ArticleNeuroinformatics2015
Clinical prediction from structural brain MRI scans: a large-scale empirical study.
Article in Neuroinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 3 of them syntheses that pooled it.
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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
63 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- A meta-analysis and systematic review of single vs. multimodal neuroimaging techniques in the classification of psychosis.Molecular psychiatry · 2023Pooled it
- Diagnostic Performance of Generative Adversarial Network-Based Deep Learning Methods for Alzheimer's Disease: A Systematic Review and Meta-Analysis.Frontiers in aging neuroscience · 2022Pooled it
- Reproducible Evaluation of Diffusion MRI Features for Automatic Classification of Patients with Alzheimer's Disease.Neuroinformatics · 2021Pooled it
- Research on Construction and Risk Management of Operating Room Based on IoTs and Digitization.Journal of healthcare engineering · 2022Trial
- Brain MRI findings in relation to clinical characteristics and outcome of tuberculous meningitis.PloS one · 2020Trial
- A Framework for Comparison and Interpretation of Machine Learning Classifiers to Predict Autism on the ABIDE Dataset.Human brain mapping · 2025Article
- GAMing the Brain: Investigating the Cross-modal Relationships between Functional Connectivity and Structural Features using Generalized Additive Models.Machine learning in clinical neuroimaging : 7th international workshop, MLCN 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, proceedings. MLCN (Workshop) (7th : 2024 : Marrakesh, Morocco) · 2025Article
- Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model.IEEE transactions on neural networks and learning systems · 2024Article
- Article
- Individualized multi-modal MRI biomarkers predict 1-year clinical outcome in first-episode drug-naïve schizophrenia patients.Frontiers in psychiatry · 2024Article
- Structural Brain Imaging Biomarkers of Autism Spectrum Disorder.Advances in neurobiology · 2024Review
- Translating phenotypic prediction models from big to small anatomical MRI data using meta-matching.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- A comprehensive survey of complex brain network representation.Meta-radiology · 2023Article
- Machine learning in neuroimaging: from research to clinical practice.Radiologie (Heidelberg, Germany) · 2022Review
- Multi-Task Weakly-Supervised Attention Network for Dementia Status Estimation With Structural MRI.IEEE transactions on neural networks and learning systems · 2022Article
- Population heterogeneity in clinical cohorts affects the predictive accuracy of brain imaging.PLoS biology · 2022Article
- The Role of Structure MRI in Diagnosing Autism.Diagnostics (Basel, Switzerland) · 2022Article
- A Hierarchical Graph Learning Model for Brain Network Regression Analysis.Frontiers in neuroscience · 2022Article
- Analysis of Cases with Cerebrospinal Fluid Characteristics Similar to Tuberculous Meningitis.BioMed research international · 2022Article
- MPC-STANet: Alzheimer's Disease Recognition Method Based on Multiple Phantom Convolution and Spatial Transformation Attention Mechanism.Frontiers in aging neuroscience · 2022Article
3 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Multivariate pattern analysis (MVPA) methods have become an important tool in neuroimaging, revealing complex associations and yielding powerful prediction models. Despite methodological developments and novel application domains, there has been little effort to compile benchmark results that researchers can reference and compare against. This study takes a significant step in this direction. We employed three classes of state-of-the-art MVPA algorithms and common types of structural measurements from brain Magnetic Resonance Imaging (MRI) scans to predict an array of clinically relevant variables (diagnosis of Alzheimer's, schizophrenia, autism, and attention deficit and hyperactivity disorder; age, cerebrospinal fluid derived amyloid-β levels and mini-mental state exam score). We analyzed data from over 2,800 subjects, compiled from six publicly available datasets. The employed data and computational tools are freely distributed ( https://www.nmr.mgh.harvard.edu/lab/mripredict), making this the largest, most comprehensive, reproducible benchmark image-based prediction experiment to date in structural neuroimaging. Finally, we make several observations regarding the factors that influence prediction performance and point to future research directions. Unsurprisingly, our results suggest that the biological footprint (effect size) has a dramatic influence on prediction performance. Though the choice of image measurement and MVPA algorithm can impact the result, there was no universally optimal selection. Intriguingly, the choice of algorithm seemed to be less critical than the choice of measurement type. Finally, our results showed that cross-validation estimates of performance, while generally optimistic, correlate well with generalization accuracy on a new dataset.
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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.