期刊
BIPOLAR DISORDERS
卷 19, 期 4, 页码 259-272出版社
WILEY
DOI: 10.1111/bdi.12507
关键词
artificial intelligence; bipolar disorder; fMRI; fuzzy logic; genetic algorithm; lithium; machine learning; mania; region-of-interest; spectroscopy
资金
- NIMH CIDAR [P50 MH077138]
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.
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