Prediction of lithium response in first‐episode mania using the LITHium Intelligent Agent ( LITHIA LITHIA ): Pilot data and proof‐of‐concept

摘要

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 LITH ium Intelligent Agent ( LITHIA ). Using multiple objectively defined functional magnetic resonance imaging (fMRI ) and proton magnetic resonance spectroscopy ( 1 H‐ 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 1 H‐ MRS scans at baseline pre‐treatment. We trained LITHIA using 18 1 H‐ 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.