Abnormal white matter changes in Alzheimer's disease based on diffusion tensor imaging: A systematic review
Abstract
Alzheimer's disease (AD) is a degenerative neurological disease in elderly individuals. Subjective cognitive decline (SCD), mild cognitive impairment (MCI) and further development to dementia (d-AD) are considered to be major stages of the progressive pathological development of AD. Diffusion tensor imaging (DTI), one of the most important modalities of MRI, can describe the microstructure of white matter through its tensor model. It is widely used in understanding the central nervous system mechanism and finding appropriate potential biomarkers for the early stages of AD. Based on the multilevel analysis methods of DTI (voxelwise, fiberwise and networkwise), we summarized that AD patients mainly showed extensive microstructural damage, structural disconnection and topological abnormalities in the corpus callosum, fornix, and medial temporal lobe, including the hippocampus and cingulum. The diffusion features and structural connectomics of specific regions can provide information for the early assisted recognition of AD. The classification accuracy of SCD and normal controls can reach 92.68% at present. And due to the further changes of brain structure and function, the classification accuracy of MCI, d-AD and normal controls can reach more than 97%. Finally, we summarized the limitations of current DTI-based AD research and propose possible future research directions.
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Microstructural damage in the white matter of Alzheimer's disease patients has been reported in the corpus callosum, fornix, and cingulum.
“Microstructural damage in the white matter of AD patients has been reported in the corpus callosum, fornix, and cingulum. 19 , 20 , 21”
From Apolipoprotein ε4 exacerbates white matter impairment in a mouse model of Aβ amyloidosis by decreasing actively myelinating oligodendrocytes (2025) · cites this paper as [19]
White matter microstructural damage in Alzheimer's disease is associated with disrupted structural connectivity and impaired network functions.
“This damage is associated with disrupted structural connectivity and impaired network functions. 19 , 20 , 21”
From Apolipoprotein ε4 exacerbates white matter impairment in a mouse model of Aβ amyloidosis by decreasing actively myelinating oligodendrocytes (2025) · cites this paper as [19]
At symptomatic onset, absolute diffusivity measures may be more sensitive than fractional anisotropy, whereas increasing radial diffusivity and declining fractional anisotropy may better track disease severity longitudinally.
“At symptomatic onset, absolute diffusivity measures may be more sensitive than fractional anisotropy, whereas increasing radial diffusivity and declining fractional anisotropy may better track disease severity longitudinally (Acosta-Cabronero et al., 2010, 2012; Chen et al., 2023; Fu et al., 2014; Lo Buono et al., 2020).”
From 40 Years of diffusion MRI in the brain: From history to emerging frontiers (2026) · cites this paper as Chen et al., 2023
Cites
McKhann, G. M., Knopman, D. S., Chertkow, H., Hyman, B. T., Jack, C. R., Kawas, C. H., Klunk, W. E., Koroshetz, W. J., Manly, J. J., Mayeux, R., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.03.005 · Cited by 18,657 · see citing works
NIA‐AA Research Framework: Toward a biological definition of Alzheimer's disease
Jack, C. R., Bennett, D. A., Blennow, K., Carrillo, M. C., Dunn, B., Haeberlein, S. B., Holtzman, D. M., Jagust, W., Jessen, F., Karlawish, J., et al. (2018) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2018.02.018 · Cited by 10,537 · see citing works
The basis of anisotropic water diffusion in the nervous system—A technical review.
