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In Brain connectivity

** : Background/Introduction: Sex classification using functional connectivity (FC) from resting-state fMRI (rs-fMRI) has shown promising results. This suggested that sex difference might also be embedded in the blood-oxygen-level-dependent (BOLD) properties like the amplitude of low-frequency fluctuation (ALFF) and the fraction of ALFF (fALFF). This study comprehensively investigates sex differences using a reliable and explainable machine learning (ML) pipeline. Five independent cohorts of resting-state fMRI with over than 5500 samples were used to assess sex classification performance and map the spatial distribution of the important brain regions.

METHODS : Five rsfMRI samples were used to extract ALFF and fALFF features from predefined brain parcellations and then were fed into an unbiased and explainable ML pipeline with a wide range of methods. The pipeline comprehensively assessed unbiased performance for within-sample and across sample validation. Additionally, the parcellation effect, classifiers selection, scanning length, spatial distribution, reproducibility, and feature importance were analyzed and evaluated thoroughly in the study.

RESULTS : The results demonstrated high sex classification accuracies from healthy adults (area under the curve (AUC) > 0.89) while degrading for non-healthy subjects. Sex classification showed moderate to good intraclass correlation coefficient (ICC) based on parcellation. Linear classifiers outperform non-linear classifiers. Sex differences could be detected even with a short rs-fMRI scan (e.g., 2 minutes). The spatial distribution of important features overlaps with previous results from Studies.

DISCUSSION : Sex differences are consistent in rs-fMRI and should be considered seriously in any study design, analysis, or interpretation. Features that discriminate males and females were found to be distributed across several different brain regions, suggesting a complex mosaic for sex differences in resting-state fMRI.

Al Zoubi Obada, Misaki Masaya, Tsuchiyagaito Aki, Zotev Vadim, White Evan, Paulus Martin, Bodurka Jerzy


Blood oxygen level dependent (BOLD) signal, Classification, Functional magnetic resonance imaging (fMRI), Resting-state networks, Slow frequency fluctuations