White Matter Hyperintensities Burden Research Articles

White matter hyperintensity burden mediates impact of vascular risk factors on cognitive impairment in SOL-INCA

White matter hyperintensity burden mediates impact of vascular risk factors on cognitive impairment in SOL-INCA

Associations Between Cardiac Function and Brain Health in Diverse Middle-Aged Adults: The Dallas Heart Study-2

BackgroundPrevious studies have linked cardiovascular risk factors during midlife to cognitive function in later life. However, few studies have looked at the association between cardiac function, brain structure, and cognitive function and even less have included diverse middle-aged populations. ObjectivesThe objective of this study was to determine associations between cardiac and brain structure and function in a multiethnic cohort of middle-aged adults. MethodsA cross-sectional study was conducted in participants of the Dallas Heart Study phase 2 (N = 1,919; 46% Black participants). Left ventricular (LV) mass, LV ejection fraction, LV concentricity, and peak systolic strain (LV Ecc) were assessed by cardiac magnetic resonance imaging. White matter hyperintensities (WMH) volume was measured by fluid attenuated inversion recovery magnetic resonance imaging. The Montreal Cognitive Assessment was used to measure cognitive functioning. Associations between cardiac and brain measures were determined using multivariable linear regression after adjusting for cardiovascular risk factors, education level, and physical activity. ResultsLV ejection fraction was associated with total Montreal Cognitive Assessment score (β = 0.06 [95% CI: 0.003-0.12], P = 0.042) and LV Ecc was associated with WMH volume (β = 0.08 [95% CI: 0.01-0.14], P = 0.025) in the overall cohort without significant interaction by race/ethnicity. Higher LV mass and concentricity were associated with larger WMH volume in the overall cohort (β = 0.13 [95% CI: 0.03-0.23], P = 0.008 and 0.10 [95% CI: 0.03-0.17], P = 0.005). These associations were more predominant in Black than White participants (β = 0.17 [95% CI: 0.04-0.30] vs β = −0.009 [95% CI: −0.16 to 0.14], P = 0.036 and β = 0.22 [95% CI: 0.13-0.32] vs β = −0.11 [95% CI: −0.21 to −0.01], P < 0.0001, for LV mass and concentricity, respectively). ConclusionsSubclinical cardiac dysfunction indicated by LVEF was associated with lower cognitive function. Moreover, LV mass and concentric remodeling were associated with higher WMH burden, particularly among Black individuals.

Correlation between body composition and white matter hyperintensity in patients with acute ischemic stroke.

White matter hyperintensity (WMH) burden is associated with a higher risk of ischemic stroke. The relationship between WMH and obesity is somewhat controversial which might be interfered by different body composition such as skeletal muscle, fat and bone density. However, few researchers have evaluated the relationship between WMH burden and disaggregated body constituents in acute ischemic stroke (AIS) patients systematically. A total of 352 AIS patients were enrolled in this study. The subcutaneous adipose tissue, erector spinae muscle area and bone density were evaluated on the computed tomography scanning. The burden of WMH was evaluated using the Fazekas scale based on the fluid-attenuated inversion recovery sequence. The severity of overall WMH was defined as none-mild WMH (total Fazekas score 0-2) or moderate-severe WMH (total Fazekas score 3-6). Based on the severity of periventricular WMH (P-WMH) and deep WMH, patients were categorized into either a none-mild (Fazekas score 0-1) group or a moderate-severe (Fazekas score 2-3) group. We found that patients with moderate-severe WMH showed lower bone density and smaller erector spinae muscle area and subcutaneous adipose tissue than none-mild. The logistic regression analysis showed that the bone density was independently associated with moderate-severe overall WMH (odds radio = 0.98, 95% confidence interval, 0.972-0.992, P < .001) and similar results were found in the analyses according to P-WMH (odds radio = 0.98, 95% confidence interval, 0.972-0.992, P < .001). These findings suggest that among the AIS body composition, the bone density is independently associated with the severity of overall WMH and P-WMH.

