A comparative machine learning study of schizophrenia biomarkers derived from functional connectivity

• Autores: Shevchenko, Victoria; Benn, R. Austin; Scholz, Robert; Wei, Wei; Pallavicini, Carla; Klatzmann, Ulysse; Alberti, Francesco; Satterthwaite, Theodore D.; Wassermann, Demian; Bazin, Pierre-Louis; Margulies, Daniel S.
• Año de publicación: 2025
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: fmri; schizophrenia; machine learning; Functional connectivity
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

Functional connectivity holds promise as a biomarker of schizophrenia. Yet, the high dimensionality of predictive models trained on functional connectomes, combined with small sample sizes in clinical research, increases the risk of overfitting. Recently, low-dimensional representations of the connectome such as macroscale cortical gradients and gradient dispersion have been proposed, with studies noting consistent gradient and dispersion differences in psychiatric conditions. However, it is unknown which of these derived measures has the highest predictive capacity and how they compare to raw functional connectivity specifically in the case of schizophrenia. Our study evaluates which connectome features derived from resting state functional MRI — functional connectivity, gradients, or gradient dispersion — best identify schizophrenia. To this end, we leveraged data of 936 individuals from three large open-access datasets: COBRE, LA5c, and SRPBS-1600. We developed a pipeline which allows us to aggregate over a million different features and assess their predictive potential in a single, computationally efficient experiment. We selected top 1% of features with the largest permutation feature importance and trained 13 classifiers on them using 10-fold cross-validation. Our findings indicate that functional connectivity outperforms its low-dimensional derivatives such as cortical gradients and gradient dispersion in identifying schizophrenia (Mann–Whitney test conducted on test accuracy: connectivity vs. 1st gradient: U = 142, p < 0.003; connectivity vs. neighborhood dispersion: U = 141, p = 0.004). Additionally, we demonstrated that the edges which contribute the most to classification performance are the ones connecting primary sensory regions. Functional connectivity within the primary sensory regions showed the highest discrimination capabilities between subjects with schizophrenia and neurotypical controls. These findings along with the feature selection pipeline proposed here will facilitate future inquiries into the prediction of schizophrenia subtypes and transdiagnostic phenomena.
Fil: Shevchenko, Victoria. University of Oxford; Reino Unido
Fil: Benn, R. Austin. Universite de Paris. Umr - S1134 Biologie Integree Du Globule Rouge;
Fil: Scholz, Robert. University of Oxford; Reino Unido
Fil: Wei, Wei. Universite de Paris. Umr - S1134 Biologie Integree Du Globule Rouge;
Fil: Pallavicini, Carla. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina
Fil: Klatzmann, Ulysse. Universite de Paris. Umr - S1134 Biologie Integree Du Globule Rouge;
Fil: Alberti, Francesco. Universite de Paris. Umr - S1134 Biologie Integree Du Globule Rouge;
Fil: Satterthwaite, Theodore D.. University of Pennsylvania; Estados Unidos
Fil: Wassermann, Demian. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Bazin, Pierre-Louis. No especifíca;
Fil: Margulies, Daniel S.. Universite de Paris. Umr - S1134 Biologie Integree Du Globule Rouge;