Lesion Segmentation Toolbox Research Articles

Early detection of white matter hyperintensities using SHIVA-WMH detector.

White matter hyperintensities (WMHs) are well-established markers of cerebral small vessel disease, and are associated with an increased risk of stroke, dementia, and mortality. Although their prevalence increases with age, small and punctate WMHs have been reported with surprisingly high frequency even in young, neurologically asymptomatic adults. However, most automated methods to segment WMH published to date are not optimized for detecting small and sparse WMH. Here we present the SHIVA-WMH tool, a deep-learning (DL)-based automatic WMH segmentation tool that has been trained with manual segmentations of WMH in a wide range of WMH severity. We show that it is able to detect WMH with high efficiency in subjects with only small punctate WMH as well as in subjects with large WMHs (i.e., with confluency) in evaluation datasets from three distinct databases: magnetic resonance imaging-Share consisting of young university students, MICCAI 2017 WMH challenge dataset consisting of older patients from memory clinics, and UK Biobank with community-dwelling middle-aged and older adults. Across these three cohorts with a wide-ranging WMH load, our tool achieved voxel-level and individual lesion cluster-level Dice scores of 0.66 and 0.71, respectively, which were higher than for three reference tools tested: the lesion prediction algorithm implemented in the lesion segmentation toolbox (LPA: Schmidt), PGS tool, a DL-based algorithm and the current winner of the MICCAI 2017 WMH challenge (Park et al.), and HyperMapper tool (Mojiri Forooshani et al.), another DL-based method with high reported performance in subjects with mild WMH burden. Our tool is publicly and openly available to the research community to facilitate investigations of WMH across a wide range of severity in other cohorts, and to contribute to our understanding of the emergence and progression of WMH.

Cholinergic white matter hyperintensity volume, cognitive decline, and incident dementia in older adults

AbstractBackgroundWhite matter hyperintensities (WMHs) are frequently observed in the elderly and their severity tends to increase with age. Previous studies have revealed that WMHs are closely related to cognitive dysfunction. However, as inter‐individual variations and a clinicoradiological discrepancy exist, not all individuals with severe WMH burden experience worse cognitive performance. Furthermore, the location of WMHs appears to be a critical factor in the relationship between WMHs and cognitive function. Few studies take both volume and location of white matter hyperintensities (WMHs) into account to explore the association between WMH burden and cognitive impairment. We therefore investigated associations of cholinergic WMH volume (WMHV) with global cognitive function, cognitive decline, and incident dementia in older adults.MethodA total of 752 older adults (mean age 60 years, 50% female) from the Taizhou Imaging Study were involved. Brain magnetic resonance imaging data at baseline and repeated measures of cognition over 5 years were collected and analyzed. WMHV in whole brain, the cholinergic pathways, and different tract classes in the Montreal Neurologic Institute standard space was obtained using the Lesion Segmentation Toolbox and tools from FMRIB Software Library. Multivariable linear regression, Cox regression, and partial correlation tests were performed.ResultOver a median 5.67 (IQR: 2.88‐6.84) years of follow‐up, cholinergic WMHV was associated with annual decline of Mini‐Mental State Examination (MMSE) (β = ‐0.206; P = 0.006), and incident dementia (HR = 3.45; 95%CI: 2.08‐5.74). Within the cholinergic pathways, WMHs in commissural fibers were related to incident dementia. However, greater global WMHV only slightly increased the risk of incident dementia (HR = 1.03; 95%CI: 1.01‐1.06). Neither global nor cholinergic WMHV was associated with MMSE in cross‐sectional analysis.ConclusionCholinergic WMHV is associated with longitudinal cognitive decline and incident dementia in older adults. Within the cholinergic pathways, commissural fiber tracts were strategic locations correlated to incident dementia. Our findings suggest cholinergic WMHV might be a potential indicator of cognitive deterioration.

Cognitively healthy Indians with higher Framingham Cardiovascular Risk Scores have higher WMH burden with lesser grey matter, white matter and hippocampal volumes

AbstractBackgroundIncreasing evidence indicates the important role of cardiovascular risk factors in dementia risk, particularly in Indians. It is known that cardiovascular risk factors tend to cluster together, and a widely used multivariable risk scoring is the Framingham Cardiovascular Risk Score (FRS). However, very little is known about the association between FRS and structural brain MRI parameters in cognitively healthy Indians.MethodBaseline clinical and brain MRI data of 487 cognitively healthy Indians (Clinical Dementia Rating score of ‘0’), who are part of two ongoing, large, aging cohort studies in India – Srinivaspura Aging, Neuro Senescence and COGnition study (SANSCOG) study (n = 200) and Tata Longitudinal Study of Aging (TLSA, n = 289) were utilized. FRS was calculated using clinical and blood biochemistry data, whereas brain MRI scans were performed on 3T MRI scanners. White matter hyperintensity (WMH) lesion segmentation was performed using Lesion Segmentation Toolbox (LST) and brain volumes (total grey matter, total white matter, 3rd ventricle, right and left hippocampus) were derived using FreeSurfer software and normalized according to total intracranial volumes of the respective subjects. Linear regression analysis was performed with FRS as independent variable and various structural MRI parameters as dependent variables. A p value of <0.05 was considered significant.ResultUrban subjects with higher FRS had significantly higher WMH volume (β = 0.00011, p<0.001, R2 = 0.130), lower grey matter (β = ‐0.00285, p<0.001, R2 = 0.206), white matter (β = ‐0.00180, p<0.001, R2 = 0.106), right hippocampal (β = ‐0.00004, p<0.001, R2 = 0.180), left hippocampal (β = ‐0.00003, p<0.001, R2 = 0.156) volumes and higher 3rd ventricle volumes (β = 0.00003, p<0.001, R2 = 0.150). Rural subjects higher FRS had lower grey matter (β = ‐0.00166, p<0.001, R2 = 0.118, white matter (β = ‐0.0011, p<0.001, R2 = 0.060), right hippocampal (β = ‐0.00002, p<0.001, R2 = 0.087), left hippocampal (β = ‐0.00002, p<0.001, R2 = 0.084) volumes and higher 3rd ventricle volumes (β = 0.00004, p<0.001, R2 = 0.264) but no significant association was seen with WMH volume.ConclusionThese findings indicate that cardiovascular risk is associated with structural brain changes in cognitively healthy Indians. Since cardiovascular risk can be reduced through cost‐effective community level strategies, such as lifestyle‐based interventions, promptly addressing this risk in Indians could be an important part of dementia prevention strategies.

