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. Author manuscript; available in PMC: 2018 Aug 1.
Published in final edited form as: Neurobiol Aging. 2017 May 4;56:172–179. doi: 10.1016/j.neurobiolaging.2017.04.024

White Matter Integrity on DTI and the Pathologic Staging of Alzheimer’s Disease

Kejal Kantarci 1, Melissa E Murray 2, Christopher G Schwarz 1, Robert Reid 3, Scott A Przybelski 4, Timothy Lesnick 4, Samantha M Zuk 1, Mekala R Raman 1, Matthew L Senjem 3, Jeffrey L Gunter 3, Bradley F Boeve 5, David S Knopman 5, Joseph E Parisi 6, Ronald C Petersen 5, Clifford R Jack Jr 1, Dennis Dickson 2
PMCID: PMC5523458  NIHMSID: NIHMS880095  PMID: 28552181

Abstract

Pattern of diffusion tensor MRI (DTI) alterations were investigated in pathologically-staged Alzheimer’s disease (AD) patients (n=46). Patients with antemortem DTI studies and a range of AD pathology at autopsy were included. Patients with a high neurofibrillary tangle (NFT) stage (Braak IV–VI) had significantly elevated mean diffusivity (MD) in the crus of fornix and ventral cingulum tracts, precuneus, and entorhinal white matter on voxel-based analysis after adjusting for age and time from MRI to death (p<0.001). Higher MD and lower fractional anisotropy (FA) in the ventral cingulum tract, entorhinal and precuneus white matter was associated with higher Braak NFT stage and clinical disease severity. There were no MD and FA differences among the low (none and sparse) and high (moderate and frequent) β-amyloid neuritic plaque groups. The NFT pathology of AD is associated with DTI alterations involving the medial temporal limbic connections and medial parietal white matter. This pattern of diffusion abnormalities is also associated with clinical disease severity.

Keywords: Diffusion tensor imaging, DTI, Alzheimer’s disease, limbic tracts, white matter, pathology

1. Introduction

Diffusion tensor imaging (DTI) is sensitive to the alterations in white matter microstructure such as loss of myelin and axonal membranes that restrict the random motion of water molecules in the tissue. DTI has demonstrated that the integrity of white matter is disrupted as early as the preclinical stage of Alzheimer’s disease (AD) (Adluru, et al., 2014, Fischer, et al., 2015, Kantarci, et al., 2014a, Prescott, et al., 2014). These white matter alterations on DTI are initially localized to the medial temporal limbic association tracts, and tend to spread to the temporal and parietal white matter as the clinical disease progresses (Demirhan, et al., 2015, Kantarci, et al., 2010, Konukoglu, et al., 2016, Nowrangi, et al., 2013). Anatomically, involvement of these tracts follows the topographic progression of gray matter neurodegeneration, atrophy and metabolic disruption in AD (Jack, et al., 2012, Kuczynski, et al., Villain, et al., 2008). This anatomic concordance between gray and white matter degeneration in AD suggest that the disruption in white matter tracts is associated with the cortical AD pathology, particularly the neurofibrillary tangle (NFT) tau pathology of AD.

Despite a large body of literature on DTI findings within the clinical AD spectrum, little is known about the pathologic basis of these findings. In vivo DTI and histological examination in transgenic (PDAPP) mouse model of amyloid pathology of AD suggest that myelin loss may be contributing to the diffusivity changes in the white matter (Song, et al., 2004). However, ex vivo DTI of the white matter tissue from clinically normal older adults and AD cases showed an association between axonal density and fractional anisotropy (FA). No association was identified between white matter pathology and mean diffusivity (MD) from ex vivo DTI, which was attributed to the expected gradual decrease in diffusivity after cell death and formalin fixation (Gouw, et al., 2008). Ex vivo DTI studies are hindered by the change in tissue microstructure after cell death and formalin fixation. In vivo DTI studies may reveal the pattern of white matter alterations associated with AD pathology during life. In a prospective cohort of older adults who were followed to autopsy, we investigated the patterns of white matter alterations on DTI in pathologically confirmed AD, and the relationship to clinical disease severity, NFT staging and the neuritic β-amyloid plaque (NP) severity.