Beaulieu, C. (2002) · NMR in Biomedicine · doi:10.1002/nbm.782 · Cited by 4,653 · see citing works
Le Bihan, D., Breton, E., Lallemand, D., Grenier, P., Cabanis, E., & Laval-Jeantet, M. (1986) · Radiology · doi:10.1148/radiology.161.2.3763909 · Cited by 3,912 · see citing works
MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation
Tournier, J.-D., Smith, R., Raffelt, D., Tabbara, R., Dhollander, T., Pietsch, M., Christiaens, D., Jeurissen, B., Yeh, C.-H., & Connelly, A. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116137 · Cited by 2,974 · see citing works
Dysmyelination revealed through MRI as increased radial (but unchanged axial) diffusion of water
Song, S.-K., Sun, S.-W., Ramsbottom, M. J., Chang, C., Russell, J., & Cross, A. H. (2002) · NeuroImage · doi:10.1006/nimg.2002.1267 · Cited by 2,582 · see citing works
White matter integrity, fiber count, and other fallacies: The do's and don'ts of diffusion MRI
Jones, D. K., Knösche, T. R., & Turner, R. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2012.06.081 · Cited by 2,479 · see citing works
Tractography-based connectomes are dominated by false-positive connections
Maier-Hein, K. H., Neher, P., Houde, J.-C., Côté, M.-A., Garyfallidis, E., Zhong, J., Chamberland, M., Yeh, F.-C., Lin, Y.-C., Ji, Q., et al. (2016) · doi:10.1101/084137 · Cited by 1,361 · see citing works
Twenty‐five pitfalls in the analysis of diffusion MRI data
Jones, D. K., & Cercignani, M. (2010) · NMR in Biomedicine · doi:10.1002/nbm.1543 · Cited by 827 · see citing works
Investigating white matter fibre density and morphology using fixel-based analysis.
Raffelt, D. A., Tournier, J.-D., Smith, R. E., Vaughan, D. N., Jackson, G., Ridgway, G. R., & Connelly, A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.09.029 · Cited by 620 · see citing works
Diffusion MRI fiber tractography of the brain
Jeurissen, B., Descoteaux, M., Mori, S., & Leemans, A. (2019) · NMR in Biomedicine · doi:10.1002/nbm.3785 · Cited by 574 · see citing works
Douaud, G., Jbabdi, S., Behrens, T. E. J., Menke, R. A., Gass, A., Monsch, A. U., Rao, A., Whitcher, B., Kindlmann, G., Matthews, P. M., et al. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.12.008 · Cited by 514 · see citing works
Applications of diffusion‐weighted and diffusion tensor MRI to white matter diseases – a review
Horsfield, M. A., & Jones, D. K. (2002) · NMR in Biomedicine · doi:10.1002/nbm.787 · Cited by 468 · see citing works
Understanding the Physiopathology Behind Axial and Radial Diffusivity Changes—What Do We Know?
Winklewski, P. J., Sabisz, A., Naumczyk, P., Jodzio, K., Szurowska, E., & Szarmach, A. (2018) · Frontiers in Neurology · doi:10.3389/fneur.2018.00092 · Cited by 463 · see citing works
Slot, R. E. R., Sikkes, S. A. M., Berkhof, J., Brodaty, H., Buckley, R., Cavedo, E., Dardiotis, E., Guillo‐Benarous, F., Hampel, H., Kochan, N. A., et al. (2019) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2018.10.003 · Cited by 428 · see citing works
Plitt, M., Barnes, K. A., & Martin, A. (2015) · NeuroImage: Clinical · doi:10.1016/j.nicl.2014.12.013 · Cited by 335 · see citing works
How and how not to correct for CSF-contamination in diffusion MRI
Metzler-Baddeley, C., O'Sullivan, M. J., Bells, S., Pasternak, O., & Jones, D. K. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.08.043 · Cited by 291 · see citing works
Nowrangi, M. A., Lyketsos, C. G., Leoutsakos, J. S., Oishi, K., Albert, M., Mori, S., & Mielke, M. M. (2013) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2012.05.2186 · Cited by 120 · see citing works
Ryu, S. Y., Lim, E. Y., Na, S., Shim, Y. S., Cho, J. H., Yoon, B., Hong, Y. J., & Yang, D. W. (2017) · International Psychogeriatrics · doi:10.1017/s1041610216002349 · Cited by 78 · see citing works
