Profiling Autism Spectrum Disorders from fMRI data using Leading Eigenvector Dynamics Analysis
Poster No:
1406
Submission Type:
Abstract Submission
Authors:
Vânia Miguel1, Miguel Farinha2, Álvaro Deleglise3, Joana Cabral1
Institutions:
1Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal, 2Department of Computer Science, University of Oxford, United Kingdom, 3Institute of Physiology and Biophysics, University of Buenos Aires, Argentina
First Author:
Vânia Miguel
Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho
Braga, Portugal
Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho
Braga, Portugal
Co-Author(s):
Joana Cabral
Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho
Braga, Portugal
Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho
Braga, Portugal
Introduction:
Functional magnetic resonance imaging (fMRI) studies reveal that brain activity at rest spontaneously engages in dynamic coupling modes, momentarily forming functional networks. These networks, whose occurrences vary across psychiatric conditions, are emerging as biomarkers for precision psychiatry. However, the relationship between these functional networks and distinct psychiatric phenotypes remains unclear. Autism Spectrum Disorder (ASD), a highly heterogeneous condition, provides a compelling case to explore this relationship. This study applies Leading Eigenvector Dynamics Analysis (LEiDA) to fMRI data from the Autism Brain Imaging Data Exchange (ABIDE I) to investigate its sensitivity in differentiating ASD subtypes from neurotypical controls.
Methods:
The study analyzed fMRI data from 945 participants, including neurotypical controls (n=508), and ASD subtypes: Autistic Disorder (n=331), Pervasive Developmental Disorder (PDD, n=31), and Asperger Syndrome (n=75). LEiDA was used to extract leading eigenvector dynamics, identifying recurring patterns of brain-wide functional connectivity. Statistical analyses assessed group-level differences in network occurrences, focusing on key functional networks such as the Default Mode Network (DMN), somatomotor, and frontoparietal networks. Multiple comparisons were corrected using the Bonferroni method to ensure robust findings.
Results:
LEiDA revealed significant differences in network occurrences between ASD subtypes and neurotypical controls. Participants with Autistic Disorder exhibited a markedly reduced occurrence of a DMN mode involving the Frontal Medial Orbital cortex, Posterior Cingulate, and Angular Gyrus (Bonferroni-corrected p=0.000054). PDD participants showed an increased occurrence of a more diffuse DMN configuration, whereas Asperger participants exhibited a reduced occurrence of somatomotor networks and a significant reduction in frontoparietal network occurrences compared to controls. These findings highlight distinct connectivity patterns across ASD subtypes, underscoring the heterogeneity of ASD and its impact on brain network dynamics.
Conclusions:
This study demonstrates the utility of LEiDA in discriminating ASD subtypes based on distinct functional network occurrences. By capturing the complex, dynamic nature of brain connectivity, LEiDA provides insights into the neurological underpinnings of ASD and its subtypes. These findings underscore the potential of LEiDA as a computational tool in psychiatric research, advancing precision diagnostics and theranostics in mental health. The release of improved LEiDA scripts in Matlab and Python enhances accessibility, paving the way for broader application in neuroimaging studies.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism)
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
fMRI Connectivity and Network Modeling 1
Keywords:
Autism
Computational Neuroscience
Data analysis
FUNCTIONAL MRI
Open Data
Other - LEiDA
1|2Indicates the priority used for review
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Provide references using APA citation style.
Lord, L.D. (2019). Dynamical exploration of the repertoire of brain networks at rest is modulated by psilocybin. NeuroImage, 199, 127-142.
Vohryzek, J. (2020). Ghost attractors in spontaneous brain activity: Recurrent excursions into functionally-relevant BOLD phase-locking states. Frontiers in Systems Neuroscience, 14, 20.