Unraveling the Autism spectrum heterogeneity: Insights from ABIDE I Database using data/model-driven permutation testing approaches
F.J. Alcaide, I.A. Illan, J. Ramirez, J.M. Gorriz

TL;DR
This study evaluates the impact of multi-center variability on detecting brain differences in autism using ABIDE I sMRI data, revealing limitations due to sample size, noise, and heterogeneity, and comparing two statistical mapping methods.
Contribution
It introduces a permutation testing approach to assess structural brain differences in autism across multiple centers, highlighting the influence of data heterogeneity.
Findings
No significant brain region differences found between groups.
Multicenter variability affects the detection of structural differences.
SAM method shows greater consistency with existing literature.
Abstract
Autism Spectrum Condition (ASC) is a neurodevelopmental condition characterized by impairments in communication, social interaction and restricted or repetitive behaviors. Extensive research has been conducted to identify distinctions between individuals with ASC and neurotypical individuals. However, limited attention has been given to comprehensively evaluating how variations in image acquisition protocols across different centers influence these observed differences. This analysis focuses on structural magnetic resonance imaging (sMRI) data from the Autism Brain Imaging Data Exchange I (ABIDE I) database, evaluating subjects' condition and individual centers to identify disparities between ASC and control groups. Statistical analysis, employing permutation tests, utilizes two distinct statistical mapping methods: Statistical Agnostic Mapping (SAM) and Statistical Parametric Mapping…
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Taxonomy
TopicsAutism Spectrum Disorder Research · Genomic variations and chromosomal abnormalities · Genetic Associations and Epidemiology
MethodsSegment Anything Model
