Gray and white matter changes in Alzheimer's disease: a diffusion tensor imaging study
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Purpose: To investigate microstructural changes in cortical and white matter pathways in patients with Alzheimer's disease using diffusion tensor imaging (DTI). Materials and Methods: Measures of mean diffusivity (MD) and fractional anisotropy (FA) were compared in the brains of 13 Alzheimer's disease (AD) patients and a group of 13 aged‐matched control participants employing an optimized DTI technique involving a fully automated, voxel‐based morphometric (VBM) analysis. Results: After rigorous control for anatomical variation and confounding partial volume effects, we found significantly elevated MD measures within the hippocampus, amygdala, and medial temporal, parietal, and frontal lobe gray matter regions in the AD participants. The largest number of pixels with increased MD was localized bilaterally, within the posterior cingulate gyrus. The FA was significantly reduced within the thalamus, parietal white matter, and posterior limbs of the internal capsule, indicating significant involvement of corticothalamic and thalamocortical radiations. Conclusion: This study demonstrates that rigorous VBM analysis of DTI data can be used to investigate microstructural changes in cortical, subcortical, and white matter regions in AD. J. Magn. Reson. Imaging 2007. © 2007 Wiley‐Liss, Inc.
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Rose et al. (2008) proposed excluding all voxels exceeding a certain threshold of mean diffusivity of 1300 × 10−6 mm2/s.
“First, Rose et al. (2008) proposed to exclude all voxels exceeding a certain threshold of MD (1300 × 10− 6 mm2/s).”
From Mean diffusivity in cortical gray matter in Alzheimer's disease: The importance of partial volume correction (2018) · cites this paper as Rose et al. (2008)
Data Processing and Analysis states that pixels with MD greater than 1300 × 10−6 mm²/s were removed, supporting the threshold and exclusion method. The exclusion applied to the gray-matter mask, however, not to all voxels generally; the white-matter mask was thresholded using FA instead. (LLM, conf=0.85)
Experimental — accuracy not yet measuredLeft and right hippocampi and the posterior cingulate cortex were chosen as regions of interest following the literature on peak differences in regional mean diffusivity and volumes in Alzheimer's disease.
“Left and right hippocampi as well as the posterior cingulate cortex (PCC) were chosen as regions of interest following the literature on peak differences in regional MD and volumes in AD (Cherubini et al., 2010, Eustache et al., 2016, Rose et al., 2008).”
From Mean diffusivity in cortical gray matter in Alzheimer's disease: The importance of partial volume correction (2018) · cites this paper as Rose et al., 2008
The Results and Discussion (page not provided) report elevated mean diffusivity in the posterior cingulate in AD, with the largest number of affected voxels there. The paper also discusses elevated hippocampal diffusivity, including reports involving both hippocampi, but it does not report regional volume differences in these regions; it only notes earlier findings of reduced gray-matter density. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe MD measurements in grey matter regions are in good agreement with previous studies that have detected elevated values in the brains of AD patients and in mouse models exhibiting amyloid pathology at late stages of progression.
“The MD measurements in grey matter regions are in good agreement with previous studies that have detected elevated values in the brains of AD patients (Fellgiebel et al., 2004, Kantarci et al., 2001, Rose et al., 2008) and in mouse models exhibiting amyloid pathology at late stages of progression (Sun et al., 2005).”
From In vivo imaging of tau pathology using multi-parametric quantitative MRI (2015) · cites this paper as Rose et al., 2008
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Cites
Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space
Collins, D. L., Neelin, P., Peters, T. M., & Evans, A. C. (1994) · Journal of Computer Assisted Tomography · doi:10.1097/00004728-199403000-00005 · Cited by 3,431 · cited 2× · see citing works
“To refine this model, all participants’ normalized b 0 images in raw image space were then nonlinearly registered to the linear model in a hierarchical fitting strategy using a step size of 8 mm (27).”
MethodDiffusion tensor MR imaging of the human brain
Pierpaoli, C., Jezzard, P., Basser, P. J., Barnett, A., & Di Chiro, G. (1996) · Radiology · doi:10.1148/radiology.201.3.8939209 · Cited by 2,664 · see citing works
Mild cognitive impairment and Alzheimer disease: regional diffusivity of water
Kantarci, K., Jack, C. R., Xu, Y. C., Campeau, N. G., O’Brien, P. C., Smith, G. E., Ivnik, R. J., Boeve, B. F., Kokmen, E., Tangalos, E. G., et al. (2001) · Radiology · doi:10.1148/radiology.219.1.r01ap14101 · Cited by 300 · see citing works
Functional implications of hippocampal volume and diffusivity in mild cognitive impairment
Müller, M. J., Greverus, D., Dellani, P. R., Weibrich, C., Wille, P. R., Scheurich, A., Stoeter, P., & Fellgiebel, A. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.06.029 · Cited by 210 · see citing works
Flagged first, then by citations
Research works printed but not linked: 34
- Kantarci K, Petersen RC, Boeve BF, et al. DWI predicts future progression to Alzheimer's disease in amnestic mild cognitive im- pairment. Neurology 2005;64:902-904.
