TY - JOUR
T1 - Discriminating VCID subgroups
T2 - A diffusion MRI multi-model fusion approach
AU - Raja, Rajikha
AU - Caprihan, Arvind
AU - Rosenberg, Gary A.
AU - Rachakonda, Srinivas
AU - Calhoun, Vince D.
N1 - Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2020/4/1
Y1 - 2020/4/1
N2 - 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.
AB - 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.
KW - Constrained spherical deconvolution model
KW - Diffusion kurtosis imaging
KW - Diffusion tensor imaging
KW - Fusion
KW - Tractography
KW - Vascular cognitive impairment and dementia
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U2 - 10.1016/j.jneumeth.2020.108598
DO - 10.1016/j.jneumeth.2020.108598
M3 - Review article
C2 - 32004594
AN - SCOPUS:85078659915
SN - 0165-0270
VL - 335
JO - Journal of Neuroscience Methods
JF - Journal of Neuroscience Methods
M1 - 108598
ER -