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Objectives Individualized treatment for bipolar disorder based on neuroimaging treatment targets remains elusive. To address this shortcoming, we developed a linguistic machine learning system based on a cascading genetic fuzzy tree ( GFT) design called the LITHium Intelligent Agent ( LITHIA). Using multiple objectively defined functional magnetic resonance imaging (fMRI) and proton magnetic resonance spectroscopy (1H- MRS) inputs, we tested whether LITHIA could accurately predict the lithium response in participants with first-episode bipolar mania. Methods We identified 20 subjects with first-episode bipolar mania who received an adequate trial of lithium over 8 weeks and both fMRI and 1H- MRS scans at baseline pre-treatment. We trained LITHIA using 18 1H- MRS and 90 fMRI inputs over four training runs to classify treatment response and predict symptom reductions. Each training run contained a randomly selected 80% of the total sample and was followed by a 20% validation run. Over a different randomly selected distribution of the sample, we then compared LITHIA to eight common classification methods. Results LITHIA demonstrated nearly perfect classification accuracy and was able to predict post-treatment symptom reductions at 8 weeks with at least 88% accuracy in training and 80% accuracy in validation. Moreover, LITHIA exceeded the predictive capacity of the eight comparator methods and showed little tendency towards overfitting. Conclusions The results provided proof-of-concept that a novel GFT is capable of providing control to a multidimensional bioinformatics problem-namely, prediction of the lithium response-in a pilot data set. Future work on this, and similar machine learning systems, could help assign psychiatric treatments more efficiently, thereby optimizing outcomes and limiting unnecessary treatment. [ABSTRACT FROM AUTHOR]

Objectives: Functional and postmortem studies suggest that the orbitofrontal cortex (OFC) is involved in the pathophysiology of bipolar disorder (BD). This anatomical magnetic resonance imaging (MRI) study examined whether BD patients have smaller OFC gray matter volumes compared to healthy comparison subjects (HC). Methods: Twenty-eight BD patients were compared to 28 age- and gender-matched HC. Subjects underwent a 1.5T MRI with 3D spoiled gradient recalled acquisition. Total OFC and medial and lateral subdivisions were manually traced by a blinded examiner. Images were segmented and gray matter volumes were calculated using an automated method. Results: Analysis of covariance, with intracranial volume as covariate, showed that BD patients and HC did not differ in gray matter volumes of total OFC or its subdivisions. However, total OFC gray matter volume was significantly smaller in depressed patients (n = 10) compared to euthymic patients (n = 18). Moreover, total OFC gray matter volumes were inversely correlated with depressive symptom intensity, as assessed by the Hamilton Depression Rating Scale. OFC gray matter volumes were not related to lithium treatment, age at disease onset, number of episodes, or family history of mood disorders. Conclusions: Our results suggest that abnormal OFC gray matter volumes are not a pervasive characteristic of BD, but may be associated with specific clinical features of the disorder. [ABSTRACT FROM AUTHOR]