Atypical Flexibility in Dynamic Functional Connectivity Quantifies the Severity in Autism Spectrum Disorder

Harlalka, Vatika; Bapi, Raju S.; Vinod, P. K.; Roy, Dipanjan

doi:10.3389/fnhum.2019.00006

Cited by 96 publications

(106 citation statements)

References 52 publications

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“…7). This follows previous work showing the engagement of brain networks is specific to the current behavior (Mattar et al, 2015; Telesford et al, 2016) and that disrupting these networks underlies numerous pathologies (Badhwar et al, 2017; Braun et al, 2016; Harlalka et al, 2019).…”

Section: Discussionsupporting

confidence: 84%

Low-Dimensional Spatio-Temporal Dynamics Underlie Cortex-Wide Neural Activity

MacDowell

Buschman

2020

Preprint

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12Cognition arises from the dynamic flow of neural activity through the brain. To capture these 13 dynamics, we used mesoscale calcium imaging to record neural activity across the dorsal cortex 14 of awake mice. We found that the large majority of variance in cortex-wide activity (~75%) could 15 be explained by a limited set of ~14 'motifs' of neural activity. Each motif captured a unique spatio-16 temporal pattern of neural activity across the cortex. These motifs generalized across animals 17 and were seen in multiple behavioral environments. Motif expression differed across behavioral 18 states and specific motifs were engaged by sensory processing, suggesting the motifs reflect core 19 cortical computations. Together, our results show that cortex-wide neural activity is highly 20 dynamic, but that these dynamics are restricted to a low-dimensional set of motifs, potentially to 21 allow for efficient control of behavior. Zanos et al., 2015). Together, this work suggests cortical activity is highly dynamic, evolving over 36 both time and space, and that these dynamics play a computational role in cognition (Buonomano 37 and Maass, 2009; Miller and Wilson, 2008). 38However, despite this work, the nature of cortical dynamics is still not well understood. Previous 39 work has been restricted to specific regions and/or specific behavioral states and so, we do not 40 yet know how neural activity evolves across the entire cortex, whether dynamics are similar across 41 individuals, or how dynamics relate to behavior. This is due, in part, to the difficulty of quantifying 42 the spatio-temporal dynamics of neural activity across the brain. 43To address this, we used mesoscale imaging to measure neural activity across the dorsal cortical 44 surface of the mouse brain (Silasi et al., 2016). Then, using a convolutional factorization 45 approach, we identified dynamic 'motifs' of cortex-wide neural activity. Each motif captured a Surprisingly, the motifs clustered into a limited set of ~14 different spatio-temporal 'basis' motifs 50 that were consistent across all animals. The basis motifs captured the majority of the variance in 51 neural activity in different behavioral states and in multiple sensory and social environments. 52Specific motifs were selectively engaged by each environment and by sensory stimuli, suggesting 53 the motifs reflect core cortical computations, such as visual or tactile processing. Together, our 54 results suggest cortex-wide neural activity is highly dynamic but that these dynamics are low-55 dimensional: they are constrained to a small set of possible spatio-temporal patterns. 56 Results 57Discovery of spatio-temporal motifs of cortical activity in awake, head-fixed mice 58We performed widefield 'mesoscale' calcium imaging of the dorsal cerebral cortex of awake, 59 head-fixed mice expressing the fluorescent calcium indicator GCaMP6f in cortical pyramidal 60 neurons ( Fig. 1A; see Methods for details, Chen et al., 2013). A translucent-skull prep provided 61 optical access to dorsal cortex, a...

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Section: Discussionsupporting

confidence: 84%

Low-Dimensional Spatio-Temporal Dynamics Underlie Cortex-Wide Neural Activity

MacDowell

Buschman

2020

Preprint

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“…Meanwhile, increased variance of connectivity of the PCC to other DMN regions during the resting state was associated with slower reaction times on a subsequent attention task [Lin et al, ]. Moreover, recent DFC analyses showed that connection variability was positively correlated with the ADOS scores [Chen et al, ; Harlalka, Raju, Krishnanunni, & Roy, ], and higher variability was also observed in another study [Falahpour et al, ], which could cause unstable transmission between major brain hubs [Chen et al, ].…”

