The effect of white matter hyperintensities on statistical analysis of diffusion tensor imaging in cognitively healthy elderly and prodromal Alzheimer's disease

Daniel Svärd; Markus Nilsson; Björn Lampinen; Jimmy Lätt; Pia C. Sundgren; Erik Stomrud; Lennart Minthon; Oskar Hansson; Danielle van Westen

doi:10.1371/journal.pone.0185239

Abstract

Diffusion tensor imaging (DTI) has been used to study microstructural white matter alterations in a variety of conditions including normal aging and Alzheimer's disease (AD). White matter hyperintensities (WMH) are common in cognitively healthy elderly as well as in AD and exhibit elevated mean diffusivity (MD) and reduced fractional anisotropy (FA). However, the effect of WMH on statistical analysis of DTI estimates has not been thoroughly studied. In the present study we address this in two ways. First, we investigate the effect of WMH on MD and FA in the dorsal and ventral cingulum, the superior longitudinal fasciculus, and the corticospinal tract, by comparing two matched groups of cognitively healthy elderly (n = 21 + 21) with unequal WMH load. Second, we assess the effects of adjusting for WMH load when comparing MD and FA in prodromal AD subjects (n = 83) to cognitively healthy elderly (n = 132) in the abovementioned white matter tracts. Results showed the WMH in cognitively healthy elderly to have a generally large effect on DTI estimates (Cohen’s d = 0.63 to 1.27 for significant differences in MD and −1.06 to −0.69 for FA). These effect sizes were comparable to those of various neurological and psychiatric diseases (Cohen’s d = 0.57 to 2.20 for differences in MD and −1.76 to −0.61 for FA). Adjusting for WMH when comparing DTI estimates in prodromal AD subjects to cognitively healthy elderly improved the explanatory power as well as the outcome of the analysis, indicating that some of the differences in MD and FA were largely driven by unequal WMH load between the groups rather than alterations in normal-appearing white matter (NAWM). Thus, our findings suggest that if the purpose of a study is to compare alterations in NAWM between two groups using DTI it may be necessary to adjust the statistical analysis for WMH.

Citation: Svärd D, Nilsson M, Lampinen B, Lätt J, Sundgren PC, Stomrud E, et al. (2017) The effect of white matter hyperintensities on statistical analysis of diffusion tensor imaging in cognitively healthy elderly and prodromal Alzheimer's disease. PLoS ONE 12(9): e0185239. https://doi.org/10.1371/journal.pone.0185239

Editor: Yong Liu, Brainnetome Center & The National Laboratory of Pattern Recognition, CHINA

Received: March 29, 2017; Accepted: September 9, 2017; Published: September 21, 2017

Copyright: © 2017 Svärd et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Raw image data cannot be made publicly available due to restrictions imposed by Swedish law and the Regionala Etikprövningsnämnden of Lund, Sweden. Requests for data can be sent to the corresponding author, Daniel Svärd (daniel.svard@med.lu.se).

Funding: The study was supported by the European Research Council, the Swedish Research Council, the Strategic Research Area MultiPark (Multidisciplinary Research in Parkinson’s disease) at Lund University, the Crafoord Foundation, the Swedish Brain Foundation, the Skåne University Hospital Foundation, the Swedish Alzheimer Association, Stiftelsen för gamla tjänarinnor, and the Swedish Federal Government under the ALF Agreement. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Diffusion-weighted MRI is a non-invasive technique capable of detecting microstructural tissue alterations such as axonal loss and demyelination in the human brain in vivo [1–3]. As such, its popularity has been growing steadily during the last two decades. Diffusion tensor imaging (DTI) yields estimates of e.g. the mean diffusivity (MD) and the fractional anisotropy (FA), and has been used to study human brain development and normal aging as well as a wide variety of neurological and psychiatric conditions [4–8].

White matter hyperintensities (WMH) are, compared to normal-appearing white matter (NAWM), visualized as hyperintense regions in the white matter on T2-weighted MRI and are most commonly thought to be due to small vessel disease [9–11]. They are typically located in periventricular or deep cortical regions and are histopathologically characterized by ischemic changes and a varying degree of gliosis, axonal loss, and demyelination. WMH are interchangeably called white matter lesions, white matter changes, and leukoaraiosis amongst others [11–13]. These changes are relatively common in cognitively healthy elderly with the prevalence varying substantially between different studies [14–16]. Some studies report that more than 90% of individuals older than 60 years exhibit WMH [17].

DTI is sensitive to WMH, exhibiting elevated MD and reduced FA in affected white matter regions [18–20]. However, the relationship between alterations in NAWM, detectable only with DTI, and WMH, also detectable with T2-weighted MRI, has only recently been investigated more thoroughly [21,22]. It has been proposed that WMH constitute a histopathological continuum of varying degree of white matter pathology [23]. Several recent studies have also investigated the effect of WMH on DTI estimates in normal aging and concluded that WMH only partially explain the elevated MD and reduced FA seen in normal aging, suggesting that alterations in NAWM as quantified with DTI may represent an early phase and WMH the late phase of the same common pathophysiological phenomenon [24,25].

Given that WMH are seen in normal aging, it is reasonable to suspect that the mere presence of WMH in cognitively healthy elderly are associated to elevated MD and reduced FA. Two recent studies investigated this and observed that subjects having WMH were more likely to have elevated MD and reduced FA in general compared to healthy subjects not having WMH [26,27].

Due to the association between DTI estimates and the presence of WMH, unequal WMH load between two groups may confound group comparisons, and lead to differences being partially due to the difference in WMH load rather than more disease-specific effects of alterations in NAWM. However, this methodological issue is not always considered and accounted for in DTI research, and the magnitude of the effect of WMH on DTI estimates in healthy subjects has not yet been fully investigated. The two above referenced studies included only a small number of subjects with WMH (n = 12 and n = 8, respectively) [26,27]. Also, most of the subjects were in the fourth to fifth decades of life, wherefore it is uncertain to what degree the results apply to subjects in the sixth to eight decades of life, which are known to have a higher prevalence of WMH. Moreover, to our knowledge, no previous study has evaluated how the level of WMH load in a control group affects the comparison of DTI estimates to a diseased group.

Amnestic mild cognitive impairment (aMCI) is characterized by cognitive impairment greater than expected for a particular age and educational level, but not significant enough to interfere with activities of daily life or fulfilling the criteria for dementia [28]. Although aMCI is etiologically a heterogeneous entity, in a majority of cases aMCI is an expression of prodromal Alzheimer's disease (AD) and it has been shown that the ratio between Aβ₄₂ and Aβ₄₀ in cerebrospinal fluid (CSF) predicts conversion to AD rather well [29]. aMCI and AD are both associated with alterations in NAWM that are thought to be disease-specific, as quantified with DTI, but they are also associated with elevated levels of WMH [5,30]. Thus, it is possible that including controls with high WMH load in a study of prodromal AD subjects versus cognitively healthy elderly may lead to improper conclusions about disease-specific alterations in NAWM detectable only with DTI in the prodromal AD group. That is, inclusion of controls with high WMH load may lead to false negative results. On the other hand, inclusion of controls with low WMH load in a study of prodromal AD subjects with high WMH load may lead to an overestimation of disease-specific alterations in NAWM.

We therefore hypothesized that WMH can affect the results of a statistical analysis in DTI when there is a difference in WMH load between the investigated groups. The aim of this study was thus to, first, study the effect of unequal WMH load between two matched groups of cognitively healthy elderly on DTI estimates in several major white matter tracts and, second, to assess the effect of WMH when comparing prodromal AD subjects to cognitively healthy elderly. This aim was addressed in two ways. First, we compared MD and FA in the right and left dorsal and ventral cingulum, the right and left superior longitudinal fasciculus (SLF), and the right and left corticospinal tract (CST) in cognitively healthy elderly with different levels of WMH load and estimated the effect size of WMH on DTI estimates. Second, we compared MD and FA in the abovementioned white matter tracts between prodromal AD subjects and cognitively healthy elderly in two analyses, unadjusted and adjusted for WMH, respectively. The abovementioned tracts were selected because the dorsal and ventral cingulum and the SLF, but not the CST, is associated with elevated MD and reduced FA in AD subjects, and the dorsal cingulum and the SLF, but not the ventral cingulum and the CST, are at least partially located in areas were WMH are common [5,17,30,31]. This selection thereby allowed us to more carefully disentangle the effect of unequal WMH load between groups on DTI estimates from more disease-specific alterations in NAWM in prodromal AD.

Methods

Ethics statement

The study was approved by the local Ethics Committee at Lund University and conducted according to the provisions of the Helsinki Declaration. Written informed consent was obtained from all subjects.

