Detecting Schizophrenia With 3D Structural Brain MRI Using Deep Learning Article Swipe
Jia Guo
,
Junhao Zhang
,
Vish Rao
,
Ye Tian
,
Yanting Yang
,
Nicolas Acosta
,
Zihan Wan
,
Pin-Yu Lee
,
Chloe Zhang
,
Larry Kegeles
,
Scott A. Small
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-1895500/v1
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-1895500/v1
Schizophrenia is a chronic neuropsychiatric disorder that causes distinct structural alterations within the brain. We hypothesize that deep learning applied to a structural neuroimaging dataset could detect disease-related alteration and improve classification and diagnostic accuracy. We tested this hypothesis using a single, widely available, and conventional T1-weighted MRI scan, from which we extracted the 3D whole-brain structure using standard post-processing methods. A deep learning model was then developed, optimized, and evaluated on three open datasets with T1-weighted MRI scans of patients with schizophrenia. Our proposed model outperformed the benchmark model, which was also trained with structural MR images using a 3D CNN architecture. Our model is capable of almost perfectly (area under the ROC curve = 0.987) distinguishing schizophrenia patients from healthy controls on unseen structural MRI scans. Regional analysis localized subcortical regions and ventricles as the most predictive brain regions. Subcortical structures serve a pivotal role in cognitive, affective, and social functions in humans, and structural abnormalities of these regions have been associated with schizophrenia. Our finding corroborates that schizophrenia is associated with widespread alterations in subcortical brain structure and the subcortical structural information provides prominent features in diagnostic classification. Together, these results further demonstrate the potential of deep learning to improve schizophrenia diagnosis and identify its structural neuroimaging signatures from a single, standard T1-weighted brain MRI.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-1895500/v1
- OA Status
- gold
- Cited By
- 6
- References
- 36
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4283693703Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-1895500/v1Digital Object Identifier
- Title
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Detecting Schizophrenia With 3D Structural Brain MRI Using Deep LearningWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-08-10Full publication date if available
- Authors
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Jia Guo, Junhao Zhang, Vish Rao, Ye Tian, Yanting Yang, Nicolas Acosta, Zihan Wan, Pin-Yu Lee, Chloe Zhang, Larry Kegeles, Scott A. SmallList of authors in order
- Landing page
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https://doi.org/10.21203/rs.3.rs-1895500/v1Publisher landing page
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.21203/rs.3.rs-1895500/v1Direct OA link when available
- Concepts
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Schizophrenia (object-oriented programming), Neuroimaging, Neuroscience, Psychology, Deep learning, Artificial intelligence, Computer science, PsychiatryTop concepts (fields/topics) attached by OpenAlex
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6Total citation count in OpenAlex
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2025: 2, 2024: 1, 2023: 1, 2022: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| publication_year | 2022 |
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