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      Transient states of network connectivity are atypical in autism: A dynamic functional connectivity study

      1 , 2 , 1 , 1 , 2 , 1 , 3 , 1 , 2
      Human Brain Mapping
      Wiley

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          Abstract

          There is ample evidence of atypical functional connectivity (FC) in autism spectrum disorders (ASDs). However, transient relationships between neural networks cannot be captured by conventional static FC analyses. Dynamic FC (dFC) approaches have been used to identify repeating, transient connectivity patterns (“states”), revealing spatiotemporal network properties not observable in static FC. Recent studies have found atypical dFC in ASDs, but questions remain about the nature of group differences in transient connectivity, and the degree to which states persist or change over time. This study aimed to: (a) describe and relate static and dynamic FC in typical development and ASDs, (b) describe group differences in transient states and compare them with static FC patterns, and (c) examine temporal stability and flexibility between identified states. Resting‐state functional magnetic resonance imaging (fMRI) data were collected from 62 ASD and 57 typically developing (TD) children and adolescents. Whole‐brain, data‐driven regions of interest were derived from group independent component analysis. Sliding window analysis and k‐means clustering were used to explore dFC and identify transient states. Across all regions, static overconnnectivity and increased variability over time in ASDs predominated. Furthermore, significant patterns of group differences emerged in two transient states that were not observed in the static FC matrix, with group differences in one state primarily involving sensory and motor networks, and in the other involving higher‐order cognition networks. Default mode network segregation was significantly reduced in ASDs in both states. Results highlight that dynamic approaches may reveal more nuanced transient patterns of atypical FC in ASDs.

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          Cortical activation and synchronization during sentence comprehension in high-functioning autism: evidence of underconnectivity.

          The brain activation of a group of high-functioning autistic participants was measured using functional MRI during sentence comprehension and the results compared with those of a Verbal IQ-matched control group. The groups differed in the distribution of activation in two of the key language areas. The autism group produced reliably more activation than the control group in Wernicke's (left laterosuperior temporal) area and reliably less activation than the control group in Broca's (left inferior frontal gyrus) area. Furthermore, the functional connectivity, i.e. the degree of synchronization or correlation of the time series of the activation, between the various participating cortical areas was consistently lower for the autistic than the control participants. These findings suggest that the neural basis of disordered language in autism entails a lower degree of information integration and synchronization across the large-scale cortical network for language processing. The article presents a theoretical account of the findings, related to neurobiological foundations of underconnectivity in autism.
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            Autism and abnormal development of brain connectivity.

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              BOLD correlates of EEG topography reveal rapid resting-state network dynamics.

              Resting-state functional connectivity studies with fMRI showed that the brain is intrinsically organized into large-scale functional networks for which the hemodynamic signature is stable for about 10s. Spatial analyses of the topography of the spontaneous EEG also show discrete epochs of stable global brain states (so-called microstates), but they remain quasi-stationary for only about 100 ms. In order to test the relationship between the rapidly fluctuating EEG-defined microstates and the slowly oscillating fMRI-defined resting states, we recorded 64-channel EEG in the scanner while subjects were at rest with their eyes closed. Conventional EEG-microstate analysis determined the typical four EEG topographies that dominated across all subjects. The convolution of the time course of these maps with the hemodynamic response function allowed to fit a linear model to the fMRI BOLD responses and revealed four distinct distributed networks. These networks were spatially correlated with four of the resting-state networks (RSNs) that were found by the conventional fMRI group-level independent component analysis (ICA). These RSNs have previously been attributed to phonological processing, visual imagery, attention reorientation, and subjective interoceptive-autonomic processing. We found no EEG-correlate of the default mode network. Thus, the four typical microstates of the spontaneous EEG seem to represent the neurophysiological correlate of four of the RSNs and show that they are fluctuating much more rapidly than fMRI alone suggests. Copyright 2010 Elsevier Inc. All rights reserved.
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                Author and article information

                Journal
                Human Brain Mapping
                Hum Brain Mapp
                Wiley
                1065-9471
                1097-0193
                January 25 2019
                June 2019
                January 25 2019
                June 2019
                : 40
                : 8
                : 2377-2389
                Affiliations
                [1 ]Brain Development Imaging Laboratories, Department of PsychologySan Diego State University San Diego California
                [2 ]Joint Doctoral Program in Clinical PsychologySan Diego State University/University of California San Diego San Diego California
                [3 ]Center for Functional MRI, Department of RadiologyUniversity of California San Diego San Diego California
                Article
                10.1002/hbm.24529
                6549695
                30681228
                e6aa4ea1-2f7d-4505-8e41-0b42ce0b5f57
                © 2019

                http://onlinelibrary.wiley.com/termsAndConditions#am

                http://onlinelibrary.wiley.com/termsAndConditions#vor

                http://doi.wiley.com/10.1002/tdm_license_1.1

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