Comparison of white matter hyperintensities between Latin American and American individuals with Alzheimer’s disease and cognitively normal older adults

AbstractBackgroundWhite matter hyperintensities (WMH) are Flair signal abnormalities frequently seen in periventricular and subcortical brain regions in the elderly. They are presumed to be, at least in part, a sign of cardiovascular disease (CVD). WMHs are associated with stroke, cognitive decline, and dementia. Given that most of the literature on dementia comes from high‐income countries, little is known about dementia in Latin American countries (LAC), which have an increased risk of dementia and CVD due to unique demographic, socioeconomic, cultural and ethnic factors. Our study aims to characterize the effects of white matter hyperintensities (WMH) in patients with AD within LAC and to compare these findings with a sample from the USA.MethodA total of 491 latin american subjects (235 cognitively normal (CN), 256 AD) and 271 american subjects (165 CN, 106 AD), underwent a brain MRI. WMH were extracted from T2‐FLAIR images using the Lesion Segmentation Toolbox and were divided into periventricular (pWMH) or deep (dWMH) depending on their distance from the ventricles. Linear mixed‐effects were used to model the volumes of WMH as a function of age, gender, years of education, intracranial volume, average cortical thickness, and region (LAC or USA).ResultAD groups from both regions showed larger total, dWMH and pWMH volumes compared to HC. Higher pWMH from AD and HC was associated with age, female sex and inversely associated with years of education. On the other hand, the increase in dWMH was found to be associated with female sex for the HC group and with age for AD. Cortical thinning was associated with an increase in total, subcortical and periventricular WMH for both groups. No statistically significant association was found between the region (USA or LAC) and WMH.ConclusionAD showed higher WMH compared to HC. Our findings suggest that pWMH and dWMH have different clinical correlates. No associations between region and WMH load or distribution were found. Future studies need to elucidate if there is an increase in other signs of CVD (such as cerebral microbleeds, brain lacunae, enlarged perivascular spaces) in Latin America and if they are linked to cognition and social determinants of health.

Longitudinal white matter hyperintensities in Down’s syndrome

AbstractBackgroundDown’s syndrome (DS) is a genetically determined form of Alzheimer’s disease (AD). Typically not complicated by systemic vascular risk factors, the DS population represents a unique opportunity to unravel the complex relationship between AD and cerebrovascular disease. Here, we aim at studying the evolution over time of white matter hyperintensities (WMHs), a key hallmark of cerebrovascular disease frequently found in AD, and to assess their topographical progression and associations with AD biomarkers in adults with DS.MethodThis longitudinal study included 43 DS adults (age = 39.8±10.4y; females = 41.9%; n = 37 asymptomatic AD, n = 6 symptomatic AD) and 81 euploid controls (age = 53.7±6.2y; females = 67.3%) from the Down‐Alzheimer Barcelona Neuroimaging Initiative, with at least 2 MRI visits with T1 and FLAIR acquisitions (time delay = 3.4±1.6y). WMHs were segmented using the Lesion Prediction Algorithm implemented in the Lesion Segmentation Toolbox (SPM12). WMH volumes were extracted in 36 regions of interest and used to compute total and regional annual volume changes. Non‐parametric statistical tests were performed to assess the effect of disease severity on WMH volume changes over time and define associations between longitudinal WMHs and baseline demographic and genetic data, and fluid biomarkers.ResultTotal annual WMH volume change increased in symptomatic DS compared to both asymptomatic DS and controls (Figure 1A). Regionally, WMH load increased in regions showing WMH at baseline in all groups (Figure 2). These changes were greater and more distributed, involving layers more distant from the ventricles (especially in occipito‐parietal regions) in symptomatic DS. Age, but not sex, intellectual disability, or APOEε4 status, was associated with longitudinal WMH in DS (Figure 1B‐F): the annual WMH volume changes in DS differed from controls at age ∼45y. Finally, WMH volume changes were negatively related to CSF‐Aβ42/40 and positively related to plasma and CSF ptau‐181 and neurofilament light (Figure 3).ConclusionPathological increases in WMH volume start ∼10 years before the mean age at onset for AD (53.8y) in DS and relate to underlying AD pathology and neurodegeneration. Regional analyses suggest that WMH volume changes increase with disease progression and progressively involve deeper white matter areas. Together, these results support that WMHs are tightly related to AD pathogenesis.

Clinical Manifestations.

