Magnetoencephalography Studies of the Envelope Following Response During Amplitude-Modulated Sweeps: Diminished Phase Synchrony in Autism Spectrum Disorder

Prevailing theories of the neural basis of at least a subset of individuals with autism spectrum disorder (ASD) include an imbalance of excitatory and inhibitory neurotransmission. These circuitry imbalances are commonly probed in adults using auditory steady-state responses (ASSR, driven at 40 Hz) to elicit coherent electrophysiological responses (EEG/MEG) from intact circuitry. Challenges to the ASSR methodology occur during development, where the optimal ASSR driving frequency may be unknown. An alternative approach (more agnostic to driving frequency) is the amplitude-modulated (AM) sweep in which the amplitude of a tone (with carrier frequency 500 Hz) is modulated as a sweep from 10 to 100 Hz over the course of ∼15 s. Phase synchrony of evoked responses, measured via intra-trial coherence, is recorded (by EEG or MEG) as a function of frequency. We applied such AM sweep stimuli bilaterally to 40 typically developing and 80 children with ASD, aged 6–18 years. Diagnoses were confirmed by DSM-5 criteria as well as autism diagnostic observation schedule (ADOS) observational assessment. Stimuli were presented binaurally during MEG recording and consisted of 20 AM swept stimuli (500 Hz carrier; sweep 10–100 Hz up and down) with a duration of ∼30 s each. Peak intra-trial coherence values and peak response frequencies of source modeled responses (auditory cortex) were examined. First, the phase synchrony or inter-trial coherence (ITC) of the ASSR is diminished in ASD; second, hemispheric bias in the ASSR, observed in typical development (TD), is maintained in ASD, and third, that the frequency at which the peak response is obtained varies on an individual basis, in part dependent on age, and with altered developmental trajectories in ASD vs. TD. Finally, there appears an association between auditory steady-state phase synchrony (taken as a proxy of neuronal circuitry integrity) and clinical assessment of language ability/impairment. We concluded that (1) the AM sweep stimulus provides a mechanism for probing ASSR in an unbiased fashion, during developmental maturation of peak response frequency, (2) peak frequencies vary, in part due to developmental age, and importantly, (3) ITC at this peak frequency is diminished in ASD, with the degree of ITC disturbance related to clinically assessed language impairment.

Altered Temporal Structure of Neural Phase Synchrony in Patients With Autism Spectrum Disorder

Functional connectivity, quantified by phase synchrony, between brain regions is known to be aberrant in patients with autism spectrum disorder (ASD). Here, we evaluated the long-range temporal correlations of time-varying phase synchrony (TV-PS) of electrocortical oscillations in patients with ASD as well as typically developing people using detrended fluctuation analysis (DFA) after validating the scale-invariance of the TV-PS time series. By comparing the DFA exponents between the two groups, we found that those of the TV-PS time series of high-gamma oscillations were significantly attenuated in patients with ASD. Furthermore, the regions involved in aberrant TV-PS time series were mainly within the social ability and cognition-related cortical networks. These results support the notion that abnormal social functions observed in patients with ASD may be caused by the highly volatile phase synchrony states of electrocortical oscillations.

Controlling phase synchrony in the mean field coupled Hindmarsh–Rose neurons

The phase synchronization in a network of mean field coupled Hindmarsh–Rose neurons and the control of phase synchrony by an external input has been analyzed in this work. The analysis of interspike interval, with varying coupling strength, reveals the dynamical change induced in each neuron in the network. The bursting phase lines depict that mean field coupling induces phase synchrony in excitatory mode and desynchrony in inhibitory mode. The coefficient of variability, in spatial and temporal domain, signifies the deviations in firing times of neurons, in a collective manner. The Kuramoto order parameter quantifies the intermittent and complete phase synchrony, induced by excitatory mean field coupling. The capability of external input, in the form of spikes, to control the intermittent and complete phase synchrony has been analyzed. The coefficient of variability and Kuramoto order parameter has been studied by varying the amplitude, pulse width and frequency of the input. The studies have shown that high-frequency spike input, with optimum amplitude and pulse width, has high desynchronizing ability, which is substantiated by the parameter space analysis. The control of synchrony in the network of neurons may find application in rectifying neural disorders.

Emulation of Astrocyte Induced Neural Phase Synchrony in Spin-Orbit Torque Oscillator Neurons

Astrocytes play a central role in inducing concerted phase synchronized neural-wave patterns inside the brain. In this article, we demonstrate that injected radio-frequency signal in underlying heavy metal layer of spin-orbit torque oscillator neurons mimic the neuron phase synchronization effect realized by glial cells. Potential application of such phase coupling effects is illustrated in the context of a temporal “binding problem.” We also present the design of a coupled neuron-synapse-astrocyte network enabled by compact neuromimetic devices by combining the concepts of local spike-timing dependent plasticity and astrocyte induced neural phase synchrony.

