Interpretable machine learning model for characterizing magnetic susceptibility-based biomarkers in first episode psychosis.
Authors
Affiliations (11)
Affiliations (11)
- Energy Transformation Center, Faculty of Engineering, Universidad Andrés Bello, Santiago, Chile.
- Millennium Institute for Intelligent Healthcare Engineering - iHEALTH, Santiago, Chile; Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile; Radiology Department, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile.
- Department of Informatics and Computing, Faculty of Engineering, Universidad Tecnológica Metropolitana, Santiago, Chile.
- School of Electrical Engineering, Pontificia Universidad Católica de Valparaíso, Valparaiso, Chile.
- Department of Psychiatry, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile; School of Medicine, Universidad Finis Terrae, Chile.
- Department of Psychiatry, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile.
- Department of Neurology and Psychiatry. Faculty of Medicine, Clínica Alemana Universidad del Desarrollo. Santiago, Chile.
- Millennium Institute for Intelligent Healthcare Engineering - iHEALTH, Santiago, Chile; Biomedical Engineering School, Faculty of Engineering, Universidad de Valparaíso, Valparaíso, Chile.
- Millennium Institute for Intelligent Healthcare Engineering - iHEALTH, Santiago, Chile; Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile; Department of Psychiatry, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile.
- Millennium Institute for Intelligent Healthcare Engineering - iHEALTH, Santiago, Chile; Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile; Department of Electrical Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile.
- Department of Medical Imaging and Radiation Sciences, School of Primary and Allied Health Care, Faculty of Medicine, Nursing and Health Sciences, Monash University, Australia. Electronic address: [email protected].
Abstract
Several studies have shown changes in neurochemicals within the deep-brain nuclei of patients with psychosis. These alterations indicate a dysfunction in dopamine within subcortical regions affected by fluctuations in iron concentrations. Quantitative Susceptibility Mapping (QSM) is a method employed to measure iron concentration, offering a potential means to identify dopamine dysfunction in these subcortical areas. This study employed a random forest algorithm to predict susceptibility features of the First-Episode Psychosis (FEP) and the response to antipsychotics using Shapley Additionality Explanation (SHAP) values. 3D multi-echo Gradient Echo (GRE) and T1-weighted GRE were obtained in 61 healthy-volunteers (HV) and 76 FEP patients (32 % Treatment-Resistant Schizophrenia (TRS) and 68 % treatment-Responsive Schizophrenia (RS)) using a 3T Philips Ingenia MRI scanner. QSM and R2* were reconstructed and averaged in twenty-two segmented regions of interest. We used a Sequential Forward Selection as a feature selection algorithm and a Random Forest as a model to predict FEP patients and their response to antipsychotics. We further applied the SHAP framework to identify informative features and their interpretations. Finally, multiple correlation patterns from magnetic susceptibility parameters were extracted using hierarchical clustering. Our approach accurately classifies HV and FEP patients with 76.48 ± 10.73 % accuracy (using four features) and TRS vs RS patients with 76.43 ± 12.57 % accuracy (using four features), using 10-fold stratified cross-validation. The SHAP analyses indicated the top four nonlinear relationships between the selected features. Hierarchical clustering revealed two groups of correlated features for each study. Early prediction of treatment response enables tailored strategies for FEP patients with treatment resistance, ensuring timely and effective interventions.