Depression as a disease of network disruption: learning from multiple sclerosis

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PROJECT ABSTRACT/SUMMARY Multiplesclerosis (MS) is an immune-mediatedneurological disorder that affects one million people in the United States. Up to 50% of patients with MS experience depression, yet the mechanisms of depression in MS remain under-investigated. MS is characterized by white matter lesions, suggesting that brain network disruption may underly depression symptoms. Studies of medically healthy participants with depression have described associations between white matter variability and depressive symptoms, but frequently exclude participants with medical comorbidities and thus cannot be extrapolated to people with intracranial diseases. Previous research using lesion network mapping, a technique for mapping heterogeneous gray matter lesions to neuropsychiatric symptoms, has demonstrated that strokes in gray matter associated with depression disrupt a reproducible depression network. However, such techniques have never been applied to white matter disease or MS. Studying white matter lesions associated with depression in MS may provide a way to understand both the pathophysiology of depression in MS and general network mechanisms of depression more broadly. The purpose of this current study is to investigate how brain network disruption underlies depression by learning from the example of multiple sclerosis. In Aim 1, I will delineate how depression in adults with MS is associated with white matter lesion location and burden in a retrospective sample of 1,554 MS patients with research-grade 3T MRIs acquired as part of clinical care. Depression and MS diagnoses will be obtained from the electronic medical record. While this sample provides an ideal dataset for developing a model, the electronic medical record does not contain granular depression measures. In Aim 2, I will obtain structured clinical and cognitive assessments for MS patients and prospectively evaluate white matter integrity as a predictor of dimensional depressive symptoms. However, it is possible that symptoms of depression may reflect heterogenous brain network disruption patterns. Therefore, in Aim 3, I will use advanced semi-supervised machine learning methods to parse heterogeneity in MS white matter lesion burden in the retrospective sample and test whether this model predicts phenotypic heterogeneity in our deeply-phenotyped prospective sample. The support of the K23 award will provide the applicant with the training necessary to achieve these aims. The training objectives will be accomplished with the support of an outstanding mentorship team, Drs. Satterthwaite, Shinohara, Bassett, Bar- Or, Fox, McCoy, and the world class resources of the University of Pennsylvania. Together, the proposed scientific aims and training objectives will form the foundation for an independent research program that will use techniques from computational psychiatry to understand depression in patients with medical comorbidities.