ArticleJournal of neuroscience methods2017
A robust sparse-modeling framework for estimating schizophrenia biomarkers from fMRI.
Article in Journal of neuroscience methods, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- Identification of male schizophrenia patients using brain morphology based on machine learning algorithms.World journal of psychiatry · 2024Article
- Novel machine learning approaches for improving the reproducibility and reliability of functional and effective connectivity from functional MRI.Journal of neural engineering · 2023Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review.JMIR mental health · 2023Review
- Machine Learning of Schizophrenia Detection with Structural and Functional Neuroimaging.Disease markers · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundOur goal is to identify the brain regions most relevant to mental illness using neuroimaging. State of the art machine learning methods commonly suffer from repeatability difficulties in this application, particularly when using large and heterogeneous populations for samples. NEW
methodWe revisit both dimensionality reduction and sparse modeling, and recast them in a common optimization-based framework. This allows us to combine the benefits of both types of methods in an approach which we call unambiguous components. We use this to estimate the image component with a constrained variability, which is best correlated with the unknown disease mechanism.
resultsWe apply the method to the estimation of neuroimaging biomarkers for schizophrenia, using task fMRI data from a large multi-site study. The proposed approach yields an improvement in both robustness of the estimate and classification accuracy. COMPARISON WITH EXISTING
methodsWe find that unambiguous components incorporate roughly two thirds of the same brain regions as sparsity-based methods LASSO and elastic net, while roughly one third of the selected regions differ. Further, unambiguous components achieve superior classification accuracy in differentiating cases from controls.
conclusionsUnambiguous components provide a robust way to estimate important regions of imaging data.
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Registered trials
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