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DTI measures in crossing-fibre areas: increased diffusion anisotropy reveals early white matter alteration in MCI and mild Alzheimer’s disease
Open accessAbstract
Though mild cognitive impairment is an intermediate clinical state between healthy aging and Alzheimer's disease (AD), there are very few whole-brain voxel-wise diffusion MRI studies directly comparing changes in healthy control, mild cognitive impairment (MCI) and AD subjects. Here we report whole-brain findings from a comprehensive study of diffusion tensor indices and probabilistic tractography obtained in a very large population of healthy controls, MCI and probable AD subjects. As expected from the literature, all diffusion indices converged to show that the cingulum bundle, the uncinate fasciculus, the entire corpus callosum and the superior longitudinal fasciculus are the most affected white matter tracts in AD. Significant differences between MCI and AD were essentially confined to the corpus callosum. More importantly, we introduce for the first time in a degenerative disorder an application of a recently developed tensor index, the "mode" of anisotropy, as well as probabilistic crossing-fibre tractography. The mode of anisotropy specifies the type of anisotropy as a continuous measure reflecting differences in shape of the diffusion tensor ranging from planar (e.g., in regions of crossing fibres from two fibre populations of similar density or regions of "kissing" fibres) to linear (e.g., in regions where one fibre population orientation predominates), while probabilistic crossing-fibre tractography allows to accurately trace pathways from a crossing-fibre region. Remarkably, when looking for whole-brain diffusion differences between MCI patients and healthy subjects, the only region with significant abnormalities was a region of crossing fibres in the centrum semiovale, showing an increased mode of anisotropy. The only white matter region demonstrating a significant difference in correlations between neuropsychological scores and a diffusion measure (mode of anisotropy) across the three groups was the same region of crossing fibres. Further examination using probabilistic tractography established explicitly and quantitatively that this previously unreported increase of mode and co-localised increase of fractional anisotropy was explained by a relative preservation of motor-related projection fibres (at this early stage of the disease) crossing the association fibres of the superior longitudinal fasciculus. These findings emphasise the benefit of looking at the more complex regions in which spared and affected pathways are crossing to detect very early alterations of the white matter that could not be detected in regions consisting of one fibre population only. Finally, the methods used in this study may have general applicability for other degenerative disorders and, beyond the clinical sphere, they could contribute to a better quantification and understanding of subtle effects generated by normal processes such as visuospatial attention or motor learning.
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“In particular, major fibre tracts such as the forceps major and forceps minor, inferior fronto-occipital fasciculus as well as the posterior cingulum show white matter alterations in Alzheimer’s disease dementia (Taylor et al., 2001; Teipel et al., 2002; Douaud et al., 2011; Mito et al., 2018).”
From White matter diffusion alterations precede symptom onset in autosomal dominant Alzheimer’s disease (2018) · cites this paper as Douaud et al., 2011
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“DTI alterations such as those observed in the corticospinal tract in the current study should be interpreted with caution, as the current TBSS approach cannot be unambiguously attributed to that tract, but potentially are because of alterations in crossing fibres such as the superior longitudinal fasciculus or corpus callosum (Douaud et al., 2011; Teipel et al., 2014; Mito et al., 2018).”
From White matter diffusion alterations precede symptom onset in autosomal dominant Alzheimer’s disease (2018) · cites this paper as Douaud et al., 2011
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“In healthy tissue with crossing fibers, the DTI model may show low FA. FADTI may paradoxically appear to increase in regions where crossing fibers deteriorate in neurodegenerative diseases such as AD (Douaud et al., 2011).”
From Diffusion MRI Indices and Their Relation to Cognitive Impairment in Brain Aging: The Updated Multi-protocol Approach in ADNI3 (2019) · cites this paper as Douaud et al., 2011
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Citations
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Scheck, S. M. (2016) · The University of Queensland · doi:10.14264/uql.2016.526
Longitudinal analysis of diffusion-weighted MRI with a ball-and-sticks model
Arkesteijn, G. A. M., Poot, D. H. J., Niestijl, M., Vernooij, M. W., Niessen, W. J., A, L. J. V. V., & Vos, F. M. (2017) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2017.7950635
“Therefore, diffusion descriptors computed from this model may lack sensitivity [6], or may suggest spurious change in the white matter microstructure [7, 8].”
Mehta, R., Kim, H. J., Wang, S., Johnson, S. C., Yuan, M., & Singh, V. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.07575
“…left and right corticospinal tract and the left and right cerebral peduncle were identified on both PiB and DTI measures (supported by the literature (Douaud et al., 2011)), together with Part A of the Trail Making Test (see Table 4) which happens to be used in AD diagnosis (Albert et al., 2011).”
