Brain Fractal Dimension and Machine Learning can predict first-episode psychosis and risk for transition to psychosis.
Authors
Affiliations (4)
Affiliations (4)
- Institute of Neuro- and Bioinformatics, University of Luebeck, Ratzeburger Allee 160, Luebeck, 23562, Schleswig-Holstein, Germany; Pattern Recognition Company GmbH, Maria-Goeppert-Straße 3, Luebeck, 23562, Schleswig-Holstein, Germany. Electronic address: [email protected].
- Schleswig-Holstein University Medical Center, Ratzeburger Allee 160, Luebeck, 23562, Schleswig-Holstein, Germany.
- Faculty of Medicine, University of Basel, Klingelbergstr. 61 CH, Basel, 4056, Switzerland.
- Institute of Neuro- and Bioinformatics, University of Luebeck, Ratzeburger Allee 160, Luebeck, 23562, Schleswig-Holstein, Germany.
Abstract
Although there are notable structural abnormalities in the brain associated with psychotic diseases, it is still unclear how these abnormalities relate to clinical presentation. However, the fractal dimension (FD), which offers details on the complexity and irregularity of brain microstructures, may be a promising feature, as demonstrated by neuropsychiatric disorders such as Parkinson's and Alzheimer's. It may offer a possible biomarker for the detection and prognosis of psychosis when paired with machine learning. The purpose of this study is to investigate FD as a structural magnetic resonance imaging (sMRI) feature from individuals with a high clinical risk of psychosis who did not transit to psychosis (CHR_NT), clinical high risk who transit to psychosis (CHR_T), patients with first-episode psychosis (FEP) and healthy controls (HC). Using a machine learning approach that ultimately classifies sMRI images, the goals are (a) to evaluate FD as a potential biomarker and (b) to investigate its ability to predict a subsequent transition to psychosis from the high-risk clinical condition. We obtained sMRI images from 194 subjects, including 44 HCs, 77 FEPs, 16 CHR_Ts, and 57 CHR_NTs. We extracted the FD features and analyzed them using machine learning methods under five classification schemas (a) FEP vs. HC, (b) FEP vs. CHR_NT, (c) FEP vs. CHR_T, (d) CHR_NT vs. CHR_T, (d) CHR_NT vs. HC and (e) CHR_T vs. HC. In addition, the CHR_T group was used as external validation in (a), (b) and (d) comparisons to examine whether the progression of the disorder followed the FEP or CHR_NT patterns. The proposed algorithm resulted in a balanced accuracy greater than 0.77. This study has shown that FD can function as a predictive neuroimaging marker, providing fresh information on the microstructural alterations triggered throughout the course of psychosis. The effectiveness of FD in the detection of psychosis and transition to psychosis should be established by further research using larger datasets.