Joint embedding: A scalable alignment to compare individuals in a connectivity space.

ABSTRACT: A common coordinate space enabling comparison across individuals is vital to understanding human brain organization and individual differences. By leveraging dimensionality reduction algorithms, high-dimensional fMRI data can be represented in a low-dimensional space to characterize individual features. Such a representative space encodes the functional architecture of individuals and enables the observation of functional changes across time. However, determining comparable functional features across individuals in resting-state fMRI in a way that simultaneously preserves individual-specific connectivity structure can be challenging. In this work we propose scalable joint embedding to simultaneously embed multiple individual brain connectomes within a common space that allows individual re

SUBMITTER: Nenning KH 

PROVIDER: S-EPMC7779372 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature