Towards Automated Classification of Visual Hallucination Presence in Psychosis using Resting-State fMRI

Visual hallucinations can severely impact the quality of life of affected individuals and are linked to greater disease severity in psychosis. To facilitate the detection of imaging biomarkers of visual hallucinations, we developed an automated pipeline to compare and evaluate feature extraction and classification methods using resting-state functional MRI scans from individuals with and without visual hallucinations. Five common functional connectivity features were assessed in this study: Regional Homogeneity, Voxel-Mirrored Homotopic Connectivity, Amplitude of Low Frequency Fluctuations, Fractional Amplitude of Low Frequency Fluctuations, and Eigenvector Centrality Mapping. We further evaluated the use of Pearson correlation in feature selection with different cutoff-values and employed a linear support vector machine for classification. The pipeline was validated on a dataset of 45 individuals, including people with psychosis and healthy controls. The model performance was evaluated based on the classification accuracy, sensitivity, specificity, as well as the interpretability of the feature weights. The code for the created pipeline is publicly available: https://github.com/LEO-UMCG/Visual_Hallucinations_Classification.Clinical relevance- Classification of patients based on biomarkers holds potential for complementing clinical measures, predicting future cases, and guiding personalized treatment of schizophrenia. The comparison of feature types and identification of imaging-based biomarkers in this study provide valuable insights for future research on VH classification and the underlying mechanisms of visual hallucinations.