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Machine Learning Evidence for Sex Differences Consistently Influences Resting-State Functional Magnetic Resonance Imaging Fluctuations Across Multiple Independently Acquired Data Sets.

Obada Al ZoubiMasaya MisakiAki TsuchiyagaitoVadim ZotevEvan WhiteMartin PaulusJerzy Bodurka
Published in: Brain connectivity (2021)
Background/Introduction: Sex classification using functional connectivity from resting-state functional magnetic resonance imaging (rs-fMRI) has shown promising results. This suggested that sex difference might also be embedded in the blood-oxygen-level-dependent properties such as 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 rs-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 rs-fMRI 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. In addition, the parcellation effect, classifier 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 >0.89), while degrading for nonhealthy subjects. Sex classification showed moderate to good intraclass correlation coefficient based on parcellation. Linear classifiers outperform nonlinear classifiers. Sex differences could be detected even with a short rs-fMRI scan (e.g., 2 min). 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 rs-fMRI.
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