Skip to main content
NCBI home page
Human Brain Mapping logoLink to Human Brain Mapping
. 2023 May 6;44(10):3943–3953. doi: 10.1002/hbm.26326

Young versus older subject diffusion magnetic resonance imaging data for virtual white matter lesion tractography

Mohammad Taghvaei 1, Philip Cook 2, Shokufeh Sadaghiani 1, Banafsheh Shakibajahromi 1, William Tackett 1, Sudipto Dolui 2, Debarun De 3, Christopher Brown 1, Pulkit Khandelwal 4, Paul Yushkevich 2, Sandhitsu Das 1, David A Wolk 1, John A Detre 1,✉
PMCID: PMC10258527  PMID: 37148501

Abstract

White matter hyperintensity (WMH) lesions on T2 fluid‐attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) and changes in adjacent normal‐appearing white matter can disrupt computerized tract reconstruction and result in inaccurate measures of structural brain connectivity. The virtual lesion approach provides an alternative strategy for estimating structural connectivity changes due to WMH. To assess the impact of using young versus older subject diffusion MRI data for virtual lesion tractography, we leveraged recently available diffusion MRI data from the Human Connectome Project (HCP) Lifespan database. Neuroimaging data from 50 healthy young (39.2 ± 1.6 years) and 46 healthy older (74.2 ± 2.5 years) subjects were obtained from the publicly available HCP‐Aging database. Three WMH masks with low, moderate, and high lesion burdens were extracted from the WMH lesion frequency map of locally acquired FLAIR MRI data. Deterministic tractography was conducted to extract streamlines in 21 WM bundles with and without the WMH masks as regions of avoidance in both young and older cohorts. For intact tractography without virtual lesion masks, 7 out of 21 WM pathways showed a significantly lower number of streamlines in older subjects compared to young subjects. A decrease in streamline count with higher native lesion burden was found in corpus callosum, corticostriatal tract, and fornix pathways. Comparable percentages of affected streamlines were obtained in young and older groups with virtual lesion tractography using the three WMH lesion masks of increasing severity. We conclude that using normative diffusion MRI data from young subjects for virtual lesion tractography of WMH is, in most cases, preferable to using age‐matched normative data.

Keywords: brain connectivity, tractography, virtual lesion, white matter hyperintensity

The major drawback of using target diffusion magnetic resonance imaging (MRI) data from older subjects for virtual lesion tractography is the detection of fewer streamlines. For some white matter (WM) tracts, the presence of WM hyperintensity (WMH) in older subjects further contributes to an underestimation of actual tract disconnection. We conclude that using diffusion MRI data of young subjects for virtual lesion tractography of WMH is, in most cases, preferable to using age‐matched normative diffusion MRI data.

graphic file with name HBM-44-3943-g006.jpg

1. INTRODUCTION

White matter hyperintensities (WMH) are a common finding on T2‐weighted fluid‐attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) in older subjects, where they are thought to represent the sequelae of small vessel ischemic disease (Prins & Scheltens, 2015). Neuropathologically, WMH lesions are characterized by demyelination, axonal loss, and gliosis (Fazekas et al., 1993). Clinically, WMH have been associated with cognitive decline and dementia (Gouw et al., 2011) though the mechanisms by which they cause cognitive decline are not well understood. White matter (WM) lesions classically cause disconnection syndromes by interrupting communication between the gray matter regions they connect. However, age‐associated WMH are predominantly observed in the periventricular region where multiple WM tracts cross (Habes et al., 2018) and can potentially interrupt multiple WM communication pathways (Ritchie et al., 2015). Tract disconnection is a proposed mechanism of associations between WMH and cognitive symptoms (Langen et al., 2018; Reginold et al., 2015; Seiler et al., 2018).

Brain structural connectivity can be studied non‐invasively using diffusion tensor imaging (DTI)‐based streamline tractography (Johansen‐Berg & Behrens, 2006). While in principle this approach can also be used to detect and quantify changes in structural connectivity due to WMH, previous studies have demonstrated that WMH lesions and normal‐appearing WM (NAWM) adjacent to them can alter the microstructural integrity and diffusion parameters of WM (Bastin et al., 2009; De Groot et al., 2013). Wallerian degeneration along the length of the tract crossing the lesion and the effects of surrounding ischemic penumbra are two proposed mechanisms of disruption in NAWM adjacent to the lesions (Liu et al., 2021; Maillard et al., 2011; Maniega et al., 2015; Muñoz Maniega et al., 2019; Reginold et al., 2015; Reginold et al., 2016; Reginold et al., 2018; Taylor et al., 2001). Consequently, computerized tract reconstruction of diffusion MRI data by tensor tracing algorithms may be disrupted, resulting in inaccurate measures of structural brain connectivity (Hua et al., 2008; Johansen‐Berg & Behrens, 2006; Muñoz Maniega et al., 2019; Reginold et al., 2016; Reginold et al., 2018; Seiler et al., 2018; Wiegell et al., 2000).

The virtual lesion approach provides an alternative strategy for estimating structural connectivity changes due to WMH (Li et al., 2021). In this method, a WMH lesion mask derived from FLAIR MRI in an affected individual is applied as the region of avoidance (ROA) for diffusion tractography on high‐quality diffusion data from healthy subjects without brain lesions. An advantage of the virtual lesion approach is that while high quality diffusion tractography data are rarely available in clinical populations, FLAIR MRI is routinely acquired. However, a limitation of this approach is uncertainties about coregistration errors between the lesion mask from older subjects and the target diffusion MRI data from young subjects to which it is typically applied. In addition, any effects of age‐related changes in brain structural connectivity and WM microstructural integrity in NAWM, which are known to occur (Davis et al., 2009; de Groot et al., 2015; McWhinney et al., 2016; Pinto et al., 2021; Sala et al., 2012; Zhao et al., 2015), will not be measured.

To date, virtual lesion tractography has mainly leveraged high‐quality diffusion MRI data acquired from young healthy subjects in the Human Connectome Project (HCP), even when analyzing lesion masks derived from older individuals' MRI (Geller et al., 2019; Li et al., 2021; Monai et al., 2020; Pacella et al., 2019; Salvalaggio et al., 2020; Silvestri et al., 2022; Thiebaut de Schotten et al., 2020). Salvalaggio et al. used virtual lesion tractography to assess the extent of structural disconnection resulting from ischemic lesions and found that indirect estimation of structural connectivity could predict poststroke behavioral impairment (Salvalaggio et al., 2020). Monai et al. employed virtual lesion tractography to compare the structural disconnection in hemiparetic patients with and without anosognosia following a stroke. Patients with anosognosia demonstrated a broader disconnection of cortical networks, which was not limited to motor pathways (Monai et al., 2020). Thiebaut de Schotten et al. applied 1333 ischemic stroke lesions on 163 young healthy subjects' diffusion MRI data obtained from the HCP database to estimate the stroke lesion disconnectome. By integrating the results of estimated tracts' disconnection with the neuropsychological data of patients, they were able to develop an atlas of WM function (Thiebaut de Schotten et al., 2020). While applying virtual lesion masks to multiple HCP target datasets captures some of variability in tract locations, little is known about whether this sufficiently addresses age‐related variability in connectivity and tract locations. The main question is whether it is reasonable to use young healthy subjects' diffusion tractography data to indirectly assess the effect of WMH on brain connectivity in older subjects. The recent availability of high‐quality diffusion MRI data from older subjects in the Human Connectome Lifespan Project (Bookheimer et al., 2019) provides an opportunity to directly compare virtual lesion tractography of WMH using target diffusion MRI data acquired in young versus older subjects. In this study, we leverage virtual lesion tractography and HCP Lifespan data to compare structural connectivity changes based on three WMH masks with low, moderate, and high WMH lesion burden. We also assessed the impact of age and WMH burden on intact tractography parameters in the HCP Aging cohort.

