Diffusion tensor distribution function metrics boost power to detect deficits in Alzheimer's disease

Talia M. Nir; Artemis Zavaliangos-Petropulu; Neda Jahanshad; Julio E. Villalon-Reina; Liang Zhan; Alex D. Leow; Matt A. Bernstein; Clifford R. Jack; Michael W. Weiner; Paul M. Thompson

doi:10.1109/ISBI.2016.7493455

Diffusion tensor distribution function metrics boost power to detect deficits in Alzheimer's disease

Talia M. Nir, Artemis Zavaliangos-Petropulu, Neda Jahanshad, Julio E. Villalon-Reina, Liang Zhan, Alex D. Leow, Matt A. Bernstein, Clifford R. Jack, Michael W. Weiner, Paul M. Thompson

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

Fractional anisotropy derived from the single-tensor model (FADTI) in diffusion MRI (dMRI) is the most widely used metric to characterize white matter (WM) micro-architecture in disease, despite known limitations in regions with extensive fiber crossing. Due to time constraints and interest in collecting multiple clinical samples and MRI scan types, complex HARDI acquisition protocols are rare in clinical population dMRI studies. Under such constraints, the tensor distribution function (TDF) can still be used to reconstruct multiple underlying fibers by representing the diffusion profile as a probabilistic mixture of tensors. Here we set out to better profile WM deficits in Alzheimer's disease (AD) by comparing the standard FADTI and TDF-derived FA (FATDF) in (1) WM network connectivity and voxel-based analyses of diagnostic differences, and (2) for picking up associations with clinical cognitive ratings and hippocampal volume. Ultimately, the TDF approach may be more sensitive and accurate than corresponding DTI-derived measures.

Original language	English (US)
Title of host publication	2016 IEEE International Symposium on Biomedical Imaging
Subtitle of host publication	From Nano to Macro, ISBI 2016 - Proceedings
Publisher	IEEE Computer Society
Pages	1088-1092
Number of pages	5
ISBN (Electronic)	9781479923502
DOIs	https://doi.org/10.1109/ISBI.2016.7493455
State	Published - Jun 15 2016
Event	2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Prague, Czech Republic Duration: Apr 13 2016 → Apr 16 2016

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
Volume	2016-June
ISSN (Print)	1945-7928
ISSN (Electronic)	1945-8452

Other

Other	2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016
Country/Territory	Czech Republic
City	Prague
Period	4/13/16 → 4/16/16

Keywords

Alzheimer's disease
TDF
diffusion imaging
fractional anisotropy
white matter

ASJC Scopus subject areas

Biomedical Engineering
Radiology Nuclear Medicine and imaging

Access to Document

10.1109/ISBI.2016.7493455

Cite this

Nir, T. M., Zavaliangos-Petropulu, A., Jahanshad, N., Villalon-Reina, J. E., Zhan, L., Leow, A. D., Bernstein, M. A., Jack, C. R., Weiner, M. W., & Thompson, P. M. (2016). Diffusion tensor distribution function metrics boost power to detect deficits in Alzheimer's disease. In 2016 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Proceedings (pp. 1088-1092). Article 7493455 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2016-June). IEEE Computer Society. https://doi.org/10.1109/ISBI.2016.7493455

Diffusion tensor distribution function metrics boost power to detect deficits in Alzheimer's disease. / Nir, Talia M.; Zavaliangos-Petropulu, Artemis; Jahanshad, Neda et al.
2016 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Proceedings. IEEE Computer Society, 2016. p. 1088-1092 7493455 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2016-June).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Nir, TM, Zavaliangos-Petropulu, A, Jahanshad, N, Villalon-Reina, JE, Zhan, L, Leow, AD, Bernstein, MA , Jack, CR, Weiner, MW & Thompson, PM 2016, Diffusion tensor distribution function metrics boost power to detect deficits in Alzheimer's disease. in 2016 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Proceedings., 7493455, Proceedings - International Symposium on Biomedical Imaging, vol. 2016-June, IEEE Computer Society, pp. 1088-1092, 2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016, Prague, Czech Republic, 4/13/16. https://doi.org/10.1109/ISBI.2016.7493455

Nir TM, Zavaliangos-Petropulu A, Jahanshad N, Villalon-Reina JE, Zhan L, Leow AD et al. Diffusion tensor distribution function metrics boost power to detect deficits in Alzheimer's disease. In 2016 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Proceedings. IEEE Computer Society. 2016. p. 1088-1092. 7493455. (Proceedings - International Symposium on Biomedical Imaging). doi: 10.1109/ISBI.2016.7493455

Nir, Talia M. ; Zavaliangos-Petropulu, Artemis ; Jahanshad, Neda et al. / Diffusion tensor distribution function metrics boost power to detect deficits in Alzheimer's disease. 2016 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Proceedings. IEEE Computer Society, 2016. pp. 1088-1092 (Proceedings - International Symposium on Biomedical Imaging).

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AU - Zhan, Liang

AU - Leow, Alex D.

AU - Bernstein, Matt A.

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Diffusion tensor distribution function metrics boost power to detect deficits in Alzheimer's disease

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