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Proceedings Paper

Identification of dementia subtypes based on a diffusion MRI multi-model approach
Author(s): Rajikha Raja; Arvind Caprihan; Gary A. Rosenberg; Vince D. Calhoun
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Paper Abstract

Dementia refers to symptoms associated with cognitive decline which is widespread in aging population. Among the various subtypes of dementia, Alzheimer’s disease (AD) and vascular cognitive impairment (VCI) are the two most prevalent types. The main aim of this study is to identify biomarkers which could accurately distinguish between the two dementia subtypes, AD and VCI, in order to aid the physician in planning disease specific treatments. Diffusion weighted MRI (DW-MRI) studies have been widely reported in neuroimaging research as an efficient biomarker in identifying the pathologies associated with dementia. Generally, these studies utilize the metrics estimated from a specific DW-MRI model. For the first time, we attempted to use diffusion derived metrics from more than a single model through fusion technique. In this study, the metrics from two well known DW-MRI models such as diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) are fused using a multiset canonical correlation analysis combined with joint independent component analysis (mCCA+jICA) fusion framework to investigate the potential differences between AD and VCI groups. The participants include 35 healthy controls, 24 AD subjects and 23 VCI subjects. DWMRI data acquired with maximum b-value greater than or equal to 2000 s/mm2 which is suitable for DKI fitting. DTI derived metrics such as mean diffusivity (MD), fractional anisotropy (FA), axial diffusivity (AxD) and radial diffusivity (RD) and DKI metrics such as mean kurtosis (MK), axial kurtosis (AK) and radial kurtosis (RK) are the diffusion features fused to obtain 8 independent components for each feature along with corresponding mixing coefficients. Performance of the proposed multi-model fusion framework is evaluated by comparing the group level testing carried out on features from individual diffusion models with the fused features from proposed method. Results showed that fusion methodology outperformed conventional unimodel approach in terms of distinguishing between subject groups. Diffusion features from individual models successfully distinguished between HC and disease groups (HC Vs AD and HC Vs VCI) with a minimum p-value of 0.00123 but failed to differentiate AD and VCI. On the other hand, the group differences between mixing coefficients obtained from fusion, showed differences between all pairs of subject groups (HC Vs AD, HC Vs VCI and AD Vs VCI). The significant p-value between AD and VCI obtained was 0.000897. The independent spatial components corresponding to mixing coefficient of minimum p-value was overlapped on MNI white matter (WM) tract atlas to identify the prominent WM tracts which showed a significant difference between AD and VCI. The WM tracts thus identified were superior longitudinal fasciculus, anterior thalamic radiation, optic radiation, cingulum and arcuate fasciculus. ROC analysis showed increased area under curve for fused features (average AUC=0.913) as compared to that of unimodel features (average AUC=0.77) which shows the increased sensitivity of proposed method.

Paper Details

Date Published: 28 February 2020
PDF: 7 pages
Proc. SPIE 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1131709 (28 February 2020); doi: 10.1117/12.2549279
Show Author Affiliations
Rajikha Raja, Ctr. for Translational Research in Neuroimaging and Data Science (United States)
Emory Univ. (United States)
Arvind Caprihan, The Mind Research Network (United States)
Gary A. Rosenberg, The Univ. of New Mexico Health Sciences Ctr. (United States)
Vince D. Calhoun, Ctr. for Translational Research in Neuroimaging and Data Science (United States)
The Univ. of New Mexico Health Sciences Ctr. (United States)


Published in SPIE Proceedings Vol. 11317:
Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging
Andrzej Krol; Barjor S. Gimi, Editor(s)

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