Elsevier

Neuroscience & Biobehavioral Reviews

Volume 71, December 2016, Pages 590-600
Neuroscience & Biobehavioral Reviews

Review article
Variability of cortical oscillation patterns: A possible endophenotype in autism spectrum disorders?

https://doi.org/10.1016/j.neubiorev.2016.09.031Get rights and content

Highlights

  • Stimulus-related cortical oscillations patterns in ASD are reviewed.

  • No clear-cut picture of specific dysfunctional oscillation patterns has emerged.

  • Recent evidence points to increased intra-individual trial-to-trial variability in behavioral and neurophysiological responses in ASD.

  • Intra-individual variability represents a new avenue for the investigation of oscillatory activity in ASD.

Abstract

Autism spectrum disorders (ASD) have been associated with altered neural oscillations, especially fast oscillatory activity in the gamma frequency range, suggesting fundamentally disturbed temporal coordination of activity during information processing. A detailed review of available cortical oscillation studies in ASD does not convey a clear-cut picture with respect to dysfunctional oscillation patterns in the gamma or other frequency ranges. Recent evidence suggests that instead of a general failure to activate or synchronize the cortex, there is greater intra-participant variability across behavioral, fMRI and EEG responses in ASD. Intra-individual fluctuations from one trial to another have been largely ignored in task-related neural oscillation studies of ASD, which instead have focused on mean changes in power. We highlight new avenues for the analysis of cortical oscillation patterns in ASD which are sensitive to trial-to-trial variability within the participant, in order to validate the significance of increased response variability as possible endophenotype of the disorder.

Introduction

Autism spectrum disorders (ASD) represent early-onset neurodevelopmental syndromes, which are characterized by social and communication deficits, restricted interests, repetitive behavior, and sensory dysfunction (American Psychiatric Association, 2013). The ever-rising prevalence estimates for ASD render the quest to identify the underlying pathophysiology increasingly important (Autism and Developmental Disabilities Monitoring Network Surveillance Year 2008 Principal Investigators and Centers for Disease Control and Prevention, 2012). Despite high heritability rates for ASD (at ∼90%), no primarily responsible gene has been identified to date (Ronemus et al., 2014). Similarly, no generally accepted consensus with respect to the principal pathophysiological mechanism exists.

While early accounts of the neurobiological basis of ASD focused on structural abnormalities concerning overall brain volume or specific brain regions such as the amygdala or cerebellum (Amaral et al., 2008, Brambilla et al., 2004), the broad psychopathology of ASD suggests a fundamental and distributed neural system abnormality (Minshew and Williams, 2007). Furthermore, using the Autism Brain Imaging Data Exchange (ABIDE) database, a recent study investigating more than 500 anatomical MRI scans was not able to replicate many existing anatomical findings such as significantly different total brain, amygdala or cerebellar volume in ASD (Haar et al., 2014). Thus, in contrast to former “focal approaches”, scientists and clinicians have started to reject the notion that an abnormality within a single brain area can account for the variety of symptoms associated with ASD. Instead, the current zeitgeist is characterized by increased attempts to identify aspects of disturbed brain communication, on microscopic and macroscopic levels (Belmonte et al., 2004, Dinstein et al., 2011, Hahamy et al., 2015, Just et al., 2012, Kennedy and Courchesne, 2008, Müller, 2007, Uhlhaas et al., 2010, Uhlhaas and Singer, 2012). For example, fundamental disturbances of experience-dependent synaptic pruning and cortical plasticity as well as an imbalance of excitatory and inhibitory synaptic processes have been proposed (Markram and Markram, 2010, Rubenstein, 2011).