A Unified Framework for Inattention Estimation From Resting State Phase Synchrony Using Machine Learning

Inattention is one of the most significant clinical symptoms for evaluating attention deficit hyperactivity disorder (ADHD). Previous inattention estimations were performed using clinical scales. Recently, predictive models for inattention have been established for brain-behavior estimation using neuroimaging features. However, the performance of inattention estimation could be improved for conventional brain-behavior models with additional feature selection, machine learning algorithms, and validation procedures. This paper aimed to propose a unified framework for inattention estimation from resting state fMRI to improve the classical brain-behavior models. Phase synchrony was derived as raw features, which were selected with minimum-redundancy maximum-relevancy (mRMR) method. Six machine learning algorithms were applied as regression methods. 100 runs of 10-fold cross-validations were performed on the ADHD-200 datasets. The relevance vector machines (RVMs) based on the mRMR features for the brain-behavior models significantly improve the performance of inattention estimation. The mRMR-RVM models could achieve a total accuracy of 0.53. Furthermore, predictive patterns for inattention were discovered by the mRMR technique. We found that the bilateral subcortical-cerebellum networks exhibited the most predictive phase synchrony patterns for inattention. Together, an optimized strategy named mRMR-RVM for brain-behavior models was found for inattention estimation. The predictive patterns might help better understand the phase synchrony mechanisms for inattention.

Phase-amplitude coupling and phase synchronization between medial temporal, frontal and posterior brain regions support episodic autobiographical memory recall

Episodic autobiographical memory (EAM) is a complex cognitive function that emerges from the coordination of specific and distant brain regions. Specific brain rhythms, namely theta and gamma oscillations and their synchronization, are thought of as putative mechanisms enabling EAM. Yet, the mechanisms of inter-regional interaction in the EAM network remain unclear in humans at the whole brain level. To investigate this, we analyzed EEG recordings of participants instructed to retrieve autobiographical episodes. EEG recordings were projected in the source space, and time-courses of atlas-based brain regions-of-interest (ROIs) were derived. Directed phase synchrony in high theta (7-10 Hz) and gamma (30-80 Hz) bands and high theta-gamma phase-amplitude coupling were computed between each pair of ROIs. Using network-based statistics, a graph-theory method, we found statistically significant networks for each investigated mechanism. In the gamma band, two sub-networks were found, one between the posterior cingulate cortex (PCC) and the medial temporal lobe (MTL) and another within the medial frontal areas. In the high theta band, we found a PCC to ventromedial prefrontal cortex (vmPFC) network. In phase-amplitude coupling, we found the high theta phase of the left MTL biasing the gamma amplitude of posterior regions and the vmPFC. Other regions of the temporal lobe and the insula were also phase biasing the vmPFC. These findings suggest that EAM, rather than emerging from a single mechanism at a single frequency, involves precise spatio-temporal signatures mapping on distinct memory processes. We propose that the MTL orchestrates activity in vmPFC and PCC via precise phase-amplitude coupling, with vmPFC and PCC interaction via high theta phase synchrony and gamma synchronization contributing to bind information within the PCC-MTL sub-network or valuate the candidate memory within the medial frontal sub-network.

Time-frequency analysis methods and their application in developmental EEG data

EEG provides a rich measure of brain activity that can be characterized as neuronal oscillations. However, most developmental EEG work to date has focused on analyzing EEG data as Event-Related Potentials (ERPs) or power based on the Fourier transform. While these measures have been productive, they do not leverage all the information contained within the EEG signal. Namely, ERP analyses ignore non-phase-locked signals and Fourier-based power analyses ignore temporal information. Time-frequency analyses can better characterize the oscillations contained in the EEG data. By separating power and phase information across different frequencies, time-frequency measures provide a closer interpretation of the neurophysiological mechanisms, facilitate translation across neurophysiology disciplines, and capture processes not observed by ERP or Fourier-based analyses (e.g., connectivity). Despite their unique contributions, a literature review of this journal reveals that time-frequency analyses of EEG are yet to be embraced by the developmental cognitive neuroscience field. This manuscript presents a conceptual introduction to time-frequency analyses for developmental researchers. To facilitate the use of time-frequency analyses, we include a tutorial of accessible scripts, based on Cohen (2014), to calculate time-frequency power (signal strength), inter-trial phase synchrony (signal consistency), and two types of phase-based connectivity (inter-channel phase synchrony and weighted phase lag index).