Shades of white: diffusion properties of T1- and FLAIR-defined white matter signal abnormalities differ in stages from cognitively normal to dementia

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.

White-matter abnormalities in presymptomatic GRN and C9orf72 mutation carriers

A large proportion of familial frontotemporal dementia is caused by TAR DNA-binding protein 43 (transactive response DNA-binding protein 43 kDa) proteinopathies. Accordingly, carriers of autosomal dominant mutations in the genes associated with TAR DNA-binding protein 43 aggregation, such as Chromosome 9 open reading frame 72 (C9orf72) or progranulin (GRN), are at risk of later developing frontotemporal dementia. Brain imaging abnormalities that develop before dementia onset in mutation carriers may serve as proxies for the presymptomatic stages of familial frontotemporal dementia due to a genetic cause. Our study objective was to investigate brain MRI-based white-matter changes in predementia participants carrying mutations in C9orf72 or GRN genes. We analysed mutation carriers and their family member controls (noncarriers) from the University of British Columbia familial frontotemporal dementia study. First, a total of 42 participants (8 GRN carriers; 11 C9orf72 carriers; 23 noncarriers) had longitudinal T₁-weighted MRI over ∼2 years. White-matter signal hypointensities were segmented and volumes were calculated for each participant. General linear models were applied to compare the baseline burden and the annualized rate of accumulation of signal abnormalities among mutation carriers and noncarriers. Second, a total of 60 participants (9 GRN carriers; 17 C9orf72 carriers; 34 noncarriers) had cross-sectional diffusion tensor MRI available. For each participant, we calculated the average fractional anisotropy and mean, radial and axial diffusivity parameter values within the normal-appearing white-matter tissues. General linear models were applied to compare whether mutation carriers and noncarriers had different trends in diffusion tensor imaging parameter values as they neared the expected age of onset. Baseline volumes of white-matter signal abnormalities were not significantly different among mutation carriers and noncarriers. Longitudinally, GRN carriers had significantly higher annualized rates of accumulation (estimated mean: 15.87%/year) compared with C9orf72 carriers (3.69%/year) or noncarriers (2.64%/year). A significant relationship between diffusion tensor imaging parameter values and increasing expected age of onset was found in the periventricular normal-appearing white-matter region. Specifically, GRN carriers had a tendency of a faster increase of mean and radial diffusivity values and C9orf72 carriers had a tendency of a faster decline of fractional anisotropy values as they reached closer to the expected age of dementia onset. These findings suggest that white-matter changes may represent early markers of familial frontotemporal dementia due to genetic causes. However, GRN and C9orf72 mutation carriers may have different mechanisms leading to tissue abnormalities.

Cerebrovascular damage in subjective cognitive decline: A systematic review and meta-analysis

BACKGROUND

Subjective cognitive decline (SCD) has been postulated as an early marker of Alzheimer's Disease (AD) but it can also be associated to other non-AD pathologies such as Vascular Dementia (VaD). Nevertheless, there is scarce data about SCD as a potential harbinger of cerebrovascular pathology. Thus, we conducted a systematic review and meta-analysis on the association between SCD and cerebrovascular damage measured by neuroimaging markers.

METHOD

This study was performed following the PRISMA guidelines. The search was conducted in 3 databases (PubMed, Scopus and Web of Science) from origin to December 8th, 2021. Primary studies including cognitively unimpaired adults with SCD and neuroimaging markers of cerebrovascular damage (i.e., white matter signal abnormalities, WMSA) were selected. Qualitative synthesis and meta-analysis of studies with a case-control design was performed.

RESULTS

Of 241 articles identified, 21 research articles were selected. Eight case-control studies were included for the meta-analysis. A significant overall effect-size was observed for the mean WMSA burden in SCD relative to controls, where the WMSA burden was higher in SCD.

CONCLUSION

Our findings show the potential usefulness of SCD as a harbinger of cerebrovascular disease in cognitively healthy individuals. Further research is needed in order to elucidate the role of SCD as a preclinical marker of vascular cognitive impairment.

