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- Title
Prediction of lithium response in first-episode mania using the LITHium Intelligent Agent ( LITHIA): Pilot data and proof-of-concept.
- Authors
Fleck, David E; Ernest, Nicholas; Adler, Caleb M; Cohen, Kelly; Eliassen, James C; Norris, Matthew; Komoroski, Richard A; Chu, Wen‐Jang; Welge, Jeffrey A; Blom, Thomas J; DelBello, Melissa P; Strakowski, Stephen M
- 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 (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.
- Subjects
THERAPEUTICS; BIPOLAR disorder; BRAIN imaging; MACHINE learning; INTELLIGENT agents; FUNCTIONAL magnetic resonance imaging
- Publication
Bipolar Disorders, 2017, Vol 19, Issue 4, p259
- ISSN
1398-5647
- Publication type
Article
- DOI
10.1111/bdi.12507