Study population

Cognitively healthy elderly (n = 132; mean age 71.6±4.5 years; 44.7% males) with a non-pathological Aβ₄₂/Aβ₄₀ ratio (i.e. Aβ₄₂/Aβ₄₀ > 0.1) were recruited from the Malmö Diet and Cancer Study, an epidemiological study part of the European Prospective Investigation into Diet and Cancer in the city of Malmö, Sweden (Table 1) [29,32]. Inclusion and exclusion criteria have been described previously [33]. Briefly, subjects were eligible for inclusion if they were above 60 years old, scored 27−30 points on Mini Mental State Examination (MMSE) at screening visit [34], did not suffer from any subjective cognitive impairment, and were fluent in Swedish. Exclusion criteria included presence of severe neurological disease (e.g. stroke, Parkinson’s disease or multiple sclerosis) or psychiatric disease (e.g. severe depression or psychotic syndromes).

Download:

Table 1. Sample characteristics of the study population.

https://doi.org/10.1371/journal.pone.0185239.t001

aMCI subjects (n = 83; mean age 71.4±5.4 years; 53.0% males) with pathological Aβ₄₂/Aβ₄₀ ratio (i.e. Aβ₄₂/Aβ₄₀ ≤ 0.1) indicating high risk of conversion to AD were selected from the prospective and longitudinal Swedish BioFINDER study (Biomarkers For Identifying Neurodegenerative Disorders Early and Reliably; http://www.biofinder.se; Table 1) [29]. Subjects were classified as aMCI based on a neuropsychological battery of examinations and clinical assessments. They were enrolled consecutively at three memory outpatient clinics in Sweden and were assessed by physicians with special interest in dementia disorders. Subjects were eligible for inclusion if they were referred to the memory clinics due to cognitive impairment, presented objective memory impairment according to neuropsychological assessments, did not fulfill the criteria for dementia, scored 24−30 points on MMSE, were aged 60−80 years, and were fluent in Swedish. Exclusion criteria included cognitive impairment that without doubt could be explained by another condition (other than prodromal dementia), severe somatic disease, and refusing lumbar puncture or neuropsychological investigation.

MRI acquisition and post-processing

Data acquisition was performed on a Siemens Trio 3 T MRI scanner using a standard 12-channel head coil. DTI data were acquired using a single-shot EPI sequence (TR/TE = 8200/86 ms/ms) with diffusion encoding in 64 directions using b values of 0 and 1000 s/mm². In total, 60 contiguous axial slices with a spatial resolution of 2×2×2 mm³ were acquired. For the assessment of WMH load, 27 axial slices of T2-weighted FLAIR imaging were acquired (TR/TE/TI = 9000/89/2500 ms/ms/ms) at a spatial resolution of 0.7×0.7×5.2 mm³. The volumetric analysis was based on a coronal MPRAGE sequence (TR/TE/TI = 1950/3.37/900 ms/ms/ms), with a spatial resolution of 1.0×1.0×1.2 mm³ and a flip angle of 9°.

All DTI data were corrected for motion and eddy current induced artifacts by registering images to the first non-diffusion weighted image using Elastix [35,36]. Parameter maps of MD and FA were calculated from the diffusion tensor eigenvalues using in-house developed software implemented in Matlab (MATLAB 2013a, The MathWorks Inc., Natick, MA, USA). This software fitted the diffusion tensor using heteroscedasticity-corrected linear least squares regression. DTI volumes were registered to MNI152 standard-space using the registration algorithm of FLIRT and FNIRT, parts of the FMRIB Software Library [37,38].

Subjects that exhibited gross motion artifacts on DTI parameter maps or incidental findings such as meningioma, severe atrophy or old infarction were excluded from subsequent analysis.

Tractography

The investigated white matter tracts were generated using a semi-automated tractography method (Fig 1). The method utilized multiple regions of interest (ROIs) defined in MNI152 standard-space and warped to native-space using FNIRT results. The ROIs were then used to segment streamlines from a whole-brain tractography generated in TrackVis (thresholds were set to 0.2 for FA and 30° for the angle) [39]. This method was chosen over manual ROI definition in native-space, which would have required an inordinate analysis time due to the high number of subjects and white matter tracts. To include streamlines running along the whole white matter tract, as well as shorter streamlines in the same anatomical region, an ‘AND’-ROI covering the entire white matter tract was defined for each of the eight tracts in MNI152 standard-space, according to the ICBM-DTI-81 white matter labels atlas [40]. That is, the volume of the entire tract, as defined in the atlas, was projected from the atlas to standard-space and further to subject-space for each subject. To exclude streamlines from nearby white matter tracts intersecting the desired tract, a ‘NOT’-ROI was defined around their anatomical region of the tract according to the JHU WM tractography atlas [41]. Technically, the same procedure for creating the 'AND'-ROIs was used to create the 'NOT'-ROIs, except that before the ROIs were projected to subject-space they were automatically enlarged by approximately five voxels from the border of the ROI. Then the actual tract volume was subtracted resulting in a shell-like ROI, with a thickness that corresponded to the enlargement of the original ROI, that surrounded the tract volume as defined in the atlas. The procedure was performed using in-house developed software implemented in Matlab, utilizing the warp-fields generated by FNIRT to warp ROIs from standard-space to subject-space, and TrackVis for streamline management. For each subject, the segmented white matter tracts were visually inspected and, if necessary, adjusted in subject-space. This semi-automated method was found to be more robust because automated delineation of the ROIs was less biased by inter- and intra-individual anatomical variation or different position of the head in the MRI scanner than manual placement of ROIs [42]. The average value of MD and FA for each white matter tract was used in subsequent analysis.

Download:

Fig 1. Graphical rendering of tractographies in a representative subject.

Tractographies of the left-hand side of the dorsal cingulum (A), the ventral cingulum (B), the superior longitudinal fasciculus (SLF; C), and the corticospinal tract (CST; D) segmented from a whole-brain tractography and superimposed on a mid-sagittal FA map.

https://doi.org/10.1371/journal.pone.0185239.g001

Volumetric assessment

White matter atrophy is associated with elevated MD and reduced FA [21]. To be able to adjust subsequent analysis for this confound, ventricle volume was normalized by intracranial volume and was used as a proxy for white matter atrophy. FreeSurfer was used to perform automated segmentation of the MPRAGE volumes to calculate ventricle volume (ml) and intracranial volume (ml) (Table 1) [43].

Assessment of WMH load

Automated segmentation of WMH volume (ml) was performed with Lesion segmentation tool (LST as implemented in SPM8) using T2-weighted FLAIR imaging and the MPRAGE volumes. WMH volume was normalized by intracranial volume and was used to describe WMH load quantitatively (Table 1) [44]. In addition, WMH load was evaluated qualitatively by one trained operator using the Fazekas rating scale (grade 0–3) [12].

Assessment of cardiovascular disease

Cardiovascular disease is associated with both WMH and elevated MD and reduced FA [45–47]. To be able to adjust subsequent analysis for this confound the presence of indirect markers of cardiovascular disease, diabetes, hypertension, ischemic heart disease, transient ischemic attack, and stroke [48] were used to classify each subject as either having or not having cardiovascular disease (Table 1).

Definition of subgroups of cognitively healthy elderly with unequal WMH load

Two subgroups with unequal WMH load, matched for age, male-to-female sex ratio, prevalence of cardiovascular disease, MMSE score, and ventricle volume, were selected from the group of cognitively healthy elderly (Table 1 and Fig 2). Lower WMH load was defined as having a WMH volume ≤ 0.5% of the total intracranial volume, which corresponded approximately to Fazekas grade 0–1, and higher WMH load as having a WMH volume ≥ 1%, which corresponded approximately to Fazekas grade 2–3 [12].

Download:

Fig 2. FLAIR images of two different representative subjects with different WMH load.

Subject A represents lower WMH load (i.e. a WMH volume ≤ 0.5% of the intracranial volume that corresponded approximately to Fazekas grade 0–1) and subject B higher WMH load (i.e. a WMH volume ≥ 1% of the intracranial volume that corresponded approximately to Fazekas grade 2–3), with arrows indicating example regions with WMH.

https://doi.org/10.1371/journal.pone.0185239.g002

Assessment of Aβ₄₂/Aβ₄₀ ratio in cerebrospinal fluid

The procedure and analysis of the CSF followed the Alzheimer’s Association Flow Chart for CSF biomarkers [49]. Lumbar CSF samples were collected at the three centers and analyzed according to a standardized protocol [49,50]. CSF Aβ₄₂ and Aβ₄₀ were analyzed by Euroimmun (EI) (EUROIMMUN AG, Lübeck, Germany).

Statistical analysis

Statistical analysis was performed using the Statistical Package for Social Sciences (SPSS version 22, IBM Corp., Chicago, IL, USA).

