SynthesisHuman brain mapping2020
Towards a brain-based predictome of mental illness.
Synthesis in Human brain mapping, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 95 papers, 4 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
95 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Neural substrates of motivational dysfunction across neuropsychiatric conditions: Evidence from meta-analysis and lesion network mapping.Clinical psychology review · 2022Pooled it
- Clinical prediction models in psychiatry: a systematic review of two decades of progress and challenges.Molecular psychiatry · 2022Pooled it
- Great Expectations: A Critical Review of and Suggestions for the Study of Reward Processing as a Cause and Predictor of Depression.Biological psychiatry · 2021Pooled it
- Towards a brain-based predictome of mental illness.Human brain mapping · 2020Pooled it
- Individualized fMRI connectivity defines signatures of antidepressant and placebo responses in major depression.Molecular psychiatry · 2023Trial
- Subtyping psychotic disorders using a data-driven approach reveals divergent cortical and cellular signatures.Molecular psychiatry · 2026Article
- Analysis of electroencephalogram microstates and spectral power in children and adolescents with attention deficit hyperactivity disorder.European child & adolescent psychiatry · 2026Article
- Identifying transdiagnostic neurobiomarkers for precision major psychiatric disordersJournal of translational internal medicine · 2026Article
- Individual-specific functional connectivity predicts clinical symptoms severity in patients with post-traumatic stress disorder.BMC psychiatry · 2026Article
- Multimodal machine learning reveals neurobiological signatures of binge-type eating disorders.Frontiers in neuroscience · 2026Article
- Leveraging Machine Learning to Advance Alcohol Research: Current Applications, Challenges, and Opportunities.Alcohol research : current reviews · 2026Review
- Investigating structural-functional brain covariation in bipolar disorder using a multimodal fusion approach.Brain imaging and behavior · 2025Article
- Neuroimaging Data Informed Mood and Psychosis Diagnosis Using an Ensemble Deep Multimodal Framework.Human brain mapping · 2025Article
- Predicting Mental and Neurological Illnesses Based on Cerebellar Normative Features.Biological psychiatry global open science · 2025Article
- Using deep learning to predict internalizing problems from brain structure in youth.Translational psychiatry · 2025Article
- Assessing the impact of artifact correction and artifact rejection on the performance of SVM- and LDA-based decoding of EEG signals.NeuroImage · 2025Article
- Accelerated brain aging in alcohol users: Insights from machine learning.Alcohol, clinical & experimental research · 2025Article
- Using deep learning to predict internalizing problems from brain structure in youth.bioRxiv : the preprint server for biology · 2025Article
- Brain health services for the secondary prevention of cognitive impairment and dementia: Opportunities, challenges, and the business case for existing and future facilities.The journal of prevention of Alzheimer's disease · 2025Review
- Schizophrenia and Neurodevelopment: Insights From Connectome Perspective.Schizophrenia bulletin · 2025Review
35 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
2 authors.
Funding
Abstract
Neuroimaging-based approaches have been extensively applied to study mental illness in recent years and have deepened our understanding of both cognitively healthy and disordered brain structure and function. Recent advancements in machine learning techniques have shown promising outcomes for individualized prediction and characterization of patients with psychiatric disorders. Studies have utilized features from a variety of neuroimaging modalities, including structural, functional, and diffusion magnetic resonance imaging data, as well as jointly estimated features from multiple modalities, to assess patients with heterogeneous mental disorders, such as schizophrenia and autism. We use the term "predictome" to describe the use of multivariate brain network features from one or more neuroimaging modalities to predict mental illness. In the predictome, multiple brain network-based features (either from the same modality or multiple modalities) are incorporated into a predictive model to jointly estimate features that are unique to a disorder and predict subjects accordingly. To date, more than 650 studies have been published on subject-level prediction focusing on psychiatric disorders. We have surveyed about 250 studies including schizophrenia, major depression, bipolar disorder, autism spectrum disorder, attention-deficit hyperactivity disorder, obsessive-compulsive disorder, social anxiety disorder, posttraumatic stress disorder, and substance dependence. In this review, we present a comprehensive review of recent neuroimaging-based predictomic approaches, current trends, and common shortcomings and share our vision for future directions.
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.