Maturation trajectories of cortical resting-state networks depend on the mediating frequency band.

The functional significance of resting state networks and their abnormal manifestations in psychiatric disorders are firmly established, as is the importance of the cortical rhythms in mediating these networks. Resting state networks are known to undergo substantial reorganization from childhood to adulthood, but whether distinct cortical rhythms, which are generated by separable neural mechanisms and are often manifested abnormally in psychiatric conditions, mediate maturation differentially, remains unknown. Using magnetoencephalography (MEG) to map frequency band specific maturation of resting state networks from age 7 to 29 in 162 participants (31 independent), we found significant changes with age in networks mediated by the beta (13-30 Hz) and gamma (31-80 Hz) bands. More specifically, gamma band mediated networks followed an expected asymptotic trajectory, but beta band mediated networks followed a linear trajectory. Network integration increased with age in gamma band mediated networks, while local segregation increased with age in beta band mediated networks. Spatially, the hubs that changed in importance with age in the beta band mediated networks had relatively little overlap with those that showed the greatest changes in the gamma band mediated networks. These findings are relevant for our understanding of the neural mechanisms of cortical maturation, in both typical and atypical development.

Somatosensory cortex functional connectivity abnormalities in autism show opposite trends, depending on direction and spatial scale.

Functional connectivity is abnormal in autism, but the nature of these abnormalities remains elusive. Different studies, mostly using functional magnetic resonance imaging, have found increased, decreased, or even mixed pattern functional connectivity abnormalities in autism, but no unifying framework has emerged to date. We measured functional connectivity in individuals with autism and in controls using magnetoencephalography, which allowed us to resolve both the directionality (feedforward versus feedback) and spatial scale (local or long-range) of functional connectivity. Specifically, we measured the cortical response and functional connectivity during a passive 25-Hz vibrotactile stimulation in the somatosensory cortex of 20 typically developing individuals and 15 individuals with autism, all males and right-handed, aged 8-18, and the mu-rhythm during resting state in a subset of these participants (12 per group, same age range). Two major significant group differences emerged in the response to the vibrotactile stimulus. First, the 50-Hz phase locking component of the cortical response, generated locally in the primary (S1) and secondary (S2) somatosensory cortex, was reduced in the autism group (P < 0.003, corrected). Second, feedforward functional connectivity between S1 and S2 was increased in the autism group (P < 0.004, corrected). During resting state, there was no group difference in the mu-α rhythm. In contrast, the mu-ß rhythm, which has been associated with feedback connectivity, was significantly reduced in the autism group (P < 0.04, corrected). Furthermore, the strength of the mu-ß was correlated to the relative strength of 50 Hz component of the response to the vibrotactile stimulus (r = 0.78, P < 0.00005), indicating a shared aetiology for these seemingly unrelated abnormalities. These magnetoencephalography-derived measures were correlated with two different behavioural sensory processing scores (P < 0.01 and P < 0.02 for the autism group, P < 0.01 and P < 0.0001 for the typical group), with autism severity (P < 0.03), and with diagnosis (89% accuracy). A biophysically realistic computational model using data driven feedforward and feedback parameters replicated the magnetoencephalography data faithfully. The direct observation of both abnormally increased and abnormally decreased functional connectivity in autism occurring simultaneously in different functional connectivity streams, offers a potential unifying framework for the unexplained discrepancies in current findings. Given that cortical feedback, whether local or long-range, is intrinsically non-linear, while cortical feedforward is generally linear relative to the stimulus, the present results suggest decreased non-linearity alongside an increased veridical component of the cortical response in autism.

Local and long-range functional connectivity is reduced in concert in autism spectrum disorders.