2. Methods

2.1. Cases

Patients from the Mayo Clinic AD Research Center, a dementia clinic based cohort, and Mayo Clinic Study of Aging, a population-based cohort, were included in this study. We retrospectively identified consecutive patients (n=46) who had an antemortem MRI and autopsy from July 2010 until January 2015 with a range of AD pathology according to the National Institute on Aging-Alzheimer’s Association guidelines for the neuropathologic assessment of Alzheimer’s disease (Hyman, et al., 2012, Montine, et al., 2012). Individuals participating in these longitudinal studies undergo approximately annual clinical examinations including assessments of clinical dementia rating (CDR) sum of boxes and mini mental state examination (MMSE), brain MRI and routine laboratory tests.

Inclusion of subjects in this study was solely based on the neuropathologic findings and clinical diagnosis was not used for inclusion. For inclusion in this study, patients underwent antemortem MR examinations, and had autopsies that showed a range of AD pathology but no other dementia-related pathologic diagnosis. Cases were excluded if they had a stroke, neoplasm, or other neurological illnesses that typically interfere with cognitive function and brain structure at the time of MRI.

Clinical diagnosis at the time of the MRI was made according to the established clinical criteria during a consensus conference involving neurologists, neuropsychologists, and nurses who evaluated the patients.

2.2. MRI and DTI Methods

MRIs were performed at 3 Tesla using eight-channel phased array receiver recoil (GE, Milwaukee, WI) between 0.2 to 4.6 years before death. A 3D high resolution MPRAGE acquisition was performed for anatomic segmentation and labeling of the DTI scans. A single-shot echo-planar DTI pulse sequence was performed using parallel imaging with a SENSE factor of two in the axial plane. The DTI sequence’s parameters included TR= 10,200 ms; an in-plane matrix of 128/128; field of view of 35 cm. The slice thickness was 2.7mm. The DTI volumes consisted of 41 diffusion-encoding gradient directions and a set of five volumes of non-diffusion T2-weighted images with 2.7 mm. isotropic resolution.

Although we excluded cases with a clinically diagnosed stroke at the time of MRI, we assessed incidental infarcts identified on MRIs (i.e. silent infarcts), which may influence DTI findings. Assessment of cerebrovascular lesions was performed using methods that were previously described. (Kantarci, et al., 2008, Raz, et al., 2013) Briefly, white matter hyperintensity volumes were calculated using a semiautomated segmentation algorithm based on fluid-attenuated inversion recovery MRI and silent infarcts were identified by a trained image analyst and confirmed by a radiologist (KK).

We used a previously tested and validated method to process DTI scans (Schwarz, et al., 2014). First, DTI images were corrected for subject motion and residual eddy current distortion by affine-registering each volume to the first image volume with no diffusion-weighting. Weighted linear least squares optimization was used to fit diffusion tensors on extracted voxels and FA images were calculated from the eigenvalues of the tensors using Dipy(Garyfallidis, et al., 2014). Advanced normalization tools-Symmetric Normalization (ANTS-SyN) version 1.9.y(Avants, et al., 2011) algorithm was used for generating a study-specific template that included all FA images. Each subject’s FA and MD images were non-linearly registered to the template, a white matter mask that included voxels with an FA >0.20 was applied and smoothed with a 8mm full-width at half-maximum Gaussian kernel. Voxel-based analysis (VBA) was performed on SPM5(Ashburner and Friston, 2000) for investigating the differences in pathologic groups, and displayed on a rendered single-subject transparent brain for visualization using the MRIcroGL program (http://www.mccauslandcenter.sc.edu/mricrogl/). In addition, a secondary quantitative analysis was performed with the John’s Hopkins University (JHU) DTI atlas (Oishi, et al., 2008) by registering the white matter ROIs to the study-specific template using ANTS-SyN.

2.3. Neuropathologic Assessment

Standardized methods were used for the neuropathologic assessment by two expert neuropathologists (DWD and JEP). Sampling was done according to the CERAD protocol (Mirra, et al., 1991) and the Third Report of the DLB Consortium (McKeith, et al., 2005) from the left (n=36) or the right (n=10) hemisphere. NFT and corresponding Braak NFT stage (Braak and Braak, 1991) and the CERAD NP score (Mirra, et al., 1991) was determined using thioflavin-S microscopy or Bielshowsky silver stain.

