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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
J Alzheimers Dis. Author manuscript; available in PMC 2012 March 6.
Published in final edited form as:
PMCID: PMC3294372

DTI Analyses and Clinical Applications in Alzheimer’s Disease


DTI is one of the most effective MR tools for the investigation of the brain anatomy. In addition to the gray matter, histopathological studies indicate that white matter is also a good target for both the early diagnosis of AD and for monitoring disease progression, which motivates us to use DTI to study AD patients in vivo. There are already a large amount of studies reporting significant differences between AD patients and controls, as well as to predict progression of disease in symptomatic non-demented individuals. Application of these findings in clinical practice remains to be demonstrated.

Keywords: Alzheimer’s disease, mild cognitive impairment, white matter, diffusion tensor imaging, clinical application


Diffusion Tensor Imaging (DTI) is one of the MRI techniques that can measure the thermal motion of water molecules. Water molecules move randomly in all directions if there is no structure that prevents their free motion; this probability distribution is isotropic. In the brain, there are many structures that restrict the free motion of water molecules, such as tightly packed axons [1, 2], that alter the magnitude and shape of the probability distribution. For example, water molecules in white matter tend to diffuse more easily along axonal bundles, leading to anisotropic diffusion. On the other hand, the gray matter often does not have a clear structural alignment, leading to a more isotropic diffusion. We can use this water diffusion property as a probe to infer the brain anatomy. In DTI, we quantify the diffusion property by fitting the measured water diffusion to a simple tensor model with a 3 × 3 symmetric matrix. In this way, we can quantitatively describe the diffusion properties using eigenvalues (λ1, λ2, and λ3, describing the extent of anisotropy) and eigenvectors, (v1, v2, and v3, describing the orientation of anisotropy).

Once a tensor is calculated in each pixel, several contrasts can be generated. For example, we can measure the mean diffusivity (MD), which is the average of three eigenvalues: (λ1 + λ2 + λ3)/3, indicating the magnitude of overall water diffusion in each pixel (Fig. 1C). We can also measure the degree of diffusion anisotropy. One of the most widely used metrics of diffusion anisotropy is “fractional anisotropy (FA),” which is [3, 4] (Fig. 1D):


Fig. 1
Comparison of conventional T1- (A) and T2- (B) weighted images, and DTI-derived mean diffusivity (MD) (C), fractional anisotropy (FA) (D), and color-coded orientation (E) maps of cognitively normal 72-year-old woman (upper row) and 70-year-old woman with ...

This is a convenient index because it is scaled from 0 (isotropic) – 1 (anisotropic). If diffusion is isotropic (λ1 = λ2 = λ3), this measure becomes 0. An FA close to 1 indicates high diffusion anisotropy. In addition to these scalar measures, we can also visualize orientation information. A color-coded orientation map of the first eigenvector (v1) [5, 6] is one method to visualize orientation information, in which red (R), green (G), and blue (B) colors are assigned to right-left, anterior-posterior, and superior-inferior orientations, respectively. in Fig. 1, images created from DTI measurements are compared with conventional MR images. In conventional MRI (Fig. 1A and B), the white matter area looks homogeneous. However, the color-coded orientation map in Fig. 1E contains various colors in the white matter area, which represent the orientation of aligned structures.

DTI is a powerful method by which to identify specific fiber bundles that are affected by diseases. Various pathological conditions alter DTI-derived parameters and we can three-dimensionally map the area(s) that show such alterations. The other important feature of DTI is the ability to parcellate white matter structures. Using the images shown in Fig. 1E, we can identify and study the degeneration of specific white matter structures, such as the cingulum (Fig. 1F). It should be emphasized that, in imaging studies, we can investigate the anatomy of a specific structure only when it is discretely identifiable. Because of the capability to provide detailed anatomical information about the white matter, DTI could be one of the most effective MR tools for the investigation of the white matter anatomy. Although the use of DTI is not restricted to studies of the white matter, the majority of DTI studies are related to the normal or diseased anatomy of the white matter. However, directional information derived from DTI is less useful for investigating gray matter structures, except for several fiber-rich gray matter structures [712] and the fetal cortex, with the columnar structures showing clear directionality [13]. For the gray matter analysis, MD is often used to quantify the effects of various diseases.

Although DTI meets the requirements of many basic and clinical research purposes, the two main limitations should be noted. First, water diffusion is an INDIRECT indicator of the underlying neuroanatomy, and there are numerous microscopic structures that may affect the diffusion. Therefore, different histopathological conditions may result in similar alterations of DTI-derived parameters. Second, the diffusion process (1–10 µm during the 20–100 ms of diffusion time) is averaged over a large voxel volume, with typically 2–3 mm resolution. This leads to the sensitivity of DTI to macroscopic configuration of fiber bundles, such as the mixture of multiple fiber populations with different fiber orientations in a voxel or partial volume effects. Therefore, we cannot immediately conclude whether the source of changes in diffusion lies in cellular level structures or is due to the macroscopic reorganization of fiber structures. There have been several attempts to ameliorate the latter limitation (thus reducing the impact of the macroscopic averaging effect). For example, we can increase the total number of voxels within the brain( = increased image resolution).We can also extract more parameters from each voxel( = increased intra-voxel information) by using sophisticated non-tensor diffusion analysis methods rather than using a simple tensor approach [1421]. The application of these new approaches to AD studies could be an important future research endeavor.