- Rose SE, McMahon KL, Janke AL, et al. MRI diffusion indices and neuropsychological performance in amnestic mild cognitive impair- ment. J Neurol Neurosurg Psychiatry 2006;77:1122-1128.
- Basser PJ, Jones DK. Diffusion-tensor MRI: theory, experimental design and data analysis-a technical overview. NMR Biomed 2002; 15:456 -467.
- Bronge L, Bogdanovic N, Wahlund LO. Postmortem MRI and histo- pathology of white matter changes in Alzheimer's disease. Dement Geriatr Cogn Disord 2002;13:205-212.
- Brun A, Englund E. A white matter disorder in dementia of the Alzheimer's type: a pathoanatomical study. Ann Neurol 1986;19: 253-262.
- Englund E . Neuropathology of white matter changes in Alzheimer's disease and vascular dementia. Dement Geriatr Cogn Disord 1998; 9:6 -12.
- Fellgiebel A, Wille P, Muller M, et al. Ultrastructural hippocampal and white matter alterations in mild cognitive impairment: a diffu- sion tensor imaging study. Dement Geriatr Cogn Disord 2004;18: 101-108.
- Hanyu H, Sakurai H, Iwamoto T, Takasaki M, Shindo H, Abe K. Diffusion-weighted MR imaging of the hippocampus and temporal white matter in Alzheimer's disease. J Neurol Sci 1998;156:195-200.
- Naggara O, Oppenheim C, Rieu et al. Diffusion tensor imaging in early Alzheimer's disease. Psychiatry Res: Neuroimaging 2006;146: 243-249.
- Bozzali M, Falini A, Franceschi M, et al. White matter damage in Alzheimer's disease assessed in vivo using diffusion tensor magnetic resonance imaging. J Neurol Neurosurg Psychiatry 2002;72:742-746.
- Choi SJ, Lim KO, Monteiro I, Reisberg B. Diffusion tensor imaging of frontal white matter microstructure in early Alzheimer's disease: a preliminary study. J Geriatr Psychiatry Neurol 2005;18:12-19.
- Fellgiebel A, Muller MJ, Wille P, et al. Colour-coded diffusion tensor imaging of posterior cingulate fiber tracts in mild cognitive impair- ment. Neurobiol Aging 2005;26:1193-1198.
- Head D, Buckner RL, Shimony JS, et al. Differential vulnerability of anterior white matter in nondemented aging with minimal acceler- ation in dementia of the Alzheimer's type: evidence from diffusion tensor imaging. Cereb Cortex 2004;14:410 -423.
- Kalus P, Slotboom J, Gallinat J, et al. Examining the gateway to the limbic system with diffusion tensor imaging: the perforant pathway in dementia. Neuroimage 2006;15:713-720.
- Medina D, deToledo-Morrell L, Urresta F, et al. White matter changes in cognitive impairment and AD: a diffusion tensor imag- ing study. Neurobiol Aging 2006;27:663-672.
- Rose SE, Chen F, Chalk JB, et al. Loss of connectivity in Alzhei- mer's disease: an evaluation of white matter tract integrity with colour coded diffusion tensor imaging. J Neurol Neurosurg Psychi- atry 2000;69:528 -530.
- Takahashi S, Yonezawa H, Takahashi J, Kudo M, Inoue T, Tohgi H. Selective reduction of diffusion anisotropy in white matter of Alz- heimer's disease brains measured by 3.0T magnetic resonance imaging. Neurosci Lett 2002;332:45-48.
- Yoshiura T, Mihara F, Ogomori K, Tanaka A, Kaneko K, Masuda K. Diffusion tensor in posterior cingulate gyrus: correlation with cogni- tive decline in Alzheimer's disease. Neuroreport 2002;13:2299 -2302.
- Jones DK, Horsefield MA, Simmons A. Optimal strategies for mea- suring diffusion in anisotropic systems by magnetic resonance imaging. Magn Reson Med 1999;42:515-525.
