Title: A Generative-Predictive Framework to Capture Altered Brain Activity in fMRI and its Association with Genetic Risk: Application to Schizophrenia
Abstract: We present a generative-predictive framework that captures the differences in regional brain activity between a neurotypical cohort and a clinical population, as guided by patient-specific genetic risk. Our model assumes that the functional activations in the neurotypical subjects are distributed around a population mean, and that the altered brain activity in neuropsychiatric patients is dened via deviations from this neurotypical mean. We employ group sparsity to identify a set of brain regions that simultaneously explain the salient functional differences and specify a set of basis vectors, which span the low dimensional data subspace. The patient-specific projections onto this subspace are used as feature vectors to identify multivariate associations with genetic risk. We have evaluated our model on a task-based fMRI dataset from a population study of schizophrenia. We compare our model with two baseline methods, LASSO regression and Random Forest (RF) regression, where we establish a direct association between the brain activity during a working memory task and a schizophrenia polygenetic risk score. Our model demonstrates greater consistency and robustness across bootstrapping experiments than the machine learning baselines. Moreover, the differential activation in the set of brain regions implicated by our model underlie the well documented executive cognitive deficits in schizophrenia.