Machine learning based differential diagnosis of schizophrenia, major depression disorder and bipolar disorder using structural magnetic resonance imaging.
Authors
Affiliations (9)
Affiliations (9)
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, China.
- Duke University, 2080 Duke University Road, Durham, NC 27708, United States. Electronic address: [email protected].
- Huzhou Third People's Hospital, China.
- Huai'an No. 3 People's Hospital, China.
- Nanjing Drum Tower Hospital, China.
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, China.
- Nanjing Medical University, China.
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, China. Electronic address: [email protected].
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China; Lab for Artificial Intelligence in Medical Imaging (LAIMI), School of Medical Imaging, Nanjing Medical University, Nanjing, Jiangsu, China. Electronic address: [email protected].
Abstract
Cortical morphological abnormalities in schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BD) have been identified in past research. However, their potential as objective biomarkers to differentiate these disorders remains uncertain. Machine learning models may offer a novel diagnostic tool. Structural MRI (sMRI) of 220 SCZ, 220 MDD, 220 BD, and 220 healthy controls were obtained using a 3T scanner. Volume, thickness, surface area, and mean curvature of 68 cerebral cortices were extracted using FreeSurfer. 272 features underwent 3 feature selection techniques to isolate important variables for model construction. These features were incorporated into 3 classifiers for classification. After model evaluation and hyperparameter tuning, the best-performing model was identified, along with the most significant brain measures. The univariate feature selection-Naive Bayes model achieved the best performance, with an accuracy of 0.66, macro-average AUC of 0.86, and sensitivities and specificities ranging from 0.58-0.86 to 0.81-0.93, respectively. Key features included thickness of right isthmus-cingulate cortex, area of left inferior temporal gyrus, thickness of right superior temporal gyrus, mean curvature of right pars orbitalis, thickness of left transverse temporal cortex, volume of left caudal anterior-cingulate cortex, area of right banks superior temporal sulcus, and thickness of right temporal pole. The machine learning model based on sMRI data shows promise for aiding in the differential diagnosis of SCZ, MDD, and BD. Cortical features from the cingulate and temporal lobes may highlight distinct biological mechanisms underlying each disorder.