2.4. Statistical Analysis

Subject characteristics were compared using chi-squared test for differences in proportions or two-sided two-sample t-test. Correlations of Braak NFT stage with the tract-based MD and FA values were tested with Spearman correlations adjusted for age and time from MRI to death. Correlations of CDR sum of boxes and MMSE scores with the tract-based MD and FA values were tested with Spearman correlations adjusted for age and time from MRI to death. We did not adjust for multiple comparisons in reporting the results. (Rothman, 1990) We show the actual p-values, so if a reader wanted to adjust for multiple comparisons, it is possible.

3. Results

3.1. Characteristics of the Study Cohort

Subject characteristics classified according to the pathologic groups are listed in Table 1. To investigate the DTI findings with VBA, we grouped Braak NFT stage I–III as the low NFT stage (n=21) and Braak NFT stage IV–VI as high NFT stage (n=25) (Table 1a). We grouped cases with none and sparse CERAD NP score as low NP score (n=13) and cases with moderate to frequent NP score as high NP score (n=33) (Table 1b). Patients with a low NFT stage and low NP score were older than those with high NFT stage and high NP score. Furthermore, there was a trend toward longer time intervals between the DTI scan acquisition and death in the high NFT stage and high NP score group, compared to the low NFT stage and low NP score group. Therefore all analysis on the DTI data was adjusted for age and time from DTI scan to death. As expected, patients with high NFT stage and high NP score scored worse on MMSE and had a higher score on CDR sum of boxes. A majority of the patients with low NFT stage and low NP score were clinically normal. However, there were cases who were diagnosed with mild cognitive impairment (n=4) in the low NFT stage group and one case was diagnosed with mild cognitive impairmentin the low NP score group. One case was diagnosed with probable AD dementia at the time of MRI in the low NFT stage and low NP score groups. In the high NFT stage group, there were cases who were clinically normal (n=5; 20%), who were diagnosed with mild cognitive impairment (n=4; 16%), probable AD dementia (n=8; 32%). In the high NP score group, there were cases who were clinically normal (n=10; 30%), who were diagnosed with mild cognitive impairment (n=7; 21%), probable AD dementia (n=8; 24%). In both the high NFT stage and high NP stage groups, other diagnosis included dementia with Lewy bodies (n=3) and others (n=5) such as corticobasal syndrome, progressive nonfluent aphasia or progressive fluent aphasia/semantic dementia at the time of MRI.

Table 1a.

Patient characteristics at the time of MRI classified by low and high NFT stage

Low NFT Stage
n = 21		 High NFT Stage
n = 25		 P-value
No. of females (%) 7 (33) 8 (32) 0.92
No. of ε4 carriers (%) 5 (24) 10 (42) 0.20
Age 82.7 (5.7) 75.0 (11.4) 0.008
Education 13.9 (3.0) 14.6 (2.9) 0.40
CDR sum of boxes 0.5 (1.4) 6.6 (5.4) <0.001
MMSE 27.3 (2.1) 16.7 (8.7) <0.001
Braak Stage 2.0 (0.8) 5.3 (0.7) <0.001
Neuritic Plaques <0.001
 None 9 (43) 0
 Sparse 4 (19) 0
 Moderate 8 (38) 6 (25)
 Frequent 0 18 (75)
Diagnosis <0.001
 Cognitively Normal 16 (76) 5 (20)
 MCI 4 (19) 4 (16)
 AD 1 (5) 8 (32)
 DLB 0 (0) 3 (12)
 Other 0 (0) 5 (20)
White matter hyperintensity volume cc 31.2 (19.6) 28.7 (20.9) 0.66†
No. with silent infarct on MRI (%) 8 (42%) 5 (21%) 0.13
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The mean (standard deviation) is indicated for the continuous variables The p-values are from the two-sided two-sample t-test for the continuous variables and the chi-squared test for differences in proportions.

*

This p-value is being reported on a square root transformation of the regular values. This transformation was used because distribution of time from scan to death was skewed.

†

This p-value is being reported on a log transformation of the regular values and adjusted for the total intracranial volume.

Table 1b.

