ArticleNeuroImage2020
Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics.
Article in NeuroImage, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 129 papers, 2 of them syntheses that pooled it.
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Who cites it
129 citing papers in PubMed, 2 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
- Deep learning applications for the classification of psychiatric disorders using neuroimaging data: Systematic review and meta-analysis.NeuroImage. Clinical · 2021Pooled it
- Trial
- Effect sizes for brain-based predictive models of cognition across the lifespan.Communications psychology · 2026Review
- Article
- Multimodal MRI prediction of cognitive functioning across the lifespan: separating between-person differences from within-person changes.GeroScience · 2026Article
- Racialized Heteroscedasticity in Neuroimaging Features, Behavior Measures, and Neuroimaging-Based Predictive Models.Research square · 2026Article
- Discrimination of generalized anxiety and major depressive disorders by machine learning of resting-state functional MRI.BMC psychiatry · 2026Article
- Feature overlap in transdiagnostic connectome-based models of sustained attention and autism symptoms.medRxiv : the preprint server for health sciences · 2026Article
- Using connectome-based predictive models to reveal the systems standardized tests and clinical symptoms are reflecting.Nature communications · 2026Article
- Article
- From Big to Small: Emerging Methods for Enhancing Precision Psychiatry Through Transfer Learning.Biological psychiatry · 2026Review
- Mapping Individualized Developmental Imbalance in Youth and Its Association with Psychopathology.bioRxiv : the preprint server for biology · 2026Article
- Sex differences in dynamic and static measures of brain integration derived from resting-state functional magnetic resonance imaging.Biology of sex differences · 2026Article
- Cognition, but not affect, rests upon a segregated intrinsic network architecture.bioRxiv : the preprint server for biology · 2026Article
- Is the whole more than the sum of its parts? Considering global and local features of the connectome improves prediction of individuals and phenotypes.bioRxiv : the preprint server for biology · 2026Article
- Individual identifiability following Procrustes alignment of functional gradients: effect of subspace dimensionality.Communications biology · 2026Article
- The motion sensitivity and predictive utility of different estimates of interregional functional coupling in resting-state functional MRI.Network neuroscience (Cambridge, Mass.) · 2026Article
- Digital Twin Brain: Generating Multitask Behavior from Connectomes for Personalized Therapy.BME frontiers · 2026Article
- NeuroAgeFusionNet an ensemble deep learning framework integrating CNN, transformers, and GNN for robust brain age estimation using MRI scans.Scientific reports · 2025Article
69 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
There is significant interest in the development and application of deep neural networks (DNNs) to neuroimaging data. A growing literature suggests that DNNs outperform their classical counterparts in a variety of neuroimaging applications, yet there are few direct comparisons of relative utility. Here, we compared the performance of three DNN architectures and a classical machine learning algorithm (kernel regression) in predicting individual phenotypes from whole-brain resting-state functional connectivity (RSFC) patterns. One of the DNNs was a generic fully-connected feedforward neural network, while the other two DNNs were recently published approaches specifically designed to exploit the structure of connectome data. By using a combined sample of almost 10,000 participants from the Human Connectome Project (HCP) and UK Biobank, we showed that the three DNNs and kernel regression achieved similar performance across a wide range of behavioral and demographic measures. Furthermore, the generic feedforward neural network exhibited similar performance to the two state-of-the-art connectome-specific DNNs. When predicting fluid intelligence in the UK Biobank, performance of all algorithms dramatically improved when sample size increased from 100 to 1000 subjects. Improvement was smaller, but still significant, when sample size increased from 1000 to 5000 subjects. Importantly, kernel regression was competitive across all sample sizes. Overall, our study suggests that kernel regression is as effective as DNNs for RSFC-based behavioral prediction, while incurring significantly lower computational costs. Therefore, kernel regression might serve as a useful baseline algorithm for future studies.
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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.