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Atypical Core-Periphery Brain Dynamics in Autism

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Atypical Core-Periphery Brain Dynamics in Autism REVIEW Atypical core-periphery brain dynamics in autism 1 2 Dipanjan Roy and Lucina Q. Uddin 1Cognitive Brain Dynamics Lab, National Brain Research Centre, Manesar, India 2Department of Psychology, University of Miami, Coral Gables, FL, USA Keywords: Core-periphery dynamics, Atypical timescales, Caudate, Core and contextual symptom severity, Sensory-motor network, Restricted and repetitive behaviors ABSTRACT The intrinsic function of the human brain is dynamic, giving rise to numerous behavioral an open access journal subtypes that fluctuate distinctively at multiple timescales. One of the key dynamical processes that takes place in the brain is the interaction between core-periphery brain regions, which undergoes constant fluctuations associated with developmental time frames. Core-periphery dynamical changes associated with macroscale brain network dynamics span multiple timescales and may lead to atypical behavior and clinical symptoms. For example, recent evidence suggests that brain regions with shorter intrinsic timescales are located at the periphery of brain networks (e.g., sensorimotor hand, face areas) and are implicated in perception and movement. On the contrary, brain regions with longer timescales are core hub regions. These hubs are important for regulating interactions between the brain and the body during self-related cognition and emotion. In this review, we summarize a large body of converging evidence derived from time-resolved fMRI studies in autism to characterize atypical core-periphery brain dynamics and how they relate to core and contextual sensory and cognitive profiles. Citation: Roy, D., & Uddin, L. Q. (2021). INTRODUCTION Atypical core-periphery brain dynamics in autism. Network Neuroscience, 5(2), Sensory Processing in Autism 295–321. https://doi.org/10.1162 /netn_a_00181 Perhaps the most remarkable feature of autism spectrum disorder (ASD) is profound behavioral DOI: diversity across different individuals, which pertains to all factors involved in interactions with https://doi.org/10.1162/netn_a_00181 the physical and social environment (Baron-Cohen, Ashwin, Ashwin, Tavassoli, & Chakrabarti, Received: 2 September 2020 2009; Blakemore, Burnett, & Dahl, 2010; Bolton, Morgenroth, Preti, & Van De Ville, 2020; Accepted: 31 December 2020 Lawson et al., 2015; Robertson & Baron-Cohen, 2017; Shafritz, Dichter, Baranek, & Belger, Competing Interests: The authors have 2008; Uddin, 2021). This diversity underlies variability in personality, physiology, and mental declared that no competing interests exist. capacity, which in turn are sculpted by not only complex biological influences (e.g., med- ication, genetic and epigenetic factors) but also various sociocultural factors (e.g., multilin- Corresponding Author: Dipanjan Roy gual environments, social learning, trauma; Baron-Cohen et al., 2009; Baum et al., 2017; [email protected] Bolton et al., 2020; Robertson & Baron-Cohen, 2017; Uddin, 2021). Developmental re- Handling Editor: search suggests that sensory symptoms manifest early in life, and contribute unique variance Olaf Sporns to the diagnostic criteria of autism (Andreae, 2019; Chen, Nomi, Uddin, Duan, & Chen, 2017; Ciarrusta et al., 2019; Courchesne, Campbell, & Solso, 2011; Uddin, 2021). Neuroimaging evidence suggests that sensory symptoms originate from differences in low-level processing Copyright: © 2021 Massachusetts Institute of Technology in sensory-dedicated regions in the autistic brain, and offers insight into circuit-level alter- Published under a Creative Commons Attribution 4.0 International ations (Abbott et al., 2018; Alaerts et al., 2015; Alaerts, Swinnen, & Wenderoth, 2016; Alaerts (CC BY 4.0) license et al. 2014; Anderson et al., 2011; Baum et al., 2017; Collignon et al., 2013; Courchesne et al., 2011; Uddin, 2021). Studying the brain at rest has demonstrated that although the environment The MIT Press Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/netn_a_00181 by guest on 02 October 2021 Core-periphery brain network dynamics in autism has an influence, the brain operates intrinsically and is modulated by, rather than controlled by, the external world (Baum et al., 2017; Bolton et al., 2020; Liégeois et al., 2019; Lin et al., 2016; Liu, Liao, Xia, & He, 2018). This modulation is a recursive process between the brain and the environment mediated by perception and action (Friston, 2009; Kiebel, Daunizeau, & Friston, 2008). This process is highly dynamic, as are both the environment and the brain (Bolton et al., 2020; Friston, 2009; Huang et al., 2015). Network core: A set of densely connected brain Individuals with severe autism usually have intellectual impairments and develop little