Topology-guided cyclic brain connectivity generation using geometric deep learning

Sserwadda A., Rekık I.

JOURNAL OF NEUROSCIENCE METHODS, vol.353, 2021 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 353
  • Publication Date: 2021
  • Doi Number: 10.1016/j.jneumeth.2020.108988
  • Journal Name: JOURNAL OF NEUROSCIENCE METHODS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, Biotechnology Research Abstracts, CAB Abstracts, EMBASE, MEDLINE, Veterinary Science Database
  • Istanbul Technical University Affiliated: Yes

Abstract

Background: There is a growing need for analyzing medical data such as brain connectomes. However, the unavailability of large-scale training samples increases risks of model over-fitting. Recently, deep learning (DL) architectures quickly gained momentum in synthesizing medical data. However, such frameworks are primarily designed for Euclidean data (e.g., images), overlooking geometric data (e.g., brain connectomes). A few existing geometric DL works that aimed to predict a target brain connectome from a source one primarily focused on domain alignment and were agnostic to preserving the connectome topology.