The Hachinski ischemic score (HIS) scale is used to exclude as much as possible, individuals with dominant cerebrovascular pathology from those with dominant AD pathology. White matter hyperintensities (WMH) are the gold-standard for evaluating cerebrovascular pathology and linked to worsening cognitive impairment. HIS ≤ 4 are used to minimize non-AD pathology in ADNI participants. Here we sought to understand whether HIS scores for hypertension (HMHYPERT) in ADNI3 were associated with greater WMH burden (and hence cerebrovascular pathology) even within individuals with HIS ≤ 4. We compared HMHYPERT scores between individuals with normal cognition (CN) and with mild cognitive impairment (MCI), and participants and correlated with percent WMH burden. A total of 263 subjects screened for ADNI3 (92 MCI [38 F], age = 73 ± 6; 146 CN [94 F], age = 74 ± 7) who had HIS scores and FLAIR MRI acquisitions within a year of each other. 25 All AD participants were excluded. WMH burden was calculated by segmenting them using the Lesion Segmentation Toolbox and normalizing their volume by total intracranial volume. We used the HMHYPERT as a vascular risk factor, excluding those with any focal neurological symptoms. For analysis, we dichotomized participants into those with HMHYPERT = 0 and HMHYPERT>0. To examine whether a non-zero HMHYPERT score corresponds to greater WMH burden, we examined the prevalence of WMH in between these 2 groups and within the MCI and CN groups. Although not significant, HMHYPERT scores were higher in MCI. (Fig. 1A, MCI = 0.48±0.50, CN = 0.39±0.49). We found a greater proportion of MCI subjects had HMHYPERT>0 than CN subjects (Fig. 1B, HMHYPERT = 0: CN = 61.0% and MCI = 52.2%; HMHYPERT > 0: CN = 40.0% and MCI = 48.1%). WMH burden was greater in MCI (0.33±0.53) than CN (0.20±0.32) (Fig. 2A). MCI participants with HMHYPERT>0 had the highest WMH burden followed by MCI participants with HMHYPERT = 0, CN with HMHYPERT >0, and CN with HMHYPERT = 0 had the lowest WMH burden. (Fig. 2B). Cerebrovascular pathology increases significantly with diagnosis, but its severity increases further with findings of hypertension even when total HIS ≤ 4.

Developing Topics.

The novel diffusion-based Neurite Orientation and Dispersion Imaging (NODDI) model may provide complementary information to Diffusion Tensor Imaging (DTI) to increase sensitivity in characterizing tissue microarchitecture. We examined if one or a combination of both approaches can improve our understanding of at-risk tissue in and around white matter hyperintensities (WMH). From ADNI3, we identified individuals [n = 54, age = 78 ± 7, 46% F) with concurrent measures of multi-shell DTI, 3D FLAIR, and 3D MPRAGE. We segmented WMH from structural data using Lesion Segmentation Toolbox. We derived DTI (FA, MD) and NODDI parameters (NDI, ODI, Fiso) using QSIprep. We dilated up to 5 mm outside the WMH, applied an FA threshold of ≥ 0.2 to exclude gray matter, calculated DTI and NODDI parameters at 1 mm intervals within this region. We performed Pearson correlations between all parameters. Assuming normal white matter at 5 mm, we normalized these parameters with their respective values at 5 mm and plotted the spatial variation of each parameter in and outside the WMH. Normalized DTI and NODDI parameters representing spatial variations within the 5 mm region are shown in Fig 1A. Table 1 shows p-values comparing DTI and NODDI parameters between WMH the subsequent regions outside it. Compared to WMH, Fiso was significantly different at all distances. FA showed similar spatial characteristics with NDI and was negatively correlated with ODI. Fiso correlated very strongly with MD. In the WMHs and its immediate vicinity, low FA, NDI, ODI suggest lower cell density, while low Fiso and MD suggest low extracellular fluid volume. Together these observations may reflect cytotoxic edema, cell loss, and injury. Normal appearing white matter (3-5 mm) showed high FA, NDI, ODI, and low MD consistent with high cell density and normal extracellular water. In the transition between WMH vicinity and normal white matter, low FA, NDI, ODI, high MD and Fiso suggest cell loss only. This transition region could be at risk of converting to WMH. Future work will include longitudinal evaluation of WMH growth in the at-risk tissue. However, final validation of WMH pathology will need histological confirmation.

Baseline cognitive dispersion is related to increases in white matter hyperintensities in cognitively preserved older individuals