Patient characteristics at the time of MRI classified by low and high neuritic plaque scores

None or Sparse Neuritic Plaques
n = 13		 Moderate or Frequent Neuritic Plaques
n = 33		 P-value
No. of females (%) 5 (38%) 10 (30%) 0.60
No. of e4 carriers (%) 1 (8%) 14 (44%) 0.02
Age (years) 83.2 (6.0) 76.7 (10.6) 0.04
Education (years) 13.5 (2.7) 14.6 (3.0) 0.27
CDR sum of boxes 0.6 (1.7) 5.1 (5.4) 0.005
MMSE 26.9 (2.0) 19.7 (9.0) 0.009
Time from scan to death (years) 1.7 (1.3) 2.4 (1.3) 0.06*
Braak Staging, n (%) <0.001
 I–III 13 (100%) 8 (24%)
 IV–VI 0 (0%) 25 (76%)
Clinical Diagnosis, n (%)
 Clinically normal 11 (85%) 10 (30%)
 Mild cognitive impairment 1 (8%) 7 (21%)
 AD dementia 1 (8%) 8 (24%)
 Dementia with Lewy bodies 0 (0%) 3 (9%)
 Other 0 (0%) 5 (15%)
White matter hyperintensity volume cc. 29.6 (21.4) 29.9 (19.9) 0.83†
No. with silent infarct on MRI (%) 5 (38%) 8 (27%) 0.44
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The mean (standard deviation) is indicated for the continuous variables The p-values are from the two-sided two-sample t-test for the continuous variables and the chi-squared test for differences in proportions.

*

This p-value is being reported on a square root transformation of the regular values. This transformation was used because distribution of time from scan to death was skewed.

†

This p-value is being reported on a log transformation of the regular values and adjusted for the total intracranial volume.

Cerebrovascular lesions were not different between the pathologic groups. The volume of WMH was not different between the low NFT stage and low NP score groups compared to high NFT stage and high NP score groups after adjusting for the total intracranial volume. Similarly, the frequency of cases with silent infarcts was not different between the pathologic groups. Except for one case in the high NFT stage and high NP score groups with a small temporal lobe cortical infarct, all silent infarcts were lacunar infarcts (Table 1a and 1b).

3.2. Voxel-based Analysis (VBA)

VBA did not reveal any differences in FA values between the high NFT stage and low NFT stage groups after adjusting for age and time from DTI scan to death. Compared to the low NFT stage group, high NFT stage group had elevated MD in the crus of fornix, cingulum, temporal, and precuneus white matter as shown in Figure 1 (p<0.001). No differences were identified after correcting for multiple comparisons using false discovery rate. Furthermore, VBA did not reveal any differences in MD and FA values between the high NP score and low NP score groups after adjusting for age and time from DTI scan to death.

Figure 1.

Figure 1

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Voxel-based analysis comparing mean diffusivity values. Voxels with higher mean diffusivity in the high neurofibrillary tangle stage group than the low neurofibrillary tangle stage group after adjusting for age and time from DTI scan to death are plotted on a 3-dimensional transparent render and also displayed on individual slices from the common template (p<0.001; uncorrected for multiple comparisons). The enlarged sections show the involvement of the crus of the fornix, cingulum and precuneus white matter.

3.3. Atlas-based Analysis

To determine the specific WM tracts involved and their relationship to NFT stage and clinical disease severity, we performed a Johns Hopkins University (JHU) DTI atlas-based (Oishi, et al., 2008) analysis on tracts and white matter regions that had higher MD in the high NFT stage compared to the low NFT stage group on visual inspection of VBA projections.

We used the JHU atlas-based MD and FA values from the pathologically sampled hemisphere used for Braak NFT staging and found a significant association between Braak NFT stage and the ventral cingulum tract FA (Rho= −0.34; p=0.02) and MD (Rho =0.56; p<0.001); precuneus white matter FA (Rho = −0.50; p<0.001) and MD (Rho=0.40; p=0.01); after adjusting for age and time from DTI scan to death (Figure 2). Although the VBA analysis showed higher MD in the crus of the fornix in high NFT stage, compared to the low NFT stage group, no association was found between Braak NFT stage and the entire fornix FA from the JHU atlas (r= −0.09; p=0.56), and this association did not reach statistical significance with MD Rho= 0.26; p=0.087) after adjusting for age and time from DTI scan to death.

Figure 2.

Figure 2

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Associations of MD and FA values from specific white matter tracts and Braak neurofibrillary tangle staging at autopsy adjusted for age and scan to death time interval

The JHU atlas-based analysis also demonstrated that higher MD and lower FA in the ventral cingulum tract and precuneus white matter were associated with lower MMSE and higher CDR sum of boxes scores. In the entorhinal WM, higher MD was associated with lower MMSE and higher CDR sum of boxes scores. In the fornix, an association was found between lower fornix FA and higher CDR sum of boxes scores. (Table 2).