Recent technological advancements in both hardware (machine) and software (scan sequence) have greatly facilitated the use of DTI, and broadened the applications much more than was previously possible. The typical scan time required to gather whole brain DTI data using a 3 T scanner is only approximately 5 min, with 30 gradient axes and 2–3 mm cubic resolution. The short scanning time is very important because AD patients have difficulty remembering and following instructions during the scan, and thus, scanning in as short a time as possible is optimal. MRI scanners, equipped with devices for parallel imaging techniques, which are required to gather less-distorted DTI, are now widely available. In addition, modern clinical MRI scanners are equipped with DTI calculation tools, and the calculated images can be visualized in a filmless image-reading system. Thus, it may be possible for the clinician to utilize DTI-derived images as part of the diagnostic evaluation.


Researchers have long been focused on the cortical pathology of Alzheimer’s disease (AD), and have primarily used conventional MRI to characterize that pathology. Indeed, the most important pathologic features of AD are the senile plaques and the neurofibrillary tangles found in the cortex, as well as the cortical neuron loss. For instance, the loss of layer III and layer V large pyramidal neurons is seen in cortical association areas [22] and the loss of layer II pyramidal neurons is seen in the entorhinal cortex [23]. Loss of neurons in these areas results in GM atrophy that can be measured on conventional MR images [24, 25]. DTI has also identified altered water diffusivity in the GM. Increased MD was consistently found in the areas with the neurofibrillary pathology of AD [10, 2630], such as the hippocampus, the entorhinal cortex, the parahippocampal gyrus, the temporo-parietal association cortex, and the posterior cingulate gyrus. Although the exact cause of these changes in DTI measurements are difficult to identify, the fact that the degree of MD increase is more evident in the areas with GM atrophy [26] suggest that neuronal loss [31] is one of the reasons. In addition, several studies indicate that the addition of MD measurements to GM morphometry in the hippocampus and parahippocampal gyrus improved the ability to distinguish an AD group from a control group [26, 28], which suggests the possibility of the existence of additional pathologies sensitively detected by DTI over conventional MRI.

In addition to the GM pathology, increasing evidence shows that neuronal degeneration begins in the neuronal periphery, such as in the axons and dendrites [3235]. White matter atrophy also could be an indirect indicator of nerve cell loss, since the volume of the cell body is much smaller than its myelinated fiber [36]. Consistently, pathological studies have revealed various types of white matter alterations in AD, such as altered myelin and oligodendrocytes, axonal degeneration, and vascular pathologies [3739]. Therefore, white matter seems to be a good target for both the early diagnosis of AD and for monitoring disease progression, which motivates us to use DTI to study AD patients in vivo. Indeed, DTI has identified WM alterations in many WM tracts and superficially located WM areas. A meta-analysis of 41 DTI studies, published from 2002 to 2010 [40], indicates that the white matter abnormality of AD is widespread through the entire WM area. The limbic fibers, in particular, which have a direct connection to the medial temporal lobe, were repeatedly reported as the vulnerable WM structures [29, 4149]. WM damage quantified by DTI has been correlated with atrophy in the anatomically connected GM areas in AD patients, but the correlation was not clear in patients with amnestic MCI in most of the WM tracts [50]. These findings suggest that primary WM damage that precedes GM atrophy may possibly exists in the pre-diagnostic phase of AD, but the WM damage seen in clinically diagnosed AD patients may reflect secondary degenerative processes after neuronal loss. An attempt to identify the earliest DTI-detectable WM abnormality using an AD mouse model identified a reduced first eigenvalue (l1, parallel diffusion) in the WM, suggesting that the earliest anatomical change is axonal damage [51]. However, in contrast to this finding, an increased first eigenvalue, as well as increased MD, is often found in in vivo DTI scans of human AD patients [48, 52, 53]. Again, we would like to emphasize that, because of the oversimplification of the anatomical information during the multiple steps of DTI acquisition and calculation, interpretation of the DTI-derived parameters is not straightforward. DTI is useful for localizing and quantifying the anatomical abnormalities, but apparently not adequate to investigate the histopathological background of the diseases.


There are many important questions we want to answer through DTI analysis of AD. For example, what are the AD-specific features that can be observed with DTI? Can these features be observed in persons with mild cognitive impairment, thought to be the earliest symptomatic stage of AD, or even in the presymptomatic phase? How does DTI reflect or predict the progression of AD? Are there correlations between DTI and cognitive functions? To answer these questions, the quantification of DTI parameters is the first important step.

Several issues arise when we quantify DTI results. First, regions of interest (ROI) should be defined to measure DTI-derived parameters, such as FA or MD. The smallest ROI we can define is a single voxel, and the maximum ROI is the whole brain. The localization information is maximized when the smallest ROI (single voxel) is adopted, but the statistical power is lost because of the low signal-to-noise ratio and the difficulty of identifying the corresponding voxel across subjects. The statistical power is maximized when the size and shape of the ROI exactly follow pathological locations. If we have an a priori hypothesis about the locations of the pathologic tissues, we could pre-define the size and shape of ROIs according to the hypothesis. If we hypothesize that the pathology is seen in specific fiber tracts, we can use tractography to draw ROIs (tract-specific analysis, see, eg. [54, 55]). ROI-based DTI analyses have been widely used for AD studies and have successfully identified reduced FA or increased MD, or both, within the splenium of the corpus callosum [47, 5659], the cingulum bundle [11, 27, 42, 6062], and the fornix [43, 44]. However, this approach is hypothesis-dependent, and the majority of the brain area remains unexamined, which makes it difficult to evaluate the localization specificity.

Second, we need to decide whether we should explore the whole brain or limited areas of the brain. Whole brain analysis is ideal for evaluating the regional specificity of the findings. However, drawing a number of ROIs manually that would cover the whole brain is a tremendous effort. Thus, automated method, such as those based on image normalization (transformation), are typically used. After transforming images to a common template space (atlas), we can even quantify the image at the voxel level (voxel-based analysis). Although image normalization has been widely used to analyze conventional MRI contrasts, the transformation of DTI poses a unique challenge. DTI data consists of tensor fields (as opposed to scalar fields for conventional MRI), and white matter tracts revealed by the tensor field must be registered after normalization [63, 64].To avoid false-positive and false-negative findings, the accuracy of the registration is a crucial requirement. Accuracy is especially critical when dealing with small structures, string-like structures, and sheet-like structures, which are often found in the white matter. In these structures, only a few pixel gaps between the subject and template image will cause significant missregistration.

