.g., see Brock et al., 2002
), although evidence to support this position is mixed. While some studies have shown lower levels of evoked γ-band power in those with ASD (Wilson et al., 2007
), more recent data indicates that while evoked γ-band power may be reduced in those with ASD, induced γ-band power is increased, and inter-trial γ-band phase coherence (ITPC) is reduced (Rojas et al., 2008
). The concept of “evoked” or “induced” EEG is defined by whether or not single-trial activity is time- and phase-locked to a stimulus (evoked activity), or whether it is perturbed by the stimulus, but neither time- nor phase-locked to it (induced activity). However, as the data presented above, and numerous other estimations of ITPC (e.g., Tallon-Baudry and Bertrand, 1999
) illustrate, complete phase-locking across trials (i.e., ITPC
1) is physiologically unrealistic. Therefore the boundary for defining whether stimulus-related activity should be considered to be evoked or induced is unclear. Rojas et al. (2008
) point out that their data fit a model in which total (evoked
induced) stimulus-related γ-band power is equivalent in the participants with and without ASD, and that reduced inter-trial phase consistency, computationally, leads to a reduction of what is classed as evoked activity and an increase in what is classed as induced activity. Thus these authors conclude that the production of γ-band oscillations in response to external stimulation is no different in those with and without ASD, rather their data point toward dysfunction in the timing of γ-band oscillations in the participants with ASD.
The data reported here provide evidence of reduced ITPC in the α-band in ASD. Together with the result of Rojas et al. (2008
), these data indicate widespread dysfunction of neural timing in ASD, rather than a specific deficit of high-frequency γ-band oscillations as some authors have predicted. Reduced ITPC in ASD indicates that those with ASD are less able to synchronize the activity of stimulus-related cell assembly(ies) consistently across trials, and provide evidence for temporal disruptions in the organization and recruitment of cell assemblies. It is not clear whether this temporal disruption underpins, is caused by, or is unrelated to, postulated neural de-synchrony in ASD.
A number of possible etiologies of atypical neural oscillations in ASD have been suggested, including: a surfeit in local connectivity – especially in primary sensory areas (Belmonte et al., 2004
); smaller and more dispersed cortical mini-columns leading to a reduction in inhibitory inter-neuronal activity (Casanova et al., 2002
); an imbalance of cortical excitation and inhibition due to increased glutamergic/reduced GABAergic signaling (Rubenstein and Merzenich, 2003
); and impairment in the inferior olive – a structure that that mediates electrical synapses and that drives neural synchrony, and has been found to be structurally atypical in some individuals with ASD (Welsh et al., 2005
). No theory has yet linked any of these putative impairments with increased intra-participant variability in those with ASD. However within the literature on ADHD, intra-participant variability has been theoretically linked with inconsistent and inefficient neuronal transmission, which may arise from impairment in astrocytes, a type of glial cell that plays a critical role in fueling neuronal oscillations (Russell et al., 2006
). Astrocyte impairment in ASD could therefore account for a range of features of ASD including neural de-synchrony, EEG single-trial variability, and behavioral (response time) variability. Given the important role of glia in synapse formation and maintenance (Bolton and Eroglu, 2009
) the suggestion that astrocyte impairment may be a critical factor in ASD is a tantalizing one. It is important to note however that, in addition to the theoretically formulated suggestions described above, a variety of neuronal characteristics (e.g., synaptic transmission, channel gating, fluctuation in transmitter release, postsynaptic receptor activation, ion concentrations, membrane conductance) may contribute to variability of evoked EEG response (Sannita, 2006
), therefore it is not currently possible to identify the precise source(s) of EEG variability.