A Mann-Whitney U-test for independent samples was performed to determine differences in age, male-to-female sex ratio, prevalence of cardiovascular disease, MMSE score, Aβ₄₂/Aβ₄₀ ratio, ventricle volume, and WMH volume between cognitively healthy elderly and prodromal AD subjects.

A two-tailed Student’s t-test for independent samples was performed to compare MD and FA in all eight white matter tracts between the two matched subgroups of cognitively healthy elderly with unequal WMH load. The effect size was based on differences in group means and described using Cohen’s d, which facilitates comparison of effect sizes between studies.

Two multivariate linear regression models were used to test if and to what degree the WMH load affected DTI estimates and their change in prodromal AD. In model 1, the dependent variable was the DTI estimate (MD or FA) for each tract (the dorsal and ventral cingulum, the SLF, and the CST) while the independent variables were having prodromal AD or not and ventricle volume. In model 2, WMH volume was added as an additional independent variable. Standardized β was calculated to describe the effect of the independent variables. In both models, variables tested using the Mann-Whitney U-test described above and with significant differences between the prodromal AD subjects and cognitively healthy elderly were also added as independent variables to be able to adjust the analyses for these. R² was calculated to describe the proportion of the variance in the dependent variable that was predictable from the independent variable (i.e. how well the regression model explained the real data). ΔR² was calculated to describe how R² changes between model 1 and 2 (i.e. if adjusting the analysis for WMH volume improved the explanatory power of the analysis or not).

Results

Demographic analysis

In prodromal AD subjects, MMSE score was significantly higher (p = 1.23×10⁻²⁶), Aβ₄₂/Aβ₄₀ ratio was significantly lower (p = 6.50×10⁻⁷⁰), ventricular volume was significantly higher (p = 3.07×10⁻³), and WMH volume was significantly higher (p = 2.60×10⁻⁴) compared to cognitively healthy controls (Table 1). There were no significant differences in age, male-to-female sex ratio, or prevalence of cardiovascular disease between these groups.

Effect of WMH on DTI estimates in cognitively healthy elderly

In cognitively healthy elderly with higher compared to lower WMH load (Table 2), MD was significantly elevated in the right SLF (0.81±0.06 vs. 0.75±0.03 μm²/ms, p = 2.47×10⁻⁴), the left SLF (0.79±0.06 vs. 0.73±0.03 μm²/ms, p = 1.83×10⁻⁴), and the right dorsal cingulum (0.76±0.04 vs. 0.74±0.03 μm²/ms, p = 0.04). Also, FA was significantly reduced in the right SLF (0.41±0.03 vs. 0.43±0.02, p = 1.61×10⁻³), the left SLF (0.41±0.03 vs. 0.44±0.02, p = 1.35×10⁻³), and the left dorsal cingulum (0.45±0.03 vs. 0.47±0.03, p = 0.03). Cohen’s d ranged from small (ventral cingulum, d < 0.3) to very high (SLF, d > 1.0).

Download:

Table 2. Comparison of MD and FA between the matched subgroups of cognitively healthy elderly with lower^{^a} and higher^{^b} WMH load, respectively, using a two-tailed Student’s t-test.

https://doi.org/10.1371/journal.pone.0185239.t002

Effect of WMH load in prodromal AD subjects compared to cognitively healthy elderly

Table 3 shows results from using regression model 1, where the dependent variable was MD or FA for each of the eight investigated tracts and the independent variables were having prodromal AD or not and ventricle volume. Tests showed that standardized β for having prodromal AD was significant for both MD and FA in the right SLF (β = 0.19 for MD and β = −0.13 for FA), the left SLF (β = 0.20 for MD and β = −0.16 for FA), the right dorsal cingulum (β = 0.16 for MD and β = −0.23 for FA), the left dorsal cingulum (β = 0.15 for MD and β = −0.20 for FA), the right ventral cingulum (β = 0.23 for MD and β = −0.18 for FA), and the left ventral cingulum (β = 0.27 for MD and β = −0.24 for FA). R² ranged from 0.02 to 0.09. Thus, prodromal AD subjects had elevated MD and reduced FA in the right and left SLF and in the right and left dorsal and ventral cingulum compared to cognitively healthy elderly in the analysis not adjusted for WMH volume.

Download:

Table 3. Multivariate linear regression analyses of MD and FA in the dorsal and ventral cingulum, the SLF, and the CST in cognitively healthy elderly (n = 132) and prodromal AD subjects (n = 83) unadjusted (model 1) and adjusted (model 2) for WMH volume, respectively.

https://doi.org/10.1371/journal.pone.0185239.t003

In regression model 2 (see Table 3), where WMH volume was added as an additional independent variable, standardized β for having prodromal AD was significant for both MD and FA in the right ventral cingulum (β = 0.23 for MD and β = −0.18 for FA), the left ventral cingulum (β = 0.24 for MD and β = −0.21 for FA), and for FA in the right dorsal cingulum (β = −0.16) and left ventral cingulum (β = −0.16). In this model, standardized β for WMH volume was significant for both MD and FA in the right SLF (β = 0.74 for MD and β = −0.61 for FA), the left SLF (β = 0.74 for MD and −0.61 for FA), the right dorsal cingulum (β = 0.21 for MD and β = −0.28 for FA), the left dorsal cingulum (β = 0.26 for MD and −0.17 for FA), the right CST (β = 0.29 for MD and β = −0.33 for FA), and the left CST (β = 0.24 for MD and β = −0.21 for FA). ΔR² was significant in all analyses where standardized β for WMH volume was significant and ranged from 0.02 to 0.46. Thus, in all investigated tracts except the ventral cingulum, the explanatory power of the model was improved (i.e. a higher R² indicating better explained variance) when adjusting the analysis for WMH. Furthermore, in model 2, standardized β for having prodromal AD was no longer significant for MD or FA in neither the right nor the left SLF and no longer significant for MD in neither the right nor the left dorsal cingulum. That is, when adjusting the analysis for WMH there was no longer a significant difference in MD or FA in neither the right nor the left SLF and no longer a significant difference in MD in neither the right nor the left dorsal cingulum when comparing prodromal AD subjects to cognitively healthy elderly.

Discussion

In the present study, we set out to investigate if unequal WMH load affects the comparison of DTI estimates between groups. First, we studied the effect of WMH in several major white matter tracts in cognitively healthy elderly. Second, we assessed the effect of WMH when comparing prodromal AD subjects to cognitively healthy elderly. We observed that the effect of WMH on MD and FA were in general large (Table 2). Moreover, when comparing MD and FA between prodromal AD subjects and cognitively healthy elderly, we observed that adjusting the analysis for WMH load improved the explanatory power as well as the outcome of the analysis. Our interpretation is that the effect of WMH on DTI estimates is comparable to that of and sometimes larger than disease-specific alterations in NAWM as reported by previous studies and that differences in DTI estimates were in some white matter tracts largely attributed to WMH load rather than being specific to prodromal AD (Table 3). These results are important because they suggest that unequal WMH load between two investigated groups could affect the result of a comparison of DTI estimates and may thus lead to an over- or underestimation of disease-specific alterations in NAWM. For example, a high WMH load in a control group may lead to false negative results. On the other hand, a high WMH load in a diseased group, in e.g. prodromal AD, may lead to false positive results. That is, if the aim of the study was to study disease-specific alterations in NAWM rather than WMH.

DTI estimates are affected by alterations in NAWM but also by WMH probably due to their similar composition on a microscopic level. Compared to normal white matter that largely consists of densely packed axons that restrict the diffusion of water molecules, alterations in NAWM are thought to represent axonal loss and demyelination leading to less restricted diffusion [51]. WMH, on the other hand, are thought to have a microstructure that consists of a varying degree of gliosis but also more severe axonal loss and demyelination that also leads to less restricted diffusion measured as elevated MD and reduced FA [11–13, 18]. Early alterations in NAWM, detectable only with DTI, are thought to be more disease-specific whereas WMH, detectable with DTI and T2-weigted MRI, are also encountered in cognitively healthy elderly [4–8, 14–17].

We found that the effect of WMH on MD and FA in cognitively healthy elderly was large and comparable to what has been reported previously for various pathological conditions. For example, effect sizes for MD in MCI are between 0.57 and 1.09, in AD between 0.95 and 1.04, in Parkinson’s disease between 0.78 and 2.20, and in schizophrenia between 0.78 and 1.24 [52−57]. Although the effect size is dependent on factors such as the cohort selection, the MRI scanner, the protocol used, and the method of analysis [58], it is worth noting that the significant effects we found on MD of high WMH load in matched subgroups of cognitively healthy elderly was between 0.63 and 1.27 for different tracts (Table 2). This is on par with several different pathological conditions (Table 4). Corresponding results were observed for FA. Adjusting for potentially unequal levels of WMH is thus warranted to disentangle effects of the WMH from more disease-specific alterations in NAWM.

