Neural Responses to Monetary Incentives in Bipolar Disorder (Hershel Mehta, advisor: Brian Knutson, Psychology)
Behavioral research implicates elevated reward sensitivity in bipolar disorder, which predicts both onset and more severe course of mania. To neurally probe reward responsiveness in the context of bipolar disorder, we elicited neural responses to anticipation and receipt of monetary gains and losses among persons with remitted bipolar I disorder (n=24) and a well-matched control group (n=24). Participants completed the Monetary Incentive Delay (MID) task while being scanned with functional magnetic resonance imaging (FMRI), during which they anticipated and responded to receive different amounts of monetary gains and losses. Compared to control participants, bipolar participants showed less NAcc activity during anticipation of gains, consistent with profiles observed in reward-related externalizing disorders.
Classifying Bipolar Disorder from fMRI Data - Machine Learning Approaches (Andrew Barakat, advisor: James McClelland, Psychology)
Bipolar Disorder remains one of the most costly and dangerous psychiatric disorders, in large part due to misdiagnosis. Machine learning approaches lend themselves well to the problem of classification, and the use of functional magnetic resonance imaging (fMRI) to examine the neurological basis of psychiatric disorders has great promise as a biomarker. A special type of Recurrent Neural Network (RNN) called an LSTM successfully classified subjects with remitted Bipolar I Disorder (n = 24) from a set of healthy controls (n = 24) with an accuracy of 85.25% over 5 cross-validated instances. From a set of 8300 trials, the data were split in the ratio 70%, 10% and 20% for training, validation, and testing, respectively. Single and multivariate approaches were examined and discussed. This result supports the notion that neurological data can be used as a biomarker for the diagnosis of Bipolar Disorder.
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