Discriminating VCID subgroups: A diffusion MRI multi-model fusion approach

Rajikha Raja; Arvind Caprihan; Gary A Rosenberg; Srinivas Rachakonda; Vince D Calhoun

doi:10.1016/j.jneumeth.2020.108598

Discriminating VCID subgroups: A diffusion MRI multi-model fusion approach

J Neurosci Methods. 2020 Apr 1:335:108598. doi: 10.1016/j.jneumeth.2020.108598. Epub 2020 Jan 28.

Authors

Rajikha Raja¹, Arvind Caprihan², Gary A Rosenberg³, Srinivas Rachakonda⁴, Vince D Calhoun⁵

Affiliations

¹ The Mind Research Network, Albuquerque, NM 87106, USA; Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA 30303, USA. Electronic address: rraja@gsu.edu.
² The Mind Research Network, Albuquerque, NM 87106, USA.
³ UNM Health Sciences Center, University of New Mexico, Albuquerque, NM 87106, USA.
⁴ Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA 30303, USA.
⁵ The Mind Research Network, Albuquerque, NM 87106, USA; Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA 30303, USA.

Abstract

Background: Vascular cognitive impairment and dementia (VCID) and Alzheimer's disease are predominant diseases among the aging population resulting in decline of various cognitive domains. Diffusion weighted MRI (DW-MRI) has been shown to be a promising aid in the diagnosis of such diseases. However, there are various models of DW-MRI and the interpretation of diffusion metrics depends on the model used in fitting data. Most previous studies are entirely based on parameters calculated from a single diffusion model.

New method: We employ a data fusion framework wherein diffusion metrics from different models such as diffusion tensor imaging, diffusion kurtosis imaging and constrained spherical deconvolution model are fused using well known blind source separation approach to investigate white matter microstructural changes in population comprising of controls and VCID subgroups. Multiple comparisons between subject groups and prediction analysis using features from individual models and proposed fusion model are carried out to evaluate performance of proposed method.

Results: Diffusion features from individual models successfully distinguished between controls and disease groups, but failed to differentiate between disease groups, whereas fusion approach showed group differences between disease groups too. WM tracts showing significant differences are superior longitudinal fasciculus, anterior thalamic radiation, arcuate fasciculus, optic radiation and corticospinal tract.

Comparison with existing method: ROC analysis showed increased AUC for fusion (AUC = 0.913, averaged across groups and tracts) compared to that of uni-model features (AUC = 0.77) demonstrating increased sensitivity of proposed method.

Conclusion: Overall our results highlight the benefits of multi-model fusion approach, providing improved sensitivity in discriminating VCID subgroups.

Keywords: Constrained spherical deconvolution model; Diffusion kurtosis imaging; Diffusion tensor imaging; Fusion; Tractography; Vascular cognitive impairment and dementia.

Publication types

Research Support, N.I.H., Extramural
Review

MeSH terms

Aged
Cognitive Dysfunction*
Diffusion Magnetic Resonance Imaging
Diffusion Tensor Imaging
Humans
Pyramidal Tracts
White Matter* / diagnostic imaging

Abstract

Publication types

MeSH terms

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