Various non-linear transformation methods have been proposed for DTI analyses, such as tensor-to-tensor matching [6569], scalar measures matching [69, 70], or other DTI-derived information, as well as some combination of these methods [7175]. Transformation methods based on non-DTI contrasts have also been applied to transform DTI, with high registration accuracy [64, 76].

To perform whole brain analysis with no a priori hypothesis, voxel-based analysis is one of the most widely used approaches [29, 52, 7779]. Fig. 2A is an example of such an analysis, designed to find brain areas with AD-specific white matter alterations. This analysis indicates significant FA reduction in the fornix, the splenium of the corpus callosum, as well as several small areas in the superficial white matter in the frontal lobe. Although this result seems to be consistent with previous ROI-based investigations, we must interpret the results with great caution, since this approach tends to miss the widely distributed regions that show only small changes in the parameters [80]. One of the attempts to overcome such limitations is to apply multivariate models [81], such principal component analysis (PCA) [78] or canonical correlation analysis (CCA) [82], which have already been applied to DTI analysis of AD. Other methods include voxel grouping. For example, if we hypothesize that the white matter pathology of AD is tract-specific or structure-specific, especially in the early stages, we may apply tract-based voxel grouping, such as Tract-Based Spatial Statistics (TBSS) [83], or structure-based voxel grouping, such as atlas-based analysis (ABA) [84]. Indeed, TBSS has already revealed important findings, such as deteriorations in the limbic fibers, the fronto-occipital fasciculi, the inferior longitudinal fasciculi, and the forceps major, even in the early-symptomatic patients or in participants at high risk for developing AD [45, 53, 8590]. ABA is a method that uses a set of pre-defined ROIs, called a parcellation map (Fig. 3), which covers the entire brain, in the atlas space. The parcellation map can be overlaid on the images normalized to the atlas space to measure DTI-derived parameters (eg., FA or MD)in each ROI (parcel), or can be transformed to each image to measure DTI-derived parameters as well as the volumes of each ROI. One additional feature that sets the ABA apart from TBSS is that ABA provides morphometric (volume) information about brain atrophy in the volume of each parcel. Our initial results from ABA (Fig. 2B) indicate a higher sensitivity for ABA in detecting changes in FA, especially in the areas with widely distributed small FA reductions, compared to the voxel-based analysis. The drawback of this approach is that if the region is limited in the small portion of the structure (parcel), the effect is diluted and sensitivity is decreased.

Fig. 2
Statistical group comparison of FA after image normalization. Nineteen patients with Alzheimer’s disease and 22 age-matched cognitively normal participants were compared, and the areas with significance (t-test, p < 0.05 after correction ...
Fig. 3
Atlas-based analysis. The original FA map from a patient with Alzheimer’s disease (A) was normalized to the atlas space (B). The atlas used as the template is shown in (C). After image normalization, pre-defined three-dimensional ROIs (color-contours, ...


In the previous section, we discussed the detection of AD-specific pathology using DTI. There are already a large amount of studies reporting structure-specific brain abnormalities even in the very early stages of AD. In this section, we would like to discuss the application of DTI to studies of AD from the following two points of views.

Functional correlation

One of the important questions after the identification and quantification of structure-specific brain abnormalities is whether the degree of abnormalities correlates with cognitive functions. For example, even if we find reduced FA in a specific brain area, if the observed FA reduction does not correlate with the cognitive decline, the finding is not valuable as a marker to visualize the disease progression or to see the effects of therapeutic interventions. Research has identified the disruption of global WM [56], the frontal WM [91], or specific WM tracts, including the fornix, the cingulum, the cingulate WM, the genu and splenium of the corpus callosum, and the perforant pathway, which are related to global cognitive decline [44, 47, 92, 93]. Less is known about the tract or structure-specific contribution of cognitive domains, although several researchers have discovered a contribution by the temporal lobe WM [94, 95] and the posterior cingulate WM [10] to delayed recall, the frontal WM to executive function [95], and the posterior part of the corpus callosum to verbal fluency and figural memory [96]. However, the localization specificity of these findings has not been established, since these studies are based on a limited number of ROIs selected by an a priori hypothesis. Unfortunately, the anatomical background of the behavioral and psychiatric symptoms of AD, which are the most challenging and distressing effects of the disease, has not been fully explored.

Clinical application

AD studies typically adopt cognitively normal participants as a control group to identify AD-specific features. However, in the clinical situation, identification of AD-specific DTI markers would be of great value to differentiate it from other neurodegenerative dementias, such as frontotemporal dementia (FTD) and dementia with Lewy bodies (DLB). There are studies reporting more prominent DTI-detectable WM damage, especially in the frontal WM and the genu of the corpus callosum, in FTD than in AD [52, 82]. Although DTI findings about the DLB are controversial [26, 28, 97, 98], elevated MD without atrophy in the amygdala was proposed as a potential marker to differentiate DLB from AD [26]. However, the sensitivity and the specificity of the DTI-based indices to differentiate AD from these neurodegenerative dementias is still not established.

