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

Abstract

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 (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.

Keywords: artificial intelligence; bipolar disorder; fMRI; fuzzy logic; genetic algorithm; lithium; machine learning; mania; region-of-interest; spectroscopy.

Figure 1

Voxel-wise contrast showing regional activation differences between bipolar and healthy groups at baseline to define regions-of-interest (ROIs) for machine learning input (a). Fifteen mm spheres of different colors centered around local cluster maxima defining 30 functionally-derived ROIs including: 1 = L parahippocampal gyrus; 2 = R parahippocampal gyrus; 3 = B medial frontal cortex; 4 = L caudate; 5 = R caudate; 6 = L putamen; 7 = R putamen; 8 = B superior frontal cortex; 9 = L middle frontal cortex; 10 = R middle frontal cortex; 11 = R middle temporal gyrus; 12 = L anterior insula; 13 = L posterior insula; 14 = R insula; 15 = R precuneus; 16 = L cerebellar lobule VI/VIIa; 17 = R cerebellar lobule VI/VIIa; 18 = L cerebellar lobule VI; 19 = L fusiform gyrus; 20 = L lingual gyrus; 21 = R lingual gyrus; 22 = R inferior occipital gyrus; 23 = L parietal cortex; 24 = R parietal cortex; 25 = L inferior temporal gyrus; 26 = L dorsal posterior cingulate; 27 = L ventrolateral prefrontal cortex; 28 = B medial prefrontal cortex; 29 = B anterior cingulate cortex; 30 = R amygdala (b).

Figure 2

The post-training Genetic Fuzzy Tree (GFT) of multiple cascades showing 93 individual Fuzzy Inference Systems (FISs) that help control the final output of Young Mania Rating Scale (YMRS) reduction % and lithium response classification.