2. MATERIALS AND METHODS

2.1. Subjects

We obtained neuroimaging data of 50 subjects under 45 years old (young group) and 50 subjects over 70 years old selected at random from the publicly available HCP‐Aging lifespan 2.0 release (Bookheimer et al., 2019). After processing the images, four older subjects' data were excluded due to the low quality of the processed image. Finally, neuroimaging data of 50 healthy young (27 female, mean [SD] age of 39.2 [1.6] years) and 46 healthy older (24 females, mean [SD] age of 74.2 [2.5] years) subjects were included in this study. We also selected structural MRI data of 394 (mean [SD] age of 69.3 [12.8] years) subjects from The Penn Alzheimer Disease Research Center (ADRC) database. In both studies, images were acquired using protocols approved by the Institutional Review Board of each center.

2.2. MRI acquisition

The diffusion MRI data in HCP subjects were acquired using a Siemens Prisma 3 Tesla scanner. The following parameters were used: TR/TE = 3230/89.20 ms, 1.5 mm isotropic resolution, 92 slices, and multiband acceleration factor of 4. A total of 185 diffusion‐weighting directions of 2 shells of b = 1500 and 3000 s/mm2 in addition to 28 images with b = 0 s/mm2 were acquired. A variable‐flip‐angle turbo‐spin‐echo sequence was used for the T2‐weighted scan. Parameters include TR/TE = 3200/564 ms, turbo factor = 314, resolution 0.8 mm isotropic, and acquisition matrix = 320 × 300 × 208 slices (Harms et al., 2018). A Siemens Prisma 3 Tesla MRI scanner was used to acquire FLAIR MRI scans from older subjects recruited from the Penn ADRC. FLAIR parameters used were: TR/TE/TI = 6000/289/2200 ms, 1 mm isotropic resolution, and acquisition matrix = 256 × 220 × 160 slices.

2.3. WMH classification in HCP lifespan data

The HCP Lifespan dataset does not include FLAIR MRI. Instead, in both young and older groups, T2‐weighted images were inspected by two expert raters (MT and SS) and WMH severity was scored according to the Fazekas classification system (Fazekas et al., 1987). Total Fazekas score was defined as the sum of periventricular and deep WM scores and subjects were classified into four groups based on that: no lesion (Fazekas score: 0), low lesion burden (Fazekas scores: 1–2), moderate lesion burden (Fazekas scores: 3–4), and high lesion burden (Fazekas scores: 4–6). The interrater reliability (kappa) was 0.91.

2.4. Lesion segmentation of FLAIR MRI images

Supratentorial WMH on FLAIR MRI of Penn ADRC subjects were segmented using nnU‐Net, a deep learning‐based image segmentation method (Isensee et al., 2021). PyTorch 1.5.1 and Nvidia Quadro RTX 5000 GPUs were used to train the model. The model was initially trained using user‐annotated images of 100 ADRC subjects with the WMH as the foreground and the rest of the image as the background. WMH were defined as hyperintense lesions in the periventricular and deep WM compared to NAWM on FLAIR MRI and were segmented manually using ITK‐SNAP (Yushkevich et al., 2006). Images were skull stripped using automated brain extraction HD‐BET (Isensee et al., 2019), and then standardized and normalized between 0 and 1. We implemented nnU‐Net with the default settings for 500 epochs. The model was tested on a total of 100 subjects. The Dice coefficient for the fivefold cross‐validation was 86.06% (±1.48). Subsequently, this model was employed to segment WMH lesions of the remaining 294 ADRC subjects. Then, these FLAIR‐based segmentation images were manually verified, co‐registered to the T1 images, and warped to MNI space using Advanced Normalization Tools (Avants et al., 2011). This mapping included deformable registration. Lesion frequency map were calculated using all unique WMH masks. We used three frequency thresholds of 0.05, 0.10, and 0.15 to extract three different WMH masks representative of low, moderate, and high levels of lesion burden. The 0.05 threshold mask denoted a high level of lesion burden, as it encompassed voxels where at least 5% of subjects displayed lesions. In contrast, the 0.15 threshold mask denoted a low level of lesion burden, as it encompassed only voxels where 15 or greater percent of subjects displayed lesions. Left hemisphere lesions were selected for this study to minimize the impact of lesion asymmetry, and the right hemisphere without lesions was used as a control (Figure 1).

FIGURE 1.

FIGURE 1

Open in a new tab

(a) Three different masks were extracted from the lesion frequency of the Penn Alzheimer Disease Research Center (ADRC) subjects with low, moderate, and high white matter hyperintensity (WMH) volumes. (b) 3D illustration of WMH masks (blue: ventricles, yellow: low WMH volume mask, green: moderate WMH volume mask, red: high WMH volume mask).

2.5. DTI structural connectivity analysis

HCP diffusion MRI data of young and older subjects were processed using the HCP pipelines including eddy current and head motion correction (Glasser et al., 2013). Then, the images were reconstructed using q‐space diffeomorphic reconstruction (QSDR) with a diffusion sampling length ratio of 1.25 to calculate the spin distribution function (SDF) and were normalized to the MNI quantitative anisotropy (QA) map on DSI Studio (Yeh & Tseng, 2011). The QSRD is an extension of the generalized q‐sampling imaging (GQI) method, which enables users to construct the SDF in any given desired space. GQI tractography was found to offer highly valid connections with a lower rate of false‐positive connections (Maier‐Hein et al., 2017). To calculate tract‐based structural connectivity, deterministic tractography was conducted to extract 21 WM bundles using the HCP1065 WM tractography atlas in right and left brain hemispheres (Yeh et al., 2018). Extracted WM pathways included: acoustic radiation, arcuate fasciculus, cingulum, corpus callosum, corticobulbar tract, corticopontine tract, corticospinal tract, corticostriatal tract, extreme capsule, fornix, frontal aslant tract, inferior fronto‐occipital fasciculus, inferior longitudinal fasciculus, middle longitudinal fasciculus (MLF), superior longitudinal fasciculus, medial lemniscus, optic radiation, parietal aslant tract, thalamic radiation, uncinate fasciculus, and vertical occipital fasciculus. For each WM pathway, 106 seeds were placed within the atlas tract volume, and streamlines were generated using the following tracking parameters: tracking index QA, angular threshold 0, minimum length 20 mm, maximum length 300 mm, termination of the process after 106 seeds, 0 iterations of topology‐informed pruning, and an autotrack tolerance of 16. The total streamline count reported in the intact tractography is the sum of right and left hemispheric streamline numbers for each pathway. For the DTI parameters, fractional anisotropy (FA) and mean diffusivity (MD) are the average of right and left hemispheric values for each tract.

