# Mapping longitudinally consistent intrinsic connectivity networks in macaque brain via longitudinal sparse dictionary learning

## TL;DR

This paper introduces a new method to consistently map brain connectivity networks in macaques over time, improving understanding of brain development and disorders.

## Contribution

The novel Longitudinal Sparse Dictionary Learning method ensures temporal consistency in mapping brain connectivity networks.

## Key findings

- LSDL successfully extracted 21 consistent longitudinal intrinsic connectivity networks in macaque brains.
- LSDL outperformed FICA and SDL in modeling longitudinal fMRI data.
- The method enables robust tracking of brain connectivity evolution over time.

## Abstract

Mapping consistent longitudinal intrinsic connectivity networks (ICNs) is crucial for understanding brain functional development over various life stages. However, achieving consistent longitudinal ICNs has been challenging due to the lack of methodologies that maintain temporal consistency. To address this gap, we introduce an innovative approach named Longitudinal Sparse Dictionary Learning (LSDL). This method utilizes an additional Frobenius norm to bridge gaps between consecutive ICNs, facilitating the continuous transfer of the learned feature matrix to subsequent stages. Moreover, Matrix Backpropagation (MBP) is employed to effectively mitigate potential accumulative errors. Our validation results demonstrate that LSDL can successfully extract 21 consistent longitudinal ICNs in macaque brains. In comparative empirical evaluations with established methodologies, Fast Independent Component Analysis (FICA) and Sparse Dictionary Learning (SDL), LSDL showcases superior efficacy in modeling longitudinal functional Magnetic Resonance Imaging (fMRI) data. This approach opens new avenues for research into developmental brain dynamics and neurodegenerative disorders, providing a robust framework for tracking the evolution of brain connectivity over time.