Impact of white matter hyperintensities on structural connectivity and cognition in cognitively intact ADNI participants

We used indirect brain mapping with virtual lesion tractography to test the hypothesis that the extent of white matter tract disconnection due to white matter hyperintensities (WMH) is associated with corresponding tract-specific cognitive performance decrements. To estimate tract disconnection, WMH masks were extracted from FLAIR MRI data of 481 cognitively intact participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and used as regions of avoidance for fiber tracking in diffusion MRI data from 50 healthy young participants from the Human Connectome Project. Estimated tract disconnection in the right inferior fronto-occipital fasciculus, right frontal aslant tract, and right superior longitudinal fasciculus mediated the effects of WMH volume on executive function. Estimated tract disconnection in the left uncinate fasciculus mediated the effects of WMH volume on memory and in the right frontal aslant tract on language. In a subset of ADNI control participants with amyloid data, positive status increased the probability of periventricular WMH and moderated the relationship between WMH burden and tract disconnection in executive function performance.

Effect of pre-stroke antiplatelet use on stroke outcomes in acute small vessel occlusion stroke with moderate to severe white matter burden

BackgroundCerebral small vessel disease is characterized by white matter hyperintensities (WMH) and acute small vessel occlusion (SVO) stroke. We investigated the effect of prior antiplatelet use (APU) on stroke outcome in 1151 patients with acute SVO stroke patients and moderate to severe WMH. MethodsUsing a multicenter database, this retrospective study used quantitative WMH volume measurements and propensity score matching (PSM) for comparisons between patients with prior APU and without APU. Primary outcomes were stroke progression and poor functional outcome (modified Rankin Scale>2) at 3 months. Logistic regression analyses assessed associations between prior APU, WMH burden, and stroke outcomes. ResultsStroke progression was lower in the prior APU group in both the total cohort (14.8% vs. 6.9%, p < 0.001) and the PSM cohort (16.3% vs. 6.9%, p < 0.001). The proportion of poor functional outcomes at 3 months was not significantly different in the total cohort, but the PSM cohort showed a lower proportion in the prior APU group (30.8% vs. 20.2%, p = 0.002). Logistic regression analysis confirmed that prior APU was associated with a reduced risk of stroke progression (OR, 0.39; 95% CI, 0.22–0.70; p = 0.001) and poor functional outcome at 3 months (OR, 0.37; 95% CI, 0.23–0.59; p < 0.001). ConclusionPrior APU is associated with reduced stroke progression and improved functional outcome at 3 months in acute SVO stroke patients with moderate to severe WMH. Early treatment of WMH and acute SVO stroke may have potential benefits in improving stroke outcomes.

Accuracy of TrUE-Net in comparison to established white matter hyperintensity segmentation methods: An independent validation study

White matter hyperintensities (WMH) are nearly ubiquitous in the aging brain, and their topography and overall burden are associated with cognitive decline. Given their numerosity, accurate methods to automatically segment WMH are needed. Recent developments, including the availability of challenge data sets and improved deep learning algorithms, have led to a new promising deep-learning based automated segmentation model called TrUE-Net, which has yet to undergo rigorous independent validation. Here, we compare TrUE-Net to six established automated WMH segmentation tools, including a semi-manual method. We evaluated the techniques at both global and regional level to compare their ability to detect the established relationship between WMH burden and age. We found that TrUE-Net was highly reliable at identifying WMH regions with low false positive rates, when compared to semi-manual segmentation as the reference standard. TrUE-Net performed similarly or favorably when compared to the other automated techniques. Moreover, TrUE-Net was able to detect relationships between WMH and age to a similar degree as the reference standard semi-manual segmentation at both the global and regional level. These results support the use of TrUE-Net for identifying WMH at the global or regional level, including in large, combined datasets.

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.

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.

