ArticleBipolar disorders2017
Prediction of lithium response in first-episode mania using the LITHium Intelligent Agent (LITHIA): Pilot data and proof-of-concept.
Article in Bipolar disorders, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 67 citations in OpenAlex.
- Optimizing Treatment Strategies in the Bipolar Disorder Spectrum With Classical AI Approaches: Systematic Review of Performance, Bias, and Clinical Applicability.JMIR mental health · 2026Pooled it
- Brain structural connectomic topology predicts medication response in youth with bipolar disorder: A randomized clinical trial.Journal of affective disorders · 2025Trial
- Effects of short-term quetiapine and lithium therapy for acute manic or mixed episodes on the limbic system and emotion regulation circuitry in youth with bipolar disorder.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2023Trial
- Neurobiological Markers for Predicting Treatment Response in Patients with Bipolar Disorder.Biomedicines · 2022Article
- Artificial intelligence (AI) in medicine as a strategic valuable tool.The Pan African medical journal · 2021Article
- Imaging-Based Subtyping for Psychiatric Syndromes.Neuroimaging clinics of North America · 2020Review
- Challenges and Future Prospects of Precision Medicine in Psychiatry.Pharmacogenomics and personalized medicine · 2020Review
- Preliminary analysis of resting state functional connectivity in young adults with subtypes of bipolar disorder.Journal of affective disorders · 2019Article
- Discrete patterns of cortical thickness in youth with bipolar disorder differentially predict treatment response to quetiapine but not lithium.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2018Article
- Prediction of response to drug therapy in psychiatric disorders.Open biology · 2018Review
Corrections and comments
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Authors and funding
12 authors at 3 institutions in 1 country.
Funding
Abstract
objectivesIndividualized 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 (
methodsWe identified 20 subjects with first-episode bipolar mania who received an adequate trial of lithium over 8 weeks and both fMRI and
resultsLITHIA 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.
conclusionsThe 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.