Download:

Table 4. Effect size of group mean expressed as Cohen’s d and group size (n, patient group + control group) for WMH in the present study and various pathological conditions previously studied using DTI.

https://doi.org/10.1371/journal.pone.0185239.t004

The present study showed WMH to have a relatively large effect on DTI estimates even though we only took the WMH load into account, and not their exact location. This was probably due to a local effect of WMH on DTI estimates, large enough to affect the mean value of the white matter tract. The largest effect sizes were observed in the SLF and the dorsal cingulum, which are both at least partially located in regions where WMH are commonly observed (i.e. in periventricular and deep cortical regions) [5,17,30,31]. This suggests that there is a correlation between the effect of WMH on DTI estimates in a specific tract and its local WMH load. However, the current study was not designed to study this correlation and is therefore not able to neither confirm nor discard it.

When comparing DTI estimates in the eight investigated white matter tracts between prodromal AD subjects and cognitively healthy elderly using regression analysis we observed that MD was elevated and FA reduced, respectively, in prodromal AD subjects in the right and left dorsal and ventral cingulum and in the right and left SLF when the analysis was unadjusted for WMH load. These results are in accordance with most previous work although it has also been reported that FA can be unexpectedly elevated in the SLF of aMCI subjects due to a reduction of crossing fibers [5,59].

Adjusting for WMH load allowed us to separately test the effects of disease-specific alterations in NAWM and WMH in prodromal AD. When adjusting the analysis for WMH load we observed that the explanatory power as well as the outcome was improved for investigated white matter tracts at least partially located in regions where WMH are common (e.g. the SLF and the dorsal cingulum in the deep cortical regions of the frontal and parietal lobes) but not in regions were WMH are less common (e.g. the ventral cingulum in the temporal lobe) [17,30,31]. Furthermore, when adjusting the analysis for WMH load we still observed an elevated MD in the ventral cingulum and a reduced FA in the dorsal and ventral cingulum in prodromal AD subjects but no significant differences in MD in the SLF and dorsal cingulum or in FA in the SLF. This suggests that differences in DTI estimates in the SLF and partially in the dorsal cingulum between prodromal AD subjects and cognitively healthy elderly in the analysis unadjusted for WMH were largely driven by differences in WMH load between the two groups rather than disease-specific alterations in NAWM (Tables 1 and 3). This is not that surprising given that there was a group mean difference in WMH load between the prodromal AD subjects and the cognitively healthy elderly and that the largest effect size in group mean difference between the matched subgroups of cognitively healthy elderly with unequal WMH load were seen in the SLF and the dorsal cingulum. It is possible that these results extrapolate to studies of other conditions, and in particular in the elderly since the prevalence of WMH is known to increase with age [14–16].

We identified three limitations of the present study. First, the exact etiology of WMH in the study population was unknown. Even though we matched and adjusted the analyses for the influence of cardiovascular disease, WMH could in some subjects be an expression of other incipient disease (e.g. multiple sclerosis) that DTI could be sensitive to. However, we made an effort to minimize this potential confounding effect when designing the inclusion and exclusion criteria. Second, since WMH have a different microscopic structure compared to NAWM, WMH may interfere with fiber tracking and thus the presence of WMH in the investigated groups may potentially have biased the results. Even though this is a theoretical limitation, visual inspection of the generated tractographies awoke no suspicions that this was the case in this study. Third, we did not correct for multiple comparisons due to the explorative nature of the study. This means that we preferred the risk of accepting false positive differences over the risk of discarding true positive differences.

Conclusion

The effect of unequal WMH load in cognitively healthy elderly on the DTI estimates MD and FA was generally large and comparable to previous reports from using DTI in various pathological conditions such as neurodegenerative disease. Adjusting the statistical analysis for WMH load when comparing MD and FA between prodromal AD subjects and cognitively healthy elderly improved the explanatory power as well as the outcome of the analysis indicating that some differences in MD and FA were largely driven by unequal WMH load between the groups rather than disease-specific alterations in NAWM. This suggests that the effect of differences in WMH load between groups is large enough to affect the results of a statistical analysis of DTI estimates and that this should be taken into consideration in the design and interpretation of DTI studies. If the purpose of a DTI study is to compare disease-specific alterations in NAWM rather than WMH between groups with unequal WMH load this can be achieved by adjusting the statistical analysis for WMH load.