Such fundamental changes to neural activity could also be indirectly reflected in changes of signals measured by non-invasive methods such as electro- or magnetoencephalography (EEG/MEG; Lopes da Silva, 2013). EEG and MEG signals usually exhibit a mixture of fast and slowly oscillating activity which are thought to reflect the activity of functionally related cell assemblies that dynamically synchronize their discharges for transient periods (Engel et al., 2001, Fries, 2005, Singer and Gray, 1995, Varela et al., 2001). This may involve groups of neurons located in small patches of cortex or in distributed regions (Donner and Siegel, 2011, Siegel et al., 2012). The synchronization of neural activity is now widely accepted as an important mechanism for the functional organization of the brain and the communication within or between cortical networks, as it is considered to gate neuronal information flow via fluctuating temporal windows of excitability (Engel et al., 2013, Fries, 2005, Salinas and Sejnowski, 2001). As the development of neural networks hinges on such temporal coordination of brain activity (Uhlhaas et al., 2010), investigating the synchronization of neural activity in neurodevelopmental disorders such as ASD is an important line of enquiry.

Here, we provide a thorough summary of the current evidence on stimulus-related oscillatory neural activity in ASD, and outline inconsistencies in the literature and potential methodological limitations. We consider the recent notion of increased intra-individual trial-to-trial variability as a promising new avenue for the investigation of cortical oscillation patterns in ASD, highlight methodological possibilities to address variability in EEG/MEG studies of ASD, and discuss the relationship to connectivity and complexity. Finally, we suggest directions for future research that may validate the hypothesis of increased response variability as possible endophenotype of the disorder.

Section snippets

Stimulus-related cortical oscillation patterns in ASD

An increasing number of research papers have highlighted that synchronization of neural activity may be atypical in ASD, and that this could be observed in EEG and/or MEG data (Uhlhaas and Singer, 2012, Uhlhaas and Singer, 2007; Table 1). By means of spectral transformation, the MEG or EEG signal can be decomposed into functionally-specific cortical rhythms or frequencies (i.e., the number of cycles contained in a second, measured in Hz). Usually, the ongoing M/EEG signal at rest is dominated

Intra-individual variability in ASD

Intra-participant trial-to-trial variability in clinical conditions represents a sensitive marker for pathophysiological processing and appears to be a promising approach to developing a clearer understanding of the neural aetiology in ASD. Attention deficit hyperactivity disorder (ADHD) has classically been associated with a high degree of intra-individual variability such as in reaction time data (Karalunas et al., 2014). ASD and ADHD are intimately linked as suggested by high comorbidity

Pathophysiological mechanisms underlying increased neural variability in ASD and their relationship to neural oscillations

Several neurophysiological mechanisms are thought to contribute to moment-to-moment variability in neural activity (see also Dinstein et al., 2015, Fontanini and Katz, 2008), including changes in E/I balance (Turrigiano, 2011). ASD theories related to the notion of increased variability have considered ASD as a disorder of neural noise, synaptic pruning, sensory gating, probabilistic learning/inference and prediction and also as disorder of E/I balance (Markram and Markram, 2010, Orekhova et

Measuring intra-participant trial-to-trial variability in neural activity

Increased sensory-evoked trial-to-trial variability in participants with ASD has been observed even when no differences in average response amplitude are seen compared to control participants (Gandal et al., 2010, Milne, 2011, Dinstein et al., 2012). Thus, the concept of intra-participant inter-trial variability may also be more informative for the investigation and analysis of neural oscillations, especially since measuring oscillations at an average level as indexed by abnormal power spectra

Relationship to measures of connectivity and complexity

Complexity and variability are related concepts. Complex systems –such as large-scale neuronal networks– are balanced between perfect regularity and complete randomness (Sporns et al., 2000). This characteristic of neuronal systems can be measured, e.g. by some forms of entropy, which represent computational, complexity-sensitive tools to assess signal dynamics in neural time series data (for a comparison of different entropy measures, see Liang et al., 2015). As neuronal systems are neither

Conclusions and future directions

Intra-individual inter-trial variability in neural activity has recently been demonstrated in ASD, possibly reflecting less efficient or noisier neural communication within the brain of individuals with ASD. Oscillatory neuronal synchronization is considered to be one mechanism by which communication within the brain is achieved. Yet, to date, intra-individual variability is notably absent from analyses of cortical oscillation patterns in ASD. M/EEG studies, which investigated oscillatory

Conflicts of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

This work was supported by the EU (FP7-ICT-270212, ERC-AdG-269716, H2020-641321, A.K.E.). We thank M. Siegel for helpful comments on earlier versions of this manuscript.

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