# Integration of temporal & spatial properties of dynamic functional connectivity based on two-directional two-dimensional principal component analysis for disease analysis

## TL;DR

This paper introduces a new method to analyze brain connectivity data by combining both spatial and temporal features, improving disease classification accuracy.

## Contribution

A novel framework using two-directional two-dimensional PCA and Fourier transform to integrate spatial and temporal features of dynamic functional connectivity.

## Key findings

- The proposed framework outperforms existing methods in classification tasks.
- Combining spatial and temporal features enhances the diagnostic potential of dynamic functional connectivity.
- Fourier transform helps extract temporal-invariance properties effectively.

## Abstract

Dynamic functional connectivity, derived from resting-state functional magnetic resonance imaging (rs-fMRI), has emerged as a crucial instrument for investigating and supporting the diagnosis of neurological disorders. However, prevalent features of dynamic functional connectivity predominantly capture either temporal or spatial properties, such as mean and global efficiency, neglecting the significant information embedded in the fusion of spatial and temporal attributes. In addition, dynamic functional connectivity suffers from the problem of temporal mismatch, i.e., the functional connectivity of different subjects at the same time point cannot be matched. To address these problems, this article introduces a novel feature extraction framework grounded in two-directional two-dimensional principal component analysis. This framework is designed to extract features that integrate both spatial and temporal properties of dynamic functional connectivity. Additionally, we propose to use Fourier transform to extract temporal-invariance properties contained in dynamic functional connectivity. Experimental findings underscore the superior performance of features extracted by this framework in classification experiments compared to features capturing individual properties.