The canonical pattern of Alzheimer's disease atrophy is linked to white matter hyperintensities in normal controls, differently in normal controls compared to in AD

White matter signal abnormalities (WMSA), either hypo- or hyperintensities in MRI imaging, are considered a proxy of cerebrovascular pathology and contribute to, and modulate, the clinical presentation of Alzheimer's disease (AD), with cognitive dysfunction being apparent at lower levels of amyloid and/or tau pathology when lesions are present. To what extent the topography of cortical thinning associated with AD may be explained by WMSA remains unclear. Cortical thickness group difference maps and subgroup analyses show that the effect of WMSA on cortical thickness in cognitively normal participants has a higher overlap with the canonical pattern of AD, compared to AD participants. (Age and sex-matched group of 119 NC (AV45 PET negative, CDR = 0) versus 119 participants with AD (AV45 PET-positive, CDR > 0.5). The canonical patterns of cortical atrophy thought to be specific to Alzheimer's disease are strongly linked to cerebrovascular pathology supporting a reinterpretation of the classical models of AD suggesting that a part of the typical AD pattern is due to co-localized cortical loss before the onset of AD.

Cerebrovascular Disease and Depressive Symptomatology in Individuals With Subjective Cognitive Decline: A Community-Based Study

Subjective cognitive decline (SCD) may be the first sign of Alzheimer's disease (AD), but it can also reflect other pathologies such as cerebrovascular disease or conditions like depressive symptomatology. The role of depressive symptomatology in SCD is controversial. We investigated the association between depressive symptomatology, cerebrovascular disease, and SCD. We recruited 225 cognitively unimpaired individuals from a prospective community-based study [mean age (SD) = 54.64 (10.18); age range 35-77 years; 55% women; 123 individuals with one or more subjective cognitive complaints, 102 individuals with zero complaints]. SCD was assessed with a scale of 9 memory and non-memory subjective complaints. Depressive symptomatology was assessed with established questionnaires. Cerebrovascular disease was assessed with magnetic resonance imaging markers of white matter signal abnormalities (WMSA) and mean diffusivity (MD). We combined correlation, multiple regression, and mediation analyses to investigate the association between depressive symptomatology, cerebrovascular disease, and SCD. We found that SCD was associated with more cerebrovascular disease, older age, and increased depressive symptomatology. In turn, depressive symptomatology was not associated with cerebrovascular disease. Variability in MD was mediated by WMSA burden, presumably reflecting cerebrovascular disease. We conclude that, in our community-based cohort, depressive symptomatology is associated with SCD but not with cerebrovascular disease. In addition, depressive symptomatology did not influence the association between cerebrovascular disease and SCD. We suggest that therapeutic interventions for depressive symptomatology could alleviate the psychological burden of negative emotions in people with SCD, and intervening on vascular risk factors to reduce cerebrovascular disease should be tested as an opportunity to minimize neurodegeneration in SCD individuals from the community.

Cognitive variability, brain aging, and cognitive decline in late-life major depression

OBJECTIVES

Older adults with late-life major depression (LLMD) are at increased risk of dementia. Dispersion, or within-person performance variability across cognitive tests, is a potential marker of cognitive decline. This study examined group differences in dispersion between LLMD and nondepressed healthy controls (HC) and investigated whether dispersion was a predictor of cognitive performance 1 year later in LLMD. We also explored demographic, clinical, and structural imaging correlates of dispersion in LLMD and HC. We hypothesized that dispersion would be greater in LLMD compared with HC and would be associated with worse cognitive performance 1 year later in LLMD.

DESIGN

Participants were enrolled in the Neurobiology of Late-Life Depression, a naturalistic longitudinal investigation of the predictors of poor illness course in LLMD.

PARTICIPANTS

The baseline sample consisted of 121 older adults with LLMD and 39 HC; of these subjects, 94 LLMD and 35 HC underwent magnetic resonance imaging (MRI). One-year cognitive data were available for 107 LLMD patients.

MEASUREMENTS

All participants underwent detailed clinical and structural MRI at baseline. LLMD participants also completed a comprehensive cognitive evaluation 1 year later.

RESULTS

Higher test dispersion was evident in LLMD when compared with nondepressed controls. Greater baseline dispersion predicted 1-year cognitive decline in LLMD patients even when controlling for baseline cognitive functioning and demographic and clinical confounders. Dispersion was correlated with white matter lesions in LLMD but not HC. Dispersion was also correlated with anxiety in both LLMD and HC.

CONCLUSIONS

Dispersion is a marker of neurocognitive integrity that requires further exploration in LLMD.