AbstractBackgroundCognitive dispersion (CD), understood as intra‐individual variability in performance, is considered a sensitive marker of prodromal stages of Alzheimer’s Disease (AD; Halliday et al. J. Intell 2018;6(1):12), and it is thought to complement cognitive performance (CP) in predicting age‐related changes (Costa et al. ClinicNeuropsychol 2019;33(2)). Moreover, white matter hyperintensities (WMH) are a common age‐associated finding that especially impacts processing speed (Boutzoukas et al. Front in AgingNeurosci 2021;13) and executive function (Papp et al. NeuropsycholDev 2014;21(2)). Therefore, we aimed to investigate (i) associations between CD, CP, and WMH, and (ii) whether WMH age‐related changes can be better predicted by baseline CD or CP in a cognitively normal aging sample.MethodOne hundred and three healthy elders (age: 68.61±3.01 years; 72 females) underwent a neuropsychological assessment and a magnetic resonance image acquisition and repeated them after 2 years. Based on processing speed and executive function scores, we computed a baseline CD (i.e., CD‐tp1; using the intraindividual standard deviation method, Costa et al. Clinic Neuropsychol 2019;33(2)) and a CP (calculated with principal component analysis) at both time‐points (i.e., CP‐tp1, CP‐tp2). 3D‐FLAIR sequence was used to estimate WMH volumes (i.e., WMH‐tp1, WMH‐tp2) using the SPM12 Lesion Segmentation Toolbox (Schmidt et al. Neuroimage 2012;59(4)). The baseline volumes were subtracted from the follow‐up ones to obtain the difference (i.e., CP‐diff, WMH‐diff). Statistical analyses were linear regressions adjusted by age, sex, years of education, and intracranial volume for WMH analyses. Paired t‐tests were used for the longitudinal approach.ResultThere was a negative cross‐sectional relationship between CD‐tp1 and CP‐tp1 (B = ‐.240, t = ‐3.034, p = .004; Fig1), while those cognitive measures were unrelated to WMH‐tp1 volumes. Longitudinally, we detected a significant increase in WMH (t = 7.244, p<.001), while CP remained stable and there were no significant associations between CP‐diff and WMH‐diff. The main finding was the significant relationship found between WMH volume increases (i.e., WMH‐diff) and CD‐tp1 (B = ‐.320, t = ‐3.060, p = .003; Fig2), but not with CP‐tp1.ConclusionThe results show that in healthy elderly individuals who evidenced cognitive stability in processing speed and executive function, the CD is a sensitive marker to predict significant age‐related increases in WMH, which would be beneficial in the AD‐continuum.

The Influence of White Matter Lesion Volume on Hippocampal Shape in Patients Along the Alzheimer’s Disease Continuum

AbstractBackgroundAlzheimer’s Disease (AD) is a progressive neurocognitive disorder with irreversible hippocampal atrophy and degradation of cognitive function. Hippocampal atrophy can be assessed by volume and shape changes on MRI, and hippocampal volume is a sensitive marker for AD diagnosis and progression; however, it does not reflect regional changes within the hippocampus. Interest in shape analysis of the hippocampus is growing, as it provides more detailed assessment of morphological changes that highlight regional atrophic differences. Cerebral small vessel disease (CSVD) burden quantified as white matter lesion volume from MRI is strongly associated with hippocampal volume loss in AD and mild cognitive impairment (MCI). Our goal was to examine relationships between hippocampal morphometric features and white matter hyperintensity (WMH) volume, a measure of CSVD burden, across the AD continuum.MethodWe analyzed baseline data from 186 participants from the Wake Forest Alzheimer’s Disease Research Center (ADRC) Clinical Core cohort whose cognitive diagnosis was adjudicated by an expert panel in accordance with NIA‐AA guidelines (without reference to AD biomarkers). Participants ranged from cognitively normal (CN) (n = 62, 72±8y), to MCI (n = 62, 73±7y), and dementia (n = 62, 73±9y). We focused on the left hippocampus and used T1‐weighted images from 3T brain MRI to generate left hippocampal segmentations and total intracranial volume estimates. Total WMH volume was estimated from FLAIR images using the lesion segmentation toolbox for SPM, it was then normalized to intracranial volume and log transformed to address skewness. Hippocampal shape analysis was performed using SlicerSALT, and a multivariate linear model was implemented to relate WMH volume to hippocampal shape features while adjusting for diagnosis, age, sex, and years of education.ResultOn the superior aspect of the hippocampus, greater WMH volume was associated with more surface contraction of the anterior portions of the CA1, CA2, CA3 and subiculum as shown in Figure 1A. Similar effects were seen on the inferior aspect as shown in Figure 1B.ConclusionThese finding suggest that higher WMH volume is associated with selective hippocampal subfield degeneration in the anterior portions of the hippocampus which may be more vulnerable to CSVD buildup.

Cocaine use is associated with cerebral white matter hyperintensities in HIV disease.

White matter hyperintensities (WMH), a marker of cerebral small vessel disease and predictor of cognitive decline, are observed at higher rates in persons with HIV (PWH). The use of cocaine, a potent central nervous system stimulant, is disproportionately common in PWH and may contribute to WMH. The sample included of 110 PWH on antiretroviral therapy. Fluid-attenuated inversion recovery (FLAIR) and T1-weighted anatomical MRI scans were collected, along with neuropsychological testing. FLAIR images were processed using the Lesion Segmentation Toolbox. A hierarchical regression model was run to investigate predictors of WMH burden [block 1: demographics; block 2: cerebrovascular disease (CVD) risk; block 3: lesion burden]. The sample was 20% female and 79% African American with a mean age of 45.37. All participants had persistent HIV viral suppression, and the median CD4+ T-cell count was 750. Nearly a third (29%) currently used cocaine regularly, with an average of 23.75 (SD = 20.95) days in the past 90. In the hierarchical linear regression model, cocaine use was a significant predictor of WMH burden (β = .28). WMH burden was significantly correlated with poorer cognitive function (r = -0.27). Finally, higher WMH burden was significantly associated with increased serum concentrations of interferon-γ-inducible protein 10 (IP-10) but lower concentrations of myeloperoxidase (MPO); however, these markers did not differ by COC status. WMH burden is associated with poorer cognitive performance in PWH. Cocaine use and CVD risk independently contribute to WMH, and addressing these conditions as part of HIV care may mitigate brain injury underlying neurocognitive impairment.

