Regularized Functional Connectivity in Schizophrenia - Salvador, Raymond; Fuentes-Claramonte, Paola; García-León, María Ángeles; Ramiro, Núria; Soler-Vidal, Joan; Torres, María Llanos; Salgado-Pineda, Pilar; Munuera, Josep; Voineskos, Aristotle; Pomarol-Clotet, Edith (Frontiers in Human Neuroscience, 2022)
Regularization may be used as an alternative to dimensionality reduction when the number of variables in a model is much larger than the number of available observations. In a recent study from our group regularized regression was employed to quantify brain functional connectivity in a sample of healthy controls using a brain parcellation and resting state fMRI images. Here regularization is applied to evaluate resting state connectivity abnormalities at the voxel level in a sample of patients with schizophrenia Specifically, ridge regression is implemented with different degrees of regularization and results are compared to those delivered by the weighted global brain connectivity method (GBC), which is based on averaged bivariate correlations. Ridge regression is able to detect a much extended set of abnormally connected regions than the GBC, including schizophrenia related connectivity reductions in fronto-medial, somatosensory and occipital structures. Due to its multivariate nature, the proposed method is much more sensitive to group abnormalities than the GBC. Voxel based regularized regression is a simple and sensitive alternative for quantifying brain functional connectivity.