References

1. Basser PJ, Mattiello J, LeBihan D. MR diffusion tensor spectroscopy and imaging. Biophys J 1994;66:259–67. pmid:8130344
- View Article
- PubMed/NCBI
- Google Scholar
2. Le Bihan D, Mangin JF, Poupon C, Clark CA, Pappata S, Molko N, et al. Diffusion tensor imaging: concepts and applications. J Magn Reson Imaging 2001;13:534–46. pmid:11276097
- View Article
- PubMed/NCBI
- Google Scholar
3. Nilsson M, van Westen D, Ståhlberg F, Sundgren PC, Lätt J. The role of tissue microstructure and water exchange in biophysical modelling of diffusion in white matter. MAGMA 2013;26:345–70. pmid:23443883
- View Article
- PubMed/NCBI
- Google Scholar
4. Kubicki M, McCarley R, Westin CF, Park HJ, Maier S, Kikinis R, et al. A review of diffusion tensor imaging studies in schizophrenia. J Psychiatr Res 2007;41:15–30. pmid:16023676
- View Article
- PubMed/NCBI
- Google Scholar
5. 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 2011;32:2322.e5–2322.e18.
- View Article
- Google Scholar
6. Lebel C, Gee M, Camicioli R, Wieler M, Martin W, Beaulieu C. Diffusion tensor imaging of white matter tract evolution over the lifespan. NeuroImage 2012;60:340–52. pmid:22178809
- View Article
- PubMed/NCBI
- Google Scholar
7. Santillo A, Mårtensson J, Lindberg O, Nilsson M, Manzouri A, Landqvist Waldö M, et al. Diffusion tensor tractography versus volumetric imaging in the diagnosis of behavioral variant frontotemporal dementia. PLoS ONE 2013.
- View Article
- Google Scholar
8. Surova Y, Nilsson M, Lätt J, Lampinen B, Lindberg O, Widner H, et al. Disease-specific structural changes in thalamus and dentatorubrothalamic tract in progressive supranuclear palsy. Neuroradiology 2015;57:1079–91. pmid:26253801
- View Article
- PubMed/NCBI
- Google Scholar
9. Potter GM, Doubal FN, Jackson CA, Chappell FM, Sudlow CL, Dennis MS, et al. Counting cavitating lacunes underestimates the burden of lacunar infarction. Stroke 2010;41:267–72. pmid:20044528
- View Article
- PubMed/NCBI
- Google Scholar
10. Schmidt R, Schmidt H, Haybaeck J, Loitfelder M, Weis S, Cavalieri M, et al. Heterogeneity in age-related white matter changes. Acta Neuropathol 2011;122:171–85. pmid:21706175
- View Article
- PubMed/NCBI
- Google Scholar
11. Wardlaw JM, Smith EE, Biessels GJ, Cordonnier C, Fazekas F, Frayne R, et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol 2013;12:822–38. pmid:23867200
- View Article
- PubMed/NCBI
- Google Scholar
12. Fazekas F, Chawluk JB, Alavi A, Hurtig HI, Zimmerman RA. MR signal abnormalities at 1.5 T in Alzheimer's dementia and normal aging. AJR Am J Roentgenol. 1987;149:351–56. pmid:3496763
- View Article
- PubMed/NCBI
- Google Scholar
13. Gouw AA, Seewann A, van der Flier WM, Barkhof F, Rozemuller AM, Scheltens P, et al. Heterogeneity of small vessel disease: a systematic review of MRI and histopathology correlations. J Neurol Neurosurg Psychiatry 2011;82:126–35. pmid:20935330
- View Article
- PubMed/NCBI
- Google Scholar
14. Breteler MMB, van Swieten JC, Bots ML, Grobbee DE, Claus JJ, van den Hout JHW, et al. Cerebral white matter lesions, vascular risk factors, and cognitive function in a population based study The Rotterdam Study. Neurology 1994;44:1246. pmid:8035924
- View Article
- PubMed/NCBI
- Google Scholar
15. Ylikoski A, Erkinjuntti T, Raininko R, Sarna S, Sulkava R, Tilvis R. White matter hyperintensities on MRI in the neurologically nondiseased elderly—analysis of cohorts of consecutive subjects aged 55 to 85 years living at home. Stroke 1995;26:1171–77. pmid:7604409
- View Article
- PubMed/NCBI
- Google Scholar
16. Longstreth WT, Manolio TA, Arnold A, Burke GL, Bryan N, Jungreis CA, et al. Clinical correlates of white matter findings on cranial magnetic resonance imaging of 3301 elderly people The Cardiovascular Health Study. Stroke 1996; 27:1274–82. pmid:8711786
- View Article
- PubMed/NCBI
- Google Scholar
17. de Leeuw FE, de Groot JC, Achten E, Oudkerk M, Ramos LMP, Heijboer R, et al. Prevalence of cerebral white matter lesions in elderly people: a population based magnetic resonance imaging study. The Rotterdam Scan Study. J Neurol Neurosurg Psychiatry 2001;70:9–14. pmid:11118240
- View Article
- PubMed/NCBI
- Google Scholar
18. Jones DK, Lythgoe D, Horsfield MA, Simmons A, Williams SCR, Markus HS. Characterization of white matter damage in ischemic leukoaraiosis with diffusion tensor MRI. Stroke 1999;30:393–97. pmid:9933277
- View Article
- PubMed/NCBI
- Google Scholar
19. Maillard P, Fletcher E, Harvey D, Carmichael O, Reed B, Mungas D, et al. White matter hyperintensity penumbra. Stroke 2011;42:1917–22. pmid:21636811
- View Article
- PubMed/NCBI
- Google Scholar
20. Svärd D, Szczepankiewicz F, Nilsson M, Lampinen B, Lätt J, Sundgren PC, et al. White matter hyperintensities as a confounder in diffusion tensor imaging analysis of elderly cohorts. Proc Intl Soc Mag Reson Med 2014;22,1839.
- View Article
- Google Scholar
21. Vernooij MW, de Groot M, van der Lugt A, Ikram MA, Krestin GP, Hofman A, et al. White matter atrophy and lesion formation explain the loss of structural integrity of white matter in aging. NeuroImage 2008;43:470–7. pmid:18755279
- View Article
- PubMed/NCBI
- Google Scholar
22. Leritz EC, Shepel J, Williams VJ, Lipsitz LA, McGlinchey RE, Milberg WP, et al. Associations between T1 white matter lesion volume and regional white matter microstructure in aging. Hum Brain Mapp 2014;35:1085–1100. pmid:23362153
- View Article
- PubMed/NCBI
- Google Scholar
23. Maillard P, Fletcher E, Lockhart SN, Roach AE, Reed B, Mungas D, et al. White matter hyperintensities and their penumbra lie along a continuum of injury in the aging brain. Stroke 2014;45:1721–26. pmid:24781079
- View Article
- PubMed/NCBI
- Google Scholar
24. de Groot M, Ikram MA, Akoudad S, Krestin GP, Hofman A, van der Lugt A, et al. Tract-specific white matter degeneration in aging: the Rotterdam Study. Alzheimers Dement 2015;11:321–30. pmid:25217294
- View Article
- PubMed/NCBI
- Google Scholar
25. Pelletier A, Periot O, Dilharreguy B, Hiba B, Bordessoules M, Chanraud S, et al. Age-related modifications of diffusion tensor imaging parameters and white matter hyperintensities as inter-dependent processes. Front Aging Neurosci 2016;7:255. pmid:26834625
- View Article
- PubMed/NCBI
- Google Scholar
26. Iverson GL, Hakulinen U, Wäljas M, Dastidar P, Lange RT, Soimakallio S, et al. To exclude or not to exclude: white matter hyperintensities in diffusion tensor imaging research. Brain Inj 2011;25:1325–32. pmid:22077537
- View Article
- PubMed/NCBI
- Google Scholar
27. Lange RT, Shewchuk JR, Heran MKS, Rauscher A, Jarett M, Brubacher JR, et al. To exclude or not to exclude: further examination of the influence of white matter hyperintensities in diffusion tensor imaging research. J Neurotraum 2014;31:198–205.
- View Article
- Google Scholar
28. Petersen RC, Smith GE, Waring SC, Ivnik RJ, Tangalos EG, Kokmen E. Mild cognitive impairment: Clinical characterization and outcome. Arch Neurol 1999;56:303–8. pmid:10190820
- View Article
- PubMed/NCBI
- Google Scholar
29. Janelidze S, Zetterberg H, Mattsson N, Palmqvist S, Vanderstichele H, Lindberg O, et al. CSF Aβ42/Aβ40 and Aβ42/Aβ38 ratios: better diagnostic markers of Alzheimer disease. Ann Clin Transl Neurol 2016;3:154–65. pmid:27042676
- View Article
- PubMed/NCBI
- Google Scholar
30. Yoshita M, Fletcher E, Harvey D, Ortega M, Martinez O, Mungas DM, et al. Extent and distribution of white matter hyperintensities in normal aging, MCI, and AD. Neurol 2006;67:2192–8.
- View Article
- Google Scholar
31. Catani M, de Schotten MT. A diffusion tensor imaging tractography atlas for virtual in vivio dissections. Cortex 2008;44:1105–32. pmid:18619589
- View Article
- PubMed/NCBI
- Google Scholar
32. Manjer J, Carlsson S, Elmståhl S, Gullberg B, Janzon L, Lindström M, et al. The Malmö diet and cancer study: representativity, cancer incidence and mortality in participants and non-participants. Eur J Cancer Prev 2001;10:489–99. pmid:11916347
- View Article
- PubMed/NCBI
- Google Scholar
33. Gustavsson A-M, Stomrud E, Abul-Kasim K, Minthon L, Nilsson PM, Hansson O, et al. Cerebral microbleeds and white matter hyperintensities in cognitively healthy elderly: a cross-sectional cohort study evaluating the effect of arterial stiffness. Cerebrovasc Dis Extra 2015;5:41–51. pmid:26120319
- View Article
- PubMed/NCBI
- Google Scholar
34. Folstein MF, Folstein SE, McHugh PR. A practical method for grading the cognitive state of patients for the clinician. J Psychiat Res 1975;12:189–98. pmid:1202204
- View Article
- PubMed/NCBI
- Google Scholar
35. Klein S, Staring M, Murphy K, Viergever MA, Pluim JPW. Elastix: a toolbox for intensity based medical image registration. IEEE Trans Med Img 2010;29:196–205.
- View Article
- Google Scholar
36. Nilsson M, Szczepankiewicz F, van Westen D, Hansson O. Extrapolation-based references improve motion and eddy-current correction of high b-value DWI data: application in Parkinson’s disease dementia. PLoS One 10(11):e0141825. pmid:26528541
- View Article
- PubMed/NCBI
- Google Scholar
37. Mazziotta J, Toga A, Evans A, Fox P, Lancaster J, Zilles K, et al. A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). Phil Trans R Soc Lond 2001;356:1293–322.
- View Article
- Google Scholar
38. Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM. FSL. NeuroImage 2012;62:782–90. pmid:21979382
- View Article
- PubMed/NCBI
- Google Scholar
39. Wang R, Benner T, Sorensen A. Diffusion toolkit: a software package for diffusion imaging data processing and tractography. Proc Int Soc Magn Reson Med 2007;15, 3720.
- View Article
- Google Scholar
40. Mori S, Wakana S, Nagae-Poetscher LM, van Zijl PCM. MRI Atlas of Human White Matter. Elsevier, Amsterdam, The Netherlands 2005.
41. Hua K, Zhang J, Wakana S, Jiang H, Li X, Reich DS, et al. Tract probability maps in stereotaxic spaces: analysis of white matter anatomy and tract-specific quantification. NeuroImage 2008;39:336–47. pmid:17931890
- View Article
- PubMed/NCBI
- Google Scholar
42. Nucifora PG, Wu X, Melhem ER, Gur RE, Gur RC, Verma R. Automated diffusion tensor tractography: implementation and comparison to user-driven tractography. Acad Radiol 2012;19:622–9. pmid:22342650
- View Article
- PubMed/NCBI
- Google Scholar
43. Reuter M, Schmansky NJ, Rosas HD, Fischl B. Within-subject template estimation for unbiased longitudinal image analysis. NeuroImage 2012;61:1402–18. pmid:22430496
- View Article
- PubMed/NCBI
- Google Scholar
44. Schmidt P, Gaser C, Arsic M, Buck D, Förschler A, Berthele A, et al. An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis. NeuroImage 2012;59:3774–83. pmid:22119648
- View Article
- PubMed/NCBI
- Google Scholar
45. Launer LJ. Epidemiology of white matter lesions. Top Magn Reson Imaging 2004;15:365–7. pmid:16041288
- View Article
- PubMed/NCBI
- Google Scholar
46. Kodl CT, Franc DT, Rao JP, Anderson FS, Thomas W, Mueller BA, et al. Diffusion tensor imaging identifies deficits in white matter microstructure in subjects with type 1 diabetes that correlate with reduced neurocognitive function. Diabetes 2008;57:3083–9. pmid:18694971
- View Article
- PubMed/NCBI
- Google Scholar
47. Maillard P, Seshadri S, Beiser A, Himali JJ, Au R, Fletcher E, et al. Effects of systolic pressure on white-matter integrity in young adults in the Framingham heart study: a cross-sectional study. Lancet Neurol 2012;11:1039–47. pmid:23122892
- View Article
- PubMed/NCBI
- Google Scholar
48. Wilson PW, D'Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation 1998;97:1837–47. pmid:9603539
- View Article
- PubMed/NCBI
- Google Scholar
49. Blennow K, Hampel H, Weiner M, Zetterberg H. Cerebrospinal fluid and plasma biomarkers in Alzheimer disease. Nat Rev Neurol 2010;6:131–44. pmid:20157306
- View Article
- PubMed/NCBI
- Google Scholar
50. Palmqvist S, Zetterberg H, Blennow K, Vestberg S, Andreasson U, Brooks D, et al. Accuracy of brain amyloid detection in clinical practice using cerebrospinal fluid β-amyloid 42: a cross-validation study against amyloid positron emission tomography.
- View Article
- Google Scholar
51. Le Bihan D. Looking into the functional architecture of the brain with diffusion MRI. Nat Rev Neurosci 2003;4:469–80. pmid:12778119
- View Article
- PubMed/NCBI
- Google Scholar
52. Wang F, Sun Z, Cui L, Du X, Wang X, Zhang H, et al. Anterior cingulum abnormalities in male patients with schizophrenia determined through diffusion tensor imaging. Am J Psychiatry 2004;161:573–5. pmid:14992988
- View Article
- PubMed/NCBI
- Google Scholar
53. Zhang Y, Schuff N, Jahng G-H, Bayne W, Mori S, Schad L, et al. Diffusion tensor imaging of cingulum fibers in mild cognitive impairment and Alzheimer disease. Neurology 2007;68:13–9. pmid:17200485
- View Article
- PubMed/NCBI
- Google Scholar
54. Gattellaro G, Minati L, Grisoli M, Mariani C, Carella F, Osio M, et al. White matter involvement in idiopathic Parkinson disease: a diffusion tensor imaging study. Am J Neuroradiol 2009;30:1222–6. pmid:19342541
- View Article
- PubMed/NCBI
- Google Scholar
55. Stenset V, Bjørnerud A, Fjell AM, Walhovd KB, Hofoss D, Due-Tønnessen P, et al. Cingulum fiber diffusivity and CSF T-tau in patients with subjective and mild cognitive impairment. Neurobiol Aging 2011;32:581–589. pmid:19428143
- View Article
- PubMed/NCBI
- Google Scholar
56. Zhuang L, Sachdev PS, Trollor JN, Reppermund S, Kochan NA, Brodaty H, et al. Microstructural white matter changes, nor hippocampal atrophy, detect early amnestic mild cognitive impairment. PLoS One 2013;8:e58887. pmid:23516569
- View Article
- PubMed/NCBI
- Google Scholar
57. Rémy F, Vayssière N, Saint-Aubert L, Barbeau E, Pariente J. White matter disruption at the prodromal stage of Alzheimer’s disease: relationships with hippocampal atrophy and episodic memory performance. Neuroimage Clin 2015;27:482–92.
- View Article
- Google Scholar
58. Szczepankiewicz F, Lätt J, Wirestam R, Leemans A, Sundgren P, van Westen D, et al. Variability in diffusion kurtosis imaging: impact on study design, statistical power and interpretation. NeuroImage 2013;76:145–54. pmid:23507377
- View Article
- PubMed/NCBI
- Google Scholar
59. Douaud G, Jbabdi S, Behrens TE, Menke RA, Gass A, Monsch AU, et al. DTI measures in crossing-fibre areas: increased diffusion anisotropy reveals early white matter alteration in MCI and mild Alzheimer's disease. NeuroImage 2011;55:880–90. pmid:21182970
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Basser PJ, Mattiello J, LeBihan D. MR diffusion tensor spectroscopy and imaging. Biophys J 1994;66:259–67. pmid:8130344
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Le Bihan D, Mangin JF, Poupon C, Clark CA, Pappata S, Molko N, et al. Diffusion tensor imaging: concepts and applications. J Magn Reson Imaging 2001;13:534–46. pmid:11276097
View Article
PubMed/NCBI
Google Scholar