Long-range cortical functional connectivity is often reduced in autism spectrum disorders (ASD), but the nature of local cortical functional connectivity in ASD has remained elusive. We used magnetoencephalography to measure task-related local functional connectivity, as manifested by coupling between the phase of alpha oscillations and the amplitude of gamma oscillations, in the fusiform face area (FFA) of individuals diagnosed with ASD and typically developing individuals while they viewed neutral faces, emotional faces, and houses. We also measured task-related long-range functional connectivity between the FFA and the rest of the cortex during the same paradigm. In agreement with earlier studies, long-range functional connectivity between the FFA and three distant cortical regions was reduced in the ASD group. However, contrary to the prevailing hypothesis in the field, we found that local functional connectivity within the FFA was also reduced in individuals with ASD when viewing faces. Furthermore, the strength of long-range functional connectivity was directly correlated to the strength of local functional connectivity in both groups; thus, long-range and local connectivity were reduced proportionally in the ASD group. Finally, the magnitude of local functional connectivity correlated with ASD severity, and statistical classification using local and long-range functional connectivity data identified ASD diagnosis with 90% accuracy. These results suggest that failure to entrain neuronal assemblies fully both within and across cortical regions may be characteristic of ASD.

Auditory processing in noise is associated with complex patterns of disrupted functional connectivity in autism spectrum disorder.

Autism spectrum disorder (ASD) is associated with difficulty in processing speech in a noisy background, but the neural mechanisms that underlie this deficit have not been mapped. To address this question, we used magnetoencephalography to compare the cortical responses between ASD and typically developing (TD) individuals to a passive mismatch paradigm. We repeated the paradigm twice, once in a quiet background, and once in the presence of background noise. We focused on both the evoked mismatch field (MMF) response in temporal and frontal cortical locations, and functional connectivity with spectral specificity between those locations. In the quiet condition, we found common neural sources of the MMF response in both groups, in the right temporal gyrus and inferior frontal gyrus (IFG). In the noise condition, the MMF response in the right IFG was preserved in the TD group, but reduced relative to the quiet condition in ASD group. The MMF response in the right IFG also correlated with severity of ASD. Moreover, in noise, we found significantly reduced normalized coherence (deviant normalized by standard) in ASD relative to TD, in the beta band (14-25 Hz), between left temporal and left inferior frontal sub-regions. However, unnormalized coherence (coherence during deviant or standard) was significantly increased in ASD relative to TD, in multiple frequency bands. Our findings suggest increased recruitment of neural resources in ASD irrespective of the task difficulty, alongside a reduction in top-down modulations, usually mediated by the beta band, needed to mitigate the impact of noise on auditory processing. Autism Res 2016,. © 2016 International Society for Autism Research, Wiley Periodicals, Inc. Autism Res 2017, 10: 631-647. © 2016 International Society for Autism Research, Wiley Periodicals, Inc.

Normal Evoked Response to Rapid Sequences of Tactile Pulses in Autism Spectrum Disorders.

Autism spectrum disorder (ASD) is a developmental disorder diagnosed behaviorally, with many documented neurophysiological abnormalities in cortical response properties. While abnormal sensory processing is not considered core to the disorder, most ASD individuals report sensory processing abnormalities. Yet, the neurophysiological correlates of these abnormalities have not been fully mapped. In the auditory domain, studies have shown that cortical responses in the early auditory cortex in ASD are abnormal in multiple ways. In particular, it has been shown that individuals with ASD have abnormal cortical auditory evoked responses to rapid, but not slow, sequences of tones. In parallel, there is substantial evidence of somatosensory processing abnormalities in ASD, including in the temporal domain. Here, we tested the somatosensory domain in ASD for abnormalities in rapid processing of tactile pulses, to determine whether abnormalities there parallel those observed in the auditory domain. Specifically, we tested the somatosensory cortex response to a sequence of two tactile pulses with different (short and long) temporal separation. We analyzed the responses in cortical space, in primary somatosensory cortex. As expected, we found no group difference in the evoked response to pulses with long (700 ms) temporal separation. Contrary to findings in the auditory domain, we also found no group differences in the evoked responses to the sequence with a short (200 ms) temporal separation. These results suggest that rapid temporal processing deficits in ASD are not generalized across multiple sensory domains, and are unlikely to underlie the behavioral somatosensory abnormalities observed in ASD.

Altered Onset Response Dynamics in Somatosensory Processing in Autism Spectrum Disorder.

