Susan Whitfield-Gabrieli, PhD
Research Scientist, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
Research Scientist, Martinos Imaging Center, McGovern Institute for Brain Research
Title: Clinical Applications of Resting State Networks: Clinical Characterization, Novel Treatments and Prediction of Clinical Outcome
Abstract: Neuroimaging has revealed brain differences in clinical disorders, but it has not yet fundamentally altered how patients are diagnosed or treated. However, the intrinsic functional architecture of the human brain as elucidated by resting state functional connectivity holds great promise for clinical translation. I will describe how resting state networks (RSNs), provide information for (1) clinical characterization, (2) evaluating/developing novel treatments, and (3) predicting clinical outcome. We find that patients with schizophrenia and individuals with risk for schizophrenia and depression exhibit default mode network (DMN) hyperconnectivity which is positively correlated with symptom severity, and reductions in DMN anticorrelations, associated with impaired executive function. I will describe ways in which novel pharmacological (cannabinoid) and behavioral interventions (mindfulness meditation and real-time DMN feedback) ameliorate/normalize this DMN functional pathology. The finding that the DMN is plastic and can be altered by such interventions offers hope that effective treatments may help patients mitigate symptoms and potentially augment their cognitive function. In a study of patients with disorders of consciousness, such as minimally conscious state and vegetative state/unresponsive wakefulness syndrome (VS/UWS), individual differences in DMN anticorrelations predicted clinical outcomes more accurately than initial diagnosis. Finally, we used pre-treatment RSNs in patients with social anxiety disorder to predict subsequent clinical response to cognitive behavioral therapy. The intrinsic connectivity measures yielded a fivefold improvement in predicting treatment response relative to the clinical measure of initial anxiety. These results suggest that RSNs may provide biomarkers that substantially improve predictions for success of clinical interventions, and suggest that such biomarkers may offer evidence-based, personalized/precision medicine approaches for optimally selecting treatment options.