Tractography-based classification in distinguishing patients with first-episode schizophrenia from healthy individuals
Abstract
Background Schizophrenia has been characterized as a neurodevelopmental disorder of brain disconnectivity. However, whether disrupted integrity of white matter tracts in schizophrenia can potentially serve as individual discriminative biomarkers remains unclear. Methods A random forest algorithm was applied to tractography-based diffusion properties obtained from a cohort of 65 patients with first-episode schizophrenia (FES) and 60 healthy individuals to investigate the machine-learning discriminative power of white matter disconnectivity. Recursive feature elimination was used to select the ultimate white matter features in the classification. Relationships between algorithm-predicted probabilities and clinical characteristics were also examined in the FES group. Results The classifier was trained by 80% of the sample. Patients were distinguished from healthy individuals with an overall accuracy of 71.0% (95% confident interval: 61.1%, 79.6%), a sensitivity of 67.3%, a specificity of 75.0%, and the area under receiver operating characteristic curve (AUC) was 79.3% (χ2 p Conclusions Our findings suggest that the widespread connectivity disruption observed in FES patients, especially in younger patients, might be considered potential individual discriminating biomarkers.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Data-driven tract-specific analysis uses machine learning techniques to perform tasks such as disease classification and prediction, as well as prediction of behavioral measures and traits.
“Another tract-specific analysis strategy is data-driven and uses machine learning techniques to perform tasks such as disease classification and prediction (Deng et al., 2019; O’Dwyer et al., 2012; Payabvash et al., 2019; Zhang et al., 2018a), as well as prediction of behavioral measures and traits (Seguin et al., 2020a; Tian et al., 2021).”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Deng et al., 2019
The citation lists haven’t been fetched yet
References 46 · Cited by 40
- Authors
- Deng, Y., Hung, K. S. Y., Lui, S. S. Y., Chui, W. W. H., Lee, J. C. W., Wang, Y., Li, Z., Mak, H. K. F., Sham, P. C., Chan, R. C. K., & Cheung, E. F. C.
- Affiliations
- Chinese Academy of Sciences (CN)
- Castle Peak Hospital (CN)
- University of Hong Kong (HK)
- Corresponding author
- Cheung, Eric F.C.
- ORCID
- Venue
- Progress in Neuro-Psychopharmacology and Biological Psychiatry
- Publisher
- Elsevier BV
- Published
- 2019-01
- Volume
- 88
- Pages
- 66-73
- ISSN
- 0278-5846 (ISSN-L)
- OpenAlex
- W2809567684
- Cited by
- 40
- References
- 46
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 7 · 2023: 7 · 2022: 4 · 2021: 6 · 2020: 7 · 2019: 1
- FWCI
- 2.75
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Progress in Neuro-Psychopharmacology and Biological Psychiatry (publishedVersion)
- PubMed (publishedVersion)
- Institutional Repository of Institute of Psychology, Chinese Academy of Sciences (Institute of Psychology, Chinese Academy of Sciences) (submittedVersion)
- Griffith Research Online (submittedVersion)
- Funders
- Philip KH Wong Foundation
- Beijing Training Project for Leading Talents
- Beijing Municipal Science & Technology Commission Grant
- Chinese Academy of Sciences (Z151100000315020, Z161100000216138, XDB02030002, Y2CX131003)
- CAS/SAFEA International Partnership Programme for Creative Research Teams
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Schizophrenia research and treatment
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- first-episode schizophrenia, white matter connectivity, tractography, diffusion MRI, schizophrenia classification, cerebello-thalamo-cortical circuit, age as predictor, neurodevelopmental disorders
- MeSH terms
- Machine Learning, Adult, Brain, Brain Mapping, Female, Humans, Male, Neural Pathways, Psychiatric Status Rating Scales, ROC Curve, Schizophrenia, Antipsychotic Agents, Anisotropy, Young Adult, Diffusion Tensor Imaging, White Matter
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