In summary, many of the scientific findings, to date, are not readily transferrable to clinical practice, even though the findings are important for understanding of the pathology of AD. A multi-center research project, seeking imaging biomarkers for AD, the Alzheimer’s Disease Neuroimaging Initiative (ADNI, ADNI-GO) [99], and the upcoming ADNI-2, did not adopt DTI as a core protocol because of the uncertain long-term test-retest precision, questionable relevance to clinical trials, and absence of an established calibration method [100], all of which should be thoroughly investigated before clinical application.


DTI measures have been used to demonstrate significant differences between AD patients and controls, as well as to predict progression of disease in symptomatic non-demented individuals. Application of these findings in clinical practice remains to be demonstrated.


This research was supported by NIH grants R21AG033774, P41RR015241, U24RR021382, PO1EB00195, RO1AG20012, and P50AG005146 (Johns Hopkins Alzheimer’s Disease Research Center). The images acquired on Alzheimer’s patients and age-matched controls were supported by a methods development grant from Glaxo-Smith-Kline. The authors thank Mary McAllister for help with manuscript editing.


1. Beaulieu C, Allen PS. Determinants of anisotropic water diffusion in nerves. Magn Reson Med. 1994;31:394–400. [PubMed]
2. Moseley ME, Cohen Y, Kucharczyk J, Mintorovitch J, Asgari HS, Wendland MF, Tsuruda J, Norman D. Diffusion-weighted MR imaging of anisotropic water diffusion in cat central nervous system. Radiology. 1990;176:439–445. [PubMed]
3. Pierpaoli C, Basser PJ. Toward a quantitative assessment of diffusion anisotropy. Magn Reson Med. 1996;36:893–906. [PubMed]
4. Pierpaoli C, Jezzard P, Basser PJ, Barnett A, Di Chiro G. Diffusion tensor MR imaging of human brain. Radiology. 1996;201:637–648. [PubMed]
5. Makris N, Worth AJ, Sorensen AG, Papadimitriou GM, Wu O, Reese TG, Wedeen VJ, Davis TL, Stakes JW, Caviness VS, Kaplan E, Rosen BR, Pandya DN, Kennedy DN. Morphometry of in vivo human white matter association pathways with diffusion-weighted magnetic resonance imaging. Ann Neurol. 1997;42:951–962. [PubMed]
6. Pajevic S, Pierpaoli C. Color schemes to represent the orientation of anisotropic tissues from diffusion tensor data: application to white matter fiber tract mapping in the human brain. Magn Reson Med. 1999;42:526–540. [PubMed]
7. Unrath A, Klose U, Grodd W, Ludolph AC, Kassubek J. Directional colour encoding of the human thalamus by diffusion tensor imaging. Neurosci Lett. 2008;434:322–327. [PubMed]
8. Duan Y, Li X, Xi Y. Thalamus segmentation from diffusion tensor magnetic resonance imaging. Int J Biomed Imagin. 2007;2007:90216. [PMC free article] [PubMed]
9. Wakana S, Nagae-Poetscher LM, Jiang H, van Zijl P, Golay X, Mori S. Macroscopic orientation component analysis of brain white matter and thalamus based on diffusion tensor imaging. Magn Reson Med. 2005;53:649–657. [PubMed]
10. Fellgiebel A, Wille P, Muller MJ, Winterer G, Scheurich A, Vucurevic G, Schmidt LG, Stoeter P. Ultrastructural hippocampal and white matter alterations in mild cognitive impairment: a diffusion tensor imaging study. Dement Geriatr Cogn Disord. 2004;18:101–108. [PubMed]
11. Takahashi S, Yonezawa H, Takahashi J, Kudo M, Inoue T, Tohgi H. Selective reduction of diffusion anisotropy in white matter of Alzheimer disease brains measured by 3.0 Tesla magnetic resonance imaging. Neurosci Lett. 2002;332:45–48. [PubMed]
12. Solano-Castiella E, Anwander A, Lohmann G, Weiss M, Docherty C, Geyer S, Reimer E, Friederici AD, Turner R. Diffusion tensor imaging segments the human amygdala in vivo. Neuroimage. 2010;49:2958–2965. [PubMed]
13. Huang H, Xue R, Zhang J, Ren T, Richards LJ, Yarowsky P, Miller MI, Mori S. Anatomical characterization of human fetal brain development with diffusion tensor magnetic resonance imaging. J Neurosci. 2009;29:4263–4273. [PMC free article] [PubMed]
14. Tuch DS, Reese TG, Wiegell MR, Wedeen VJ. Diffusion MRI of complex neural architecture. Neuron. 2003;40:885–895. [PubMed]
15. Tuch DS, Reese TG, Wiegell MR, Makris N, Belliveau JW, Wedeen VJ. High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity. Magn Reson Med. 2002;48:577–582. [PubMed]
16. Wedeen VJ, Hagmann P, Tseng WY, Reese TG, Weisskoff RM. Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging. Magn Reson Med. 2005;54:1377–1386. [PubMed]
17. Wiegell M, Larsson H, Wedeen V. Fiber crossing in human brain depicted with diffusion tensor MR imaging. Radiology. 2000;217:897–903. [PubMed]
18. Alexander DC, Barker GJ, Arridge SR. Detection and modeling of non-Gaussian apparent diffusion coefficient profiles in human brain data. Magn Reson Med. 2002;48:331–340. [PubMed]
