Extraction of dynamic functional connectivity from brain grey matter and white matter for MCI classification

Xiaobo Chen, Han Zhang, Lichi Zhang, Celina Shen, Seong Whan Lee, Dinggang Shen

Research output: Contribution to journalArticle

35 Citations (Scopus)

Abstract

Brain functional connectivity (FC) extracted from resting-state fMRI (RS-fMRI) has become a popular approach for diagnosing various neurodegenerative diseases, including Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI). Current studies mainly construct the FC networks between grey matter (GM) regions of the brain based on temporal co-variations of the blood oxygenation level-dependent (BOLD) signals, which reflects the synchronized neural activities. However, it was rarely investigated whether the FC detected within the white matter (WM) could provide useful information for diagnosis. Motivated by the recently proposed functional correlation tensors (FCT) computed from RS-fMRI and used to characterize the structured pattern of local FC in the WM, we propose in this article a novel MCI classification method based on the information conveyed by both the FC between the GM regions and that within the WM regions. Specifically, in the WM, the tensor-based metrics (e.g., fractional anisotropy [FA], similar to the metric calculated based on diffusion tensor imaging [DTI]) are first calculated based on the FCT and then summarized along each of the major WM fiber tracts connecting each pair of the brain GM regions. This could capture the functional information in the WM, in a similar network structure as the FC network constructed for the GM, based only on the same RS-fMRI data. Moreover, a sliding window approach is further used to partition the voxel-wise BOLD signal into multiple short overlapping segments. Then, both the FC and FCT between each pair of the brain regions can be calculated based on the BOLD signal segments in the GM and WM, respectively. In such a way, our method can generate dynamic FC and dynamic FCT to better capture functional information in both GM and WM and further integrate them together by using our developed feature extraction, selection, and ensemble learning algorithms. The experimental results verify that the dynamic FCT can provide valuable functional information in the WM; by combining it with the dynamic FC in the GM, the diagnosis accuracy for MCI subjects can be significantly improved even using RS-fMRI data alone.

Original languageEnglish
JournalHuman Brain Mapping
DOIs
Publication statusAccepted/In press - 2017

Fingerprint

Brain
Magnetic Resonance Imaging
Prodromal Symptoms
Cognitive Dysfunction
White Matter
Gray Matter
Diffusion Tensor Imaging
Anisotropy
Neurodegenerative Diseases
Alzheimer Disease
Learning

Keywords

  • Alzheimer's disease
  • Functional connectivity
  • Functional correlation tensor
  • Mild cognitive impairment
  • Resting-state fMRI

ASJC Scopus subject areas

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

Cite this

Extraction of dynamic functional connectivity from brain grey matter and white matter for MCI classification. / Chen, Xiaobo; Zhang, Han; Zhang, Lichi; Shen, Celina; Lee, Seong Whan; Shen, Dinggang.

In: Human Brain Mapping, 2017.

Research output: Contribution to journalArticle

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