Information theory characteristics improve the prediction of lithium response in bipolar disorder patients using a support vector machine classifier

Utkarsh Tripathi, Liron Mizrahi, Martin Alda, Gregory Falkovich, Shani Stern

Research output: Contribution to journalArticlepeer-review

Abstract

Aim: Bipolar disorder (BD) is a mood disorder with a high morbidity and death rate. Lithium (Li), a prominent mood stabilizer, is often used as a first-line treatment. However, clinical studies have shown that Li is fully effective in roughly 30% of BD patients. Our goal in this study was to use features derived from information theory to improve the prediction of the patient's response to Li as well as develop a diagnostic algorithm for the disorder. Methods: We have performed electrophysiological recordings in patient-derived dentate gyrus (DG) granule neurons (from a total of 9 subjects) for three groups: 3 control individuals, 3 BD patients who respond to Li treatment (LR), and 3 BD patients who do not respond to Li treatment (NR). The recordings were analyzed by the statistical tools of modern information theory. We used a Support Vector Machine (SVM) and Random forest (RF) classifiers with the basic electrophysiological features with additional information theory features. Results: Information theory features provided further knowledge about the distribution of the electrophysiological entities and the interactions between the different features, which improved classification schemes. These newly added features significantly improved our ability to distinguish the BD patients from the control individuals (an improvement from 60% to 74% accuracy) and LR from NR patients (an improvement from 81% to 99% accuracy). Conclusion: The addition of Information theory-derived features provides further knowledge about the distribution of the parameters and their interactions, thus significantly improving the ability to discriminate and predict the LRs from the NRs and the patients from the controls.

Original languageEnglish
JournalBipolar Disorders
DOIs
StateAccepted/In press - 2022

Bibliographical note

Funding Information:
This material is based upon work supported by the Zuckerman STEM Leadership Program, ISF grant 1994/21, and ISF grant 3252/21 for Shani Stern and Simons foundation (662962), EU Horizon 2020 (83937, 873028) and BSF (2018033, 2020765) for Gregory Falkovich. The clinical part of this study was supported by grants from Genome Atlantic/ Reserach Nova Scotia (RPPP) and from ERA PerMed (PLOT‐BD) to Martin Alda.

Funding Information:
Zuckerman STEM Leadership Program for Shani Stern, ISF grant 1994/21, ISF grant 3252/21 for Shani Stern, Simons foundation (662962), EU Horizon 2020 (83937, 873028) and BSF (2018033, 2020765) for Gregory Falkovich.

Publisher Copyright:
© 2022 The Authors. Bipolar Disorders published by John Wiley & Sons Ltd.

Keywords

  • bipolar disorders
  • entropy
  • information theory
  • iPSC
  • lithium responders and non-responders
  • lithium response prediction
  • machine learning
  • mutual information
  • SVM classifier
  • valproic acid

ASJC Scopus subject areas

  • Psychiatry and Mental health
  • Biological Psychiatry

Fingerprint

Dive into the research topics of 'Information theory characteristics improve the prediction of lithium response in bipolar disorder patients using a support vector machine classifier'. Together they form a unique fingerprint.

Cite this