Prediction of anhedonia in patients with first-episode schizophrenia using a Wavelet-ALFF-based Support vector regression model

Abstract

Anhedonia is one of the core features of the negative symptoms of schizophrenia and can be extremely burdensome. Our study applied resting-state functional magnetic resonance imaging (fMRI)-based support vector regression (SVR) to predict anhedonia in patients with first-episode schizophrenia (FES) and analysed the correlation between the wavelet-based amplitude low-frequency fluctuation (wavelet-ALFF) of the main brain region and anhedonia. We recruited 31 patients with FES and 33 healthy controls (HCs) from the Affiliated Brain Hospital of Guangzhou Medical University. All subjects completed the Temporal Experience of Pleasure Scale (TEPS) and received resting-state fMRI (rs-fMRI). We used the wavelet-ALFF method and SVR to analyse the data. Patients with FES had lower consummatory pleasure scores than healthy subjects (t = -2.71, P<0.01). FES displays variable wavelet-ALFF in a wide range of cerebral cortices (P<0.05, GFR corrected). The SVR analysis showed that wavelet-ALFF, based primarily on the right putamen (r = 0.40, P<0.05) and right superior occipital gyrus (r = -0.39, P<0.05), was effective in predicting consummatory pleasure scores with an accuracy of 56.43 %. Our study shows that abnormal spontaneous neural activity in FES may be related to the state of consummatory anhedonia in FES. Wavelet-ALFF changes in the right putamen and superior occipital gyrus may be a biological feature of FES with anhedonia and could serve as a potential biological marker of FES with anhedonia.

Keywords: Anhedonia; Schizophrenia; Support vector regression; Wavelet-ALFF.