Early alzheimer's disease progression detection using multi-subnetworks of the brain

Jaroslav Rokicki, Hiyoshi Kazuko, Francois Benoit Vialatte, Andrius Usinskas, Andrzej Cichocki

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Alzheimer's disease is neurodegenerative disorder believed to affect 24.3 million people worldwide. Proposed MRI based disease progression markers have shown ability to perform the classification between the Alzheimer's Disease (AD), Mild Cognitive Impariment (MCI) and Normal Cognitive (NC) subjects. We exploited two approaches, first one is to use single sub-network volume as a feature, second to use a network of most discriminative sub-networks. Multi-feature approach showed improvement by 4.5% in AD/NC classification case, and 1.5 % in MCI/NC case. Study was summarized for 48 AD, 119 MCI and 66 NC subjects.

Original languageEnglish
Title of host publicationIJCCI 2012 - Proceedings of the 4th International Joint Conference on Computational Intelligence
Pages684-691
Number of pages8
Publication statusPublished - 2012
Externally publishedYes
Event4th International Joint Conference on Computational Intelligence, IJCCI 2012 - Barcelona, Spain
Duration: 5 Oct 20127 Oct 2012

Publication series

NameIJCCI 2012 - Proceedings of the 4th International Joint Conference on Computational Intelligence

Conference

Conference4th International Joint Conference on Computational Intelligence, IJCCI 2012
Country/TerritorySpain
CityBarcelona
Period5/10/127/10/12

Keywords

  • Alzheimer's Disease
  • Amygdala
  • Brain Atrophy
  • Classification
  • Early Detection
  • Entorhinal Cortex
  • Hippocampus
  • LDA
  • Multi-volume
  • Segmentation of Brain Subnetworks

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