19. Tournier JD, Calamante F, Gadian DG, Connelly A. Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. Neuroimage. 2004;23:1176–1185. [PubMed]
20. Frank LR. Anisotropy in high angular resolution diffusion-weighted MRI. Magn Reson Med. 2001;45:935–939. [PubMed]
21. Frank LR. Characterization of anisotropy in high angular resolution diffusion-weighted MRI. Magn Reson Med. 2002;47:1083–1099. [PubMed]
22. Pearson RC, Esiri MM, Hiorns RW, Wilcock GK, Powell TP. Anatomical correlates of the distribution of the pathological changes in the neocortex in Alzheimer disease. Proc Natl Acad Sci U S A. 1985;82:4531–4534. [PubMed]
23. Gomez-Isla T, Price JL, McKeel DW, Jr, Morris JC, Growdon JH, Hyman BT. Profound loss of layer II entorhinal cortex neurons occurs in very mild Alzheimer’s disease. J Neurosci. 1996;16:4491–4500. [PubMed]
24. Smith AD. Imaging the progression of Alzheimer pathology through the brain. Proc Natl Acad Sci U S A. 2002;99:4135–4137. [PubMed]
25. Stout JC, Jernigan TL, Archibald SL, Salmon DP. Association of dementia severity with cortical gray matter and abnormal white matter volumes in dementia of the Alzheimer type. Arch Neurol. 1996;53:742–749. [PubMed]
26. Kantarci K, Avula R, Senjem ML, Samikoglu AR, Zhang B, Weigand SD, Przybelski SA, Edmonson HA, Vemuri P, Knopman DS, Ferman TJ, Boeve BF, Petersen RC, Jack CR., Jr Dementia with Lewy bodies and Alzheimer disease: neurodegenerative patterns characterized by DTI. Neurology. 2010;74:1814–1821. [PMC free article] [PubMed]
27. Fellgiebel A, Muller MJ, Wille P, Dellani PR, Scheurich A, Schmidt LG, Stoeter P. Color-coded diffusion-tensor-imaging of posterior cingulate fiber tracts in mild cognitive impairment. Neurobiol Aging. 2005;26:1193–1198. [PubMed]
28. Firbank MJ, Blamire AM, Krishnan MS, Teodorczuk A, English P, Gholkar A, Harrison RM, O’Brien JT. Diffusion tensor imaging in dementia with Lewy bodies and Alzheimer’s disease. Psychiatry Res. 2007;155:135–145. [PubMed]
29. Medina D, DeToledo-Morrell L, Urresta F, Gabrieli JD, Moseley M, Fleischman D, Bennett DA, Leurgans S, Turner DA, Stebbins GT. White matter changes in mild cognitive impairment and AD:Adiffusion tensor imaging study. Neurobiol Aging. 2006;27:663–672. [PubMed]
30. Rose SE, Janke AL, Chalk JB. Gray and white matter changes in Alzheimer’s disease: a diffusion tensor imaging study. J Magn Reson Imaging. 2008;27:20–26. [PubMed]
31. Bobinski M, de Leon MJ, Wegiel J, Desanti S, Convit A, Saint Louis LA, Rusinek H, Wisniewski HM. The histological validation of post mortem magnetic resonance imaging-determined hippocampal volume in Alzheimer’s disease. Neuroscience. 2000;95:721–725. [PubMed]
32. Chevalier-Larsen E, Holzbaur EL. Axonal transport and neurodegenerative disease. Biochim Biophys Acta. 2006;1762:1094–1108. [PubMed]
33. Stokin GB, Lillo C, Falzone TL, Brusch RG, Rockenstein E, Mount SL, Raman R, Davies P, Masliah E, Williams DS, Goldstein LS. Axonopathy and transport deficits early in the pathogenesis of Alzheimer’s disease. Science. 2005;307:1282–1288. [PubMed]
34. Gunawardena S, Goldstein LS. Disruption of axonal transport and neuronal viability by amyloid precursor protein mutations in Drosophila. Neuron. 2001;32:389–401. [PubMed]
35. Pigino G, Morfini G, Pelsman A, Mattson MP, Brady ST, Busciglio J. Alzheimer’s presenilin 1 mutations impair kinesin-based axonal transport. J Neurosci. 2003;23:4499–4508. [PubMed]
36. Meier-Ruge W, Ulrich J, Bruhlmann M, Meier E. Age-related white matter atrophy in the human brain. Ann N Y Acad Sci. 1992;673:260–269. [PubMed]
37. Brun A, Englund E. A white matter disorder in dementia of the Alzheimer type: a pathoanatomical study. Ann Neurol. 1986;19:253–262. [PubMed]
38. Englund E, Brun A, Alling C. White matter changes in dementia of Alzheimer’s type. Biochemical and neuropathological correlates Brain. 1988;111(Pt(6)):1425–1439. [PubMed]
39. Sjobeck M, Haglund M, Englund E. Decreasing myelin density reflected increasing white matter pathology in Alzheimer’s disease–a neuropathological study. Int J Geriatr Psychiatry. 2005;20:919–926. [PubMed]
40. Sexton CE, Kalu UG, Filippini N, Mackay CE, Ebmeier KP. A meta-analysis of diffusion tensor imaging in mild cognitive impairment and Alzheimer’s disease. Neurobiol Aging. 2010 Epub ahead of print. [PubMed]
41. Zhou Y, Dougherty JH, Jr, Hubner KF, Bai B, Cannon RL, Hutson RK. Abnormal connectivity in the posterior cingulate and hippocampus in early Alzheimer’s disease and mild cognitive impairment. Alzheimers Dement. 2008;4:265–270. [PubMed]
