A Comparative Machine Learning Study of Connectivity-Based Biomarkers of Schizophrenia

Abstract

AbstractFunctional connectivity holds promise as a biomarker of psychiatric disorders. Yet, its high dimensionality, combined with small sample sizes in clinical research, increases the risk of overfitting when the aim is prediction. 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 connectivity. Our study evaluates which connectome features — functional connectivity, gradients, or gradient dispersion — best identify schizophrenia. Figure 1 summarizes this work.Surprisingly, our findings indicate that functional connectivity outperforms its low-dimensional derivatives such as cortical gradients and gradient dispersion in identifying schizophrenia. Additionally, we demonstrated that the edges which contribute the most to classification performance are the ones connecting primary sensory regions.