Longitudinal characterization of white matter degeneration in early stages of AD

AbstractBackgroundWhite matter (WM) degeneration in older adults is often characterized unimodally by diffusion tensor imaging (DTI), T1w and T2‐FLAIR MRI. However, approaches integrating multimodal MRI information will be necessary for better understanding WM degeneration in early stages of AD.MethodThis study characterizes the longitudinal changes of WM structure among 66 participants with marked baseline white matter hyperintensity volume (WMHv > 2000 mm3). 42 cognitively normal [CN, 72±7.2y, F = 25] and 24 mild cognitive impairment [MCI, 74±5.7y, F = 14]) adults underwent longitudinal MRI (scan interval: 2.8±0.85 years) within the Wake Forest AD Research Center. DTI metrics were estimated using FMRIB’s Diffusion Toolbox, voxel‐wise volumetric changes of WM (Jacobian Determinant [JD]) were estimated from fractional anisotropy (FA) maps using SPM CAT12, and WMHv were quantified using SPM’s Lesion Segmentation Toolbox. We segmented WM into WMH and normal‐appearing WM (NAWM) to characterize different degeneration patterns by integrating multimodal MRI information. We performed a linear regression analysis of %WMHv change with cognitive status, scan interval, and baseline WMH volume as covariates.ResultDecreased FA and increased mean diffusivity (MD) were observed in WMH regions compared with NAWM. Within WMH, interestingly, volume and FA increased, while MD decreased (p <0.001) during the longitudinal follow‐up (Figs. 1 and 2). Baseline WMHv was not significantly associated with cognitive status, but trended toward higher WMHv in MCI compared to CN (p = 0.07). Adjusted for baseline WMHv, cognitive status was associated with WMHv change (p = 0.036, MCI faster than CN, Fig. 3). While FA (p = 0.99) and MD (p = 0.16) did not change in the NAWM, volumetric change rate (JD) indicated volumetric contraction (p<0.001, Figs. 4 and 5). WMH progresses with time in older adults at risk of AD dementia by occupying neighboring NAWM regions, resulting in relative increases in FA and decreases in MD.ConclusionIn NAWM, WM degeneration can be better characterized by volumetric contraction than white matter microstructural integrity. This observation could implicate that secondary or peripheral WM fibers may reveal more vulnerable tracts in early stages of AD. Further investigation incorporating imaging makers of blood perfusion and metabolism could better explain structural mechanisms of WM degeneration.

Association of white matter lesions and brain atrophy with the development of dementia in a community: the Hisayama Study.

To investigate the association of white matter lesions volume (WMLV) levels with dementia risk and the association between dementia risk and the combined measures of WMLV and either total brain atrophy or dementia-related gray matter atrophy in a general older population. One thousand one hundred fifty-eight Japanese dementia-free community-residents aged ≥65 years who underwent brain magnetic resonance imaging were followed for 5.0 years. WMLV were segmented using the Lesion Segmentation Toolbox. Total brain volume (TBV) and regional gray matter volume were estimated by voxel-based morphometry. The WMLV-to-intracranial brain volume ratio (WMLV/ICV) was calculated, and its association with dementia risk was estimated using Cox proportional hazard models. Total brain atrophy, defined as the TBV-to-ICV ratio (TBV/ICV), and dementia-related regional brain atrophy defined based on our previous report were calculated. The association between dementia risk and the combined measures of WMLV/ICV and either total brain atrophy or the number of atrophied regions was also tested. During the follow-up, 113 participants developed dementia. The risks of dementia increased significantly with higher WMLV/ICV levels. In addition, dementia risk increased additively both in participants with higher WMLV/ICV levels and lower TBV/ICV levels and in those with higher WMLV/ICV levels and a higher number of dementia-related brain regional atrophy. The risk of dementia increased significantly with higher WMLV/ICV levels. An additive increment in dementia risk was observed with higher WMLV/ICV levels and lower TBV/ICV levels or a higher number of dementia-related brain regional atrophy, suggesting the importance of prevention or control of cardiovascular risk factors.

Performance evaluation of automated white matter hyperintensity segmentation algorithms in a multicenter cohort on cognitive impairment and dementia.

White matter hyperintensities (WMH), a biomarker of small vessel disease, are often found in Alzheimer's disease (AD) and their advanced detection and quantification can be beneficial for research and clinical applications. To investigate WMH in large-scale multicenter studies on cognitive impairment and AD, appropriate automated WMH segmentation algorithms are required. This study aimed to compare the performance of segmentation tools and provide information on their application in multicenter research. We used a pseudo-randomly selected dataset (n = 50) from the DZNE-multicenter observational Longitudinal Cognitive Impairment and Dementia Study (DELCODE) that included 3D fluid-attenuated inversion recovery (FLAIR) images from participants across the cognitive continuum. Performances of top-rated algorithms for automated WMH segmentation [Brain Intensity Abnormality Classification Algorithm (BIANCA), lesion segmentation toolbox (LST), lesion growth algorithm (LGA), LST lesion prediction algorithm (LPA), pgs, and sysu_media] were compared to manual reference segmentation (RS). Across tools, segmentation performance was moderate for global WMH volume and number of detected lesions. After retraining on a DELCODE subset, the deep learning algorithm sysu_media showed the highest performances with an average Dice's coefficient of 0.702 (±0.109 SD) for volume and a mean F1-score of 0.642 (±0.109 SD) for the number of lesions. The intra-class correlation was excellent for all algorithms (>0.9) but BIANCA (0.835). Performance improved with high WMH burden and varied across brain regions. To conclude, the deep learning algorithm, when retrained, performed well in the multicenter context. Nevertheless, the performance was close to traditional methods. We provide methodological recommendations for future studies using automated WMH segmentation to quantify and assess WMH along the continuum of cognitive impairment and AD dementia.