Rich-club in the brain’s macrostructure: Insights from graph theoretical analysis
Kim, D.-J., & Min, B.-K. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.06.039 · Cited by 73 · see citing works
Mak, E., Gabel, S., Mirette, H., Su, L., Williams, G. B., Waldman, A., Wells, K., Ritchie, K., Ritchie, C., & O’Brien, J. (2017) · Ageing Research Reviews · doi:10.1016/j.arr.2016.10.001 · Cited by 71 · see citing works
Mak, E., Gabel, S., Su, L., Williams, G. B., Arnold, R., Passamonti, L., Vazquez Rodríguez, P., Surendranathan, A., Bevan-Jones, W. R., Rowe, J. B., et al. (2017) · International Psychogeriatrics · doi:10.1017/s1041610216002143 · Cited by 66 · see citing works
White matter degeneration in subjective cognitive decline: a diffusion tensor imaging study
Li, X.-Y., Tang, Z.-C., Sun, Y., Tian, J., Liu, Z.-Y., & Han, Y. (2016) · Oncotarget · doi:10.18632/oncotarget.10091 · Cited by 65 · see citing works
Relationship between apathy and diffusion tensor imaging metrics of the brain in Alzheimer's disease
Ota, M., Sato, N., Nakata, Y., Arima, K., & Uno, M. (2012) · International Journal of Geriatric Psychiatry · doi:10.1002/gps.2779 · Cited by 58 · see citing works
Stone, D. B., Ryman, S. G., Hartman, A. P., Wertz, C. J., Vakhtin, A. A., & Initiative, A. D. N. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.711579 · Cited by 28 · see citing works
Lee, Y., Ha, J., Park, J., Lee, B., Moon, E., Chung, Y., Kim, J., Kim, H., Mun, C., Kim, T., et al. (2016) · Journal of Neuroimaging · doi:10.1111/jon.12207 · Cited by 26 · see citing works
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Shen, Y., Shen, Y., Wang, M., Jin, K., Yang, P., Cao, Z., Zhu, Q., Zhao, Z., Li, H., Han, L., et al. (2025) · Cell Reports · doi:10.1016/j.celrep.2025.116073 · Cited by 10 · see citing works
Al‐Amin, M. M., Kim, B., Karahan, H., Tate, M. D., Walsh, S. P., Puntambekar, S. S., Bissel, S. J., Lamb, B. T., Wang, N., & Kim, J. (2025) · Alzheimer's & Dementia · doi:10.1002/alz.70791 · Cited by 6 · see citing works
Le Bihan, D. (2026) · Frontiers in Neuroscience · doi:10.3389/fnins.2026.1858655 · Cited by 1 · see citing works
40 Years of diffusion MRI in the brain: From history to emerging frontiers
Le Bihan, D. (2026) · Imaging Neuroscience · doi:10.1162/imag.a.1365
“Recent longitudinal work further suggests that diffusion-derived axonal-density measures may detect early, spatially selective degeneration related to clinical decline in Alzheimer’s disease ( Gong et al., 2026; Wang et al., 2023).”
- Authors
- Chen, Y., Wang, Y., Song, Z., Fan, Y., Gao, T., & Tang, X.
- Affiliations
- Beijing Institute of Technology (CN)
- Corresponding author
- Gao, Tianxin
- Tang, Xiaoying
- Venue
- Ageing Research Reviews
- Publisher
- Elsevier BV
- Published
- 2023-06
- Volume
- 87
- Pages
- 101911
- ISSN
- 1568-1637 (ISSN-L)
- OpenAlex
- W4324344458
- Cited by
- 152
- References
- 26
- Citations by year
- 2026: 51 · 2025: 55 · 2024: 40 · 2023: 6
- FWCI
- 7.10
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Ageing Research Reviews (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Key Research and Development Program of China (2020YFC2007300, 2020YFC2007301, 2021ZD0202102)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- Alzheimer's disease, white matter microstructure, diffusion tensor imaging, structural connectomics, corpus callosum, fornix, medial temporal lobe, hippocampus, cingulum, mild cognitive impairment, subjective cognitive decline, dementia, early diagnosis, biomarkers, structural disconnection
- MeSH terms
- Aged, Alzheimer Disease, Brain, Humans, Diffusion Tensor Imaging, Cognitive Dysfunction, White Matter
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