spo- regions that aggregate long-range ken language. There are subgroups of autistic individuals who may have average or above- connectivity and serve as a backbone average IQ, but who still struggle with more subtle aspects of communication, such as body for polymodal integration. language (Jasmin et al., 2019; Ostrolenk, Bao, Mottron, Collignon, & Bertone, 2019; Robertson & Baron-Cohen, 2017; Supekar et al., 2013; Uddin, 2021; Uddin et al., 2015). In addition to Network periphery: Brain networks primarily consisting social difficulties, many individuals with autism show restricted and repetitive behaviors (RRB) of sensory and motor regions, with and sensory abnormalities (SA), and have narrow interests (Huang et al., 2015; JaoKeehnetal., more locally clustered connectivity. 2019; JaoKeehnetal., 2017; Jasmin et al., 2019; Kana, Keller, Minshew, & Just, 2007; Manning, Tibber, Charman, Dakin, & 2015; Mash, Reiter, Linke, Townsend, & Müller, 2018; Flexibility: McKinnon et al., 2019; Mottron, Belleville, Rouleau, & Collignon, 2014; Moul, Cauchi, Hawes, Flexibility is a network metric that characterizes the modular changes in Brennan, & Dadds, 2015; Robertson & Baron-Cohen, 2017; Uddin, 2021). each brain area throughout the scan period. However, this dynamic The brain as a whole shows less coordinated activity in autism, and one way of classifying measure does not capture subtypes that can include hyper- and hyporeactivity to sensory environments or unusual inter- community affiliation. est in sensory aspects of the environment could be to use brain network-based classification methods (Harlalka, Bapi, Vinod, & Roy, 2018, 2019; Nomi, Bolt, Ezie, Uddin, & Heller, 2017; Cohesion strength: Uddin et al., 2013). There is a growing body of evidence further suggesting that individual brain In a network of brain areas, node regions work in a less cohesive manner in autism, with widely distributed timescales and hi- strength is estimated as a cohesion erarchical organization of brain networks (Atasoy, Donnelly, & Pearson, 2016; Chaudhuri, matrix where the edge weights of the network denote the number of times Knoblauch, Gariel, Kennedy, & Wang, 2015; Gollo, Roberts, & Cocchi, 2017; Gollo, Zalesky, a pair of nodes changes to the Hutchison, van den Heuvel, & Breakspear, 2015; Harlalka et al., 2019; Hasson, Yang, Vallines, identical community affiliation. Heeger, & Rubin, 2008; Hong et al., 2019; Kumar et al., 2016; Lin et al., 2016; London, 2018; Nomi, Bolt, et al., 2017; Nomi & Uddin, 2015; Nomi, Vij, et al., 2017; Oldham & Fornito, Rich-club nodes: 2019; Pillai & Jirsa, 2017; Preti & Van De Ville, 2019; Raut, Snyder, & Raichle, 2020; P. Wang Strongly connected nodes in a et al., 2019; S. Wang et al., 2015; Watanabe & Rees, 2017; Watanabe, Rees, & Masuda, network. If there is a core of such 2019). Here we use the terms core and periphery (outside the core) brain regions when we nodes with a high node degree that refer to atypical timescales, flexibility, cohesion, dispersion, and functional gradients based on receives a very large proportion of connections and that are more their hierarchical organization and differences in network dynamics (Chaudhuri et al., 2015; densely interconnected among Gollo, 2019; Gollo et al., 2017; Gollo et al., 2015; Hasson et al., 2008; Hong et al., 2019; themselves than lower degree nodes van den Heuvel, Kahn, Goñi, & Sporns, 2012; P. Wang et al., 2019; S. Wang et al., 2015). in the network, they form a rich club. Specifically, studies have suggested a distinction between a network periphery containing sen- In other words, the high-degree sory and motor regions with more locally clustered connectivity, and a rich-club “core” that ag- nodes are densely connected hubs in the brain networks and form an gregates long-range connections and serves as a backbone for transmodal integration, giving rise exclusive club. to behavior and cognition (Deco, Kringelbach, Jirsa, & Ritter, 2017; Gollo, 2019; Gollo et al., 2017; Gollo et al., 2015; Griffa & van den Heuvel, 2018; Harlalka et al., 2019; Hasson et al., Transmodal cortex: 2008; Hilgetag & Goulas, 2020; Hong et al., 2019; Lin et al., 2016; Rashid et al., 2018; This overarching system is thought Shafritz et al., 2008; P. Wang et al., 2019; Watanabe & Rees, 2017). to facilitate abstract, higher order cognitive functions by segregating Perturbation of resting-state brain dynamics and distortion of timescales of sensory- information processing of the processing regions in individuals with autism compared with that of people without autism sensory environment from more self-generated and internally oriented may shed light on core and contextual neural processing and links with symptom severity in cognition emerging in transmodal, the disorder (Andreae, 2019; Baron-Cohen et al., 2009; Cerliani et al., 2015; Foxe et al., 2015; integrative
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