ArticleHuman brain mapping2023
Deep multimodal predictome for studying mental disorders.
Article in Human brain mapping, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 20 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
20 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Data-Driven Approaches for Autism Detection: A Comprehensive Review of Machine Learning Algorithms and Datasets.International journal of developmental neuroscience : the official journal of the International Society for Developmental Neuroscience · 2026Pooled it
- Artificial intelligence approaches for schizophrenia prediction and its biomarkers using medical imaging data.Frontiers in psychiatry · 2026Pooled it
- Interaction of artificial intelligence, mental disorders, and diverse data modalities: Potential treatment management based on the "method-disease-data" axis.Neural regeneration research · 2026Article
- An interpretable cross-attentive multi-modal MRI fusion framework for schizophrenia identification.Neuroimage. Reports · 2026Article
- Multimodal Representation Learning for Parsing Biological Heterogeneity in Psychiatric Neuroimaging.Biological psychiatry · 2026Review
- Individual-specific functional connectivity predicts clinical symptoms severity in patients with post-traumatic stress disorder.BMC psychiatry · 2026Article
- Conditioned Graph Reconstruction of Brain Functional Network Connectivity Reveals Interpretable Latent Axes of Sex and Fluid Intelligence.bioRxiv : the preprint server for biology · 2026Article
- Multimodal classification of mood disorders using structural MRI and polygenic risk scores: a machine learning ablation study in UK Biobank.Frontiers in psychiatry · 2026Article
- An interpretable generative multimodal neuroimaging-genomics framework for decoding Alzheimer's disease.Journal of neural engineering · 2025Article
- Semantic locality-aware biclustering for brain functional network connectivity.Scientific reports · 2025Article
- Neuromodulation techniques for the treatment of spinal cord injury: current progress, challenges, and future prospects.European journal of medical research · 2025Review
- Interpretable and integrative deep learning for discovering brain-behaviour associations.Scientific reports · 2025Article
- Exploring Schizophrenia Classification Through Multimodal MRI and Deep Graph Neural Networks: Unveiling Brain Region-Specific Weight Discrepancies and Their Association With Cell-Type Specific Transcriptomic Features.Schizophrenia bulletin · 2024Article
- Imaging-genomic spatial-modality attentive fusion for studying neuropsychiatric disorders.Human brain mapping · 2024Article
- Diagnosis of mental disorders using machine learning: Literature review and bibliometric mapping from 2012 to 2023.Heliyon · 2024Review
- SpaDE: Semantic Locality Preserving Biclustering for Neuroimaging Data.bioRxiv : the preprint server for biology · 2024Article
- Searching Reproducible Brain Features using NeuroMark: Templates for Different Age Populations and Imaging Modalities.NeuroImage · 2024Article
- Elucidating salient site-specific functional connectivity features and site-invariant biomarkers in schizophrenia via deep neural networks.Scientific reports · 2023Article
- Deep multimodal predictome for studying mental disorders.Human brain mapping · 2023Article
- Data-driven multimodal fusion: approaches and applications in psychiatric research.Psychoradiology · 2023Review
Corrections and comments
- Erratum issued
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Characterizing neuropsychiatric disorders is challenging due to heterogeneity in the population. We propose combining structural and functional neuroimaging and genomic data in a multimodal classification framework to leverage their complementary information. Our objectives are two-fold (i) to improve the classification of disorders and (ii) to introspect the concepts learned to explore underlying neural and biological mechanisms linked to mental disorders. Previous multimodal studies have focused on naïve neural networks, mostly perceptron, to learn modality-wise features and often assume equal contribution from each modality. Our focus is on the development of neural networks for feature learning and implementing an adaptive control unit for the fusion phase. Our mid fusion with attention model includes a multilayer feed-forward network, an autoencoder, a bi-directional long short-term memory unit with attention as the features extractor, and a linear attention module for controlling modality-specific influence. The proposed model acquired 92% (p < .0001) accuracy in schizophrenia prediction, outperforming several other state-of-the-art models applied to unimodal or multimodal data. Post hoc feature analyses uncovered critical neural features and genes/biological pathways associated with schizophrenia. The proposed model effectively combines multimodal neuroimaging and genomics data for predicting mental disorders. Interpreting salient features identified by the model may advance our understanding of their underlying etiological mechanisms.
Indexed as
Identifiers
What Socratic 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 Socratic graph.