Table 2.

Partial Spearman correlations of DTI atlas-based MD and FA values and clinical disease severity.

FA MD
MMSE CDR-SOB MMSE CDR-SOB
Correlations P-value Correlations P-value Correlations P-value Correlations P-value
Ventral Cingulum 0.25 (−0.07, 0.52) 0.12 −0.37 (−0.60, −0.08) 0.01 −0.59 (−0.76, −0.33) <0.001 0.48 (0.21, 0.68) <0.001
Precunueus WM 0.38 (0.07, 0.61) 0.02 −0.32 (−0.56, −0.02) 0.03 −0.43 (−0.65, −0.13) 0.005 0.31 (0.01, 0.55) 0.04
Entorhinal WM −0.07 (−0.37, 0.25) 0.69 −0.18 (−0.45, 0.12) 0.23 −0.41 (−0.64, −0.11) 0.008 0.39 (0.10, 0.61) 0.008
Fornix 0.14 (−0.19, 0.43) 0.41 −0.30 (−0.54, 0.00) 0.049 −0.06 (−0.37, 0.25) 0.70 0.13 (−0.18, 0.41) 0.41
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*

All analyses were adjusted for age and time from the DTI scan to death. Partial Rho and the 95% confidence intervals are reported.

MMSE: Mini mental state examination; CDR-SOB: Clinical dementia rating sum of boxes; FA: fractional anisotropy; MD: mean diffusivity; WM: white matter

Because there were trends of differences in DTI scan to death interval among the pathologic groups, and because long DTI scan to death intervals may have disproportionately influenced the findings in these groups, we conducted a sensitivity analysis in cases with less than 36 months of MRI scan to death interval (n=35). In this subset of cases the mean MRI scan to death interval was not different between the low (1.5 years) and high NFT stages (1.6 years) (p=0.56). The results of Spearman rank correlations of tract-based MD and FA with Braak NFT stage and the clinical disease severity did not change, although the Rho values were smaller compared to the findings in the entire cohort, likely due to reduced sample size (Data not shown)

4. Discussion

In a cohort of patients with antemortem DTI and autopsy, we demonstrated that the elevation in MD and the reduction in FA in the medial temporal and parietal white matter, particularly in the ventral limbic connections such as the ventral cingulum tract, the crus of the fornix and the entorhinal white matter were associated with a higher NFT pathologic stage of AD. Furthermore, higher MD and lower FA in a majority of these white matter regions and tracts correlated with higher CDR sum of boxes, and lower MMSE scores in patients who have a range of AD pathology but no other neurological disease that may influence these associations at autopsy. On the contrary, no differences in FA and MD were found when comparing the groups with high and low NP scores.

Alterations in white matter diffusivity on DTI are known to be associated with clinical disease severity starting from the preclinical stages of AD (Bendlin, et al., 2010). White matter DTI abnormalities have been associated with the abnormalities in cerebrospinal fluid biomarkers of AD (Bendlin, et al., 2012, Gold, et al., 2014, Li, et al., 2014, McMillan, et al., 2012), imaging biomarkers of gray matter neurodegeneration such as cortical and hippocampal atrophy on MRI (Kantarci, et al., 2014b, Ouyang, et al., 2015) and hypometabolism on PET even in clinically normal older adults (Kantarci, et al., 2014a). In carriers of the fully penetrant familial AD mutations, diffusivity alterations were present in cognitively normal individuals who were asymptomatic at the time, suggesting that DTI findings may precede hippocampal atrophy and clinical symptoms in AD (Ringman, et al., 2007).

The DTI findings on VBA in patients with high NFT stage compared to patients with low NFT stage revealed a pattern of increased MD primarily involving the limbic projections. Higher MD was observed in the entorhinal white matter, which includes the perforant pathway carrying the connections between the hippocampus and the entorhinal cortex; the crus of the fornix, which primarily carries the efferent projections from CA1 and CA3 pyramidal neurons of the hippocampus and from the subiculum to the subcortical gray matter and back; the cingulum tract, which carries hippocampal projections to the medial parietal lobe; and the precuneus white matter, which includes projections from the cingulum connecting to the precuneus. This pattern of DTI alterations involving the limbic pathways connecting the medial temporal lobe to the subcortical gray matter and the medial parietal lobes agrees with a majority of the DTI studies in preclinical AD, amnestic MCI and clinically diagnosed AD dementia patients (Chua, et al., 2009, Fellgiebel, et al., 2005, Kalus, et al., 2006, Lee, et al., 2009, Liu, et al., 2011, Medina, et al., 2006, Ray, et al., 2006, Stahl, et al., 2007, Wang, et al., 2009, Zhang, et al., 2007).