White matter hyperintensities in Down syndrome: associations with demographic, genetic, fluid and imaging biomarkers of Alzheimer's disease

AbstractBackgroundStrong associations between age, Alzheimer’s disease (AD), and vascular risk factors create challenges to delineate the specific relationships between white matter hyperintensities (WMH) and AD pathophysiology. Down syndrome (DS), a genetically determined form of AD, is not typically complicated by systematic vascular risk factors like hypertension and atherosclerosis and can therefore provide critical information in this regard. We aim to further characterize topography of WMH in DS along the AD continuum and define associations with demographic, genetic, fluid and other imaging biomarkers.MethodIn this cross‐sectional study, adults with DS from Down‐Alzheimer Barcelona Neuroimaging Initiative (DABNI) underwent a 3T‐MRI protocol, including 3D‐T1 and 3D‐FLAIR acquisitions. Gray matter (GM) volumes were computed using the CAT12 Toolbox. WMH were segmented using the lesion prediction algorithm implemented in the Lesion Segmentation Toolbox for SPM12. Segmented maps were binarized (threshold of 0.5) and WMH volumes were extracted in each brain lobe, subregions of the corpus callosum, and periventricular versus deep regions (using the standard 10mm limit from the lateral ventricles). Non‐parametric statistical tests were used to assess effects of disease severity on WMH and define associations between WMH and demographic and genetic data, CSF AD biomarkers and GM volumes.ResultsWe included 116 individuals with DS (Table 1). WMH volume was higher in prodromal and demented groups for all brain regions (Fig 1). Moreover, WMH increased with disease severity such that the demented group demonstrated larger WMH volume than the prodromal group in periventricular regions, frontal and temporal lobes, and body and splenium subregions of the corpus callosum. Total WMH volume was associated with age but not sex, intellectual disability or APOE haplotype (Fig 2). Total WMH volume was negatively related to CSF Aβ42/40 ratio and positively related to CSF t‐tau and p‐tau‐181 concentrations (Fig 3). Associations with GM volumes were widespread, but strongest in parieto‐occipital‐temporal regions.ConclusionWMH increases with AD pathology and the emergence of clinical symptoms in DS, a population with lower vascular risk factors. It also relates to neurodegeneration in typical AD regions at both asymptomatic and symptomatic stages. Altogether, this suggests that WMH might be intrinsically related to AD pathophysiological processes.

Testing the Association Between Neighborhood Disadvantage and White Matter Hyperintensities

AbstractBackgroundSocial determinants of health—the conditions in the places where people live, learn, work, and play—have been shown to affect a broad range of health outcomes, including risk for Alzheimer’s disease and related dementias (ADRD). However, the biological mechanisms by which socio‐contextual factors increase risk for ADRD are poorly known. Higher levels of neighborhood disadvantage have been linked with increased cardiovascular disease (CVD) risk. CVD risk has been associated with white matter lesions known as white matter hyperintensities (WMH) because they produce increased signal on fluid‐attenuated inversion recovery (FLAIR) MRI scans. WMH have been associated with increased risk for stroke, cognitive decline, and dementia. We investigated whether greater neighborhood‐level disadvantage was associated with greater WMH volume.MethodParticipants (n = 804) were enrolled in the Wisconsin Registry for Alzheimer’s Prevention and Wisconsin Alzheimer’s Disease Research Center Clinical Core. MRI was performed at 3 T to acquire T2‐weighted FLAIR images which were then processed using the lesion prediction algorithm from the Lesion Segmentation Toolbox to measure WMH volume. Area Deprivation Index (ADI) distributions from the state of Wisconsin were used to create a dichotomous variable that compared the highest state‐ranked quintile (most disadvantaged neighborhoods) to the lowest 4 state‐ranked quintiles (least disadvantaged neighborhoods). Multiple linear regression tested ADI as a predictor of natural log‐transformed WMH volume. Age, sex, education, intracranial volume, MRI head coil, and study cohort were controlled.ResultFour percent (n = 33) of participants were identified as living in the most disadvantaged neighborhoods (see Table 1). The association between neighborhood‐level disadvantage and log‐transformed WMH volume was not statistically significant (see Table 2 and Figure 1).ConclusionNeighborhood disadvantage was not related to WMH volume in this study. This finding contrasts with previous studies demonstrating associations between increased neighborhood disadvantage and reduced cerebral and hippocampal volume (Hunt et al., 2020) and increased odds for AD neuropathology (Powell et al., 2020). Statistical power to assess the primary effect was likely limited by the small number of participants from the most disadvantaged neighborhoods. This illustrates the need for additional research and increased population‐representative recruitment from disadvantaged contexts for ADRD research studies.

White matter hyperintensities in former American football players.