42. Zhang Y, Schuff N, Jahng GH, Bayne W, Mori S, Schad L, Mueller S, Du AT, Kramer JH, Yaffe K, Chui H, Jagust WJ, Miller BL, Weiner MW. Diffusion tensor imaging of cingulum fibers in mild cognitive impairment and Alzheimer disease. Neurology. 2007;68:13–19. [PMC free article] [PubMed]
43. Ringman JM, O’Neill J, Geschwind D, Medina L, Apostolova LG, Rodriguez Y, Schaffer B, Varpetian A, Tseng B, Ortiz F, Fitten J, Cummings JL, Bartzokis G. Diffusion tensor imaging in preclinical and presymptomatic carriers of familial Alzheimer’s disease mutations. Brain. 2007;130:1767–1776. [PubMed]
44. Mielke MM, Kozauer NA, Chan KC, George M, Toroney J, Zerrate M, Bandeen-Roche K, Wang MC, Vanzijl P, Pekar JJ, Mori S, Lyketsos CG, Albert M. Regionally-specific diffusion tensor imaging in mild cognitive impairment and Alzheimer’s disease. Neuroimage. 2009;46:47–55. [PMC free article] [PubMed]
45. Damoiseaux JS, Smith SM, Witter MP, Sanz-Arigita EJ, Barkhof F, Scheltens P, Stam CJ, Zarei M, Rombouts SA. White matter tract integrity in aging and Alzheimer’s disease. Hum Brain Mapp. 2009;30:1051–1059. [PubMed]
46. Kantarci K, Jack CR, Jr, Xu YC, Campeau NG, O’Brien PC, Smith GE, Ivnik RJ, Boeve BF, Kokmen E, Tangalos EG, Petersen RC. Mild cognitive impairment and Alzheimer disease: regional diffusivity of water. Radiology. 2001;219:101–107. [PMC free article] [PubMed]
47. Rose SE, Chen F, Chalk JB, Zelaya FO, Strugnell WE, Benson M, Semple J, Doddrell DM. Loss of connectivity in Alzheimer’s disease: an evaluation of white matter tract integrity with colour coded MR diffusion tensor imaging. J Neurol Neurosurg Psychiatry. 2000;69:528–530. [PMC free article] [PubMed]
48. Salat DH, Tuch DS, van der Kouwe AJ, Greve DN, Pappu V, Lee SY, Hevelone ND, Zaleta AK, Growdon JH, Corkin S, Fischl B, Rosas HD. White matter pathology isolates the hippocampal formation in Alzheimer’s disease. Neurobiol Aging. 2010;31:244–256. [PMC free article] [PubMed]
49. Stahl R, Dietrich O, Teipel SJ, Hampel H, Reiser MF, Schoenberg SO. White matter damage in Alzheimer disease and mild cognitive impairment: assessment with diffusion-tensor MR imaging and parallel imaging techniques. Radiology. 2007;243:483–492. [PubMed]
50. Agosta F, Pievani M, Sala S, Geroldi C, Galluzzi S, Frisoni GB, Filippi M. White matter damage in Alzheimer disease and its relationship to gray matter atrophy. Radiology. 2011;258:853–863. [PubMed]
51. Sun SW, Song SK, Harms MP, Lin SJ, Holtzman DM, Merchant KM, Kotyk JJ. Detection of age-dependent brain injury in a mouse model of brain amyloidosis associated with Alzheimer’s disease using magnetic resonance diffusion tensor imaging. Exp Neurol. 2005;191:77–85. [PubMed]
52. Zhang Y, Schuff N, Du AT, Rosen HJ, Kramer JH, Gorno-Tempini ML, Miller BL, Weiner MW. White matter damage in frontotemporal dementia and Alzheimer’s disease measured by diffusion MRI. Brain. 2009;132:2579–2592. [PMC free article] [PubMed]
53. Acosta-Cabronero J, Williams GB, Pengas G, Nestor PJ. Absolute diffusivities define the landscape of white matter degeneration in Alzheimer’s disease. Brain. 2010;133:529–539. [PubMed]
54. Pagani E, Filippi M, Rocca MA, Horsfield MA. A method for obtaining tract-specific diffusion tensor MRI measurements in the presence of disease: application to patients with clinically isolated syndromes suggestive of multiple sclerosis. Neuroimage. 2005;26:258–265. [PubMed]
55. Xue R, van Zijl PC, Crain BJ, Solaiyappan M, Mori S. In vivo three-dimensional reconstruction of rat brain axonal projections by diffusion tensor imaging. Magn Reson Med. 1999;42:1123–1127. [PubMed]
56. Bozzali M, Falini A, Franceschi M, Cercignani M, Zuffi M, Scotti G, Comi G, Filippi M. White matter damage in Alzheimer’s disease assessed in vivo using diffusion tensor magnetic resonance imaging. J Neurol Neurosurg Psychiatry. 2002;72:742–746. [PMC free article] [PubMed]
57. Duan JH,Wang HQ, Xu J, Lin X, Chen SQ, Kang Z, Yao ZB. White matter damage of patients with Alzheimer’s disease correlated with the decreased cognitive function. Surg Radiol Anat. 2006;28:150–156. [PubMed]
58. Naggara O, Oppenheim C, Rieu D, Raoux N, Rodrigo S, Dalla Barba G, Meder JF. Diffusion tensor imaging in early Alzheimer’s disease. Psychiatry Res. 2006;146:243–249. [PubMed]
59. Sydykova D, Stahl R, Dietrich O, Ewers M, Reiser MF, Schoenberg SO, Moller HJ, Hampel H, Teipel SJ. Fiber connections between the cerebral cortex and the corpus callosum in Alzheimer’s disease: a diffusion tensor imaging and voxel-based morphometry study. Cereb Cortex. 2007;17:2276–2282. [PubMed]
60. Cho H, Yang DW, Shon YM, Kim BS, Kim YI, Choi YB, Lee KS, Shim YS, Yoon B, Kim W, Ahn KJ. Abnormal integrity of corticocortical tracts in mild cognitive impairment: a diffusion tensor imaging study. J Korean Med Sci. 2008;23:477–483. [PMC free article] [PubMed]