[6] View Article

[7] PubMed/NCBI

[8] Google Scholar

[ref3] 3. Nilsson M, van Westen D, Ståhlberg F, Sundgren PC, Lätt J. The role of tissue microstructure and water exchange in biophysical modelling of diffusion in white matter. MAGMA 2013;26:345–70. pmid:23443883
View Article
PubMed/NCBI
Google Scholar

[10] View Article

[11] PubMed/NCBI

[12] Google Scholar

[ref4] 4. Kubicki M, McCarley R, Westin CF, Park HJ, Maier S, Kikinis R, et al. A review of diffusion tensor imaging studies in schizophrenia. J Psychiatr Res 2007;41:15–30. pmid:16023676
View Article
PubMed/NCBI
Google Scholar

[14] View Article

[15] PubMed/NCBI

[16] Google Scholar

[ref5] 5. 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 2011;32:2322.e5–2322.e18.
View Article
Google Scholar

[18] View Article

[19] Google Scholar

[ref6] 6. Lebel C, Gee M, Camicioli R, Wieler M, Martin W, Beaulieu C. Diffusion tensor imaging of white matter tract evolution over the lifespan. NeuroImage 2012;60:340–52. pmid:22178809
View Article
PubMed/NCBI
Google Scholar

[21] View Article

[22] PubMed/NCBI

[23] Google Scholar

[ref7] 7. Santillo A, Mårtensson J, Lindberg O, Nilsson M, Manzouri A, Landqvist Waldö M, et al. Diffusion tensor tractography versus volumetric imaging in the diagnosis of behavioral variant frontotemporal dementia. PLoS ONE 2013.
View Article
Google Scholar

[25] View Article

[26] Google Scholar

[ref8] 8. Surova Y, Nilsson M, Lätt J, Lampinen B, Lindberg O, Widner H, et al. Disease-specific structural changes in thalamus and dentatorubrothalamic tract in progressive supranuclear palsy. Neuroradiology 2015;57:1079–91. pmid:26253801
View Article
PubMed/NCBI
Google Scholar

[28] View Article

[29] PubMed/NCBI

[30] Google Scholar

[ref9] 9. Potter GM, Doubal FN, Jackson CA, Chappell FM, Sudlow CL, Dennis MS, et al. Counting cavitating lacunes underestimates the burden of lacunar infarction. Stroke 2010;41:267–72. pmid:20044528
View Article
PubMed/NCBI
Google Scholar

[32] View Article

[33] PubMed/NCBI

[34] Google Scholar

[ref10] 10. Schmidt R, Schmidt H, Haybaeck J, Loitfelder M, Weis S, Cavalieri M, et al. Heterogeneity in age-related white matter changes. Acta Neuropathol 2011;122:171–85. pmid:21706175
View Article
PubMed/NCBI
Google Scholar

[36] View Article

[37] PubMed/NCBI

[38] Google Scholar

[ref11] 11. Wardlaw JM, Smith EE, Biessels GJ, Cordonnier C, Fazekas F, Frayne R, et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol 2013;12:822–38. pmid:23867200
View Article
PubMed/NCBI
Google Scholar

[40] View Article

[41] PubMed/NCBI

[42] Google Scholar

[ref12] 12. Fazekas F, Chawluk JB, Alavi A, Hurtig HI, Zimmerman RA. MR signal abnormalities at 1.5 T in Alzheimer's dementia and normal aging. AJR Am J Roentgenol. 1987;149:351–56. pmid:3496763
View Article
PubMed/NCBI
Google Scholar

[44] View Article

[45] PubMed/NCBI

[46] Google Scholar

[ref13] 13. Gouw AA, Seewann A, van der Flier WM, Barkhof F, Rozemuller AM, Scheltens P, et al. Heterogeneity of small vessel disease: a systematic review of MRI and histopathology correlations. J Neurol Neurosurg Psychiatry 2011;82:126–35. pmid:20935330
View Article
PubMed/NCBI
Google Scholar

[48] View Article

[49] PubMed/NCBI

[50] Google Scholar

[ref14] 14. Breteler MMB, van Swieten JC, Bots ML, Grobbee DE, Claus JJ, van den Hout JHW, et al. Cerebral white matter lesions, vascular risk factors, and cognitive function in a population based study The Rotterdam Study. Neurology 1994;44:1246. pmid:8035924
View Article
PubMed/NCBI
Google Scholar