- McKhann G, Drachmann D, Foldstein M, Katzman R, Price D. Clinical diagnosis of Alzheimer's disease: report of the NINCDS- ADRADA Work Group under auspices of Department of Health and Human Services Task Force of Alzheimer's Disease. Neurology 1984;34:939 -944.
- American Psychiatric Association 1994. Diagnostic and statistical manual of mental disorders, 4th edition, revised. Washington, DC: American Psychiatric Association.
- Sorensen AG, Buonanno FS, Gonzalez RG, et al. Hyperacute stroke: evaluation with combined multisection diffusion-weighted and hemodyamically weighted echo-planar MR imaging. Radiology 1996;199:391-401.
- Collins DL, Holmes CJ, Peters TM, Evans AC. Automatic 3-D mod- el-based neuroanatomical segmentation. Hum Brain Mapp 1995; 3:190 -208.
- Mascalchi M, Filippi M, Floris R, Fonda C, Gasparotti R, Villari N. Diffusion-weighted MR of the brain: methodology and clinical ap- plication. Radiol Med (Torino) 2005;109:155-197.
- Worsley K, Lao CH, Aston J, et al. A general statistical approach for fMRI data. Neuroimage 2002;15:1-15.
- Mazziotta J, Toga A, Evans A, et al. A probabilistic atlas and refer- ence system for the human brain: International Consortium for Brain Mapping (ICBM). Philos Trans R Soc Lond B Biol Sci 2001; 356:1293-1322.
- Baron JC, Chetelat G, Desgranges B, et al. In vivo mapping of gray matter loss with voxel-based morphometry in mild Alzheimer's disease. Neuroimage 2001;14:298 -309.
- Ishii K, Kawachi T, Sasaki H, et al. Voxel-based morphometric comparison between early-and late-onset mild Alzheimer's disease and assessment of diagnostic performance of Z score images. Am J Neuroradiol 2005;26:333-240.
- Karas GB, Burton EJ, Romabouts SARB, et al. A comprehensive study of gray matter loss in patients with Alzheimer's disease using optimised voxel-based morphometry. Neuroimage 2003;18:895-907.
- Nestor PJ, Fryer TD, Hodges JR. Declarative memory impairments in Alzheimer's disease and semantic dementia. Neuroimage 2006; 30:1010 -1020.
- Smith SM, Jenkinson M, Johansen-Berg H, et al. Tract-based spa- tial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage 2006;31:1487-1505.
- Liu T, Young G, Huang L, Chen NK, Wong ST. 76-Space analysis of grey matter diffusivity: methods and applications. Neuroimage 2006;31:51-56.
- Jones DK, Symms MR, Cercignani M, Howard RJ. The effect of filter size on VBM analyses of DTI-MRI data. Neuroimage 2005;26:546-554.
- Pajevic TE, Basser PJ. Parametric and non-parametric statistical analysis of DTI-MRI data. J Magn Res 2003;161:1-14.
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- Authors
- Rose, S. E., Janke PhD, A. L., & Chalk, J. B.
- Affiliations
- The University of Queensland (AU)
- Corresponding author
- Rose, Stephen E.
- ORCID
- Rose, Stephen E.
- Venue
- Journal of Magnetic Resonance Imaging
- Publisher
- Wiley
- Published
- 2007-11-29
- Volume
- 27
- Issue
- 1
- Pages
- 20-26
- ISSN
- 1053-1807 (ISSN-L)
- 1522-2586
- OpenAlex
- W2019624613
- Cited by
- 178
- References
- 4
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 7 · 2023: 7 · 2022: 10 · 2021: 10 · 2020: 5 · 2019: 9 · 2018: 4 · 2017: 11 · 2016: 9 · 2015: 13 · 2014: 8 · 2013: 9 · 2012: 13
- FWCI
- 10.48
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Magnetic Resonance Imaging (publishedVersion)
- PubMed (publishedVersion)
- 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
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Keywords
- Alzheimer's disease, white matter microstructure, gray matter changes, mean diffusivity, fractional anisotropy, posterior cingulate cortex, hippocampus, amygdala, thalamus, internal capsule, corticothalamic pathway, thalamocortical circuits, voxel-based morphometry
- MeSH terms
- Aged, Aged, 80 and over, Alzheimer Disease, Female, Humans, Image Processing, Computer-Assisted, Male, Neuropsychological Tests, Case-Control Studies, Anisotropy, Diffusion Magnetic Resonance Imaging
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