Functional connectivity magnetic resonance imaging classification of autism

Anderson, Jeffrey S.; Nielsen, Jared A.; Froehlich, Alyson; DuBray, Molly B.; Druzgal, T. Jason; Cariello, Annahir N.; Cooperrider, Jason R.; Zielinski, Brandon A.; Ravichandran, Caitlin; Fletcher, P. Thomas; Alexander, Andrew L.; Bigler, Erin D.; Lange, Nicholas; Lainhart, Janet E.

doi:10.1093/brain/awr263

Cited by 366 publications

(345 citation statements)

References 85 publications

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“…These connections were positive on average in ASD, but negative on average in controls. Previous studies have also reported reduced negative connectivity in ASD, which was described as reduced functional segregation of networks (Rudie et al, 2012; or underconnectivity of long-range inhibitory connections (Anderson et al, 2011b). Our results show that despite distinct differences in static FC between subtypes, atypical FC within certain networks is consistent across all ASD participants compared to controls.…”

Section: Cc-by-nc-nd 40 International License Peer-reviewed) Is the supporting

confidence: 66%

Functional connectivity-based subtypes of individuals with and without autism spectrum disorder

Easson

Fatima

McIntosh

2017

Preprint

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Autism spectrum disorder (ASD) is a highly heterogeneous neurodevelopmental disorder that is characterized by impairments in social communication and restricted and repetitive behaviours.Neuroimaging studies of individuals with ASD have shown complex patterns of functional connectivity (FC), with no clear consensus on defining brain-behaviour attributes of different subtypes of the disorder. In these studies, "static FC", a metric that considers correlations across the entire time series for a given region, was used. Emerging evidence shows that FC changes over time; this "dynamic FC" can allow for unique detection of the temporal variability of functional connections over time. Here, we used k-means clustering, an unsupervised machine learning algorithm, to characterize two subtypes of ASD based on distinct patterns of static and dynamic FC. A multivariate statistical approach was then implemented to determine optimal relationships between FC patterns and group membership or behaviour. The main objective was to characterize differences in static and dynamic FC between subtypes of ASD defined in a fully data-driven manner, and between subtypes and typically developing individuals. Subtype 2 was defined by increased FC within resting-state networks and decreased FC across networks for static FC, global increases in temporal stability of dynamic FC, and robust relationships between FC and several behaviours, relative to Subtype 1. Further, both ASD subtypes exhibited different patterns of static and dynamic FC compared to controls, particularly within default mode, sensorimotor, and occipital networks. Static and dynamic FC metrics provided both overlapping and unique information about the nature of FC differences between subtypes, and between both subtypes and controls. Our results demonstrate the value of considering FC-based subtypes of ASD to elucidate different relationships between brain and behaviour among individuals with this disorder, and have important clinical implications for catering treatments and behavioural interventions to specific subtypes.

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Section: Cc-by-nc-nd 40 International License Peer-reviewed) Is the supporting

confidence: 66%

Functional connectivity-based subtypes of individuals with and without autism spectrum disorder

Easson

Fatima

McIntosh

2017

Preprint

View full text Add to dashboard Cite

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“…In particular, most task‐based studies focus their connectivity analyses on the regions that activate in response to the studied paradigm, ignoring functional interplays that may occur elsewhere in the brain (Wass, 2011). Also, despite the interest in task‐driven functional changes, the presence of intrinsic FC (iFC) variations (Damoiseaux et al, 2006; van den Heuvel & Pol, 2010; Hutchison, Womelsdorf, Gati, Everling, & Menon, 2013b) is never analytically accounted for; yet, iFC is altered throughout the brain in ASD (see for example Anderson et al, 2011; Cherkassky, Kana, Keller, & Just, 2006; Yahata et al, 2016). In some cases, ASD‐specific FC patterns put forward as caused by the studied task may thus reflect iFC group differences, or task‐driven effects may be overshadowed by iFC fluctuations and fail to be revealed.…”

Section: Introductionmentioning

confidence: 99%

Brain dynamics in ASD during movie‐watching show idiosyncratic functional integration and segregation

