ReviewNeuroImage. Clinical2018
Structural neuroimaging as clinical predictor: A review of machine learning applications.
Review in NeuroImage. Clinical, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
69 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The role of machine learning in developing non-magnetic resonance imaging based biomarkers for multiple sclerosis: a systematic review.BMC medical informatics and decision making · 2022Pooled it
- Magnetic resonance imaging for individual prediction of treatment response in major depressive disorder: a systematic review and meta-analysis.Translational psychiatry · 2021Pooled it
- [A Preliminary Study of Applying Geometric Deep Learning in Brain Morphometry for Diagnosis of Alzheimer's Disease].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2021Trial
- Interrogating cognitive and neural group variation in childhood: Examples in adolescent brain cognitive development and Oregon attention-deficit/hyperactivity disorder-1000 cohorts.Journal of psychopathology and clinical science · 2026Article
- Resting-State fMRI Co-Activation Patterns Reveal Multiscale Brain Functional Alterations in Primary Angle-Closure Glaucoma: Transcriptomic, Cellular, Neurochemical, and Machine-Learning Signatures.Clinical ophthalmology (Auckland, N.Z.) · 2026Article
- The role of machine learning in autism spectrum disorder assessment and management.Pediatric research · 2025Review
- Gaining Brain Insights by Tapping into the Black Box: Linking Structural MRI Features to Age and Cognition using Shapley-Based Interpretation Methods.Neuroinformatics · 2025Article
- Brain age as an accurate biomarker of preclinical cognitive decline: evidence from a 12-year longitudinal study.Journal of neurology · 2025Article
- Attention over vulnerable brain regions associating cerebral palsy disorder and biological markers.Journal of advanced research · 2025Article
- Gray Matter Differences in Adolescent Psychiatric Inpatients: A Machine Learning Study of Bipolar Disorder and Other Psychopathologies.Brain and behavior · 2025Article
- Article
- Multimodal Brain Growth Patterns: Insights from Canonical Correlation Analysis and Deep Canonical Correlation Analysis with Auto-Encoder.Information (Basel) · 2025Article
- Advancing Alzheimer's Diagnosis with AI-Enhanced MRI: A Review of Challenges and Implications.Current neuropharmacology · 2025Review
- Connectome-based prediction of future episodic memory performance for individual amnestic mild cognitive impairment patients.Brain communications · 2025Article
- MRI-Driven Alzheimer's Disease Diagnosis Using Deep Network Fusion and Optimal Selection of Feature.Bioengineering (Basel, Switzerland) · 2024Article
- Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning.Biological psychiatry · 2024Review
- Assessment of Sports Concussion in Female Athletes: A Role for Neuroinformatics?Neuroinformatics · 2024Review
- Multivariate analysis of multimodal brain structure predicts individual differences in risk and intertemporal preference.bioRxiv : the preprint server for biology · 2024Article
- Multimodal active subspace analysis for computing assessment oriented subspaces from neuroimaging data.Journal of neuroscience methods · 2024Article
- Article
9 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper, we provide an extensive overview of machine learning techniques applied to structural magnetic resonance imaging (MRI) data to obtain clinical classifiers. We specifically address practical problems commonly encountered in the literature, with the aim of helping researchers improve the application of these techniques in future works. Additionally, we survey how these algorithms are applied to a wide range of diseases and disorders (e.g. Alzheimer's disease (AD), Parkinson's disease (PD), autism, multiple sclerosis, traumatic brain injury, etc.) in order to provide a comprehensive view of the state of the art in different fields.
Indexed as
Identifiers
What OpenQuestion holds
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.