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.

• Authors: David E Fleck, Nicholas Ernest, Caleb M Adler, Kelly Cohen, James C Eliassen, Matthew Norris, Richard A Komoroski, Wen-Jang Chu, Jeffrey A Welge, Thomas J Blom, Melissa P DelBello, Stephen M Strakowski
• Journal: Bipolar disorders (Bipolar Disord) Vol. 19 Issue 4 Pg. 259-272 (06 2017) ISSN: 1399-5618 [Electronic] Denmark
• PMID: 28574156 (Publication Type: Journal Article)
• Copyright: © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Chemical References

• Antimanic Agents
• Lithium Compounds

Topics

• Adolescent
• Adult
• Antimanic Agents (administration & dosage, adverse effects)
• Artificial Intelligence
• Behavioral Symptoms (diagnosis, drug therapy)
• Bipolar Disorder (diagnosis, drug therapy, psychology)
• Diagnostic and Statistical Manual of Mental Disorders
• Drug Monitoring (methods)
• Drug Resistance
• Female
• Fuzzy Logic
• Humans
• Lithium Compounds (administration & dosage, adverse effects)
• Magnetic Resonance Imaging (methods)
• Male
• Multimodal Imaging (methods)
• Pilot Projects
• Predictive Value of Tests
• Prognosis
• Proton Magnetic Resonance Spectroscopy (methods)