Regardless of the precise source, increased EEG variability in those with ASD is evidence of increased intrinsic neural “noise” (Sannita, 2006
). Increased neural noise in ASD has been predicted by a number of authors (see Simmons et al., 2009
), however, the data reported here represent the first empirical demonstration of increased neural noise in ASD. Increased neural noise has the potential to influence behavior in a variety of ways, and its impact on different levels of function, e.g., perception, cognition, and behavior, may not be consistent. Whilst an increased noise-to-signal ratio leads to reduced perceptual sensitivity in many cases, one type of noise – stochastic resonance – can amplify a signal, leading to increased sensitivity. Increased levels of neural noise have therefore been discussed in relation to atypical perception in ASD, and offered as a parsimonious explanation of data in which those with ASD show both hyper- and hypo-reactivity to perceptual stimuli and enhanced and impaired perceptual sensitivity measured with psychophysical tasks (Simmons et al., 2009
Increased neural noise is less likely to have an advantageous effect on cognitive task performance however, as it may lead to a number of sub-optimal outcomes including a general decrease in response times and greater response time variability, more errors in tasks with more than one possible response, and the need for increased repetitions of a task to achieve the same outcome as those with lower levels of noise. Furthermore, noise-related reduction in task performance would be evidenced by impairments across many domains and tasks, rather than in isolated tasks, and it would also lead to increased inter-participant variability. This description of data is very similar to that represented by the literature on cognitive function in ASD. Increased neural noise is therefore a plausible, and parsimonious, explanation both for the array of cognitive tasks in which participants with ASD have been shown to perform more poorly than those without ASD, and for the significant inter-individual variability present in those with ASD. In support of this position are two demonstrations where reduced task performance can be accounted for by what may be termed “noise.” For example, thresholds for detecting coherent motion can be artificially inflated by transient lapses of attention (McAnally et al., 2001
), and intra-individual response variability is a strong predictor of success in the Go No-Go task (Bellgrove et al., 2004
), suggesting that lower sensitivity to coherent motion and failure to inhibit prepotent responses, both of which have been reported in those with ASD (see Ozonoff et al., 1994
; Milne et al., 2002
respectively), may arise due to increased neural noise rather than reflecting a specific impairment in either motion perception or in response inhibition, as is the current interpretation of these data (see also Baron-Cohen and Belmonte, 2005
for a similar argument).
Some authors have suggested that cortical hyper-excitability in ASD may be restricted to/more pronounced in, primary sensory areas (e.g., Rubenstein and Merzenich, 2003
; Mottron et al., 2006
). Therefore in order to evaluate these results in light of current theories it is necessary to consider where the neural generators of the P1 deflection analyzed here might be. The location of the electrodes selected from the CSD data, and the estimated location of the equivalent current dipole of the ICs suggests that the P1 analyzed here is generated in extra-striate cortex. This is commensurate with a number of papers that have localized the neural generators of the P1 deflection to the extra-striate cortex (e.g., Di Russo et al., 2001
; Ales et al., 2010
). Therefore, these data provide evidence for variability in extra-striate cortex rather than primary visual cortex. As noted in the Introduction
the earlier C1 deflection would be a more appropriate deflection with which to investigate variability in primary visual cortex, however this was not analyzed here as a number of participants with ASD failed to show a clear C1 deflection, either in the (averaged) ERP or in the single-trials. The specific reason for this is unclear, but is being addressed by on-going studies by our research group. Therefore, although the P1 deflection reported here does not tap the earliest stage visual processing, it was the most robust early deflection in the data, making it the best available candidate to investigate increased noise in the visual cortex in ASD. Future studies are required to establish whether areas of primary sensory cortex, generating earlier ERP deflections, show similar, or possibly greater, levels of variability in participants with ASD.
