ArticleeLife2020
Combining magnetoencephalography with magnetic resonance imaging enhances learning of surrogate-biomarkers.
Article in eLife, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 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.
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Who cites it
59 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Tracking electrophysiological signatures of Alzheimer's disease: a systematic review of multimodal studies.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Pooled it
- Source-space EEG alpha activity reveals brain age gaps due to neurodegeneration and disparity.Communications biology · 2026Article
- Individual cases of Parkinson's disease can be robustly classified using magnetoencephalography.NPJ Parkinson's disease · 2026Article
- Fifteen years on: a review of the Cam-CAN study of the cognitive neuroscience of ageing.GeroScience · 2026Review
- Capturing individual variation in children's electroencephalograms during nREM sleep.PLoS computational biology · 2026Article
- Mapping and modulating brain dynamics in aging and neurodegeneration with electroencephalography and integrated neurotechnologies in primates.Frontiers in neuroscience · 2026Review
- Multimodal neuroimaging data boosts the prediction of multifaceted cognition.Research square · 2025Article
- Article
- When Brain Models Aren't Universal: Benchmarking of Ethnic Bias in MRI-Based Cognitive Prediction Across Modalities.bioRxiv : the preprint server for biology · 2025Article
- Improving predictability, reliability, and generalizability of brain-wide associations for cognitive abilities via multimodal stacking.PNAS nexus · 2025Article
- A lightweight generative model for interpretable subject-level prediction.Medical image analysis · 2025Article
- Exploring the neuromagnetic signatures of cognitive decline from mild cognitive impairment to Alzheimer's disease dementia.EBioMedicine · 2025Article
- GREEN: A lightweight architecture using learnable wavelets and Riemannian geometry for biomarker exploration with EEG signals.Patterns (New York, N.Y.) · 2025Article
- Temporal autocorrelation is predictive of age-An extensive MEG time-series analysis.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Predicting individual traits from models of brain dynamics accurately and reliably using the Fisher kernel.eLife · 2025Article
- Integration of clinical, pathological, radiological, and transcriptomic data improves prediction for first-line immunotherapy outcome in metastatic non-small cell lung cancer.Nature communications · 2025Article
- A multimodal ensemble stacking model improves brain age prediction and reveals associations with schizophrenia symptoms.Frontiers in psychiatry · 2025Article
- Behavioral and neural effects of temporoparietal high-definition transcranial direct current stimulation in logopenic variant primary progressive aphasia: a preliminary study.Frontiers in psychology · 2025Article
- Low-Rank Tensor Fusion for Enhanced Deep Learning-Based Multimodal Brain Age Estimation.Brain sciences · 2024Article
- Machine Learning and Deep Learning Approaches in Lifespan Brain Age Prediction: A Comprehensive Review.Tomography (Ann Arbor, Mich.) · 2024Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Electrophysiological methods, that is M/EEG, provide unique views into brain health. Yet, when building predictive models from brain data, it is often unclear how electrophysiology should be combined with other neuroimaging methods. Information can be redundant, useful common representations of multimodal data may not be obvious and multimodal data collection can be medically contraindicated, which reduces applicability. Here, we propose a multimodal model to robustly combine MEG, MRI and fMRI for prediction. We focus on age prediction as a surrogate biomarker in 674 subjects from the Cam-CAN dataset. Strikingly, MEG, fMRI and MRI showed additive effects supporting distinct brain-behavior associations. Moreover, the contribution of MEG was best explained by cortical power spectra between 8 and 30 Hz. Finally, we demonstrate that the model preserves benefits of stacking when some data is missing. The proposed framework, hence, enables multimodal learning for a wide range of biomarkers from diverse types of brain signals.
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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.