Abnormalities in cortical connectivity and evoked responses have been extensively documented in autism spectrum disorder (ASD). However, specific signatures of these cortical abnormalities remain elusive, with data pointing toward abnormal patterns of both increased and reduced response amplitudes and functional connectivity. We have previously proposed, using magnetoencephalography (MEG) data, that apparent inconsistencies in prior studies could be reconciled if functional connectivity in ASD was reduced in the feedback (top-down) direction, but increased in the feedforward (bottom-up) direction. Here, we continue this line of investigation by assessing abnormalities restricted to the onset, feedforward inputs driven, component of the response to vibrotactile stimuli in somatosensory cortex in ASD. Using a novel method that measures the spatio-temporal divergence of cortical activation, we found that relative to typically developing participants, the ASD group was characterized by an increase in the initial onset component of the cortical response, and a faster spread of local activity. Given the early time window, the results could be interpreted as increased thalamocortical feedforward connectivity in ASD, and offer a plausible mechanism for the previously observed increased response variability in ASD, as well as for the commonly observed behaviorally measured tactile processing abnormalities associated with the disorder.

Altered development and multifaceted band-specific abnormalities of resting state networks in autism.

BACKGROUND: Extensive evidence indicates that cortical connectivity patterns are abnormal in autism spectrum disorders (ASD), showing both overconnectivity and underconnectivity. Since, however, studies to date have focused on either spatial or spectral dimensions, but not both simultaneously, much remains unknown about the nature of these abnormalities. In particular, it remains unknown whether abnormal connectivity patterns in ASD are driven by specific frequency bands, by spatial network properties, or by some combination of these factors. METHODS: Magnetoencephalography recordings (15 ASD, 15 control subjects) mapped back onto cortical space were used to study resting state networks in ASD with both spatial and spectral specificity. The data were quantified using graph theoretic metrics. RESULTS: The two major factors that drove the nature of connectivity abnormalities in ASD were the mediating frequency band and whether the network included frontal nodes. These factors determined whether clustering and integration were increased or decreased in cortical resting state networks in ASD. These measures also correlated with abnormalities in the developmental trajectory of resting state networks in ASD. Lastly, these measures correlated with ASD severity in some frequency bands and spatially specific subnetworks. CONCLUSIONS: Our findings suggest that network abnormalities in ASD are widespread, are more likely in subnetworks that include the frontal lobe, and can be opposite in nature depending on the frequency band. These findings thus elucidate seemingly contradictory prior findings of both overconnectivity and underconnectivity in ASD.

Encoding cortical dynamics in sparse features.

Distributed cortical solutions of magnetoencephalography (MEG) and electroencephalography (EEG) exhibit complex spatial and temporal dynamics. The extraction of patterns of interest and dynamic features from these cortical signals has so far relied on the expertise of investigators. There is a definite need in both clinical and neuroscience research for a method that will extract critical features from high-dimensional neuroimaging data in an automatic fashion. We have previously demonstrated the use of optical flow techniques for evaluating the kinematic properties of motion field projected on non-flat manifolds like in a cortical surface. We have further extended this framework to automatically detect features in the optical flow vector field by using the modified and extended 2-Riemannian Helmholtz-Hodge decomposition (HHD). Here, we applied these mathematical models on simulation and MEG data recorded from a healthy individual during a somatosensory experiment and an epilepsy pediatric patient during sleep. We tested whether our technique can automatically extract salient dynamical features of cortical activity. Simulation results indicated that we can precisely reproduce the simulated cortical dynamics with HHD; encode them in sparse features and represent the propagation of brain activity between distinct cortical areas. Using HHD, we decoded the somatosensory N20 component into two HHD features and represented the dynamics of brain activity as a traveling source between two primary somatosensory regions. In the epilepsy patient, we displayed the propagation of the epileptic activity around the margins of a brain lesion. Our findings indicate that HHD measures computed from cortical dynamics can: (i) quantitatively access the cortical dynamics in both healthy and disease brain in terms of sparse features and dynamic brain activity propagation between distinct cortical areas, and (ii) facilitate a reproducible, automated analysis of experimental and clinical MEG/EEG source imaging data.