2.6. Tractography with virtual lesions

Three WMH masks with different lesion burdens were applied as ROA on the young and older subject HCP diffusion MRI data, and tractography was conducted again for the same 21 WM bundles using the same parameters. The disconnection rate for each pathway was defined as the percentage of affected streamlines in presence of each WMH mask compared to intact tractography (Figure 2).

FIGURE 2.

FIGURE 2

Open in a new tab

Virtual lesion tact‐based disconnectome pipeline.

2.7. Statistical analyses

All statistical analyses were conducted using SPSS version 27. Independent t test and ANOVA (Mann–Whitney U and Kruskal–Wallis tests if values were not normally distributed) were used to compare disconnection rates, streamline count, and diffusion parameters in different age or Fazekas score groups. Bonferroni correction was applied to adjust the significance level in multiple comparisons. To compare categorical variables chi‐square test (or Fisher's exact test as necessary) was applied. A p‐value of less than .05 was considered statistically significant.

3. RESULTS

3.1. Comparison of intact tractography in older and young groups

Figure 3 shows the number of streamlines identified for each WM pathway in young and older subjects. In each group, there were some subjects with undetectable streamlines for certain tracts. Fornix, corticobulbar tract, and MLF were the most common undetectable tracts in older subjects, and the rate of unsuccessful tractography for these tracts was significantly higher compared to the young group (Table 1).

FIGURE 3.

FIGURE 3

Open in a new tab

Streamline count for each white matter pathway in young and older groups (AR: acoustic radiation, AF: arcuate fasciculus, CC: corpus callosum CBT: corticobulbar tract, CPT: corticopontine tract, CST: corticospinal tract, CStrT: corticostriatal tract, ExC: extreme capsule, FAT: frontal aslant tract, IFOF: inferior fronto‐occipital tract, ILF: inferior longitudinal fasciculus, ML: medial lemniscus, MLF: middle longitudinal fasciculus, OR: optic radiation, PAT: parietal aslant tract, SLF: superior longitudinal fasciculus, TR: thalamic radiation, UF: uncinate fasciculus, VOF: vertical occipital fasciculus). INSET: Magnified section of the chart showing WM pathways with low streamline count. *p < .05 after correction for multiple comparisons.

TABLE 1.

The number of subjects without a detectable tract

Tract Older group N (%) Young group N (%) p‐Value
Corticobulbar 14 (30.4%) 4 (8.0%) .005
Corticospinal 2 (4.3%) 0 (0%) .227
Extreme capsule 4 (8.7%) 4 (8.0%) .916
Fornix 15 (32.6%) 3 (6.0%) .001
IFOF 3 (6.5%) 3 (6.0%) .916
Medial lemniscus 3 (6.5%) 0 (0%) .106
MLF 9 (19.6) 2 (4.0%) .017
Optic radiation 1 (2.2%) 1 (2.0%) .952
Open in a new tab

Statistically significant p values were bolded.

Abbreviation: MLF, middle longitudinal fasciculus; IFOF, inferior fronto‐occipital fasciculus.

The streamline count was lower in older subjects that in young subjects for the majority of individual WM tracts. This difference was significant for corpus callosum, corticobulbar tract, corticospinal tract, corticostriatal tract, fornix, frontal aslant tract, and MLF. However, acoustic radiation, inferior longitudinal fasciculus, parietal aslant tract, and thalamic radiation showed more streamlines in the older group compared to the young group, though the differences were not significant (Supplementary Table 1). In the older group, there was a significant reduction in the mean FA values of the fornix, frontal aslant tract, parietal aslant tract, and uncinate fasciculus, when compared to the younger group. Additionally, the mean MD values were significantly higher for all WM pathways in the older group, with the exception of the corticospinal tract, medial lemniscus, and vertical occipital fasciculus (Supplementary Table 2).

3.2. Comparison of intact tractography in HCP subjects with Fazekas scores

Figure 4 demonstrates the baseline streamline count for each bundle across Fazekas scores for the 50 young and 46 older HCP subjects. Thirty‐nine (40.6%) subjects were classified as Fazekas 0 (no lesion), 29 (30.2%) had a low WMH lesion burden (Fazekas score 1–2), 22 (22.9%) had a moderate lesion burden (Fazekas score 4–6), and 6 (6.3%) had a high lesion burden (Fazekas score 5–6). As expected, the older group had a significantly higher WMH lesion burden compared to the young group (Table 2). A decrease in streamline count with higher WMH lesion burden was found in 10 out of 21 WM pathways and this decline was statistically significant in corpus callosum, corticostriatal tract, and fornix (Supplementary Table 3).

FIGURE 4.

FIGURE 4

Open in a new tab

Streamline count of each white matter pathway across white matter hyperintensity (WMH) lesion burdens (AR: acoustic radiation, AF: arcuate fasciculus, CC: corpus callosum CBT: corticobulbar tract, CPT: corticopontine tract, CST: corticospinal tract, CStrT: corticostriatal tract, ExC: extreme capsule, FAT: frontal aslant tract, IFOF: inferior fronto‐occipital tract, ILF: inferior longitudinal fasciculus, ML: medial lemniscus, MLF: middle longitudinal fasciculus, OR: optic radiation, PAT: parietal aslant tract, SLF: superior longitudinal fasciculus, TR: thalamic radiation, UF: uncinate fasciculus, VOF: vertical occipital fasciculus). INSET: Magnified section of the chart showing WM pathways with low streamline count. *p < .05 after correction for multiple comparisons.

TABLE 2.

Distribution of WMH lesion burden across young and older groups

Age group Fazekas score p‐Value
0 (no lesion) N (%) 1–2 (low lesion volume) N (%) 3–4 (moderate lesion volume) N (%) 5–6 (high lesion volume) N (%)
Young 39 (88.0%) 11(22.0%) 0 (0.0%) 0 (0.0%) <.001
Older 0 (0%) 18 (39.1%) 22 (47.8%) 6 (13.1%)
Total 39 (40.6%) 29 (30.2%) 22 (22.9%) 6 (6.3%)
Open in a new tab

Abbreviation: WMH, white matter hyperintensity.

