High-order resting-state functional connectivity network for MCI classification

The Alzheimer's Disease Neuroimaging Initiative

Research output: Contribution to journalArticle

60 Citations (Scopus)

Abstract

Brain functional connectivity (FC) network, estimated with resting-state functional magnetic resonance imaging (RS-fMRI) technique, has emerged as a promising approach for accurate diagnosis of neurodegenerative diseases. However, the conventional FC network is essentially low-order in the sense that only the correlations among brain regions (in terms of RS-fMRI time series) are taken into account. The features derived from this type of brain network may fail to serve as an effective disease biomarker. To overcome this drawback, we propose extraction of novel high-order FC correlations that characterize how the low-order correlations between different pairs of brain regions interact with each other. Specifically, for each brain region, a sliding window approach is first performed over the entire RS-fMRI time series to generate multiple short overlapping segments. For each segment, a low-order FC network is constructed, measuring the short-term correlation between brain regions. These low-order networks (obtained from all segments) describe the dynamics of short-term FC along the time, thus also forming the correlation time series for every pair of brain regions. To overcome the curse of dimensionality, we further group the correlation time series into a small number of different clusters according to their intrinsic common patterns. Then, the correlation between the respective mean correlation time series of different clusters is calculated to represent the high-order correlation among different pairs of brain regions. Finally, we design a pattern classifier, by combining features of both low-order and high-order FC networks. Experimental results verify the effectiveness of the high-order FC network on disease diagnosis. Hum Brain Mapp 37:3282–3296, 2016.

Original languageEnglish
Pages (from-to)3282-3296
Number of pages15
JournalHuman Brain Mapping
Volume37
Issue number9
DOIs
Publication statusPublished - 2016 Sep 1

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Brain
Magnetic Resonance Imaging
Neurodegenerative Diseases
Biomarkers

Keywords

  • brain disease diagnosis
  • functional connectivity
  • low-order and high-order networks
  • mild cognitive impairment

ASJC Scopus subject areas

  • Anatomy
  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Neurology
  • Clinical Neurology

Cite this

High-order resting-state functional connectivity network for MCI classification. / The Alzheimer's Disease Neuroimaging Initiative.

In: Human Brain Mapping, Vol. 37, No. 9, 01.09.2016, p. 3282-3296.

Research output: Contribution to journalArticle

The Alzheimer's Disease Neuroimaging Initiative 2016, 'High-order resting-state functional connectivity network for MCI classification', Human Brain Mapping, vol. 37, no. 9, pp. 3282-3296. https://doi.org/10.1002/hbm.23240
The Alzheimer's Disease Neuroimaging Initiative. / High-order resting-state functional connectivity network for MCI classification. In: Human Brain Mapping. 2016 ; Vol. 37, No. 9. pp. 3282-3296.
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