Functional connectivity differences of large-scale brain networks in autism
Poster No:
342
Submission Type:
Abstract Submission
Authors:
Elijah Gragas1, Jason Nomi1, Priyanka Sigar1, Taylor Bolt1, Lucina Uddin2,1
Institutions:
1Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA,USA, 2Department of Psychology, University of California Los Angeles, CA,USA
First Author:
Elijah Gragas, B.A.
Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles
CA,USA
Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles
CA,USA
Co-Author(s):
Jason Nomi
Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles
CA,USA
Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles
CA,USA
Priyanka Sigar
Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles
CA,USA
Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles
CA,USA
Taylor Bolt
Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles
CA,USA
Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles
CA,USA
Lucina Uddin, Ph.D.
Department of Psychology, University of California Los Angeles|Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles
CA,USA|CA,USA
Department of Psychology, University of California Los Angeles|Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles
CA,USA|CA,USA
Introduction:
Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by heterogeneous deficits within a variety of domains of social and emotional functioning. The triple network model (Menon, 2018) posits that aberrant organization of the salience, central executive, and default mode networks underlie multiple psychopathologies including ASD. Previous neuroimaging studies have examined the development of networks that underlie socioemotional processing in autism, and additional analyses are needed on larger datasets to help: 1) refine our understanding of both within and between-network connectivity in individuals with ASD 2) investigate how these functional connections change across age. The current study examined a large data set of resting state fMRI data from individuals with ASD to investigate how across and within network connections may change over age, and if these developmental changes (Uddin et al, 2013) may be different compared with typically developing (TD)individuals.
Methods:
Resting-state fMRI data were downloaded from both initiatives of the Autism Brain Imaging Data Exchange (ABIDE I & ABIDE II). A total of 753 subjects separated into cohorts of individuals with ASD and TD individuals (351 ASD; 402 TD) ranging from 5-50 years old were included.
A group independent component analysis was performed (80 components total) using the Group ICA of fMRI Toolbox (GIFT, https://trendscenter.org/software/gift/). Components were then hand classified into noise vs signal components (42 noise components, 38 signal components). A total of 6 independent component regions of interest (ROIs) were selected from the signal components; 2 regions from 3 separate networks (salience, central executive, and default mode networks). Chosen components and their average functional connectivity (FC) values are shown in Figure 1.
Individual-level FC matrices were formed by extracting time courses from each participant and correlating the average time series between each region; a total of 15 FC values were included in the matrix.
Group level-analyses were then performed by running a multiple linear regression model on each of the FC values from the 15 possible pairs between the 6 ROIs chosen. Age at scan, diagnosis group (ASD=1, TD=2), and an age x diagnosis interaction term were the independent variables modeled in the regressions.
A group independent component analysis was performed (80 components total) using the Group ICA of fMRI Toolbox (GIFT, https://trendscenter.org/software/gift/). Components were then hand classified into noise vs signal components (42 noise components, 38 signal components). A total of 6 independent component regions of interest (ROIs) were selected from the signal components; 2 regions from 3 separate networks (salience, central executive, and default mode networks). Chosen components and their average functional connectivity (FC) values are shown in Figure 1.
Individual-level FC matrices were formed by extracting time courses from each participant and correlating the average time series between each region; a total of 15 FC values were included in the matrix.
Group level-analyses were then performed by running a multiple linear regression model on each of the FC values from the 15 possible pairs between the 6 ROIs chosen. Age at scan, diagnosis group (ASD=1, TD=2), and an age x diagnosis interaction term were the independent variables modeled in the regressions.
Results:
Age x Diagnosis Interaction Between DMN and SN Components
In our preliminary regression of the FC between the precuneus/posterior cingulate cortex component and the anterior insula component pair, we did not observe a statistically significant main effect of age or diagnosis group. However, we observed a significant age x diagnosis group interaction (p=0.0178), such that FC values in the TD cohort seemed to be more dependent on age relative to the ASD cohort.
Main Effect of Age, Age:Diagnosis Interaction Within SN Components
In our preliminary regression model on the FC between the frontal medial gyrus component and anterior insula component, we observed a statistically significant main effect of age (p=0.0051) with FC increasing with age. No significant main effect of diagnosis was observed. We observed a significant age x diagnosis group interaction (p=0.0128, Fig 2), such that FC values were higher as age increased in the ASD cohort. For the TD cohort, FC values were lower as age increased.
In our preliminary regression of the FC between the precuneus/posterior cingulate cortex component and the anterior insula component pair, we did not observe a statistically significant main effect of age or diagnosis group. However, we observed a significant age x diagnosis group interaction (p=0.0178), such that FC values in the TD cohort seemed to be more dependent on age relative to the ASD cohort.
Main Effect of Age, Age:Diagnosis Interaction Within SN Components
In our preliminary regression model on the FC between the frontal medial gyrus component and anterior insula component, we observed a statistically significant main effect of age (p=0.0051) with FC increasing with age. No significant main effect of diagnosis was observed. We observed a significant age x diagnosis group interaction (p=0.0128, Fig 2), such that FC values were higher as age increased in the ASD cohort. For the TD cohort, FC values were lower as age increased.
Conclusions:
These results demonstrating FC differences between TD and ASD across age echo previous findings with smaller data sets (Nomi & Uddin, 2015). FC abnormalities may not be consistent across the lifespan in individuals with ASD. The increased FC of the salience network component pair observed in the ASD cohort (Fig 2) further iterates the notion of hyper-connectivity within the salience network for individuals with ASD (Uddin et al 2013), while also demonstrating that this hyper-connectivity may not extend beyond adolescence due to the lack of the effect beyond 10-20 year olds.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
fMRI Connectivity and Network Modeling
Keywords:
Autism
Data analysis
Development
FUNCTIONAL MRI
Other - Independent Componenet Analysis
1|2Indicates the priority used for review
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Provide references using APA citation style.
Menon V. (2018). The Triple Network Model, Insight, and Large-Scale Brain Organization in Autism. Biological psychiatry, 84(4), 236–238. https://doi.org/10.1016/j.biopsych.2018.06.012
Jason S. Nomi, Lucina Q. Uddin (2015). Developmental changes in large-scale network connectivity in autism,NeuroImage: Clinical, Volume7, 732-741,ISSN 2213-1582, https://doi.org/10.1016/j.nicl.2015.02.024
Uddin, L. Q., Supekar, K., Lynch, C. J., Khouzam, A., Phillips, J., Feinstein, C., ... & Menon, V. (2013). Salience network–based classification and prediction of symptom severity in children with autism. JAMA psychiatry, 70(8), 869-879.
Uddin, L. Q., Supekar, K., & Menon, V. (2013). Reconceptualizing functional brain connectivity in autism from a developmental perspective. Frontiers in human neuroscience, 7, 458. https://doi.org/10.3389/fnhum.2013.00458