3.3. Comparison of tract disconnection in older and young groups after applying WMH lesion masks

Three representative WMH masks from the lesion frequency map of Penn ADRC subjects with low WMH volume, Moderate WMH volume, and High WMH volume were used as virtual lesions on target HCP diffusion MRI data. We used the left hemisphere lesions on each mask as ROA for tractography in young and older target diffusion data so that the right hemisphere could be used as a control. The percent changes in computed streamline disconnection after applying each WMH lesion mask in the left hemisphere relative to the right hemisphere are illustrated in Figure 5. There was no evidence of significant tract disconnections in the right hemisphere.

FIGURE 5.

FIGURE 5

Open in a new tab

Percent tract disconnection of young and older subjects with low, moderate, and high WMH masks on the left brain hemisphere. (AR: acoustic radiation, AF: arcuate fasciculus, CC: corpus callosum CBT: corticobulbar tract, CPT: corticopontine tract, CST: corticospinal tract, CStrT: corticostriatal tract, ExC: extreme capsule, FAT: frontal aslant tract, IFOF: inferior fronto‐occipital tract, ILF: inferior longitudinal fasciculus, ML: medial lemniscus, MLF: middle longitudinal fasciculus, OR: optic radiation, PAT: parietal aslant tract, SLF: superior longitudinal fasciculus, TR: thalamic radiation, UF: uncinate fasciculus, VOF: vertical occipital fasciculus). *p < .05 after correction for multiple comparisons.

For different WM pathways, comparable rates of disconnection were observed in both groups with one exception. In the older group, the streamline percent disconnection was significantly lower in the corticostriatal tract after applying the low WMH volume mask (see also Supplementary Table 4).

4. DISCUSSION

In this study, we compared tract‐based brain streamline counts from diffusion tractography in both young and older normative subjects from the Human Connectome Aging Project with and without applying WMH masks of varying severity as ROA for fiber tracking. For intact tractography without virtual lesion masks, the majority of WM bundles had a lower number of streamlines in older subjects compared to young subjects. The rate of successful tractography for small volume pathways like fornix was significantly lower in the older group. Furthermore, a statistically significant higher WMH lesion burden was found in T2‐weighed images from the older group and WMH correlated with a significantly lower streamline count in three tracts, corpus callosum, corticostriatal tract, and fornix. However, with the exception of the corticostriatal tract, comparable percentages of affected streamlines were calculated in both groups after applying each WMH mask.

The majority of WM tracts in older individuals exhibited elevated values for MD, whereas four tracts including fornix, frontal aslant tract, parietal aslant tract, and uncinate fasciculus demonstrated lower values for FA when compared to younger individuals. Reduction in FA values corresponds to a diminished ability of water molecules to diffuse along WM fiber bundles, while increased MD values suggest a decline in the free diffusion of water molecules (Basser & Pierpaoli, 2011). Taken together, these observations indicate an age‐related disruption in WM integrity. Consistent with our findings, life‐span studies on WM diffusivity properties have demonstrated widespread age‐related changes in WM microstructural integrity with spatial variability. Nonlinear correlations of DTI parameters with age were observed and varied for each tract. Generally, in the majority of WM tracts, FA increases after birth and reaches to peak in middle age, and then gradually decreases in old age. The opposite pattern occurs in MD (Kochunov et al., 2007; Lebel et al., 2012; Mårtensson et al., 2018; Vernooij et al., 2008; Westlye et al., 2010).

A significantly lower number of streamlines was found in older subjects' corpus callosum, corticobulbar tract, corticospinal tract, corticostriatal tract, fornix, frontal aslant tract, and MLF compared to young subjects. This age‐related loss of microstructural organization may be explained by tract atrophy along with an increase in tract‐specific WMH lesions, both resulting in a lower number of streamlines in old age (de Groot et al., 2015; Vernooij et al., 2008). Our findings support the hypothesis that frontal WM connections are more likely to undergo neurodegeneration (Michielse et al., 2010) as streamline counts in WM pathways originating from the frontal lobe including corticospinal tract, corticostriatal tract, corticobulbar tract, and frontal aslant tracts were significantly reduced in older subjects, but this effect was not seen in tracts originating from other lobes. Both volumetric structural and diffusion studies have demonstrated selective age‐related volume reduction and FA decline in frontal WM, with temporal and occipital WM regions relatively preserved (Allen et al., 2005; Michielse et al., 2010; Raz et al., 2004; Salat et al., 2005). Sullivan et al. found lower FA and fewer streamlines in the frontal part of the corpus callosum in older subjects compared to younger subjects. In contrast to the frontal region, they reported slightly higher FA values in inferior temporal/occipital fibers in the older group (Sullivan et al., 2006). Similarly, a positive linear correlation between age and volume of deep temporal association fibers has been reported (Pagani et al., 2008). We also found slightly higher streamline numbers in acoustic radiation, inferior longitudinal fasciculus, and parietal aslant which are predominantly located in deep temporal, parietal, and occipital lobes.

Fornix, corticobulbar, and MLF pathways were more commonly undetectable in older subjects compared to young subjects. These three tracts are classified as small bundles and more susceptible to partial volume effects (Alexander et al., 2001). The small size, presence of prominent crossing fibers (longitudinal fibers for corticobulbar and projection fibers for MLF), and the proximity of fornix to the CSF in the ventricles make them difficult to visualize. Further, age‐related WM atrophy and superimposed WM lesions make tracing of these fibers even more challenging (Cahn et al., 2021; Jenabi et al., 2015; Valdés Cabrera et al., 2020).

After the classification of subjects into four lesion burden groups based on Fazekas scoring of their T2 weighted MRI data, corpus callosum, corticostriatal tract, and fornix showed significant decreases in streamline count with increases in WMH severity (high Fazekas score). This is consistent with expected effects of WMH lesions on the structural integrity of crossing and adjacent fibers. Corpus callosum and corticostriatal tract are the two largest tracts in our study. The volume and spatial extent of these large tracts increases the probability of tract‐lesion overlap and hence to effects of WMH lesions on tractography. In the study by Maniega et al., the highest tract‐lesion overlap was found on the forceps major of the corpus callosum in 52 older subjects (Muñoz Maniega et al., 2019). On the other hand, fornix is a relatively small tract but travels along the periventricular region in all its path. Given that WMHs predominantly develop in the periventricular region, the probability of lesions being found in the path of fornix fibers is also high despite its small size.

After applying masks with different WMH volumes as ROAs to diffusion MRI data of older and young subjects, both groups showed the same disconnection rate in the majority of pathways. However, significantly lower tract disconnection percentages were found corticostriatal tract using diffusion tractography data from older subjects. This pathway also had a lower number of streamlines in intact tractography of the older group compared to young group suggesting that they are already affected by age‐associated WMH atrophy and lesions, explaining the lower disconnection in these tracts in the older group.