The presentation, risk factors, and etiologies of white matter hyperintensities (WMH) in people exposed to repetitive head impacts are unknown. We examined the burden and distribution of WMH, and their association with years of play, age of first exposure, and clinical function in former American football players. A total of 149 former football players and 53 asymptomatic unexposed participants (all men, 45-74 years) completed fluid-attenuated inversion recovery magnetic resonance imaging, neuropsychological testing, and self-report neuropsychiatric measures. Lesion Segmentation Toolbox estimated WMH. Analyses were performed in the total sample and stratified by age 60. In older but not younger participants, former football players had greater total, frontal, temporal, and parietal log-WMH compared to asymptomatic unexposed men. In older but not younger former football players, greater log-WMH was associated with younger age of first exposure to football and worse executive function. In older former football players, WMH may have unique presentations, risk factors, and etiologies. Older but not younger former football players had greater total, frontal, temporal, and parietal lobe white matter hyperintensities (WMH) compared to same-age asymptomatic unexposed men. Younger age of first exposure to football was associated with greater WMH in older but not younger former American football players. In former football players, greater WMH was associated with worse executive function and verbal memory.

Associations in Alzheimer’s disease between intracranial vascular metrics from 4D‐flow MRI and β‐amyloid and tau PET

AbstractBackgroundCerebrovascular disease (CVD), has been linked with dementia stages of Alzheimer’s disease (AD); however, the question of whether CVD is associated with underlying AD pathophysiology remains unresolved. This work investigated the relationship between advanced MRI based cerebrovascular markers with AD biomarkers of β‐amyloid deposition and neurofibrillary tau tangles in subjects spanning the AD clinical spectrum.MethodSubjects: Study groups are summarized in figure 1. MRI: Whole brain 4D‐Flow data were acquired on a 3.0T system using a 32‐channel head coil. From 4D‐Flow images, intracranial blood flow, trans‐capillary pulse wave delay, and low frequency oscillations (LFOs) were estimated. Trans‐capillary pulse wave delay was defined as the cardiac pulse wave transit time from intracranial arteries‐to‐capillaries‐to‐veins and was measured from cardiac wave time‐to‐upstroke differences in arteries and veins. White matter hyperintensity (WMH) lesion volumes were segmented from T2‐FLAIR images using the Lesion Segmentation Toolbox in SPM12. PET: Four groups were established based on clinical diagnosis and biomarker positivity. Subjects were classified as cognitively normal (CN) or impaired (IM) (MCI or dementia) based on a clinical consensus review. Amyloid (A) (+/‐) was determined using a previously established global PiB DVR threshold >1.19 and tau (T) (+/‐) was determined using a previously established entorhinal cortex MK‐6240 SUVR threshold >1.27. Pairwise statistical differences were assessed using ANOVA followed by post‐hoc analysis (P<0.05 significance).ResultTrans‐capillary pulse wave delay was significantly longer in controls (A‐/T‐/CN) when compared with AD biomarker confirmed groups (fig 2 b) (P<0.001). Low frequency flow range and LFOs were significantly lower and diminished in A+T+ groups both cognitively normal and impaired (fig 3 P<0.001; fig 4 P=0.027, P=0.046, P=0.046). Linear regression analysis showed a weak correlation between trans‐capillary pulse wave delay and PiB and MK‐6240 (R2=0.12, R2=0.04), and between LFOs and PiB and MK‐6240 (R2=0.07, R2=0.03) (fig 5). No significant AT biomarker group differences were observed across other modalities such as traditional cardiovascular and cerebrovascular metrics, including WMH lesion volumes (fig 1).ConclusionAlthough more traditional measures of CVD were not related to biomarker groups, 4D‐Flow MRI based vascular markers suggest significant intracranial vascular stiffness and reduced autoregulation‐related vasomotion in AD biomarker positive subjects during preclinical AD.

Free water in T2 FLAIR white matter hyperintensity lesions

AbstractBackgroundWhite matter (WM) free water (FW) is likely associated with cerebral small vessel disease (CSVD). FW is the fraction of unconstrained water within an image voxel, which can be estimated from diffusion‐weighted images. T2‐weighted Fluid‐Attenuated Inversion Recovery (FLAIR) white matter hyperintensity (WMH) is a widely used index to assess the damages caused by CSVD. It is critical to characterize how FW content is altered in WMH lesions. In this work, we proposed a data processing framework to assess FW distributions in WMH and normal‐appearing WM as well as in different WM fiber tracts.MethodSingle‐shell diffusion‐weighted image (SS‐DWI) and T2 FLAIR image data of 133 cognitively normal (CN) adults (Table 1) were obtained from the ADNI dataset (http://adni.loni.usc.edu). FW maps were generated from SS‐DWI images using the DIPY software package (https://dipy.org/). WMH lesions were identified with the lesion segmentation toolbox implemented in SPM. We co‐registered FW and WMH maps of individual subjects in the MNI space. Then we derived group WM fiber tracts using the tract‐based spatial statistics (TBSS). Finally, we examined the localization of WMH and FW in the major WM tracts.ResultFigure 1 shows the FW map, FLAIR image, and WMH lesion from a representative subject. Figure 2 shows group average and individual FW distributions in WMH and normal‐appearing WM. Two sample T‐Test found that FW content in WMH is significantly higher than that in normal‐appearing WM (mean FW and standard deviation FW in WMH are 0.430 and 0.112, mean FW and standard deviation FW in normal‐appearing WM are 0.203 and 0.06, P < 1×10‐30). TBSS based analysis showed that: 1) WMH lesions are mainly located in the corona radiata, posterior thalamic radiation, tapetum, superior fronto‐occipital fasciculus, superior longitudinal fasciculus, corpus callosum, and sagittal stratum; 2) Compared with mean FW in normal‐appearing WM, elevated FW content is mainly at the fornix, tapetum, cingulum hippocampus, superior fronto‐occipital fasciculus, corpus callosum, sagittal stratum, and posterior thalamic radiation.ConclusionWe developed an image processing pipeline to estimate FW changes in WMH lesions and normal‐appearing WM. Free water content is elevated in WMH lesions, which is likely driven by CSVD.