In the entire cohort with a range of AD pathology, we correlated MD and FA values of the limbic projections and white matter regions that showed alterations on VBA with the Braak NFT staging of AD pathology. We found that the higher cortical Braak neurofibrillary tangle stage was associated with a higher MD and lower FA in many of these white matter tracts and regions. The topographic pattern of gray matter atrophy in clinically diagnosed cases with typical AD dementia follows Braak neurofibrillary tangle staging of the AD pathology involving the limbic and paralimbic cortices earlier than the neocortical regions (Whitwell, et al., 2008). In the current study, we demonstrated that a similar pattern exists for the white matter diffusivity changes, involving the limbic projections, and the medial temporal and parietal white matter regions. In fact, when considered with the pattern of NFT-related gray matter atrophy in AD, our findings indicate that the white matter and gray matter degeneration in AD is topographically concordant and likely linked to each other. Furthermore, white matter degenerative changes measured with DTI correlate with cognitive function and disease severity in this cohort with a range of AD pathology.

Contrary to the NFT stage, the VBA analysis did not reveal DTI alterations in high NP compared to the low NP cases. DTI abnormalities have been associated with high β-amyloid load on PET scans in clinically normal older adults (Chao, et al., 2013, Racine, et al., 2014, Wolf, et al., 2015). It was suggested that white matter FA decreases when β-amyloid load increases, and cognition is only affected when there is an increase in β-amyloid load together with a decrease in white matter FA (Wolf, et al., 2015). However, NFT-related neurodegenerative changes in the gray matter, which are likely present in older adults with high β-amyloid load and cognitive difficulties were not investigated. In a cohort of non-demented older adults, we had found that FA is decreased only in those who also had imaging findings of neurodegeneration in the gray matter such as atrophy or hypometabolism (Kantarci, et al., 2014a). Having a positive β-amyloid PET scan by itself, without gray matter neurodegeneration, was not sufficient for a decreased FA in the white matter. Hence, neurodegenerative changes in the gray and white matter appear to evolve simultaneously during the course of AD starting from the preclinical stage. Whether DTI can detect the neurodegenerative changes associated with AD pathophysiology earlier than gray matter atrophy on MRI or cortical hypometabolism on PET needs further investigation with longitudinal MRI and cannot be investigated in the current study with cross-sectional imaging.

Corpus callosum DTI abnormalities have been identified in individuals with amnestic MCI and AD dementia (Chua, et al., 2009, Kantarci, et al., 2014b, Lee, et al., 2009, Liu, et al., 2011, Ray, et al., 2006, Stahl, et al., 2007, Wang, et al., 2009). In the current study, corpus callosum MD and FA was not different between the high compared to the low NFT stage groups. Corpus callosum is the main connectivity pathway between the two hemispheres, therefore any neurodegenerative process involving the cerebral white matter may have an impact on corpus callosum diffusivity. Cognitive impairment increase with the number of different pathologies in older adults, so corpus callosum diffusivity alterations in cases with amnestic MCI and AD may be associated with the heterogeneity of pathological processes in the cognitively impaired individuals and likely is not specific to AD-related pathophysiology (de Groot, et al., 2015, Schneider, et al., 2007). Because we only included cases with a range of AD pathology, and excluded cases with additional neurological disorders such as stroke, or pathologies other than AD, our sample is expected to be more homogeneous than the clinically diagnosed amnestic MCI and AD dementia cohorts in the literature, and more likely reflect the diffusivity alterations in specific tracts and white matter regions associated with AD pathophysiology.