Method汪, 克. (2018) · Advances in Clinical Medicine · doi:10.12677/acm.2018.810153
“胼胝体是脑内最大的联合纤维,DTI 研究发现,AD 患者整体胼胝体 FA 值显著下降[10]。但 AD 早 期胼胝体膝部[31]还是压部[32]病理改变更明显尚存在争议。Sexton 的研究表明[32],胼胝体压部 FA 值 及 MD 值变化比膝部更加显著,这与 AD 患者颞-顶叶连接受损较额叶纤维束受损更严重的观点相符合。 也有研究[33]指出,胼胝体后部区域在疾病进程的早期首先受损,而前部区域主要在疾病晚期发生改变。 不同的研究结论可能与选取患者的认知受损的严重程度、所处疾病进程有关。目前关于胼胝体在 AD 中”
Relationship between white matter changes and aggression in methamphetamine dependence
Lederer, K. (2015) · Open University of Cape Town (University of Cape Town)
Microstructure Imaging by Diffusion MRI
Szczepankiewicz, F., & Westin, C. (2020) · Springer eBooks · doi:10.1007/978-3-030-35206-6_3
The relation among aging, dopamine-regulating genes, and neurocognition
Li, X. (2020) · Figshare
Anaby, D., Tendler, B. C., Treder, M. S., Hametner, S., Koeck, U., Tax, C. M. W., Parker, G. D., Barazany, D., Assaf, Y., & Jones, D. K. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.13.422557
“In addition to myelin, (Beaulieu, 2002), FA is modulated by fiber architecture (Basser and Pierpaoli, 1996; Douaud et al., 2011), orientation dispersion (Budde and Annese, 2013), axon density and axon diameter distribution (Takahashi et al.”
BackgroundCharacterizing Cerebral Degeneration in Amyotrophic Lateral Sclerosis with Texture Analysis
Ishaque, A. H. (2020) · ERA: Education and Research Archive (University of Alberta) · doi:10.7939/r3-65xc-5r84
Ramírez‐Toraño, F. (2021) · UPM Digital Archive (Technical University of Madrid) · doi:10.20868/upm.thesis.66243
¿Cómo contabilizar?: contabilidad de la liquidación de una empresa
Viguera, M. G. (2004) · Técnica contable
Taha, H. T., Chad, J. A., & Chen, J. J. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.27.466088
“…associations with age: (1) the posterior-inferior regions, which were previously found to display low AD sensitivity to age (Billiet et al., 2015; Coutu et al., 2014); (2) parts of the corona radiata, which is characterized by known fibre crossings (Chad et al., 2018; Douaud et al., 2011).”
BackgroundGoubran, M., Mills, B. D., Georgiadis, M., Karimpoor, M., Mouchawar, N., Sami, S., Dennis, E. L., Akers, C. S., Mitchell, L. A., Boldt, B. M., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.04.01.486632
Alves, G. S., O’Dwyer, L., Jurcoane, A., Oertel-Knöchel, V., Knöchel, C., Prvulovic, D., Sudo, F. K., Alves, C. E. D. O., Valente, L., Moreira, D., et al. (2013) · PLoS ONE · doi:10.1371/annotation/df743c15-c50e-4d00-a24d-510e15f9a73b
Neuroimaging and Cerebrospinal Fluid Biomarkers of Alzheimer’s Disease
Gordon, B. A., Vos, S. J. B., & Fagan, A. M. (2017) · Oxford University Press eBooks · doi:10.1093/med/9780190681425.003.0052
Çavuşoğlu, B., Ada, E., Emek‐Savaş, D. D., & Yener, G. (2023) · Dokuz Eylül Üniversitesi Tıp Fakültesi Dergisi · doi:10.18614/deutip.1135057
(2023) · Advances in magnetic resonance technology and applications · doi:10.1016/b978-0-323-91771-1.00003-4
Diffusion MRI with generalised gradient waveforms : methods, models, and neuroimaging applications
Boito, D. (2024) · Linköping studies in science and technology. Dissertations · doi:10.3384/9789180754439
Deep Learning Techniques for Alzheimer's Disease Detection
Farooq, B., & Ganie, S. M. (2024) · Advances in medical diagnosis, treatment, and care (AMDTC) book series · doi:10.4018/979-8-3693-1281-0.ch005
Asay, D. J., & Goodrich‐Hunsaker, N. J. (2024) · medRxiv · doi:10.1101/2024.10.09.20222463
“DTI is a method of MRI data analysis used to map and measure the white matter tracts in a subjects brain that provides insight to the presence of neurodegeneration (Douaud et al., 2011).”
MethodXu, T., Feng, Y.-H., Sun, Z., He, L., Chen, J.-H., Tian, W., Zhang, H., Zhu, M., & Xia, J. (2025) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-24-1440
“…an increase in diffusion parallel to the fiber tract orientation, which can result in a decrease in axonal density and an increase in intra-axonal space, followed by disorganization of the axonal structure, suggesting axonal loss or loss of orientation coherence of axons in the fiber tract (18).”