While we found age‐related changes in FA, MD, and the number of constructed streamlines, the estimated disconnection percentages for each mask were replicated when utilizing age‐matched diffusion MRI data, similar to when we used young subjects' diffusion MRI data. These findings suggest that estimated tract disconnection caused by a virtual lesion remains relatively constant across the lifespan (Thiebaut de Schotten et al., 2020). However, small differences may arise due to the presence of preexisting WMH lesions in older subjects on structural connectivity. We suggest that to mitigate the effect of native WMH lesions in older subjects on estimated tract disconnection, it is preferable to privilege the superior quality of structural connectivity obtained from healthy young subjects rather than age‐matched diffusion MRI data.

Most clinical research studies of brain aging and dementia include FLAIR MRI data, while high‐quality diffusion tractography data is much less commonly acquired. By employing the virtual lesion approach, FLAIR MRI can be utilized to elucidate brain‐behavior relationships between WM lesion‐based disconnection and cognitive functioning. These relationships can further be evaluated for their interactions with clinical or demographic factors as well as comorbid pathologies. For example, the virtual lesion approach can be used to assess connectivity change as a possible mechanism underlying WMH‐related cognitive deficits.

There are a few limitations of this study. First, deterministic fiber tracking was used to calculate streamline counts with and without virtual lesion masks. Deterministic fiber tracking algorithms rely on the major eigenvector of the tensor model in each voxel and can stop in voxels with isotropic tensors (such as voxels containing crossing fibers) (Descoteaux et al., 2008). On the other hand, probabilistic tractography algorithms are based on the probability distribution of diffusion direction and generate many streamlines in each voxel. Probabilistic tractography generates both more valid and more invalid bundles than deterministic tractography. Thus, although using probabilistic tractography may enhance the sensitivity of fiber detection, it can reduce selectivity (Schlaier et al., 2017; Thomas et al., 2014). Due to higher rate of false‐positive connection when using probabilistic algorithms, streamline disconnection calculated by deterministic tractography is more reliable (Sarwar et al., 2019). Second, in the virtual lesion approach, any streamlines passing through the lesion are considered fully disconnected. There is a concern that for less acute lesions like WMH, this approach likely overestimates true disconnection. Another limitation of this study was that the young and older groups in data available from HCP Aging database were not matched for cardiovascular risk factors, and some of the observed effects could conceivably be attributable to factors other than age. For example, FA changes have been found to be associated with diabetes mellitus, smoking, and hypertension (de Groot et al., 2015; Reijmer et al., 2013). We note that the HCP Aging project did not exclude subjects with cardiovascular risk factors. In general, it would be challenging to create a cohort of older subjects with no cardiovascular risks.

5. CONCLUSION

The major drawback of using target diffusion MRI data from older subjects for virtual lesion tractography is the detection of relatively small bundles. Fornix and corticobulbar were not detected in almost one‐third of older subjects. Further, in some pathways, the likely presence of WMH in older subjects would further contribute to an underestimation of actual tract disconnection. In view of the findings that the virtual lesion approach using diffusion MRI data of older subjects shows very similar brain disconnectivity results to younger subjects, whereas younger subject data yield higher streamline counts across a greater number of tracts, we conclude that using diffusion MRI data of young subjects for virtual lesion tractography of WMH is, in most cases, preferable to using age‐matched normative diffusion MRI data.

CONFLICT OF INTEREST

David A. Wolk received grants from Eli Lilly/Avid Radiopharmaceuticals, Biogen, and Merck. He has received personal fees from Eli Lilly, GE Healthcare, Neuronix, and Qynapse. He serves on a DSMB and received personal fees from Functional Neuromodulation. All other authors have no conflict of interest to declare.

Supporting information

SUPPLEMENTARY TABLE 1. Streamline count for each white matter pathway in old and young groups.

SUPPLEMENTARY TABLE 2. Mean FA and MD values for each white matter pathway in old and young groups.

SUPPLEMENTARY TABLE 3. Streamline count of each white matter pathway across Fazekas scores of WMH burden.

SUPPLEMENTARY TABLE 4. Percent of tract disconnection of old and young subjects in different WMH masks.

Click here for additional data file. (46.2KB, docx)

ACKNOWLEDGMENTS

This study was supported by NIH grants R21AG070434, R01NS111115, R03AG063213, and P30 AG072979. Diffusion MRI data used in the preparation of this article were obtained from the Lifespan Human Connectome Project in aging. The HCP Lifespan project was supported by the National Institute on Aging of the National Institutes of Health under Award Number U01AG052564 and by funds provided by the McDonnell Center for Systems Neuroscience at Washington University in St. Louis. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Taghvaei, M. , Cook, P. , Sadaghiani, S. , Shakibajahromi, B. , Tackett, W. , Dolui, S. , De, D. , Brown, C. , Khandelwal, P. , Yushkevich, P. , Das, S. , Wolk, D. A. , & Detre, J. A. (2023). Young versus older subject diffusion magnetic resonance imaging data for virtual white matter lesion tractography. Human Brain Mapping, 44(10), 3943–3953. 10.1002/hbm.26326

DATA AVAILABILITY STATEMENT

The diffusion MRI data in this article are publicly available via the lifespan Human Connectome Project Aging (https://www.humanconnectome.org/study/hcp-lifespan-aging). Other data and codes will be available upon request.

