ReviewNeuroImage. Clinical2013
Towards the identification of imaging biomarkers in schizophrenia, using multivariate pattern classification at a single-subject level.
Review in NeuroImage. Clinical, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.
What it found
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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.
The trial behind it
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
39 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Detecting neuroimaging biomarkers for schizophrenia: a meta-analysis of multivariate pattern recognition studies.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2015Pooled it
- Single-subject detection of speech network activation using functional near-infrared spectroscopy.Neurophotonics · 2026Article
- Machine learning-based morphological brain analysis in schizophrenia and unaffected siblings: a multisite study of potential risk markers.Frontiers in neuroscience · 2026Article
- Relative importance of speech and voice features in the classification of schizophrenia and depression.Translational psychiatry · 2023Article
- Syntactic complexity and diversity of spontaneous speech production in schizophrenia spectrum and major depressive disorders.Schizophrenia (Heidelberg, Germany) · 2023Article
- Classification and prediction of cognitive performance differences in older age based on brain network patterns using a machine learning approach.Network neuroscience (Cambridge, Mass.) · 2023Article
- Advances in Using MRI to Estimate the Risk of Future Outcomes in Mental Health - Are We Getting There?Frontiers in psychiatry · 2022Article
- Combining Multiple Indices of Diffusion Tensor Imaging Can Better Differentiate Patients with Traumatic Brain Injury from Healthy Subjects.Neuropsychiatric disease and treatment · 2022Article
- Random Network and Non-rich-club Organization Tendency in Children With Non-syndromic Cleft Lip and Palate After Articulation Rehabilitation: A Diffusion Study.Frontiers in neurology · 2022Article
- A multivariate neuromonitoring approach to neuroplasticity-based computerized cognitive training in recent onset psychosis.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2021Article
- Multisite schizophrenia classification by integrating structural magnetic resonance imaging data with polygenic risk score.NeuroImage. Clinical · 2021Article
- An Ensemble of Psychological and Physical Health Indices Discriminates Between Individuals with Chronic Pain and Healthy Controls with High Reliability: A Machine Learning Study.Pain and therapy · 2020Article
- Language in schizophrenia: relation with diagnosis, symptomatology and white matter tracts.NPJ schizophrenia · 2020Article
- Integrating machining learning and multimodal neuroimaging to detect schizophrenia at the level of the individual.Human brain mapping · 2020Article
- Using Machine Learning and Structural Neuroimaging to Detect First Episode Psychosis: Reconsidering the Evidence.Schizophrenia bulletin · 2020Article
- Article
- Identifying commonality and specificity across psychosis sub-groups via classification based on features from dynamic connectivity analysis.NeuroImage. Clinical · 2020Article
- Development of an Al-Based Web Diagnostic System for Phenotyping Psychiatric Disorders.Frontiers in psychiatry · 2020Article
- Recognition of Schizophrenia with Regularized Support Vector Machine and Sequential Region of Interest Selection using Structural Magnetic Resonance Imaging.Scientific reports · 2018Article
- Segmentation and texture analysis of structural biomarkers using neighborhood-clustering-based level set in MRI of the schizophrenic brain.Magma (New York, N.Y.) · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Standard univariate analyses of brain imaging data have revealed a host of structural and functional brain alterations in schizophrenia. However, these analyses typically involve examining each voxel separately and making inferences at group-level, thus limiting clinical translation of their findings. Taking into account the fact that brain alterations in schizophrenia expand over a widely distributed network of brain regions, univariate analysis methods may not be the most suited choice for imaging data analysis. To address these limitations, the neuroimaging community has turned to machine learning methods both because of their ability to examine voxels jointly and their potential for making inferences at a single-subject level. This article provides a critical overview of the current and foreseeable applications of machine learning, in identifying imaging-based biomarkers that could be used for the diagnosis, early detection and treatment response of schizophrenia, and could, thus, be of high clinical relevance. We discuss promising future research directions and the main difficulties facing machine learning researchers as far as their potential translation into clinical practice is concerned.
Identifiers
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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.