Increase in blood-brain barrier leakage in healthy, older adults

Blood-brain barrier (BBB) breakdown can disrupt nutrient supply and waste removal, which affects neuronal functioning. Currently, dynamic contrast-enhanced (DCE) MRI is the preferred in-vivo method to quantify BBB leakage. Dedicated DCE MRI studies in normal aging individuals are lacking, which could hamper value estimation and interpretation of leakage rate in pathological conditions. Therefore, we applied DCE MRI to investigate the association between BBB disruption and age in a healthy sample. Fifty-seven cognitively and neurologically healthy, middle-aged to older participants (mean age: 66 years, range: 47-91 years) underwent MRI, including DCE MRI with intravenous injection of a gadolinium-based contrast agent. Pharmacokinetic modeling was applied to contrast concentration time-curves to estimate BBB leakage rate in each voxel. Subsequently, leakage rate was calculated in the white and gray matter, and primary (basic sensory and motor functions), secondary (association areas), and tertiary (higher-order cognition) brain regions. A difference in vulnerability to deterioration was expected between these regions, with especially tertiary regions being affected by age. Higher BBB leakage rate was significantly associated with older age in the white and gray matter, and also in tertiary, but not in primary or secondary brain regions. Even in healthy individuals, BBB disruption was stronger in older persons, which suggests BBB disruption is a normal physiologically aging phenomenon. Age-related increase in BBB disruption occurred especially in brain regions most vulnerable to age-related deterioration, which may indicate that BBB disruption is an underlying mechanism of normal age-related decline.Netherlands Trial Register number: NL6358, date of registration: 2017-03-24.

Predicting Fazekas scores from automatic segmentations of white matter signal abnormalities

Different measurements of white matter signal abnormalities (WMSA) are often used across studies, which hinders combination of WMSA data from different cohorts. We investigated associations between three commonly used measurements of WMSA, aiming to further understand the association between them and their potential interchangeability: the Fazekas scale, the lesion segmentation tool (LST), and FreeSurfer. We also aimed at proposing cut-off values for estimating low and high Fazekas scale WMSA burden from LST and FreeSurfer WMSA, to facilitate clinical use and interpretation of LST and FreeSurfer WMSA data. A population-based cohort of 709 individuals (all of them 70 years old, 52% female) was investigated. We found a strong association between LST and FreeSurfer WMSA, and an association of Fazekas scores with both LST and FreeSurfer WMSA. The proposed cut-off values were 0.00496 for LST and 0.00321 for FreeSurfer (Total Intracranial volumes (TIV)-corrected values). This study provides data on the association between Fazekas scores, hyperintense WMSA, and hypointense WMSA in a large population-based cohort. The proposed cut-off values for translating LST and FreeSurfer WMSA estimations to low and high Fazekas scale WMSA burden may facilitate the combination of WMSA measurements from different cohorts that used either a FLAIR or a T1-weigthed sequence.

White matter hypointensities and hyperintensities have equivalent correlations with age and CSF β-amyloid in the nondemented elderly

INTRODUCTION

T1- and T2-weighted sequences from MRI often provide useful complementary information about tissue properties. Leukoaraiosis results in signal abnormalities on T1-weighted images, which are automatically quantified by FreeSurfer, but this marker is poorly characterized and is rarely used. We evaluated associations between white matter hyperintensity (WM-hyper) volume from FLAIR and white matter hypointensity (WM-hypo) volume from T1-weighted images and compared their associations with age and cerebrospinal fluid (CSF) β-amyloid and tau.

METHODS

A total of 56 nondemented participants (68-94 years) were recruited and gave informed consent. All participants went through MR imaging on a GE 1.5T scanner and of these 47 underwent lumbar puncture for CSF analysis. WM-hypo was calculated using FreeSurfer analysis of T1 FSPGR 3D, and WM-hyper was calculated with the Lesion Segmentation Toolbox in the SPM software package using T2-FLAIR.

RESULTS

WM-hyper and WM-hypo were strongly correlated (r = .81; parameter estimate (p.e.): 1.53 ± 0.15; p < .0001). Age was significantly associated with both WM-hyper (r = .31, p.e. 0.078 ± 0.030, p = .013) and WM-hypo (r = .42, p.e. 0.055 ± 0.015, p < .001). CSF β-amyloid levels were predicted by WM-hyper (r = .33, p.e. -0.11 ± 0.044, p = .013) and WM-hypo (r = .42, p.e. -0.24 ± 0.073, p = .002). CSF tau levels were not correlated with either WM-hyper (p = .9) or WM-hypo (p = .99).

CONCLUSIONS

Strong correlations between WM-hyper and WM-hypo, and similar associations with age, abnormal β-amyloid, and tau suggest a general equivalence between these two imaging markers. Our work supports the equivalence of white matter hypointensity volumes derived from FreeSurfer for evaluating leukoaraiosis. This may have particular utility when T2-FLAIR is low in quality or absent, enabling analysis of older imaging data sets.