REFERENCES

  1. Alexander, A. L. , Hasan, K. M. , Lazar, M. , Tsuruda, J. S. , & Parker, D. L. (2001). Analysis of partial volume effects in diffusion‐tensor MRI. Magnetic Resonance in Medicine, 45(5), 770–780. [DOI] [PubMed] [Google Scholar]
  2. Allen, J. S. , Bruss, J. , Brown, C. K. , & Damasio, H. (2005). Normal neuroanatomical variation due to age: The major lobes and a parcellation of the temporal region. Neurobiology of Aging, 26(9), 1245–1260. [DOI] [PubMed] [Google Scholar]
  3. Avants, B. B. , Tustison, N. J. , Song, G. , Cook, P. A. , Klein, A. , & Gee, J. C. (2011). A reproducible evaluation of ANTs similarity metric performance in brain image registration. NeuroImage, 54(3), 2033–2044. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Basser, P. J. , & Pierpaoli, C. (2011). Microstructural and physiological features of tissues elucidated by quantitative‐diffusion‐tensor MRI. Journal of Magnetic Resonance, 213(2), 560–570. [DOI] [PubMed] [Google Scholar]
  5. Bastin, M. E. , Clayden, J. D. , Pattie, A. , Gerrish, I. F. , Wardlaw, J. M. , & Deary, I. J. (2009). Diffusion tensor and magnetization transfer MRI measurements of periventricular white matter hyperintensities in old age. Neurobiology of Aging, 30(1), 125–136. [DOI] [PubMed] [Google Scholar]
  6. Bookheimer, S. Y. , Salat, D. H. , Terpstra, M. , Ances, B. M. , Barch, D. M. , Buckner, R. L. , Burgess, G. C. , Curtiss, S. W. , Diaz‐Santos, M. , Elam, J. S. , Fischl, B. , Greve, D. N. , Hagy, H. A. , Harms, M. P. , Hatch, O. M. , Hedden, T. , Hodge, C. , Japardi, K. C. , Kuhn, T. P. , … Yacoub, E. (2019). The lifespan Human Connectome Project in aging: An overview. NeuroImage, 185, 335–348. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Cahn, A. J. , Little, G. , Beaulieu, C. , & Tétreault, P. (2021). Diffusion properties of the fornix assessed by deterministic tractography shows age, sex, volume, cognitive, hemispheric, and twin relationships in young adults from the Human Connectome Project. Brain Structure and Function, 226(2), 381–395. [DOI] [PubMed] [Google Scholar]
  8. Davis, S. W. , Dennis, N. A. , Buchler, N. G. , White, L. E. , Madden, D. J. , & Cabeza, R. (2009). Assessing the effects of age on long white matter tracts using diffusion tensor tractography. NeuroImage, 46(2), 530–541. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. de Groot, M. , Ikram, M. A. , Akoudad, S. , et al. (2015). Tract‐specific white matter degeneration in aging: The Rotterdam study. Alzheimer's & Dementia, 11(3), 321–330. [DOI] [PubMed] [Google Scholar]
  10. De Groot, M. , Verhaaren, B. F. , De Boer, R. , et al. (2013). Changes in normal‐appearing white matter precede development of white matter lesions. Stroke, 44(4), 1037–1042. [DOI] [PubMed] [Google Scholar]
  11. Descoteaux, M. , Deriche, R. , Knosche, T. R. , & Anwander, A. (2008). Deterministic and probabilistic tractography based on complex fibre orientation distributions. IEEE Transactions on Medical Imaging, 28(2), 269–286. [DOI] [PubMed] [Google Scholar]
  12. Fazekas, F. , Chawluk, J. B. , Alavi, A. , Hurtig, H. I. , & Zimmerman, R. A. (1987). MR signal abnormalities at 1.5 T in Alzheimer's dementia and normal aging. American Journal of Neuroradiology, 8(3), 421–426. [DOI] [PubMed] [Google Scholar]
  13. Fazekas, F. , Kleinert, R. , Offenbacher, H. , Schmidt, R. , Kleinert, G. , Payer, F. , Radner, H. , & Lechner, H. (1993). Pathologic correlates of incidental MRI white matter signal hyperintensities. Neurology, 43(9), 1683–1689. [DOI] [PubMed] [Google Scholar]
  14. Geller, J. , Thye, M. , & Mirman, D. (2019). Estimating effects of graded white matter damage and binary tract disconnection on post‐stroke language impairment. NeuroImage, 189, 248–257. [DOI] [PubMed] [Google Scholar]
  15. Glasser, M. F. , Sotiropoulos, S. N. , Wilson, J. A. , Coalson, T. S. , Fischl, B. , Andersson, J. L. , Xu, J. , Jbabdi, S. , Webster, M. , Polimeni, J. R. , van Essen, D. C. , & Jenkinson, M. (2013). The minimal preprocessing pipelines for the Human Connectome Project. NeuroImage, 80, 105–124. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Gouw, A. A. , Seewann, A. , Van Der Flier, W. M. , Barkhof, F., Rozemuller, A.M., Scheltens, P., & Geurts, J.J. (2011). Heterogeneity of small vessel disease: A systematic review of MRI and histopathology correlations. Journal of Neurology, Neurosurgery & Psychiatry, 82(2), 126–135. [DOI] [PubMed] [Google Scholar]
  17. Habes, M. , Sotiras, A. , Erus, G. , Toledo, J. B. , Janowitz, D. , Wolk, D. A. , Shou, H. , Bryan, N. R. , Doshi, J. , Völzke, H. , Schminke, U. , Hoffmann, W. , Resnick, S. M. , Grabe, H. J. , & Davatzikos, C. (2018). White matter lesions: Spatial heterogeneity, links to risk factors, cognition, genetics, and atrophy. Neurology, 91(10), e964–e975. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Harms, M. P. , Somerville, L. H. , Ances, B. M. , Andersson, J. , Barch, D. M. , Bastiani, M. , Bookheimer, S. Y. , Brown, T. B. , Buckner, R. L. , Burgess, G. C. , Coalson, T. S. , Chappell, M. A. , Dapretto, M. , Douaud, G. , Fischl, B. , Glasser, M. F. , Greve, D. N. , Hodge, C. , Jamison, K. W. , … Yacoub, E. (2018). Extending the Human Connectome Project across ages: Imaging protocols for the lifespan development and aging projects. NeuroImage, 183, 972–984. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Hua, K. , Zhang, J. , Wakana, S. , Jiang, H. , Li, X. , Reich, D. S. , Calabresi, P. A. , Pekar, J. J. , van Zijl, P. C. M. , & Mori, S. (2008). Tract probability maps in stereotaxic spaces: Analyses of white matter anatomy and tract‐specific quantification. NeuroImage, 39(1), 336–347. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Isensee, F. , Jaeger, P. F. , Kohl, S. A. , Petersen, J. , & Maier‐Hein, K. H. (2021). nnU‐Net: A self‐configuring method for deep learning‐based biomedical image segmentation. Nature Methods, 18(2), 203–211. [DOI] [PubMed] [Google Scholar]
  21. Isensee, F. , Schell, M. , Pflueger, I. , Brugnara, G. , Bonekamp, D. , Neuberger, U. , Wick, A. , Schlemmer, H. P. , Heiland, S. , Wick, W. , Bendszus, M. , Maier‐Hein, K. H. , & Kickingereder, P. (2019). Automated brain extraction of multisequence MRI using artificial neural networks. Human Brain Mapping, 40(17), 4952–4964. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Jenabi, M. , Peck, K. , Young, R. , Brennan, N. , & Holodny, A. (2015). Identification of the corticobulbar tracts of the tongue and face using deterministic and probabilistic DTI fiber tracking in patients with brain tumor. American Journal of Neuroradiology, 36(11), 2036–2041. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Johansen‐Berg, H. , & Behrens, T. E. (2006). Just pretty pictures? What diffusion tractography can add in clinical neuroscience. Current Opinion in Neurology, 19(4), 379–385. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Kochunov, P. , Thompson, P. M. , Lancaster, J. L. , Bartzokis, G. , Smith, S. , Coyle, T. , Royall, D. R. , Laird, A. , & Fox, P. T. (2007). Relationship between white matter fractional anisotropy and other indices of cerebral health in normal aging: Tract‐based spatial statistics study of aging. NeuroImage, 35(2), 478–487. [DOI] [PubMed] [Google Scholar]
