ArticleHuman brain mapping2019
Machine learning identifies unaffected first-degree relatives with functional network patterns and cognitive impairment similar to those of schizophrenia patients.
Article in Human brain mapping, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 3 of them syntheses that pooled it.
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Who cites it
23 citing papers in PubMed, 3 syntheses or guidelines pooled it, 27 citations in OpenAlex.
- A meta-analysis and systematic review of single vs. multimodal neuroimaging techniques in the classification of psychosis.Molecular psychiatry · 2023Pooled it
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- Application of Support Vector Machine on fMRI Data as Biomarkers in Schizophrenia Diagnosis: A Systematic Review.Frontiers in psychiatry · 2020Pooled it
- Changes in brain functional connectivity and cognition related to white matter lesion burden in hypertensive patients from SPRINT.Neuroradiology · 2021Trial
- Classifying schizophrenia patients and healthy individuals: Whole brain SPECT functional connectivity using support vector machine classification.Neuroimage. Reports · 2026Article
- Accelerated brain aging associated with executive deficits in adolescence and early adulthood among individuals with schizophrenia and genetic high risk.BMC psychiatry · 2026Article
- Impact of schizophrenia-associated risk genes on brain functional networks and executive deficits: a study of individuals with schizophrenia and genetic high risk.Psychological medicine · 2025Article
- Mapping individualized multi-scale hierarchical brain functional networks from fMRI by self-supervised deep learning.bioRxiv : the preprint server for biology · 2025Article
- Neuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade.Schizophrenia bulletin · 2025Review
- Functional Connectivity Biomarkers in Schizophrenia.Advances in neurobiology · 2024Review
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Altered large-scale dynamic connectivity patterns in Alzheimer's disease and mild cognitive impairment patients: A machine learning study.Human brain mapping · 2023Article
- Computing personalized brain functional networks from fMRI using self-supervised deep learning.Medical image analysis · 2023Article
- Altered spatio-temporal state patterns for functional dynamics estimation in first-episode drug-naive major depression.Brain imaging and behavior · 2022Article
- Markers of Schizophrenia-A Critical Narrative Update.Journal of clinical medicine · 2022Review
- Comparison of executive functions in individuals with high and low levels of schizotypal trait.Frontiers in psychology · 2022Article
- Functional Connectivity During Visuospatial Processing in Schizophrenia: A Classification Study Using Lasso Regression.Neuropsychiatric disease and treatment · 2021Article
- Association between functional and structural connectivity of the corticostriatal network in people with schizophrenia and unaffected first-degree relatives.Journal of psychiatry & neuroscience : JPN · 2020Article
- Exploring concomitant neuroimaging and genetic alterations in patients with and patients without auditory verbal hallucinations: A pilot study and mini review.The Journal of international medical research · 2020Review
- Reversibility of cerebral blood flow in patients with Cushing's disease after surgery treatment.Metabolism: clinical and experimental · 2020Article
Corrections and comments
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Authors and funding
11 authors at 4 institutions in 2 countries.
Funding
Abstract
Schizophrenia (SCZ) patients and their unaffected first-degree relatives (FDRs) share similar functional neuroanatomy. However, it remains largely unknown to what extent unaffected FDRs with functional neuroanatomy patterns similar to patients can be identified at an individual level. In this study, we used a multivariate pattern classification method to learn informative large-scale functional networks (FNs) and build classifiers to distinguish 32 patients from 30 healthy controls and to classify 34 FDRs as with or without FNs similar to patients. Four informative FNs-the cerebellum, default mode network (DMN), ventral frontotemporal network, and posterior DMN with parahippocampal gyrus-were identified based on a training cohort and pattern classifiers built upon these FNs achieved a correct classification rate of 83.9% (sensitivity 87.5%, specificity 80.0%, and area under the receiver operating characteristic curve [AUC] 0.914) estimated based on leave-one-out cross-validation for the training cohort and a correct classification rate of 77.5% (sensitivity 72.5%, specificity 82.5%, and AUC 0.811) for an independent validation cohort. The classification scores of the FDRs and patients were negatively correlated with their measures of cognitive function. FDRs identified by the classifiers as having SCZ patterns were similar to the patients, but significantly different from the controls and FDRs with normal patterns in terms of their cognitive measures. These results demonstrate that the pattern classifiers built upon the informative FNs can serve as biomarkers for quantifying brain alterations in SCZ and help to identify FDRs with FN patterns and cognitive impairment similar to those of SCZ patients.
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