Bolton

Jochaut

Giraud

et al. 2018

Human Brain Mapping

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To refine our understanding of autism spectrum disorders (ASD), studies of the brain in dynamic, multimodal and ecological experimental settings are required. One way to achieve this is to compare the neural responses of ASD and typically developing (TD) individuals when viewing a naturalistic movie, but the temporal complexity of the stimulus hampers this task, and the presence of intrinsic functional connectivity (FC) may overshadow movie‐driven fluctuations. Here, we detected inter‐subject functional correlation (ISFC) transients to disentangle movie‐induced functional changes from underlying resting‐state activity while probing FC dynamically. When considering the number of significant ISFC excursions triggered by the movie across the brain, connections between remote functional modules were more heterogeneously engaged in the ASD population. Dynamically tracking the temporal profiles of those ISFC changes and tying them to specific movie subparts, this idiosyncrasy in ASD responses was then shown to involve functional integration and segregation mechanisms such as response inhibition, background suppression, or multisensory integration, while low‐level visual processing was spared. Through the application of a new framework for the study of dynamic experimental paradigms, our results reveal a temporally localized idiosyncrasy in ASD responses, specific to short‐lived episodes of long‐range functional interplays.

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“…7-11), and detecting brain changes in disease. Functional connectivity changes have been identified in diverse conditions including Alzheimer's disease (6,(12)(13)(14), Parkinson's disease (15,16), multiple sclerosis (17,18), autism (19), depression (20,21), and schizophrenia (22,23).…”

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confidence: 99%

Causal effect of disconnection lesions on interhemispheric functional connectivity in rhesus monkeys

O’Reilly

Croxson

Jbabdi

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

208

163

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In the absence of external stimuli or task demands, correlations in spontaneous brain activity (functional connectivity) reflect patterns of anatomical connectivity. Hence, resting-state functional connectivity has been used as a proxy measure for structural connectivity and as a biomarker for brain changes in disease. To relate changes in functional connectivity to physiological changes in the brain, it is important to understand how correlations in functional connectivity depend on the physical integrity of brain tissue. The causal nature of this relationship has been called into question by patient data suggesting that decreased structural connectivity does not necessarily lead to decreased functional connectivity. Here we provide evidence for a causal but complex relationship between structural connectivity and functional connectivity: we tested interhemispheric functional connectivity before and after corpus callosum section in rhesus monkeys. We found that forebrain commissurotomy severely reduced interhemispheric functional connectivity, but surprisingly, this effect was greatly mitigated if the anterior commissure was left intact. Furthermore, intact structural connections increased their functional connectivity in line with the hypothesis that the inputs to each node are normalized. We conclude that functional connectivity is likely driven by corticocortical white matter connections but with complex network interactions such that a near-normal pattern of functional connectivity can be maintained by just a few indirect structural connections. These surprising results highlight the importance of network-level interactions in functional connectivity and may cast light on various paradoxical findings concerning changes in functional connectivity in disease states.resting-state connectivity | macaque | fMRI | split brain R esting-state functional connectivity [intrinsic correlations in activity between brain areas, measured in the absence of overt stimulation or task demands (1, 2)] provides a powerful tool for understanding the global organization of the brain (3-6), charting its connectional structure (e.g., refs. 7-11), and detecting brain changes in disease. Functional connectivity changes have been identified in diverse conditions including Alzheimer's disease (6, 12-14), Parkinson's disease (15, 16), multiple sclerosis (17, 18), autism (19), depression (20, 21), and schizophrenia (22, 23).To relate changes in functional connectivity to physiological changes in the brain, it is important to understand how functional connectivity depends on the physical integrity of brain tissue. However, there is a disparity in the conclusions that have been drawn from work on the healthy brain and patient studies. It is generally accepted that in the healthy brain, functional connectivity correlates with structural connectivity (the presence and integrity of white matter connections) (3,24,25), and computational modeling suggests that structural connectivity shapes and constrains functional connectivity (3,24). Howeve...

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Functional connectivity magnetic resonance imaging classification of autism

Cited by 366 publications

References 85 publications

Functional connectivity-based subtypes of individuals with and without autism spectrum disorder

Functional connectivity-based subtypes of individuals with and without autism spectrum disorder

Brain dynamics in ASD during movie‐watching show idiosyncratic functional integration and segregation

Causal effect of disconnection lesions on interhemispheric functional connectivity in rhesus monkeys

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