  25. Langen, C. D. , Cremers, L. G. , de Groot, M. , et al. (2018). Disconnection due to white matter hyperintensities is associated with lower cognitive scores. NeuroImage, 183, 745–756. [DOI] [PubMed] [Google Scholar]
  26. Lebel, C. , Gee, M. , Camicioli, R. , Wieler, M. , Martin, W. , & Beaulieu, C. (2012). Diffusion tensor imaging of white matter tract evolution over the lifespan. NeuroImage, 60(1), 340–352. [DOI] [PubMed] [Google Scholar]
  27. Li, Z. , Dolui, S. , Habes, M. , Bassett, D. S. , Wolk, D. , & Detre, J. A. (2021). Predicted disconnectome associated with progressive periventricular white matter ischemia. Cerebral Circulation‐Cognition and Behavior, 2, 100022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Liu, Y. , Xia, Y. , Wang, X. , Wang, Y. , Zhang, D. , Nguchu, B. A. , He, J. , Wang, Y. , Yang, L. , Wang, Y. , Ying, Y. , Liang, X. , Zhao, Q. , Wu, J. , Liang, Z. , Ding, D. , Dong, Q. , Qiu, B. , Cheng, X. , & Gao, J. H. (2021). White matter hyperintensities induce distal deficits in the connected fibers. Human Brain Mapping, 42(6), 1910–1919. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Maier‐Hein, K. H. , Neher, P. F. , Houde, J.‐C. , Côté, M. A. , Garyfallidis, E. , Zhong, J. , Chamberland, M. , Yeh, F. C. , Lin, Y. C. , Ji, Q. , Reddick, W. E. , Glass, J. O. , Chen, D. Q. , Feng, Y. , Gao, C. , Wu, Y. , Ma, J. , He, R. , Li, Q. , … Descoteaux, M. (2017). The challenge of mapping the human connectome based on diffusion tractography. Nature Communications, 8(1), 1349. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Maillard, P. , Fletcher, E. , Harvey, D. , Carmichael, O. , Reed, B. , Mungas, D. , & DeCarli, C. (2011). White matter hyperintensity penumbra. Stroke, 42(7), 1917–1922. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Maniega, S. M. , Hernández, M. C. V. , Clayden, J. D. , et al. (2015). White matter hyperintensities and normal‐appearing white matter integrity in the aging brain. Neurobiology of Aging, 36(2), 909–918. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Mårtensson, J. , Lätt, J. , Åhs, F. , Fredrikson, M. , Söderlund, H. , Schiöth, H. B. , Kok, J. , Kremer, B. , van Westen, D. , Larsson, E. M. , & Nilsson, M. (2018). Diffusion tensor imaging and tractography of the white matter in normal aging: The rate‐of‐change differs between segments within tracts. Magnetic Resonance Imaging, 45, 113–119. [DOI] [PubMed] [Google Scholar]
  33. McWhinney, S. R. , Tremblay, A. , Chevalier, T. M. , Lim, V. K. , & Newman, A. J. (2016). Using CForest to analyze diffusion tensor imaging data: A study of white matter integrity in healthy aging. Brain Connectivity, 6(10), 747–758. [DOI] [PubMed] [Google Scholar]
  34. Michielse, S. , Coupland, N. , Camicioli, R. , Carter, R. , Seres, P. , Sabino, J. , & Malykhin, N. (2010). Selective effects of aging on brain white matter microstructure: A diffusion tensor imaging tractography study. NeuroImage, 52(4), 1190–1201. [DOI] [PubMed] [Google Scholar]
  35. Monai, E. , Bernocchi, F. , Bisio, M. , Bisogno, A. L. , Salvalaggio, A. , & Corbetta, M. (2020). Multiple network disconnection in anosognosia for hemiplegia. Frontiers in Systems Neuroscience, 14, 21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Muñoz Maniega, S. , Meijboom, R. , Chappell, F. M. , Valdés Hernández, M. C. , Starr, J. M. , Bastin, M. E. , Deary, I. J. , & Wardlaw, J. M. (2019). Spatial gradient of microstructural changes in normal‐appearing white matter in tracts affected by white matter hyperintensities in older age. Frontiers in Neurology, 10, 784. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Pacella, V. , Foulon, C. , Jenkinson, P. M. , Scandola, M. , Bertagnoli, S. , Avesani, R. , Fotopoulou, A. , Moro, V. , & Thiebaut de Schotten, M. (2019). Anosognosia for hemiplegia as a tripartite disconnection syndrome. eLife, 8, e46075. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Pagani, E. , Agosta, F. , Rocca, M. A. , Caputo, D. , & Filippi, M. (2008). Voxel‐based analysis derived from fractional anisotropy images of white matter volume changes with aging. NeuroImage, 41(3), 657–667. [DOI] [PubMed] [Google Scholar]
  39. Pinto, M. S. , Dos Santos, A. C. , & Salmon, C. E. G. (2021). Age‐related assessment of diffusion parameters in specific brain tracts correlated with cortical thinning. Neurological Sciences, 42(5), 1799–1809. [DOI] [PubMed] [Google Scholar]
  40. Prins, N. D. , & Scheltens, P. (2015). White matter hyperintensities, cognitive impairment and dementia: An update. Nature Reviews Neurology, 11(3), 157–165. [DOI] [PubMed] [Google Scholar]
  41. Raz, N. , Gunning‐Dixon, F. , Head, D. , Rodrigue, K. M. , Williamson, A. , & Acker, J. D. (2004). Aging, sexual dimorphism, and hemispheric asymmetry of the cerebral cortex: Replicability of regional differences in volume. Neurobiology of Aging, 25(3), 377–396. [DOI] [PubMed] [Google Scholar]
  42. Reginold, W. , Itorralba, J. , Luedke, A. , et al. (2016). Tractography at 3T MRI of corpus callosum tracts crossing white matter hyperintensities. American Journal of Neuroradiology, 37(9), 1617–1622. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Reginold, W. , Luedke, A. C. , Tam, A. , Itorralba, J. , Fernandez‐Ruiz, J. , Reginold, J. , Islam, O. , & Garcia, A. (2015). Cognitive function and 3‐tesla magnetic resonance imaging tractography of white matter hyperintensities in elderly persons. Dementia and Geriatric Cognitive Disorders Extra, 5(3), 387–394. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Reginold, W. , Sam, K. , Poublanc, J. , Fisher, J. , Crawley, A. , & Mikulis, D. J. (2018). Impact of white matter hyperintensities on surrounding white matter tracts. Neuroradiology, 60(9), 933–944. [DOI] [PubMed] [Google Scholar]
