ArticleNeuroscience bulletin2020
A Systematic Characterization of Structural Brain Changes in Schizophrenia.
Article in Neuroscience bulletin, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 20 citations in OpenAlex.
- Characterizing the Morphological Brain Network Topology in Patients With Migraine Using Wavelet Based Morphometry: A Descriptive Cross-Sectional Retrospective Study.Health science reports · 2025Article
- Exploring the Morphological Study of the Brain in Patients with Bipolar Disorder Based on Structural Magnetic Resonance Imaging.Neuropsychiatric disease and treatment · 2025Article
- Regional decreases of cortical thickness in major depressive disorder and their correlation with illness duration: a case-control study.Frontiers in psychiatry · 2024Article
- Voxel- and tensor-based morphometry with machine learning techniques identifying characteristic brain impairment in patients with cervical spondylotic myelopathy.Frontiers in neurology · 2024Article
- Grey matter volume and its association with cognitive impairment and peripheral cytokines in excited individuals with schizophrenia.Brain imaging and behavior · 2022Article
- Cortical and Subcortical Grey Matter Abnormalities in White Matter Hyperintensities and Subsequent Cognitive Impairment.Neuroscience bulletin · 2021Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A systematic characterization of the similarities and differences among different methods for detecting structural brain abnormalities in schizophrenia, such as voxel-based morphometry (VBM), tensor-based morphometry (TBM), and projection-based thickness (PBT), is important for understanding the brain pathology in schizophrenia and for developing effective biomarkers for a diagnosis of schizophrenia. However, such studies are still lacking. Here, we performed VBM, TBM, and PBT analyses on T1-weighted brain MR images acquired from 116 patients with schizophrenia and 116 healthy controls. We found that, although all methods detected wide-spread structural changes, different methods captured different information - only 10.35% of the grey matter changes in cortex were detected by all three methods, and VBM only detected 11.36% of the white matter changes detected by TBM. Further, pattern classification between patients and controls revealed that combining different measures improved the classification accuracy (81.9%), indicating that fusion of different structural measures serves as a better neuroimaging marker for the objective diagnosis of schizophrenia.
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