Yuan, K., Yu, Y., Ai, S., Jia, T., Xiao, L., Bao, Y., Chang, S., Sun, J., Gao, T., & Shi, J. (2025) · Research Square · doi:10.21203/rs.3.rs-6505046/v1
Sathe, A., Chatterjee, A., Shashikumar, N., Pechman, K. R., Schilling, K. G., Landman, B. A., Bolton, C. J., Zhang, P., Bender, A. R., Hohman, T. J., et al. (2025) · medRxiv · doi:10.1101/2025.05.29.25328528
“…previous research has demonstrated that the WM regions most vulnerable to disease-related alterations in cognitive impairment and Alzheimer’s disease (AD), such as the cingulum bundle and SLF, exhibit a high degree of complexity in fiber structure and orientation (Douaud et al., 2011).”
The cellular associates of late life changes in white matter microstructurePreprint
Chin, R., Zhang, X.-H., Anderson, K. M., Yendiki, A., & Holmes, A. J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.02.673734 · Cited by 1
“Developmental and lifespan trajectories of white matter tract maturation and maintenance follow complex spatial and temporal patterns, where white matter changes are observed to be non-uniform across the brain and follow a protracted course of maturation (40, 41, 71).”
Buianova, I., Silvestrin, M., Deng, J. D., & Pornpattananangkul, N. (2025) · eLife · doi:10.7554/elife.108109.1
First and Second Moments of Spherical Distributions That Are Relevant for Biological Applications
Shyntar, A., & Hillen, T. (2025) · Axioms · doi:10.3390/axioms14100743
End-to-end evaluation of white matter microstructure of the visual pathway in asymmetric glaucomaPreprint
Coutiño, D., Guerrero, J., Domínguez-Frausto, C. A., García-Guillén, M., Coronado-Leija, R., Ramírez-Manzanares, A., Hernandez-Gutierrez, E., Descoteaux, M., Ayala, M., Badillo, M., et al. (2025) · bioRxiv · doi:10.1101/2025.10.20.683506
“Moreover, in cases when one of the crossing fiber populations is degenerated, the remaining bundle dominates the diffusion signa, and the resulting tensor appears to be anisotropic (i.e., with increased FA), leading to incorrect biological interpretations [8].”
Evaluating Sample-Size Efficiency and Sensitivity of Tractometry in Alzheimer's Disease
Feng, Y., Villalón‐Reina, J. E., Gari, I. B., Alibrando, J. D., Nir, T. M., Jahanshad, N., Chandio, B. Q., & Thompson, P. M. (2026) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi61048.2026.11515697 · Cited by 2
“For the CC Body P, high along-tract resolutions ( l =5 and 10 mm) identified the largest effects in regions near the corona radiata , likely reflecting regions of crossing fibers and selective WM degeneration, consistent with prior studies us-ing ROI-based analysis [19, 17] and tractometry [2].”
Gu, H., Jiang, Y., Zhang, W., Li, W., He, J., Liu, S., Li, Y., & He, C. (2025) · Neurological Sciences · doi:10.1007/s10072-025-08619-4
“A decrease in MO values generally indicates a shift in the diffusion tensor from a linear configuration to a planar configuration, which may suggest disruption in the integrity of white matter tracts [50].”
Buianova, I., Silvestrin, M., Deng, J. D., & Pat, N. (2026) · eLife · doi:10.7554/elife.108109.2
Practical judgment in aging: examining behavioral vulnerabilities and neurobiological correlates
Sergeyev, N., Heller, A. M., Nester, C. O., Paré, N., Krishnan, A., Ellis, D. G., Moffitt, C., Roth, R. M., Warren, D. E., & Rabin, L. A. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1709372
Metadata
- Authors
- 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., & Smith, S.
- Affiliations
- University of Oxford (GB)
- University Hospital of Basel (CH)
- GlaxoSmithKline (United Kingdom) (GB)
- University of Chicago (US)
- Corresponding author
- Douaud, Gwenaëlle
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2011-04
- Volume
- 55
- Issue
- 3
- Pages
- 880-890
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC7116583
- OpenAlex
- W2151712416
- Cited by
- 514
- References
- 66
- Citations by year
- 2026: 19 · 2025: 23 · 2024: 17 · 2023: 16 · 2022: 26 · 2021: 42 · 2020: 34 · 2019: 42 · 2018: 36 · 2017: 36 · 2016: 35 · 2015: 41 · 2014: 41 · 2013: 51 · 2012: 39
- FWCI
- 26.77
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- UK Engineering and Physical Sciences Research Council
- UK Medical Research Council
- Wellcome Trust
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Keywords
- mild cognitive impairment, Alzheimer's disease, white matter microstructure, crossing fibers, diffusion anisotropy, probabilistic tractography, centrum semiovale, superior longitudinal fasciculus, corpus callosum, cingulum, uncinate fasciculus, fractional anisotropy
- MeSH terms
- Adolescent, Adult, Aged, Aged, 80 and over, Aging, Algorithms, Alzheimer Disease, Brain, Brain Mapping, Cognition Disorders, Female, Humans, Image Processing, Computer-Assisted, Male, Middle Aged, Young Adult, Diffusion Tensor Imaging
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