[52] View Article

[53] PubMed/NCBI

[54] Google Scholar

[ref15] 15. Ylikoski A, Erkinjuntti T, Raininko R, Sarna S, Sulkava R, Tilvis R. White matter hyperintensities on MRI in the neurologically nondiseased elderly—analysis of cohorts of consecutive subjects aged 55 to 85 years living at home. Stroke 1995;26:1171–77. pmid:7604409
View Article
PubMed/NCBI
Google Scholar

[56] View Article

[57] PubMed/NCBI

[58] Google Scholar

[ref16] 16. Longstreth WT, Manolio TA, Arnold A, Burke GL, Bryan N, Jungreis CA, et al. Clinical correlates of white matter findings on cranial magnetic resonance imaging of 3301 elderly people The Cardiovascular Health Study. Stroke 1996; 27:1274–82. pmid:8711786
View Article
PubMed/NCBI
Google Scholar

[60] View Article

[61] PubMed/NCBI

[62] Google Scholar

[ref17] 17. de Leeuw FE, de Groot JC, Achten E, Oudkerk M, Ramos LMP, Heijboer R, et al. Prevalence of cerebral white matter lesions in elderly people: a population based magnetic resonance imaging study. The Rotterdam Scan Study. J Neurol Neurosurg Psychiatry 2001;70:9–14. pmid:11118240
View Article
PubMed/NCBI
Google Scholar

[64] View Article

[65] PubMed/NCBI

[66] Google Scholar

[ref18] 18. Jones DK, Lythgoe D, Horsfield MA, Simmons A, Williams SCR, Markus HS. Characterization of white matter damage in ischemic leukoaraiosis with diffusion tensor MRI. Stroke 1999;30:393–97. pmid:9933277
View Article
PubMed/NCBI
Google Scholar

[68] View Article

[69] PubMed/NCBI

[70] Google Scholar

[ref19] 19. Maillard P, Fletcher E, Harvey D, Carmichael O, Reed B, Mungas D, et al. White matter hyperintensity penumbra. Stroke 2011;42:1917–22. pmid:21636811
View Article
PubMed/NCBI
Google Scholar

[72] View Article

[73] PubMed/NCBI

[74] Google Scholar

[ref20] 20. Svärd D, Szczepankiewicz F, Nilsson M, Lampinen B, Lätt J, Sundgren PC, et al. White matter hyperintensities as a confounder in diffusion tensor imaging analysis of elderly cohorts. Proc Intl Soc Mag Reson Med 2014;22,1839.
View Article
Google Scholar

[76] View Article

[77] Google Scholar

[ref21] 21. Vernooij MW, de Groot M, van der Lugt A, Ikram MA, Krestin GP, Hofman A, et al. White matter atrophy and lesion formation explain the loss of structural integrity of white matter in aging. NeuroImage 2008;43:470–7. pmid:18755279
View Article
PubMed/NCBI
Google Scholar

[79] View Article

[80] PubMed/NCBI

[81] Google Scholar

[ref22] 22. Leritz EC, Shepel J, Williams VJ, Lipsitz LA, McGlinchey RE, Milberg WP, et al. Associations between T1 white matter lesion volume and regional white matter microstructure in aging. Hum Brain Mapp 2014;35:1085–1100. pmid:23362153
View Article
PubMed/NCBI
Google Scholar

[83] View Article

[84] PubMed/NCBI

[85] Google Scholar

[ref23] 23. Maillard P, Fletcher E, Lockhart SN, Roach AE, Reed B, Mungas D, et al. White matter hyperintensities and their penumbra lie along a continuum of injury in the aging brain. Stroke 2014;45:1721–26. pmid:24781079
View Article
PubMed/NCBI
Google Scholar

[87] View Article

[88] PubMed/NCBI

[89] Google Scholar

[ref24] 24. de Groot M, Ikram MA, Akoudad S, Krestin GP, Hofman A, van der Lugt A, et al. Tract-specific white matter degeneration in aging: the Rotterdam Study. Alzheimers Dement 2015;11:321–30. pmid:25217294
View Article
PubMed/NCBI
Google Scholar

[91] View Article

[92] PubMed/NCBI

[93] Google Scholar

[ref25] 25. Pelletier A, Periot O, Dilharreguy B, Hiba B, Bordessoules M, Chanraud S, et al. Age-related modifications of diffusion tensor imaging parameters and white matter hyperintensities as inter-dependent processes. Front Aging Neurosci 2016;7:255. pmid:26834625
View Article
PubMed/NCBI
Google Scholar

[95] View Article

[96] PubMed/NCBI

[97] Google Scholar

[ref26] 26. Iverson GL, Hakulinen U, Wäljas M, Dastidar P, Lange RT, Soimakallio S, et al. To exclude or not to exclude: white matter hyperintensities in diffusion tensor imaging research. Brain Inj 2011;25:1325–32. pmid:22077537
View Article
PubMed/NCBI
Google Scholar

[99] View Article

[100] PubMed/NCBI

[101] Google Scholar

[ref27] 27. Lange RT, Shewchuk JR, Heran MKS, Rauscher A, Jarett M, Brubacher JR, et al. To exclude or not to exclude: further examination of the influence of white matter hyperintensities in diffusion tensor imaging research. J Neurotraum 2014;31:198–205.
View Article
Google Scholar

[103] View Article

[104] Google Scholar

[ref28] 28. Petersen RC, Smith GE, Waring SC, Ivnik RJ, Tangalos EG, Kokmen E. Mild cognitive impairment: Clinical characterization and outcome. Arch Neurol 1999;56:303–8. pmid:10190820
View Article
PubMed/NCBI
Google Scholar

[106] View Article

[107] PubMed/NCBI

[108] Google Scholar

[ref29] 29. Janelidze S, Zetterberg H, Mattsson N, Palmqvist S, Vanderstichele H, Lindberg O, et al. CSF Aβ42/Aβ40 and Aβ42/Aβ38 ratios: better diagnostic markers of Alzheimer disease. Ann Clin Transl Neurol 2016;3:154–65. pmid:27042676
View Article
PubMed/NCBI
Google Scholar

[110] View Article

[111] PubMed/NCBI

[112] Google Scholar

[ref30] 30. Yoshita M, Fletcher E, Harvey D, Ortega M, Martinez O, Mungas DM, et al. Extent and distribution of white matter hyperintensities in normal aging, MCI, and AD. Neurol 2006;67:2192–8.
View Article
Google Scholar

[114] View Article

[115] Google Scholar

[ref31] 31. Catani M, de Schotten MT. A diffusion tensor imaging tractography atlas for virtual in vivio dissections. Cortex 2008;44:1105–32. pmid:18619589
View Article
PubMed/NCBI
Google Scholar

[117] View Article

[118] PubMed/NCBI

[119] Google Scholar

[ref32] 32. Manjer J, Carlsson S, Elmståhl S, Gullberg B, Janzon L, Lindström M, et al. The Malmö diet and cancer study: representativity, cancer incidence and mortality in participants and non-participants. Eur J Cancer Prev 2001;10:489–99. pmid:11916347
View Article
PubMed/NCBI
Google Scholar

[121] View Article

[122] PubMed/NCBI

[123] Google Scholar

[ref33] 33. Gustavsson A-M, Stomrud E, Abul-Kasim K, Minthon L, Nilsson PM, Hansson O, et al. Cerebral microbleeds and white matter hyperintensities in cognitively healthy elderly: a cross-sectional cohort study evaluating the effect of arterial stiffness. Cerebrovasc Dis Extra 2015;5:41–51. pmid:26120319
View Article
PubMed/NCBI
Google Scholar

[125] View Article

[126] PubMed/NCBI

[127] Google Scholar

[ref34] 34. Folstein MF, Folstein SE, McHugh PR. A practical method for grading the cognitive state of patients for the clinician. J Psychiat Res 1975;12:189–98. pmid:1202204
View Article
PubMed/NCBI
Google Scholar

[129] View Article

[130] PubMed/NCBI

[131] Google Scholar

[ref35] 35. Klein S, Staring M, Murphy K, Viergever MA, Pluim JPW. Elastix: a toolbox for intensity based medical image registration. IEEE Trans Med Img 2010;29:196–205.
View Article
Google Scholar

[133] View Article

[134] Google Scholar

[ref36] 36. Nilsson M, Szczepankiewicz F, van Westen D, Hansson O. Extrapolation-based references improve motion and eddy-current correction of high b-value DWI data: application in Parkinson’s disease dementia. PLoS One 10(11):e0141825. pmid:26528541
View Article
PubMed/NCBI
Google Scholar

[136] View Article

[137] PubMed/NCBI

[138] Google Scholar

[ref37] 37. Mazziotta J, Toga A, Evans A, Fox P, Lancaster J, Zilles K, et al. A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). Phil Trans R Soc Lond 2001;356:1293–322.
View Article
Google Scholar

[140] View Article

[141] Google Scholar

[ref38] 38. Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM. FSL. NeuroImage 2012;62:782–90. pmid:21979382
View Article
PubMed/NCBI
Google Scholar

[143] View Article

[144] PubMed/NCBI

[145] Google Scholar

[ref39] 39. Wang R, Benner T, Sorensen A. Diffusion toolkit: a software package for diffusion imaging data processing and tractography. Proc Int Soc Magn Reson Med 2007;15, 3720.
View Article
Google Scholar

[147] View Article

[148] Google Scholar

[ref40] 40. Mori S, Wakana S, Nagae-Poetscher LM, van Zijl PCM. MRI Atlas of Human White Matter. Elsevier, Amsterdam, The Netherlands 2005.

