Distributional independent component analysis for diverse neuroimaging modalities.

ABSTRACT: Recent advances in neuroimaging technologies have provided opportunities to acquire brain images of different modalities for studying human brain organization from both functional and structural perspectives. Analysis of images derived from various modalities involves some common goals such as dimension reduction, denoising, and feature extraction. However, since these modalities have vastly different data characteristics, the current analysis is usually performed using distinct analytical tools that are only suitable for a specific imaging modality. In this paper, we present a Distributional Independent Component Analysis (DICA) that represents a new approach that performs decomposition on the distribution level, providing a unified framework for extracting features across imaging modalit

SUBMITTER: Wu B 

PROVIDER: S-EPMC9153395 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature