AUTHOR=Wang Yaning , Wang Yihong , Xu Xuying , Pan Xiaochuan 
  
TITLE=Dynamic mode decomposition of resting-state fMRI revealing abnormal brain region features in schizophrenia
  
JOURNAL=Frontiers in Computational Neuroscience
  
VOLUME=Volume 19 - 2025
  
YEAR=2026
  
URL=https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2025.1742563
  
DOI=10.3389/fncom.2025.1742563
  
ISSN=1662-5188
  
ABSTRACT=Extracting features from abnormal brain regions in schizophrenia patients’ brain images holds significant importance for aiding diagnosis. However, existing methods remained limited in simultaneously capturing spatiotemporal information. Dynamic mode decomposition (DMD) effectively extracts spatiotemporal features from dynamic systems, making it suitable for time-series signals such as functional magnetic resonance imaging (fMRI) and electrocorticography (ECoG). This study utilized resting-state fMRI data from 68 healthy subjects and 68 schizophrenia patients. The DMD method was employed to extract the mean amplitude of dynamic patterns as features, with feature selection conducted via Least Absolute Shrinkage and Selection Operator (LASSO) regression. A support vector machine (SVM) was further employed to validate the predictive capability of the selected features across subject groups. Based on the LASSO screening, we identified brain regions exhibiting significant inter-group differences in mean amplitude, designated these as abnormal regions, and subsequently analyzed their functional deviations. The DMD method not only provided explicit temporal dynamic representations of brain activity but also supported signal reconstruction and prediction, thereby enhancing feature interpretability. Results demonstrated that DMD effectively extracted mean amplitude features from fMRI data. Combined with LASSO and SVM, it enabled the identification of abnormal brain regions and functional abnormalities in schizophrenia patients. Furthermore, this method captured frequency-dependent signal patterns, with extracted features correlating with both regional activation intensity and functional connectivity. This approach provides novel insights for exploring potential biomarkers of psychiatric disorders.