Predicting Individual Variability in Task-Evoked Brain Activity in Schizophrenia

Abstract

BACKGROUNDPatients suffering from schizophrenia demonstrate abnormal brain activity, as well as alterations in patterns of functional connectivity assessed by functional magnetic resonance imaging (fMRI). Previous studies in healthy participants suggest a strong association between resting-state functional connectivity and task-evoked brain activity that could be detected at an individual level, and show that brain activation in various tasks could be predicted from task-free fMRI scans. In the current study we aimed to predict brain activity in patients diagnosed with schizophrenia, using a prediction model based on healthy individuals exclusively. This offers novel insights regarding the interrelations between brain connectivity and activity in schizophrenia. METHODSWe generated a prediction model using a group of 80 healthy controls that performed the well-validated N-back task, and used it to predict individual variability in task-evoked brain activation in 20 patients diagnosed with schizophrenia. RESULTSWe demonstrated a successful prediction of individual variability in the task-evoked brain activation based on resting-state functional connectivity. The predictions were highly sensitive, reflected by high correlations between predicted and actual activation maps (Median = 0.589, SD = 0.193) and specific, evaluated by a Kolomogrov-Smirnov test (D = 0.25, p < 0.0001). CONCLUSIONSA Successful prediction of brain activity from resting-state functional connectivity highlights the strong coupling between the two. Moreover, our results support the notion that even though resting-state functional connectivity and task-evoked brain activity are frequently reported to be altered in schizophrenia, the relations between them remains unaffected. This may allow to generate task activity maps for clinical populations without the need the actually perform the task.