61. Ding B, Chen KM, Ling HW, Zhang H, Chai WM, Li X, Wang T. Diffusion tensor imaging correlates with proton magnetic resonance spectroscopy in posterior cingulate region of patients with Alzheimer’s disease. Dement Geriatr Cogn Disord. 2008;25:218–225. [PubMed]
62. Fellgiebel A, Schermuly I, Gerhard A, Keller I, Albrecht J, Weibrich C, Muller MJ, Stoeter P. Functional relevant loss of long association fibre tracts integrity in early Alzheimer’s disease. Neuropsychologia. 2008;46:1698–1706. [PubMed]
63. Alexander DC, Pierpaoli C, Basser PJ, Gee JC. Spatial transformations of diffusion tensor magnetic resonance images. IEEE Trans Med Imaging. 2001;20:1131–1139. [PubMed]
64. Xu D, Mori S, Shen D, van Zijl PC, Davatzikos C. Spatial normalization of diffusion tensor fields. Magn Reson Med. 2003;50:175–182. [PubMed]
65. Zhang H, Yushkevich PA, Alexander DC, Gee JC. Deformable registration of diffusion tensorMRimages with explicit orientation optimization. Med Image Anal. 2006;10:764–785. [PubMed]
66. Cao Y, Miller MI, Mori S, Winslow RL, Younes L. Diffeomorphic Matching of Diffusion Tensor Images; Proc IEEE Comput Soc Conf Comput Vis Pattern Recogni; 2006. p. 67. [PMC free article] [PubMed]
67. Yeo BT, Vercauteren T, Fillard P, Peyrat JM, Pennec X, Golland P, Ayache N, Clatz O. DT-REFinD: diffusion tensor registration with exact finite-strain differential. IEEE Trans Med Imaging. 2009;28:1914–1928. [PubMed]
68. Ruiz-Alzola J, Westin CF, Warfield SK, Alberola C, Maier S, Kikinis R. Nonrigid registration of 3D tensor medical data. Med Image Anal. 2002;6:143–161. [PubMed]
69. Park HJ, Kubicki M, Shenton ME, Guimond A, McCarley RW, Maier SE, Kikinis R, Jolesz FA, Westin CF. Spatial normalization of diffusion tensor MRI using multiple channels. Neuroimage. 2003;20:1995–2009. [PMC free article] [PubMed]
70. Ceritoglu C, Oishi K, Li X, Chou MC, Younes L, Albert M, Lyketsos C, van Zijl PC, Miller MI, Mori S. Multi-contrast large deformation diffeomorphic metric mapping for diffusion tensor imaging. Neuroimage. 2009;47:618–627. [PMC free article] [PubMed]
71. Ziyan U, Sabuncu MR, O’Donnell LJ, Westin CF. Nonlinear registration of diffusion MR images based on fiber bundles. Med Image Comput Comput Assist Interv. 2007;10:351–358. [PubMed]
72. Van Hecke W, Leemans A, D’Agostino E, De Backer S, Vandervliet E, Parizel PM, Sijbers J. Nonrigid coregistration of diffusion tensor images using a viscous fluid model and mutual information. IEEE Trans Med Imaging. 2007;26:1598–1612. [PubMed]
73. Chiang MC, Leow AD, Klunder AD, Dutton RA, Barysheva M, Rose SE, McMahon KL, de Zubicaray GI, Toga AW, Thompson PM. Fluid registration of diffusion tensor images using information theory. IEEE Trans Med Imaging. 2008;27:442–456. [PMC free article] [PubMed]
74. Xue Z, Li H, Guo L, Wong ST. A local fast marching-based diffusion tensor image registration algorithm by simultaneously considering spatial deformation and tensor orientation. Neuroimage. 2010;52:119–130. [PMC free article] [PubMed]
75. Li H, Xue Z, Guo L, Wong ST. Simultaneous consideration of spatial deformation and tensor orientation in diffusion tensor image registration using local fast marching patterns. Inf Process Med Imaging. 2009;21:63–75. [PubMed]
76. Zollei L, Stevens A, Huber K, Kakunoori S, Fischl B. Improved tractography alignment using combined volumetric and surface registration. Neuroimage. 2010;51:206–213. [PMC free article] [PubMed]
77. Xie S, Xiao JX, Gong GL, Zang YF, Wang YH, Wu HK, Jiang XX. Voxel-based detection of white matter abnormalities in mild Alzheimer disease. Neurology. 2006;66:1845–1849. [PubMed]
78. Teipel SJ, Stahl R, Dietrich O, Schoenberg SO, Perneczky R, Bokde AL, Reiser MF, Moller HJ, Hampel H. Multivariate network analysis of fiber tract integrity in Alzheimer’s disease. Neuroimage. 2007;34:985–995. [PubMed]
79. Head D, Buckner RL, Shimony JS, Williams LE, Akbudak E, Conturo TE, McAvoy M, Morris JC, Snyder AZ. Differential vulnerability of anterior white matter in nondemented aging with minimal acceleration in dementia of the Alzheimer type: evidence from diffusion tensor imaging. Cereb Cortex. 2004;14:410–423. [PubMed]
80. Davatzikos C. Why voxel-based morphometric analysis should be used with great caution when characterizing group differences. Neuroimage. 2004;23:17–20. [PubMed]
81. Ashburner J, Kloppel S. Multivariate models of inter-subject anatomical variability. Neuroimage. 2011;56:422–439. [PMC free article] [PubMed]
82. Avants BB, Cook PA, Ungar L, Gee JC, Grossman M. Dementia induces correlated reductions in white matter integrity and cortical thickness: a multivariate neuroimaging study with sparse canonical correlation analysis. Neuroimage. 2010;50:1004–1016. [PMC free article] [PubMed]
