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

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 paper 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.

Exploring diagnosis and imaging biomarkers of Parkinson’s disease via iterative canonical correlation analysis based feature selection

“…The integrated feature information from multi-modality images can provide more comprehensive details, which can be utilized by the proposed framework for better classification performance. We will also incorporate alternative machine learning techniques in the fields of image segmentation (Zhang et al, 2016, 2017b), image super-resolution (Zhang et al, 2017c) and classification (Zhang et al, 2017d; Chen et al, 2017), and seek further performance improvements in PD diagnosis.…”

Alterations in Intra- and Interregional Intrinsic Brain Connectivity Are Differentially Associated with Memory Performance in Amnestic Mild Cognitive Impairment

“…Recent studies have shown that machine learning using structural and functional brain imaging features classify healthy elderly and MCI groups with higher accuracy [46-48]. Additionally, several studies have classified the MCI group with high accuracy by evaluating dynamic FC, considering the complex and dynamic interaction patterns among brain regions [49, 50]. In this regard, further studies, which focus on dynamic FC of the ReHo-based seeds and investigate group differences, will need to be undertaken.…”

Learning-based structurally-guided construction of resting-state functional correlation tensors

“…When the enhanced FCTs are obtained, they are non-rigidly registered to the MNI-152 space using SPM [40]. We then generate 359 fiber probability maps based on the method described in [41, 42] [43]. Each template is a probability WM mask indicating inter-subject consistent connections between any pair of Automated Anatomical labeling (AAL) brain regions, generated using the data of 500 subjects in HCP dataset.…”