Post‐mortem MRI white matter hyperintensities are associated with cortical astrocytopathy and water channel expression in the oldest‐old

AbstractBackgroundCerebrovascular small vessel disease, a common cause of cognitive impairment in older individuals, is characterized by the presence of MRI white matter hyperintensities (WMH). Our previous MRI‐targeted histopathological studies have found WMHs and the surrounding NAWM penumbra to be associated with glial markers. This study aims to assess the relationship between cortical markers of glial dysfunction and post‐mortem (PM) MRI WMH.MethodLeft brain hemispheres were obtained from 45 Oregon Alzheimer’s Disease Research Center subjects &gt;/ = 80 years of age without significant AD pathology. Intact and sliced hemispheres were scanned at 7T MRI. MRI scans were used to target histological sampling. Slides were stained for glial fibrillary acid protein (GFAP), ionized calcium binding adapter molecule‐1 (IBA1), aquaporin‐1 (AQP1), aquaporin‐4 (AQP4), vimentin and amyloid‐beta (ABETA). Slides were co‐registered to MRI. Tissue masks were hand‐drawn to delineate white matter and cortex. Cortical ROIs were quantified for mean pixel value (AQP1, AQP4, GFAP, and Vimentin) or area coverage (IBA1, ABETA). Cassettes with no local WMH were compared with those in the top third of slide WMH coverage (187 cassettes). Global WMH burden was dichotomized at the mean. Linear mixed‐effects models regressed stain quantification on local WMH (in slide) and global WMH burden, with a random effect for staining batch (levels = 4). The final model controlled for age at death and ABETA expression within a slide. Interactions between local and global WMH were included where appropriate.ResultHigh local WMH burden was associated with greater cortical vimentin (p = 0.036), GFAP (p = 0.066), and AQP4 (p = 0.004), while high global WMH burden was associated with greater IBA1 (p = 0.034) and AQP1 (p = 0.071). An interaction was identified between local and global WMH burden for AQP1 (p&lt;0.10), such that in subjects with high global WMH burden, high local WMH burden was associated with higher AQP1. Greater local cortical ABETA was associated with cortical AQP1 (p = 0.004) expression.ConclusionIn oldest‐old individuals without significant AD pathology, high local and global PM‐MRI WMH burden is associated with cortical astrocytopathy, including increased water channel expression and inflammation. Findings implicate WMH as a state marker of widespread cortical pathology that may confer increased vulnerability to cognitive decline with advanced age.

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.

Macular vessel density in the superficial plexus is not associated to cerebrovascular damage in non‐demented individuals: data from the NORFACE cohort

AbstractBackgroundOptical Coherence Tomography angiography (OCT‐A) is a novel tool that allows the detection of retinal vascular changes. The comparison of OCT‐A measures with brain vascular burden is essential to validate the former as a surrogate of cerebrovascular damage and biomarker of cognitive decline. We aimed to investigate the association of macular vessel density (VD) in the superficial plexus assessed by OCT‐A with measures of cerebrovascular pathology quantified by brain magnetic resonance imaging (MRI) in a cohort of non‐demented individuals.MethodClinical, demographical, APOE ε4 status, amyloid status, OCT‐A and brain MRI data from non‐demented participants of the FACEHBI and BIOFACE studies were included. We analyzed the association of macular VD in four quadrants (superior, nasal, inferior and temporal) with brain vascular burden using the Fazekas scale scores (dichotomized in two categories, 0‐1 vs 2‐3), assessed in a logistic regression analysis, and the volume of white matter hyperintensities (WMH) quantified by Freesurfer assessed in a multiple regression analysis. We additionally explored the associations of macular VD with hippocampal volume, ventricles volume and Alzheimer Disease cortical signature (ADCS) thickness assessed in a multiple regression analysis. All analyses were adjusted for age and dyslipidemia.ResultThe study cohort comprised 188 participants: 89 with subjective cognitive decline (SCD) and 99 with mild cognitive impairment (MCI). Logistic regression analysis showed no significant differences of regional macular VD between the two Fazekas groups (all, p&gt;0.05). Multiple regression analysis showed no significant association of regional macular VD with the volume of WMH (all, p&gt;0.05). Macular VD in the nasal and inferior quadrants were positively associated with hippocampal volume (p = 0.009 and p = 0.021, respectively), but no association was detected with ventricles volume or ADCS thickness (both p&gt;0.05).ConclusionOur study showed that retinal vascular measures (regional macular VD in the superficial vascular plexus) assessed by OCT‐A were not associated with brain vascular damage quantified by the Fazekas scale and the WMH burden n in a cohort of non‐demented research participants with SCD and MCI.