83. Smith SM, Jenkinson M, Johansen-Berg H, Rueckert D, Nichols TE, Mackay CE, Watkins KE, Ciccarelli O, Cader MZ, Matthews PM, Behrens TE. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage. 2006;31:1487–1505. [PubMed]
84. Oishi K, Faria A, Jiang H, Li X, Akhter K, Zhang J, Hsu JT, Miller MI, van Zijl PC, Albert M, Lyketsos CG, Woods R, Toga AW, Pike GB, Rosa-Neto P, Evans A, Mazziotta J, Mori S. Atlas-based whole brain white matter analysis using large deformation diffeomorphic metric mapping: application to normal elderly and Alzheimer’s disease participantstlas. Neuroimage. 2009;46:486–499. [PMC free article] [PubMed]
85. Bosch B, Arenaza-Urquijo EM, Rami L, Sala-Llonch R, Junque C, Sole-Padulles C, Pena-Gomez C, Bargallo N, Molinuevo JL, Bartres-Faz D. Multiple DTI index analysis in normal aging, amnestic MCI and AD. Relationship with neuropsychological performance. Neurobiol Aging. 2010 Epub ahead of print. [PubMed]
86. Liu Y, Spulber G, Lehtimaki KK, Kononen M, Hallikainen I, Grohn H, Kivipelto M, Hallikainen M, Vanninen R, Soininen H. Diffusion tensor imaging and Tract-Based Spatial Statistics in Alzheimer’s disease and mild cognitive impairment. Neurobiol Aging. 2009 Epub ahead of print. [PubMed]
87. Zarei M, Damoiseaux JS, Morgese C, Beckmann CF, Smith SM, Matthews PM, Scheltens P, Rombouts SA, Barkhof F. Regional white matter integrity differentiates between vascular dementia and Alzheimer disease. Stroke. 2009;40:773–779. [PubMed]
88. Stricker NH, Schweinsburg BC, Delano-Wood L, Wierenga CE, Bangen KJ, Haaland KY, Frank LR, Salmon DP, Bondi MW. Decreased white matter integrity in late-myelinating fiber pathways in Alzheimer’s disease supports retrogenesis. Neuroimage. 2009;45:10–16. [PMC free article] [PubMed]
89. Honea RA, Vidoni E, Harsha A, Burns JM. Impact of APOE on the healthy aging brain: a voxel-based MRI and DTI study. J Alzheimers Dis. 2009;18:553–564. [PMC free article] [PubMed]
90. Smith CD, Chebrolu H, Andersen AH, Powell DA, Lovell MA, Xiong S, Gold BT. White matter diffusion alterations in normal women at risk of Alzheimer’s disease. Neurobiol Aging. 2010;31:1122–1131. [PMC free article] [PubMed]
91. Ukmar M, Makuc E, Onor ML, Garbin G, Trevisiol M, Cova MA. Evaluation of white matter damage in patients with Alzheimer’s disease and in patients with mild cognitive impairment by using diffusion tensor imaging. Radiol Med(Torino) 2008;113:915–922. [PubMed]
92. Yoshiura T, Mihara F, Ogomori K, Tanaka A, Kaneko K, Masuda K. Diffusion tensor in posterior cingulate gyrus: correlation with cognitive decline in Alzheimer’s disease. Neuroreport. 2002;13:2299–2302. [PubMed]
93. Kalus P, Slotboom J, Gallinat J, Mahlberg R, Cattapan-Ludewig K, Wiest R, Nyffeler T, Buri C, Federspiel A, Kunz D, Schroth G, Kiefer C. Examining the gateway to the limbic system with diffusion tensor imaging: the perforant pathway in dementia. Neuroimage. 2006;30:713–720. [PubMed]
94. Goldstein FC, Mao H, Wang L, Ni C, Lah JJ, Levey AI. White matter integrity and episodic memory performance in mild cognitive impairment: a diffusion tensor imaging study. Brain Imaging Behav. 2009;3:132–141. [PMC free article] [PubMed]
95. Huang J, Auchus AP. Diffusion tensor imaging of normal appearing white matter and its correlation with cognitive functioning in mild cognitive impairment and Alzheimer’s disease. Ann N Y Acad Sci. 2007;1097:259–264. [PubMed]
96. Kavcic V, Ni H, Zhu T, Zhong J, Duffy CJ. White matter integrity linked to functional impairments in aging and early Alzheimer’s disease. Alzheimers Dement. 2008;4:381–389. [PMC free article] [PubMed]
97. Bozzali M, Falini A, Cercignani M, Baglio F, Farina E, Alberoni M, Vezzulli P, Olivotto F, Mantovani F, Shallice T, Scotti G, Canal N, Nemni R. Brain tissue damage in dementia with Lewy bodies: an in vivo diffusion tensor MRI study. Brain. 2005;128:1595–1604. [PubMed]
98. Ota M, Sato N, Ogawa M, Murata M, Kuno S, Kida J, Asada T. Degeneration of dementia with Lewy bodies measured by diffusion tensor imaging. NMR Biomed. 2009;22:280–284. [PubMed]
99. Mueller SG, Weiner MW, Thal LJ, Petersen RC, Jack CR, Jagust W, Trojanowski JQ, Toga AW, Beckett L. Ways toward an early diagnosis in Alzheimer’s disease: the Alzheimer’s Disease Neuroimaging Initiative (ADNI) Alzheimers Dement. 2005;1:55–66. [PMC free article] [PubMed]
100. Jack CR, Jr, Bernstein MA, Borowski BJ, Gunter JL, Fox NC, Thompson PM, Schuff N, Krueger G, Killiany RJ, Decarli CS, Dale AM, Carmichael OW, Tosun D, Weiner MW. Update on the magnetic resonance imaging core of the Alzheimer’s disease neuroimaging initiative. Alzheimers Dement. 2010;6:212–220. [PMC free article] [PubMed]