  45. Reijmer, Y. D. , Brundel, M. , De Bresser, J. , et al. (2013). Microstructural white matter abnormalities and cognitive functioning in type 2 diabetes: A diffusion tensor imaging study. Diabetes Care, 36(1), 137–144. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Ritchie, S. J. , Bastin, M. E. , Tucker‐Drob, E. M. , Maniega, S. M. , Engelhardt, L. E. , Cox, S. R. , Royle, N. A. , Gow, A. J. , Corley, J. , Pattie, A. , Taylor, A. M. , Valdés Hernández, M. C. , Starr, J. M. , Wardlaw, J. M. , & Deary, I. J. (2015). Coupled changes in brain white matter microstructure and fluid intelligence in later life. Journal of Neuroscience, 35(22), 8672–8682. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Sala, S. , Agosta, F. , Pagani, E. , Copetti, M. , Comi, G. , & Filippi, M. (2012). Microstructural changes and atrophy in brain white matter tracts with aging. Neurobiology of Aging, 33(3), 488–498. e482. [DOI] [PubMed] [Google Scholar]
  48. Salat, D. , Tuch, D. , Greve, D. , et al. (2005). Age‐related alterations in white matter microstructure measured by diffusion tensor imaging. Neurobiology of Aging, 26(8), 1215–1227. [DOI] [PubMed] [Google Scholar]
  49. Salvalaggio, A. , De Filippo De Grazia, M. , Zorzi, M. , Thiebaut de Schotten, M. , & Corbetta, M. (2020). Post‐stroke deficit prediction from lesion and indirect structural and functional disconnection. Brain, 143(7), 2173–2188. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Sarwar, T. , Ramamohanarao, K. , & Zalesky, A. (2019). Mapping connectomes with diffusion MRI: Deterministic or probabilistic tractography? Magnetic Resonance in Medicine, 81(2), 1368–1384. [DOI] [PubMed] [Google Scholar]
  51. Schlaier, J. R. , Beer, A. L. , Faltermeier, R. , Fellner, C. , Steib, K. , Lange, M. , Greenlee, M. W. , Brawanski, A. T. , & Anthofer, J. M. (2017). Probabilistic vs. deterministic fiber tracking and the influence of different seed regions to delineate cerebellar‐thalamic fibers in deep brain stimulation. European Journal of Neuroscience, 45(12), 1623–1633. [DOI] [PubMed] [Google Scholar]
  52. Seiler, S. , Fletcher, E. , Hassan‐Ali, K. , Weinstein, M. , Beiser, A. , Himali, J. J. , Satizabal, C. L. , Seshadri, S. , DeCarli, C. , & Maillard, P. (2018). Cerebral tract integrity relates to white matter hyperintensities, cortex volume, and cognition. Neurobiology of Aging, 72, 14–22. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Silvestri, E. , Villani, U. , Moretto, M. , et al. (2022). Assessment of structural disconnections in gliomas: Comparison of indirect and direct approaches. Brain Structure and Function, 227(9), 3109–3120. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Sullivan, E. V. , Adalsteinsson, E. , & Pfefferbaum, A. (2006). Selective age‐related degradation of anterior callosal fiber bundles quantified in vivo with fiber tracking. Cerebral Cortex, 16(7), 1030–1039. [DOI] [PubMed] [Google Scholar]
  55. Taylor, W. D. , Payne, M. E. , Krishnan, K. R. R. , Wagner, H. R. , Provenzale, J. M. , Steffens, D. C. , & MacFall, J. R. (2001). Evidence of white matter tract disruption in MRI hyperintensities. Biological Psychiatry, 50(3), 179–183. [DOI] [PubMed] [Google Scholar]
  56. Thiebaut de Schotten, M. , Foulon, C. , & Nachev, P. (2020). Brain disconnections link structural connectivity with function and behaviour. Nature Communications, 11(1), 5094. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Thomas, C. , Ye, F. Q. , Irfanoglu, M. O. , Modi, P. , Saleem, K. S. , Leopold, D. A. , & Pierpaoli, C. (2014). Anatomical accuracy of brain connections derived from diffusion MRI tractography is inherently limited. Proceedings of the National Academy of Sciences of the United States of America, 111(46), 16574–16579. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Valdés Cabrera, D. , Stobbe, R. , Smyth, P. , Giuliani, F. , Emery, D. , & Beaulieu, C. (2020). Diffusion tensor imaging tractography reveals altered fornix in all diagnostic subtypes of multiple sclerosis. Brain and Behavior, 10(1), e01514. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Vernooij, M. W. , de Groot, M. , van der Lugt, A. , Ikram, M. A. , Krestin, G. P. , Hofman, A. , Niessen, W. J. , & Breteler, M. M. B. (2008). White matter atrophy and lesion formation explain the loss of structural integrity of white matter in aging. NeuroImage, 43(3), 470–477. [DOI] [PubMed] [Google Scholar]
  60. Westlye, L. T. , Walhovd, K. B. , Dale, A. M. , Bjornerud, A. , Due‐Tonnessen, P. , Engvig, A. , Grydeland, H. , Tamnes, C. K. , Ostby, Y. , & Fjell, A. M. (2010). Life‐span changes of the human brain white matter: Diffusion tensor imaging (DTI) and volumetry. Cerebral Cortex, 20(9), 2055–2068. [DOI] [PubMed] [Google Scholar]
  61. Wiegell, M. R. , Larsson, H. B. , & Wedeen, V. J. (2000). Fiber crossing in human brain depicted with diffusion tensor MR imaging. Radiology, 217(3), 897–903. [DOI] [PubMed] [Google Scholar]
  62. Yeh, F.‐C. , Panesar, S. , Fernandes, D. , Meola, A. , Yoshino, M. , Fernandez‐Miranda, J. C. , Vettel, J. M. , & Verstynen, T. (2018). Population‐averaged atlas of the macroscale human structural connectome and its network topology. NeuroImage, 178, 57–68. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Yeh, F.‐C. , & Tseng, W.‐Y. I. (2011). NTU‐90: A high angular resolution brain atlas constructed by q‐space diffeomorphic reconstruction. NeuroImage, 58(1), 91–99. [DOI] [PubMed] [Google Scholar]
  64. Yushkevich, P. A. , Piven, J. , Hazlett, H. C. , Smith, R. G. , Ho, S. , Gee, J. C. , & Gerig, G. (2006). User‐guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. NeuroImage, 31(3), 1116–1128. [DOI] [PubMed] [Google Scholar]
  65. Zhao, T. , Cao, M. , Niu, H. , Zuo, X. N. , Evans, A. , He, Y. , Dong, Q. , & Shu, N. (2015). Age‐related changes in the topological organization of the white matter structural connectome across the human lifespan. Human Brain Mapping, 36(10), 3777–3792. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

SUPPLEMENTARY TABLE 1. Streamline count for each white matter pathway in old and young groups.

SUPPLEMENTARY TABLE 2. Mean FA and MD values for each white matter pathway in old and young groups.

SUPPLEMENTARY TABLE 3. Streamline count of each white matter pathway across Fazekas scores of WMH burden.

SUPPLEMENTARY TABLE 4. Percent of tract disconnection of old and young subjects in different WMH masks.

Click here for additional data file. (46.2KB, docx)

Data Availability Statement

The diffusion MRI data in this article are publicly available via the lifespan Human Connectome Project Aging (https://www.humanconnectome.org/study/hcp-lifespan-aging). Other data and codes will be available upon request.

Articles from Human Brain Mapping are provided here courtesy of Wiley

RESOURCES