Association Between Antemortem FLAIR White Matter Hyperintensities and Neuropathology in Brain Donors Exposed to Repetitive Head Impacts.

Late neuropathologies of repetitive head impacts from contact sports can include chronic traumatic encephalopathy (CTE) and white matter degeneration. White matter hyperintensities (WMH) on fluid-attenuated inversion recovery (FLAIR) MRI scans are often viewed as microvascular disease from vascular risk, but might have unique underlying pathologies and risk factors in the setting of repetitive head impacts. We investigated the neuropathologic correlates of antemortem WMH in brain donors exposed to repetitive head impacts. The association between WMH and repetitive head impact exposure and informant-reported cognitive and daily function were tested. This imaging-pathologic correlation study included symptomatic male decedents exposed to repetitive head impacts. Donors had antemortem FLAIR scans from medical records and were without evidence of CNS neoplasm, large vessel infarcts, hemorrhage, or encephalomalacia. WMH were quantified using log-transformed values for total lesion volume (TLV), calculated using the lesion prediction algorithm from the Lesion Segmentation Toolbox. Neuropathologic assessments included semiquantitative ratings of white matter rarefaction, cerebrovascular disease, hyperphosphorylated tau (p-tau) severity (CTE stage, dorsolateral frontal cortex), and β-amyloid (Aβ). Among football players, years of play was a proxy for repetitive head impact exposure. Retrospective informant-reported cognitive and daily function were assessed using the Cognitive Difficulties Scale (CDS) and Functional Activities Questionnaire (FAQ). Regression models controlled for demographics, diabetes, hypertension, and MRI resolution. Statistical significance was defined as p ≤ 0.05. The sample included 75 donors: 67 football players and 8 nonfootball contact sport athletes or military veterans. Dementia was the most common MRI indication (64%). Fifty-three (70.7%) had CTE at autopsy. Log TLV was associated with white matter rarefaction (odds ratio [OR] 2.32, 95% confidence interval [CI] 1.03, 5.24; p = 0.04), arteriolosclerosis (OR 2.38, 95% CI 1.02, 5.52; p = 0.04), CTE stage (OR 2.58, 95% CI 1.17, 5.71; p = 0.02), and dorsolateral frontal p-tau severity (OR 3.03, 95% CI 1.32, 6.97; p = 0.01). There was no association with Aβ. More years of football play was associated with log TLV (unstandardized β 0.04, 95% CI 0.01, 0.06; p = 0.01). Greater log TLV correlated with higher FAQ (unstandardized β 4.94, 95% CI 0.42, 8.57; p = 0.03) and CDS scores (unstandardized β 15.35, 95% CI -0.27, 30.97; p = 0.05). WMH might capture long-term white matter pathologies from repetitive head impacts, including those from white matter rarefaction and p-tau, in addition to microvascular disease. Prospective imaging-pathologic correlation studies are needed. This study provides Class IV evidence of associations between FLAIR white matter hyperintensities and neuropathologic changes (white matter rarefaction, arteriolosclerosis, p-tau accumulation), years of American football play, and reported cognitive symptoms in symptomatic brain donors exposed to repetitive head impacts.

White-Matter Hyperintensity Load and Differences in Resting-State Network Connectivity Based on Mild Cognitive Impairment Subtype.

“Mild cognitive impairment” (MCI) is a diagnosis characterised by deficits in episodic memory (aMCI) or in other non-memory domains (naMCI). Although the definition of subtypes is helpful in clinical classification, it provides little insight on the variability of neurofunctional mechanisms (i.e., resting-state brain networks) at the basis of symptoms. In particular, it is unknown whether the presence of a high load of white-matter hyperintensities (WMHs) has a comparable effect on these functional networks in aMCI and naMCI patients. This question was addressed in a cohort of 123 MCI patients who had completed an MRI protocol inclusive of T1-weighted, fluid-attenuated inversion recovery (FLAIR) and resting-state fMRI sequences. T1-weighted and FLAIR images were processed with the Lesion Segmentation Toolbox to quantify whole-brain WMH volumes. The CONN toolbox was used to preprocess all fMRI images and to run an independent component analysis for the identification of four large-scale haemodynamic networks of cognitive relevance (i.e., default-mode, salience, left frontoparietal, and right frontoparietal networks) and one control network (i.e., visual network). Patients were classified based on MCI subtype (i.e., aMCI vs. naMCI) and WMH burden (i.e., low vs. high). Maps of large-scale networks were then modelled as a function of the MCI subtype-by-WMH burden interaction. Beyond the main effects of MCI subtype and WMH burden, a significant interaction was found in the salience and left frontoparietal networks. Having a low WMH burden was significantly more associated with stronger salience-network connectivity in aMCI (than in naMCI) in the right insula, and with stronger left frontoparietal-network connectivity in the right frontoinsular cortex. Vice versa, having a low WMH burden was significantly more associated with left-frontoparietal network connectivity in naMCI (than in aMCI) in the left mediotemporal lobe. The association between WMH burden and strength of connectivity of resting-state functional networks differs between aMCI and naMCI patients. Although exploratory in nature, these findings indicate that clinical profiles reflect mechanistic interactions that may play a central role in the definition of diagnostic and prognostic statuses.