Artificial intelligence reveals brain aging patterns in 27,402 individuals without diagnosed cognitive impairment that are linked to genetics, biomedical measures, and cognitive decline

AbstractBackgroundUnderstanding heterogeneity of structural brain changes in aging may provide insights into susceptibility to neurodegenerative diseases. We characterize the genetics underlying brain structural heterogeneity within cognitively unimpaired (CU) individuals using data‐driven machine learning applied to a diverse dataset of 27,402 individuals from 11 neuroimaging studies from the iSTAGING consortium.MethodStructural brain morphologic patterns of CU individuals were independently examined in four decade‐long intervals spanning ages 45 to 85. Within each interval, Smile‐GAN (Yang et al., 2021) was trained on baseline anatomic and white matter hyperintensity (WMH) volumes. Smile‐GAN probability scores were used as phenotypes in genome‐wide association studies (GWAS). Specifically, we performed multiple linear regressions controlling for confounders (e.g., age) via Plink (Purcell et al., 2007). We observed longitudinal clustering stability across decades, so individuals from adjacent age groups were combined into broader age groups ([45,65), [65,85)) due to the large sample requirement of GWAS. Genomic loci, represented by the top leading single nucleotide polymorphisms (SNPs), were defined considering linkage disequilibrium. We investigated associations of SNPs with clinical traits and mapped them to genes using the GWAS Catalog (Buniello et al., 2019).ResultThree structural brain aging patterns, relative to resilient agers (A0), consistent across decades, emerged: A1, or ‘typical’ aging with low atrophy and WMHs, and two ‘advanced’ aging patterns, one showing elevated WMHs and modest atrophy (A2) and the other displaying severe, widespread atrophy and moderate WMH load (A3) (Figure 1). GWAS discovered eight and six genomic loci in [45,65) and [65,85) age groups, respectively (Table 1, Figure 2). The lead SNPs for A1 and A2 were previously associated with several cardiometabolic risk factors, WMHs, and regional brain volumes. Interestingly, rs4843552, previously associated with white matter microstructure and regional brain volumes, showed opposite effects for A1 (protection) and A2 (risk), consistent with the neuroimaging patterns. WDR41 in A3 group was previously associated with the age of Alzheimer’s disease onset (Herold et al., 2016).ConclusionReproducible neuroimaging patterns defined by regional atrophy and WMH burden were identified across CU individuals and demonstrated unique genetics. Further research is needed to elucidate the neuropathological pathways that mediate these relationships.

The Neuropathological Correlates of White Matter Hyperintensities in Cerebral Amyloid Angiopathy

