ArticleActa psychiatrica Scandinavica2018
Complexity in mood disorder diagnosis: fMRI connectivity networks predicted medication-class of response in complex patients.
Article in Acta psychiatrica Scandinavica, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Towards a brain-based predictome of mental illness.Human brain mapping · 2020Pooled it
- Identifying misdiagnosed bipolar disorder using support vector machine: feature selection based on fMRI of follow-up confirmed affective disorders.Translational psychiatry · 2024Article
- Aberrant brain dynamics and spectral power in children with ADHD and its subtypes.European child & adolescent psychiatry · 2023Article
- Multi-study evaluation of neuroimaging-based prediction of medication class in mood disorders.Psychiatry research. Neuroimaging · 2023Article
- IABC: A Toolbox for Intelligent Analysis of Brain Connectivity.Neuroinformatics · 2023Article
- Temporal multi-step predictive modeling of remission in major depressive disorder using early stage treatment data; STAR*D based machine learning approach.Journal of affective disorders · 2023Article
- Multi-scale convolutional recurrent neural network for psychiatric disorder identification in resting-state EEG.Frontiers in psychiatry · 2023Article
- Personalized Diagnosis and Treatment for Neuroimaging in Depressive Disorders.Journal of personalized medicine · 2022Review
- The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review.Journal of medical Internet research · 2021Article
- Fluoxetine and environmental enrichment similarly reverse chronic social stress-related depression- and anxiety-like behavior, but have differential effects on amygdala gene expression.Neurobiology of stress · 2021Article
- Evidence of shared and distinct functional and structural brain signatures in schizophrenia and autism spectrum disorder.Communications biology · 2021Article
- Data-driven approaches to neuroimaging biomarkers for neurological and psychiatric disorders: emerging approaches and examples.Current opinion in neurology · 2021Review
- Functional connectivity between the amygdala and subgenual cingulate gyrus predicts the antidepressant effects of ketamine in patients with treatment-resistant depression.Neuropsychopharmacology reports · 2021Article
- A multivariate neuromonitoring approach to neuroplasticity-based computerized cognitive training in recent onset psychosis.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2021Article
- Distinct Features of Cerebral Blood Flow and Spontaneous Neural Activity as Integrated Predictors of Early Response to Antidepressants.Frontiers in psychiatry · 2021Article
- Neuroimaging-based Individualized Prediction of Cognition and Behavior for Mental Disorders and Health: Methods and Promises.Biological psychiatry · 2020Review
- The Care of Patients With Complex Mood Disorders.Focus (American Psychiatric Publishing) · 2020Article
- NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders.NeuroImage. Clinical · 2020Article
- Preliminary prediction of individual response to electroconvulsive therapy using whole-brain functional magnetic resonance imaging data.NeuroImage. Clinical · 2020Article
- Identifying commonality and specificity across psychosis sub-groups via classification based on features from dynamic connectivity analysis.NeuroImage. Clinical · 2020Article
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9 authors.
Funding
Abstract
objectiveThis study determined the clinical utility of an fMRI classification algorithm predicting medication-class of response in patients with challenging mood diagnoses.
methodsNinety-nine 16-27-year-olds underwent resting state fMRI scans in three groups-BD, MDD and healthy controls. A predictive algorithm was trained and cross-validated on the known-diagnosis patients using maximally spatially independent components (ICs), constructing a similarity matrix among subjects, partitioning the matrix in kernel space and optimizing support vector machine classifiers and IC combinations. This classifier was also applied to each of 12 new individual patients with unclear mood disorder diagnoses.
resultsClassification within the known-diagnosis group was approximately 92.4% accurate. The five maximally contributory ICs were identified. Applied to the complicated patients, the algorithm diagnosis was consistent with optimal medication-class of response to sustained recovery in 11 of 12 cases (i.e., almost 92% accuracy).
conclusionThis classification algorithm performed well for the know-diagnosis but also predicted medication-class of response in difficult-to-diagnose patients. Further research can enhance this approach and extend these findings to be more clinically accessible.
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