Reveal consistent spatial-temporal patterns from dynamic functional connectivity for autism spectrum disorder identification

Yingying Zhu, Xiaofeng Zhu, Han Zhang, Wei Gao, Dinggang Shen, Guorong Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Citations (Scopus)

Abstract

Functional magnetic resonance imaging (fMRI) provides a noninvasive way to investigate brain activity. Recently,convergent evidence shows that the correlations of spontaneous fluctuations between two distinct brain regions dynamically change even in resting state,due to the conditiondependent nature of brain activity. Thus,quantifying the patterns of functional connectivity (FC) in a short time period and changes of FC over time can potentially provide valuable insight into both individual-based diagnosis and group comparison. In light of this,we propose a novel computational method to robustly estimate both static and dynamic spatial-temporal connectivity patterns from the observed noisy signals of individual subject. We achieve this goal in two folds: (1) Construct static functional connectivity across brain regions. Due to low signal-to-noise ratio induced by possible non-neural noise,the estimated FC strength is very sensitive and it is hard to define a good threshold to distinguish between real and spurious connections. To alleviate this issue,we propose to optimize FC which is in consensus with not only the low level region-to-region signal correlations but also the similarity of high level principal connection patterns learned from the estimated link-to-link connections. Since brain network is intrinsically sparse,we also encourage sparsity during FC optimization. (2) Characterize dynamic functional connectivity along time. It is hard to synchronize the estimated dynamic FC patterns and the real cognitive state changes,even using learning-based methods. To address these limitations,we further extend above FC optimization method into the spatial-temporal domain by arranging the FC estimations along a set of overlapped sliding windows into a tensor structure as the window slides. Then we employ low rank constraint in the temporal domain assuming there are likely a small number of discrete states that the brain transverses during a short period of time. We applied the learned spatial-temporal patterns from fMRI images to identify autism subjects. Promising classification results have been achieved,suggesting high discrimination power and great potentials in computer assisted diagnosis.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
EditorsGozde Unal, Mert R. Sabuncu, William Wells, Sebastian Ourselin, Leo Joskowicz
PublisherSpringer Verlag
Pages106-114
Number of pages9
ISBN (Print)9783319467191
DOIs
Publication statusPublished - 2016
Event1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 - Athens, Greece
Duration: 2016 Oct 212016 Oct 21

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9900 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016
CountryGreece
CityAthens
Period16/10/2116/10/21

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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