In addition to increased variability, the P1 peak in the participants with ASD occurred significantly sooner than in the control group. This finding, from these data, was reported previously (Milne et al., 2009
), so will not be dwelt on here. Nevertheless, reduced latency to peak is commensurate with the suggestion of local hyper-connectivity in ASD which has been predicted by some to lead to increased cortical noise (see for example, Belmonte et al., 2004
There are a number of implications from this work for existing EEG studies in those with ASD. Consistent with previous reports, these data provide no evidence for difference in P1 amplitude in participants with ASD. Although there is one report of reduced P1 amplitude in children/adolescents with pervasive developmental disorder, including ASD (Hoeksma et al., 2004
), this may have been due to latency jitter, as ERP amplitude (when calculated from the peak of the averaged single-trials) is intrinsically related to latency variability. Conversely, the suggestion that individuals with ASD may have hyper-responsive visual cortices would predict increased P1 amplitude in those with ASD, and increased latency jitter may mask this potential outcome. However, the data did not support this prediction, as when P1 amplitude was calculated as the median of the single-trial peaks, there was still no group difference in P1 amplitude. The data reported here indicate that establishing degree of latency jitter within each participant is possible, and should be an essential part of ERP analysis if conclusions are to be drawn to regarding the origin of observed group differences. Before leaving this point, it is important to point out that a number of physiological factors contribute to ERP amplitude. Although ICA was able to isolate the signal associated with perceptual encoding from the total EEG, and therefore facilitate comparison of within-participant variables such as variability, it cannot address the potentially confounding factors of individual differences that may lead to differences in ERP amplitude between groups including differences in cortical convolution, position of the calcarine sulcus, and/or conductivity of underlying tissue, etc. Given that there is some evidence of cortical folding abnormalities in children with ASD (Nordahl et al., 2007
), direct comparison of EEG amplitude or EEG power between experimental groups, without first normalizing the data, may not be a reliable technique.
When analyzing the data presented here, significant attempts were made to minimize potential confounds that could artificially inflate the estimates of variability in one group or another. Note that the within-subject estimates of variability reported here are normalized, thus validating group comparisons; note also that the two groups of participants were well matched as regards to age, IQ, and gender, and that the data from the two groups was matched in terms of quality. It is therefore unlikely that methodological factors contributed to the group differences reported here. However, as is outlined in the methodology, data is not reported from all participants who participated in the study. This was driven primarily by the goal of ensuring well matched samples, but was also a consequence of the fact that not all participants generated an IC that was considered reliable enough to be analyzed. The most likely reason for this is that only a small amount of data (approximately 51/2
min of data per participant) were recorded and available for ICA decomposition. The experiment was necessarily short given the age of the participants, but the quality of ICA decomposition would be greatly improved with longer recordings, therefore future work should aim to replicate the findings reported here with larger groups of participants and with longer data recordings.
Behavioral variability is not unique to those with ASD (Castellanos et al., 2005
), therefore future research is required to establish the universality of increased EEG variability in ASD and in other developmental disorders (such as ADHD), and to establish whether increased variability is a general characteristic of brain pathology, or whether distinctive features of variability occur in different developmental disorders. Furthermore it is necessary to establish the extent to which increased EEG variability is an enduring endophenotype of ASD, or whether it related either to external factors such as context or particular task requirements, or to internal factors such as cognitive state (e.g., awake, asleep, tired, alert). The presence of a significant correlation between EEG variability and response time variability provides preliminary evidence that response time variability and EEG variability are related, albeit in a small sample of participants. This relationship should be tested more rigorously in future studies in which larger groups of participants are tested and different types of behavioral response tasks (such as simple reaction time, choice reaction time, response inhibition, etc.) are performed. In addition, more detailed single-trial analyses should be performed in order to examine the temporally dynamic patterns of EEG fluctuations, and the relationship between EEG variability and cognitive task performance and both inter- and intra-participant variability needs to be clarified.
Note that the mean P1 peak amplitude variability, measured as the MAD estimate of the P1 peak amplitude from the raw EEG data in the TD group, was 0.37. This broadly concurs with existing data in which the coefficient of variation of VEP amplitude recorded in 100 healthy adults from electrodes positioned above the occipital cortex was reported to be 0.41 (Klistorner and Graham, 2001
). However, given that the MAD estimator is less influenced by outlying data points than the coefficient of variation, estimates of variability from this statistic tend to be lower than from the coefficient of variation, so a direct comparison between these two statistics cannot be made. For comparison, the co-efficient of variation of P1 amplitude in the TD group, based on the SD of these data was 0.58, i.e., higher than that reported by Klistorner and Graham (2001
) – possibly reflecting developmental change in amplitude variability.