Pathologic diagnosis is the reference standard for validating imaging biomarkers. However, in vivo imaging and pathologic correlation studies are usually limited by sample size. Therefore, the relatively small sample size of our cohort may have impacted several of our findings that did not reach statistical significance. For example, VBA revealed a pattern of increased MD but not decreased FA at the same p-value thresholds in cases with high NFT stage compared to low NFT stage. While the biological basis of MD and FA changes in AD is not fully understood, anisotropy analysis with FA can be influenced by crossing-fibers more than MD, limiting the power of FA to detect WM degeneration (Jeurissen, et al., 2013). Another limitation of the in vivo imaging and pathologic correlation studies is the pathologic progression that occurs during the time between image acquisition and autopsy. In this study, the time between the DTI scan and autopsy ranged from two months to almost five years, and we adjusted for this time interval in all analysis. This adjustment is based on the assumption that disease progression and the change in biomarkers associated with the disease progression have a linear trajectory, which may not be the case for different stages of the clinical and pathological progression of AD (Jack, et al., 2013). A third limitation of our study is the potential for partial volume averaging of CSF during the atlas-based analysis. CSF partial volume averaging may have influenced our results particularly in the body of the fornix that is surrounded by CSF, and this may have influenced our observation of weaker associations of fornix measurements with Braak NFT stage and clinical disease severity compared to other limbic tracts such as the ventral cingulum (Berlot, et al., 2014). Finally, cerebrovascular lesions are common in older adults and may contribute to the DTI alterations in the white matter. Because we did not find differences in WMH volume and presence of silent infarcts among the pathologic groups, the DTI-based differences we found between these groups are unlikely to be effected by the WMH.

We demonstrated the pattern of DTI alterations associated with a higher NFT stage, which involves the limbic projections, particularly those that connect the medial temporal lobe to the rest of the limbic system. These diffusivity abnormalities are associated with the Braak neurofibrillary tangle stage and clinical disease severity, which also underlie gray matter neurodegeneration in AD. Although there is a topographic concordance between the imaging patterns of gray and white matter degeneration in AD, the sequence of the white matter DTI alterations and gray matter atrophy during the progression of AD needs to be investigated in longitudinal cohorts.

Highlights.

  1. Alzheimer’s disease pathology is associated with a distinct pattern of DTI alterations involving the medial temporal limbic connections and the medial parietal white matter.

  2. These alterations in diffusivity are associated with the Braak neurofibrillary tangle stage

  3. DTI abnormalities in autopsy-confirmed Alzheimer’s disease is related to clinical disease severity

Acknowledgments

Study Funding: This work is supported by the National Institutes of Health [U01 AG06786, R01 AG040042, R01 AG11378, AG041851, C06 RR018898, R01AG034676] the Elsie and Marvin Dekelboum Family Foundation, and the Robert H. and Clarice Smith and Abigail Van Buren Alzheimer s Disease Research Program.

Disclosures:

Drs. Schwarz, Murray, Reid, Raman, and Gunter, Mr. Senjem, Mr. Lesnick, Mr. Przybelski, and Ms. Zuk report no disclosures.

Dr. Kantarci served on the Data Monitoring Committee of Pfizer, Inc. and Janssen Alzheimer Immunotherapy; serves on the data safety monitoring board for Takeda Global Research & Development Center, Inc.; and receives research support from the NIH.

Dr. Boeve has served as an investigator for a clinical trial sponsored by Cephalon, Inc. He has received honoraria from the American Academy of Neurology. He receives research support from the NIH

Dr. Knopman serves on a Data Safety Monitoring Board for Lundbeck Pharmaceuticals and for the Dominantly Inherited Alzheimer’s Disease Treatment Unit. He is participating in clinical trials sponsored by Lilly Pharmaceuticals and Tau Rx Pharmaceuticals. He receives research support from the NIH.

Dr. Petersen chaired a Data Monitoring Committee of Pfizer, Inc. and Janssen Alzheimer Immunotherapy and serves as a consultant for Hoffman La Roche, Inc., Merck, Inc., Genentech, Inc., Biogen, Inc., Eli Lilly and Co. and receives research support from the NIH

Dr. Jack reports consulting services for Eli Lilly Co, and receives research support from the NIH

Drs. Parisi and Dickson receive research support from the NIH

Abbreviations

DTI

Diffusion tensor MRI

AD

Alzheimer’s disease

MD

mean diffusivity

FA

fractional anisotropy

NFT

neurofibrillary tangles

MMSE

mini mental state examination

CDR

clinical dementia rating

VBA

voxel-based analysis

Footnotes

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