[ref41] 41. Hua K, Zhang J, Wakana S, Jiang H, Li X, Reich DS, et al. Tract probability maps in stereotaxic spaces: analysis of white matter anatomy and tract-specific quantification. NeuroImage 2008;39:336–47. pmid:17931890
View Article
PubMed/NCBI
Google Scholar

[151] View Article

[152] PubMed/NCBI

[153] Google Scholar

[ref42] 42. Nucifora PG, Wu X, Melhem ER, Gur RE, Gur RC, Verma R. Automated diffusion tensor tractography: implementation and comparison to user-driven tractography. Acad Radiol 2012;19:622–9. pmid:22342650
View Article
PubMed/NCBI
Google Scholar

[155] View Article

[156] PubMed/NCBI

[157] Google Scholar

[ref43] 43. Reuter M, Schmansky NJ, Rosas HD, Fischl B. Within-subject template estimation for unbiased longitudinal image analysis. NeuroImage 2012;61:1402–18. pmid:22430496
View Article
PubMed/NCBI
Google Scholar

[159] View Article

[160] PubMed/NCBI

[161] Google Scholar

[ref44] 44. Schmidt P, Gaser C, Arsic M, Buck D, Förschler A, Berthele A, et al. An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis. NeuroImage 2012;59:3774–83. pmid:22119648
View Article
PubMed/NCBI
Google Scholar

[163] View Article

[164] PubMed/NCBI

[165] Google Scholar

[ref45] 45. Launer LJ. Epidemiology of white matter lesions. Top Magn Reson Imaging 2004;15:365–7. pmid:16041288
View Article
PubMed/NCBI
Google Scholar

[167] View Article

[168] PubMed/NCBI

[169] Google Scholar

[ref46] 46. Kodl CT, Franc DT, Rao JP, Anderson FS, Thomas W, Mueller BA, et al. Diffusion tensor imaging identifies deficits in white matter microstructure in subjects with type 1 diabetes that correlate with reduced neurocognitive function. Diabetes 2008;57:3083–9. pmid:18694971
View Article
PubMed/NCBI
Google Scholar

[171] View Article

[172] PubMed/NCBI

[173] Google Scholar

[ref47] 47. Maillard P, Seshadri S, Beiser A, Himali JJ, Au R, Fletcher E, et al. Effects of systolic pressure on white-matter integrity in young adults in the Framingham heart study: a cross-sectional study. Lancet Neurol 2012;11:1039–47. pmid:23122892
View Article
PubMed/NCBI
Google Scholar

[175] View Article

[176] PubMed/NCBI

[177] Google Scholar

[ref48] 48. Wilson PW, D'Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation 1998;97:1837–47. pmid:9603539
View Article
PubMed/NCBI
Google Scholar

[179] View Article

[180] PubMed/NCBI

[181] Google Scholar

[ref49] 49. Blennow K, Hampel H, Weiner M, Zetterberg H. Cerebrospinal fluid and plasma biomarkers in Alzheimer disease. Nat Rev Neurol 2010;6:131–44. pmid:20157306
View Article
PubMed/NCBI
Google Scholar

[183] View Article

[184] PubMed/NCBI

[185] Google Scholar

[ref50] 50. Palmqvist S, Zetterberg H, Blennow K, Vestberg S, Andreasson U, Brooks D, et al. Accuracy of brain amyloid detection in clinical practice using cerebrospinal fluid β-amyloid 42: a cross-validation study against amyloid positron emission tomography.
View Article
Google Scholar

[187] View Article

[188] Google Scholar

[ref51] 51. Le Bihan D. Looking into the functional architecture of the brain with diffusion MRI. Nat Rev Neurosci 2003;4:469–80. pmid:12778119
View Article
PubMed/NCBI
Google Scholar

[190] View Article

[191] PubMed/NCBI

[192] Google Scholar

[ref52] 52. Wang F, Sun Z, Cui L, Du X, Wang X, Zhang H, et al. Anterior cingulum abnormalities in male patients with schizophrenia determined through diffusion tensor imaging. Am J Psychiatry 2004;161:573–5. pmid:14992988
View Article
PubMed/NCBI
Google Scholar

[194] View Article

[195] PubMed/NCBI

[196] Google Scholar

[ref53] 53. Zhang Y, Schuff N, Jahng G-H, Bayne W, Mori S, Schad L, et al. Diffusion tensor imaging of cingulum fibers in mild cognitive impairment and Alzheimer disease. Neurology 2007;68:13–9. pmid:17200485
View Article
PubMed/NCBI
Google Scholar

[198] View Article

[199] PubMed/NCBI

[200] Google Scholar

[ref54] 54. Gattellaro G, Minati L, Grisoli M, Mariani C, Carella F, Osio M, et al. White matter involvement in idiopathic Parkinson disease: a diffusion tensor imaging study. Am J Neuroradiol 2009;30:1222–6. pmid:19342541
View Article
PubMed/NCBI
Google Scholar

[202] View Article

[203] PubMed/NCBI

[204] Google Scholar

[ref55] 55. Stenset V, Bjørnerud A, Fjell AM, Walhovd KB, Hofoss D, Due-Tønnessen P, et al. Cingulum fiber diffusivity and CSF T-tau in patients with subjective and mild cognitive impairment. Neurobiol Aging 2011;32:581–589. pmid:19428143
View Article
PubMed/NCBI
Google Scholar

[206] View Article

[207] PubMed/NCBI

[208] Google Scholar

[ref56] 56. Zhuang L, Sachdev PS, Trollor JN, Reppermund S, Kochan NA, Brodaty H, et al. Microstructural white matter changes, nor hippocampal atrophy, detect early amnestic mild cognitive impairment. PLoS One 2013;8:e58887. pmid:23516569
View Article
PubMed/NCBI
Google Scholar

[210] View Article

[211] PubMed/NCBI

[212] Google Scholar

[ref57] 57. Rémy F, Vayssière N, Saint-Aubert L, Barbeau E, Pariente J. White matter disruption at the prodromal stage of Alzheimer’s disease: relationships with hippocampal atrophy and episodic memory performance. Neuroimage Clin 2015;27:482–92.
View Article
Google Scholar

[214] View Article

[215] Google Scholar

[ref58] 58. Szczepankiewicz F, Lätt J, Wirestam R, Leemans A, Sundgren P, van Westen D, et al. Variability in diffusion kurtosis imaging: impact on study design, statistical power and interpretation. NeuroImage 2013;76:145–54. pmid:23507377
View Article
PubMed/NCBI
Google Scholar

[217] View Article

[218] PubMed/NCBI

[219] Google Scholar

[ref59] 59. Douaud G, Jbabdi S, Behrens TE, Menke RA, Gass A, Monsch AU, et al. DTI measures in crossing-fibre areas: increased diffusion anisotropy reveals early white matter alteration in MCI and mild Alzheimer's disease. NeuroImage 2011;55:880–90. pmid:21182970
View Article
PubMed/NCBI
Google Scholar

[221] View Article

[222] PubMed/NCBI

[223] Google Scholar

Figures

Abstract

Introduction

Methods

Ethics statement

Study population

MRI acquisition and post-processing

Tractography

Volumetric assessment

Assessment of WMH load

Assessment of cardiovascular disease

Definition of subgroups of cognitively healthy elderly with unequal WMH load

Assessment of Aβ42/Aβ40 ratio in cerebrospinal fluid

Statistical analysis

Results

Demographic analysis

Effect of WMH on DTI estimates in cognitively healthy elderly

Effect of WMH load in prodromal AD subjects compared to cognitively healthy elderly

Discussion

Conclusion

References

Assessment of Aβ₄₂/Aβ₄₀ ratio in cerebrospinal fluid