Topographical Information-Based High-Order Functional Connectivity and Its Application in Abnormality Detection for Mild Cognitive Impairment

Han Zhang, Xiaobo Chen, Feng Shi, Gang Li, Minjeong Kim, Panteleimon Giannakopoulos, Sven Haller, Dinggang Shen

Research output: Contribution to journalArticle

21 Citations (Scopus)

Abstract

Temporal synchronization-based functional connectivity (FC) has long been used by the neuroscience community. However, topographical FC information may provide additional information to characterize the advanced relationship between two brain regions. Accordingly, we proposed a novel method, namely high-order functional connectivity (HOFC), to capture this second-level relationship using inter-regional resemblance of the FC topographical profiles. Specifically, HOFC first calculates an FC profile for each brain region, notably between the given brain region and other brain regions. Based on these FC profiles, a second layer of correlations is computed between all pairs of brain regions (i.e., correlation's correlation). On this basis, we generated an HOFC network, where "high-order" network properties were computed. We found that HOFC was discordant with the traditional FC in several links, indicating additional information being revealed by the new metrics. We applied HOFC to identify biomarkers for early detection of Alzheimer's disease by comparing 77 mild cognitive impairment patients with 89 healthy individuals (control group). Sensitivity in detection of group difference was consistently improved by ∼25% using HOFC compared to using FC. An HOFC network analysis also provided complementary information to an FC network analysis. For example, HOFC between olfactory and orbitofrontal cortices was found significantly reduced in patients, besides extensive alterations in HOFC network properties. In conclusion, our results showed promise in using HOFC to comprehensively map the human brain connectome.

Original languageEnglish
Pages (from-to)1095-1112
Number of pages18
JournalJournal of Alzheimer's Disease
Volume54
Issue number3
DOIs
Publication statusPublished - 2016

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Brain
Connectome
Neurosciences
Prefrontal Cortex
Cognitive Dysfunction
Early Diagnosis
Alzheimer Disease
Biomarkers
Control Groups

Keywords

  • Alzheimer's disease
  • biomarker
  • early detection
  • functional connectivity
  • functional magnetic resonance imaging (fMRI)
  • high-order connectivity
  • mild cognitive impairment
  • resting state fMRI

ASJC Scopus subject areas

  • Clinical Psychology
  • Geriatrics and Gerontology
  • Psychiatry and Mental health

Cite this

Topographical Information-Based High-Order Functional Connectivity and Its Application in Abnormality Detection for Mild Cognitive Impairment. / Zhang, Han; Chen, Xiaobo; Shi, Feng; Li, Gang; Kim, Minjeong; Giannakopoulos, Panteleimon; Haller, Sven; Shen, Dinggang.

In: Journal of Alzheimer's Disease, Vol. 54, No. 3, 2016, p. 1095-1112.

Research output: Contribution to journalArticle

Zhang, Han ; Chen, Xiaobo ; Shi, Feng ; Li, Gang ; Kim, Minjeong ; Giannakopoulos, Panteleimon ; Haller, Sven ; Shen, Dinggang. / Topographical Information-Based High-Order Functional Connectivity and Its Application in Abnormality Detection for Mild Cognitive Impairment. In: Journal of Alzheimer's Disease. 2016 ; Vol. 54, No. 3. pp. 1095-1112.
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