ReviewNeuroinformatics2014
A review of feature reduction techniques in neuroimaging.
Review in Neuroinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 197 papers, 5 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
197 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Magnetic resonance imaging-based machine learning classification of schizophrenia spectrum disorders: a meta-analysis.Psychiatry and clinical neurosciences · 2024Pooled it
- Application of radiomics for diagnosis, subtyping, and prognostication of medulloblastomas: a systematic review.Neurosurgical review · 2024Pooled it
- Deep learning applications for the classification of psychiatric disorders using neuroimaging data: Systematic review and meta-analysis.NeuroImage. Clinical · 2021Pooled it
- Towards a brain-based predictome of mental illness.Human brain mapping · 2020Pooled it
- Functional neuroimaging of visual creativity: a systematic review and meta-analysis.Brain and behavior · 2016Pooled it
- Random support vector machine cluster analysis of resting-state fMRI in Alzheimer's disease.PloS one · 2018Trial
- Trial
- Machine learning and individual variability in electric field characteristics predict tDCS treatment response.Brain stimulationTrial
- Metabolomic signatures of brain aging: A multimodal and genetic study.Molecular psychiatry · 2026Article
- Meniscus Morphology-Based Prediction of Backup Roll Eccentricity for Stable Slot-Die Coating on Polymer Films.Polymers · 2026Article
- FastEBM: Fast, Scalable, and Uncertainty-Aware Event-Based Disease Progression Modeling.bioRxiv : the preprint server for biology · 2026Article
- Feature selection leads to divergent neurobiological interpretations of brain-based machine learning biomarkers.Nature human behaviour · 2026Article
- Classification of familial and non-familial ADHD using auto-encoding network and binary hypothesis testing.Brain research bulletin · 2026Article
- Prediction modeling in transdiagnostic risk: results from the PROCAN study.Brain imaging and behavior · 2026Article
- Large-sample PCA eigenvectors stabilize cortical thickness components and improve small sample brain behavior prediction.Scientific reports · 2026Article
- Benchmarking Multimodal Deep Fusion Strategies for Heterogeneous Neuroimaging and Cognitive Data Using a Controlled Sex Classification Task.Brain sciences · 2026Article
- Hybrid EEG-fNIRS phoneme classification based on imagined and perceived speech.Frontiers in neuroergonomics · 2026Article
- Comparative evaluation of graph construction methods for individual brain metabolic network from FDG-PET images: an ADNI study in healthy subjects.European journal of nuclear medicine and molecular imaging · 2026Article
- Machine Learning-Based Identification of Blood Biomarkers that Distinguish Precachectic and Cachectic Patients with Pancreatic Ductal Adenocarcinoma.medRxiv : the preprint server for health sciences · 2025Article
- Predicting Remission in Schizophrenia Using Machine Learning-Assessing the Impact of Sample Size and Predictor Overinclusion.Acta psychiatrica Scandinavica · 2025Article
137 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
3 authors.
Funding
Abstract
Machine learning techniques are increasingly being used in making relevant predictions and inferences on individual subjects neuroimaging scan data. Previous studies have mostly focused on categorical discrimination of patients and matched healthy controls and more recently, on prediction of individual continuous variables such as clinical scores or age. However, these studies are greatly hampered by the large number of predictor variables (voxels) and low observations (subjects) also known as the curse-of-dimensionality or small-n-large-p problem. As a result, feature reduction techniques such as feature subset selection and dimensionality reduction are used to remove redundant predictor variables and experimental noise, a process which mitigates the curse-of-dimensionality and small-n-large-p effects. Feature reduction is an essential step before training a machine learning model to avoid overfitting and therefore improving model prediction accuracy and generalization ability. In this review, we discuss feature reduction techniques used with machine learning in neuroimaging studies.
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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.