Enhancing studies of the connectome in autism using the autism brain imaging data exchange II

Martino, Adriana Di; O’Connor, David; Chen, Bosi; Alaerts, Kaat; Anderson, Jeffrey S.; Assaf, Michal; Balsters, Joshua H.; Baxter, Leslie C.; Beggiato, Anita; Bernaerts, Sylvie; Blanken, Laura M. E.; Bookheimer, Susan Y.; Braden, B. Blair; Byrge, Lisa; Castellanos, F. Xavier; Dapretto, Mirella; Delorme, Richard; Fair, Damien A.; Fishman, Inna; Fitzgerald, Jacqueline; Gallagher, Louise; Keehn, R. Joanne Jao; Kennedy, Daniel P.; Lainhart, Janet E.; Luna, Beatríz; Mostofsky, Stewart H.; Müller, Ralph Axel; Nebel, Mary Beth; Nigg, Joel T.; O’Hearn, Kirsten; Solomon, Marjorie; Toro, Roberto; Vaidya, Chandan J.; Wenderoth, Nicole; White, Tonya; Craddock, R. Cameron; Lord, Catherine; Leventhal, Bennett L.; Milham, Michael P.

doi:10.1038/sdata.2017.10

Cited by 529 publications

(478 citation statements)

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“…It is also one of few DFC studies of ASD based on a multisite public data set [Chen et al, ; de Lacy et al, ; Falahpour et al, ; Guo et al, ]. Different from previous studies, we selected the ADIDEII data set as it offers greater phenotypic characterization [Di Martino et al, ]. Meanwhile, given the effects of age [Uddin, Supekar, & Menon, ] and handedness, only right‐handed children (<18 years old) were selected as participants.…”

Section: Discussionmentioning

confidence: 99%

“…In recent decades, the worldwide prevalence of ASD has risen to 0.7-1.5% in childhood and adolescence [Morales-Hidalgo, Roige-Castellvi, Hernandez-Martinez, Voltas, & Canals, 2018], attracting the attention of researchers. Several resting-state functional magnetic resonance imaging (rsfMRI) studies of ASD have been published, especially with the emergence of data-sharing initiatives such as the Autism Brain Imaging Data Exchange (ABIDE) [Di Martino et al, 2017;Di Martino et al, 2014]. Compared to task-based fMRI, rsfMRI has one great advantage that participants do not need to complete any specific task.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

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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“…Original resting‐state fMRI data were drawn from the open‐access ABIDE I and ABIDE II database (Di Martino et al, ; Di Martino et al, ). Specifically, analyses were limited to (a) children aged between 7 and 12 years; (b) male subjects, because they represented more than 90% of the aggregate datasets; (c) individuals with available full IQ (FIQ), handedness and eye status values (eyes open/closed); (d) subjects without excessive head motion in resting‐state scan (i.e., motion within 2 mm translation and 2 degree rotation and less than 50% frames with large frame‐wise displacement [FD], as indicated in Preprocessing ); (e) subjects with complete cortical coverage in resting‐state scan; (f) subjects with full brain anatomical images and without apparent motion artifact; (g) a well‐matched dataset of ASD and TC groups within each site created by using a data‐driven algorithm that maximized p values of group difference on age, handedness, FIQ, eye status and mean FD; (h) sites with more than 10 subjects per group after the above‐mentioned selection procedure.…”

Section: Methodsmentioning

confidence: 99%

“…Original resting-state fMRI data were drawn from the open-access ABIDE I and ABIDE II database (Di Martino et al, 2014;Di Martino et al, 2017). Specifically, analyses were limited to (a) children aged between 7 and 12 years; (b) male subjects, because they represented more than 90% of the aggregate datasets; (c) individuals with available full IQ (FIQ), handedness and eye status values (eyes open/closed);…”

Section: Participantsmentioning

confidence: 99%

Altered inter‐ and intrahemispheric functional connectivity dynamics in autistic children