Section: Discussionmentioning

confidence: 99%

Dynamic Functional Connectivity Reveals Abnormal Variability and Hyper‐connected Pattern in Autism Spectrum Disorder

Zhu

Nguchu

et al. 2019

Autism Research

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Autism spectrum disorder (ASD) is a general neurodevelopmental disorder associated with altered brain connectivity. However, most connectivity analyses in ASD focus on static functional connectivity, largely neglecting brain activity dynamics that have been reported to provide deeper insight into the underlying mechanisms of brain functions. Therefore, we anticipate that the use of dynamic functional connectivity (DFC) with interaction of clustering measures could help characterize ASD severity and reveal more information. In this study, we applied the sliding‐window and k‐means clustering methods to perform DFC and clustering analyses in ASD and typically developing (TD) groups. Data from 62 ASD and 63 TD children were acquired from the open‐access data set Autism Brain Imaging Data Exchange. Our findings revealed higher DFC variability between the posterior cingulate gyrus (PCC) and middle temporal pole (TPOmid) in subjects with ASD. The connection between the PCC and pars opercularis of inferior frontal gyrus (IFGoper) also presented greater variability in ASD, with the increase depending on ASD symptom severity. Furthermore, clustering analysis showed higher averaged dwell time and probability of transition for globally hyper‐connected state in the ASD group, which could be related to connection variability between the PCC and IFGoper. Our results demonstrate that both the PCC and IFGoper play crucial roles in characterizing symptom severity and state configuration in ASD, and brain connectivity dynamics may serve as potential indicators of ASD in future studies. Autism Res 2020, 13: 230–243. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. Lay Summary Dynamic functional connectivity (DFC) refers to functional connectivity that changes over a short time. This study found that DFC instability between the posterior cingulate gyrus and pars opercularis of inferior frontal gyrus is associated with abnormal brain pattern configurations and dysfunction of social cognitive processes in autism spectrum disorder (ASD). These findings could contribute to a deeper understanding of the neural mechanisms of ASD and help characterize ASD severity.

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“…Interestingly, global flexibility was highly negatively correlated with modularity, suggesting that more segregated networks are also more resistant to change, and thus exhibit lower variability across a certain time span (Harlalka, Bapi, Vinod, & Roy, 2019; Meunier, Lambiotte, & Bullmore, 2010; Ramos‐Nuñez et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

Longitudinal functional brain network reconfiguration in healthy aging

Malagurski

Liem

Oschwald

et al. 2020

Human Brain Mapping

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Healthy aging is associated with changes in cognitive performance and functional brain organization. In fact, cross‐sectional studies imply lower modularity and significant heterogeneity in modular architecture across older subjects. Here, we used a longitudinal dataset consisting of four occasions of resting‐state‐fMRI and cognitive testing (spanning 4 years) in 150 healthy older adults. We applied a graph‐theoretic analysis to investigate the time‐evolving modular structure of the whole‐brain network, by maximizing the multilayer modularity across four time points. Global flexibility, which reflects the tendency of brain nodes to switch between modules across time, was significantly higher in healthy elderly than in a temporal null model. Further, global flexibility, as well as network‐specific flexibility of the default mode, frontoparietal control, and somatomotor networks, were significantly associated with age at baseline. These results indicate that older age is related to higher variability in modular organization. The temporal metrics were not associated with simultaneous changes in processing speed or learning performance in the context of memory encoding. Finally, this approach provides global indices for longitudinal change across a given time span and it may contribute to uncovering patterns of modular variability in healthy and clinical aging populations.

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Atypical Flexibility in Dynamic Functional Connectivity Quantifies the Severity in Autism Spectrum Disorder

Cited by 96 publications

References 52 publications

Low-Dimensional Spatio-Temporal Dynamics Underlie Cortex-Wide Neural Activity

Low-Dimensional Spatio-Temporal Dynamics Underlie Cortex-Wide Neural Activity

Dynamic Functional Connectivity Reveals Abnormal Variability and Hyper‐connected Pattern in Autism Spectrum Disorder

Longitudinal functional brain network reconfiguration in healthy aging

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