Guo

Duan

Chen

et al. 2019

Human Brain Mapping

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Emerging evidence has associated autism spectrum disorder (ASD) with static functional connectivity abnormalities between multiple brain regions. However, the temporal dynamics of intra-and interhemispheric functional connectivity patterns remain unknown in ASD. Resting-state functional magnetic resonance imaging data were analyzed for 105 ASD and 102 demographically matched typically developing control (TC) children (age range: 7-12 years) available from the Autism Brain Imaging Data Exchange database. Whole-brain functional connectivity was decomposed into ipsilateral and contralateral functional connectivity, and sliding-window analysis was utilized to capture the intra-and interhemispheric dynamic functional connectivity density (dFCD) patterns. The temporal variability of the functional connectivity dynamics was further quantified using the standard deviation (SD) of intra-and interhemispheric dFCD across time. Finally, a support vector regression model was constructed to assess the relationship between abnormal dFCD variance and autism symptom severity. Both intra-and interhemispheric comparisons showed increased dFCD variability in the anterior cingulate cortex/medial prefrontal cortex and decreased variability in the fusiform gyrus/inferior temporal gyrus in autistic children compared with TC children. Autistic children additionally showed lower intrahemispheric dFCD variability in sensorimotor regions including the precentral/postcentral gyrus. Moreover, aberrant temporal variability of the contralateral dFCD predicted the severity of social communication impairments in autistic children.These findings demonstrate altered temporal dynamics of the intra-and interhemispheric functional connectivity in brain regions incorporating social brain network of ASD, and highlight the potential role of abnormal interhemispheric communication dynamics in neural substrates underlying impaired social processing in ASD. K E Y W O R D Sautism spectrum disorder, dynamic functional connectivity, interhemisphere, intrahemisphere, resting-state functional magnetic resonance imaging

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“…Abbreviations. Dataset—ABIDE‐II: autism brain imaging data exchange II (Martino et al, ); HCP: human connectome project (Van Essen et al, ); PPMI: Parkinson's Progression Markers Initiative (Marek et al, ); Disease—AUT: autism; PD: Parkinson's disease. Gender—F: female; M: male.…”

Section: Methodsmentioning

confidence: 99%

Test–retest reproducibility of white matter parcellation using diffusion MRI tractography fiber clustering

Zhang

Norton

et al. 2019

Human Brain Mapping

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There are two popular approaches for automated white matter parcellation using diffusion MRI tractography, including fiber clustering strategies that group white matter fibers according to their geometric trajectories and cortical‐parcellation‐based strategies that focus on the structural connectivity among different brain regions of interest. While multiple studies have assessed test–retest reproducibility of automated white matter parcellations using cortical‐parcellation‐based strategies, there are no existing studies of test–retest reproducibility of fiber clustering parcellation. In this work, we perform what we believe is the first study of fiber clustering white matter parcellation test–retest reproducibility. The assessment is performed on three test–retest diffusion MRI datasets including a total of 255 subjects across genders, a broad age range (5–82 years), health conditions (autism, Parkinson's disease and healthy subjects), and imaging acquisition protocols (three different sites). A comprehensive evaluation is conducted for a fiber clustering method that leverages an anatomically curated fiber clustering white matter atlas, with comparison to a popular cortical‐parcellation‐based method. The two methods are compared for the two main white matter parcellation applications of dividing the entire white matter into parcels (i.e., whole brain white matter parcellation) and identifying particular anatomical fiber tracts (i.e., anatomical fiber tract parcellation). Test–retest reproducibility is measured using both geometric and diffusion features, including volumetric overlap (wDice) and relative difference of fractional anisotropy. Our experimental results in general indicate that the fiber clustering method produced more reproducible white matter parcellations than the cortical‐parcellation‐based method.

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Enhancing studies of the connectome in autism using the autism brain imaging data exchange II

Cited by 529 publications

References 64 publications

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

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

Altered inter‐ and intrahemispheric functional connectivity dynamics in autistic children

Test–retest reproducibility of white matter parcellation using diffusion MRI tractography fiber clustering

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