AbstractBackgroundCerebral amyloid angiopathy (CAA) is a common cause of intracerebral hemorrhage and cognitive impairment in older individuals. Hallmark manifestations of CAA on MRI include cortical hemorrhages, as well as perivascular spaces (PVS) in the white matter centrum semiovale and white matter hyperintensities (WMH) in a subcortical multi‐spot pattern. We performed an ex vivo MRI‐neuropathological study to: 1) determine the relationship between WMH volume and CAA severity, and 2) assess the histopathological substrate of confluent WMH, WMH in a multi‐spot pattern (WM_MS), and normal‐appearing white matter (NAWM).MethodNineteen autopsy cases with pathologically confirmed CAA (age 75±8y; 7F) and five non‐CAA controls (age 88±5y; 3F) were included. Formalin‐fixed hemispheres underwent 3T MRI, including a T2‐weighted (500µm isotropic resolution, to obtain manual WMH segmentations) and a T2*‐weighted sequence (1mm isotropic resolution, to automatically segment hemisphere volume using SynthSeg). After scanning, five pre‐defined standard areas were sampled from each brain, then processed to obtain manually assessed arteriolosclerosis scores and AI‐derived cortical and leptomeningeal CAA area measurements. In the CAA cases, specific areas with ex vivo MRI‐observed WM_MS (n = 7), confluent WMH (n = 9), and NAWM (n = 9) were sampled. Sections were stained with LH&amp;E (to determine WM rarefaction, % PVS area, and degree of arteriolosclerosis), and CD68 (a marker of activated microglia, to assess density of CD68‐positive cells).ResultTotal normalized WMH volume did not differ between CAA cases (0.037±0.024) and non‐CAA controls (0.027±0.026) (Mann‐Whitney U test 36, p = 0.31). Within the CAA cases, a multivariable regression model corrected for age at death revealed that normalized WMH volume was associated with cortical CAA area (p = 0.010) and arteriolosclerosis (p = 0.066), but not leptomeningeal CAA area (p = 0.94). Targeted neuropathological assessment revealed that WM rarefaction, arteriolosclerosis score, and density of CD68‐positive cells were higher in areas with WMH compared to WM_MS and NAWM. In contrast, % PVS area was higher in WM_MS compared to WMH and NAWM.ConclusionThe observed differences in neuropathological correlates between WMH in a multi‐spot pattern and confluent WMH in CAA suggest different pathophysiological mechanisms. Future studies are needed to determine these mechanisms as well as the causal relationship between cortical CAA severity and WMH burden.

Association of white matter hyperintensities with long-term EGFR-TKI treatment and prediction of progression risk.

The purpose of this study was to test the hypothesis that brain white matter hyperintensities (WMH) are more common in patients receiving epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) and identify clinical risk factors associated with WMH. This multiple-center, prospective cohort study was conducted from March 2017 to July 2020. Two groups of patients with non-small cell lung cancer (NSCLC) who received or did not receive EGFR-TKI were included and followed up for more than 24 months. The progression of WMH was defined as an increase of ≥1 point on the Fazekas visual rating scale between the baseline and at the 2-year follow-up. A modified Poisson regression model was performed to evaluate risk factors on increased WMH load. Among 286 patients with NSCLC, 194 (68%) patients with NSCLC who received EGFR-TKI and 92 (32%) patients with NSCLC without EGFR-TKI treatment were analyzed. Modified Poisson regression analysis showed that EGFR-TKI treatment was independently associated with the WMH progression (EGFR-TKI: aRR 2.72, 95% confidence interval [CI] 1.46-5.06, p=.002). Interleukin (IL)-2, IL-4, and IL-10 were associated with increased WMH in the adjusted model (IL-2: aRR 1.55 [95% CI 1.06-2.25], p = .023; IL-4: aRR 1.66 [95% CI 1.13-2.43], p = .010; IL-10: aRR 1.48 [95% CI 1.06-2.06], p = .020). Patients with NSCLC who received EGFR-TKI may be at higher risk of developing WMH or worsening of WMH burden. The impact of increased WMH lesions in these patients is to be further assessed. IL-2, IL-4, and IL-10 may be used as potential biomarkers to monitor the risk of increased WMH burden.

White matter hyperintensity burden does not differentiate SuperAgers from average cognitive agers

AbstractBackgroundThe amount of white matter hyperintensity (WMH) found on T2‐weighted imaging has shown a strong relationship with age and is thought to be a marker of ischemia‐associated demyelination or secondary axonal degeneration associated with neurodegenerative disease. Previous healthy older aging studies have shown increased WMH are associated with poorer episodic memory performance and decreased volume of medial temporal lobe regions. Here, we examine if lower WMH are associated with a phenotype of successful cognitive aging, SuperAging. SuperAgers are participants over the age 80 with episodic memory at least as good as that of average middle‐aged adults.MethodWe quantified the WMH volume from T2‐FLAIR imaging with a pre‐trained Bayesian 3D U‐Net convolutional neural network in SuperAgers and cognitively healthy elderly controls (EC) of a similar age and education. All participants underwent the ADNI‐3 FLAIR on the same 3T Prisma MRI. WMH volume was square root transformed to account for the skewed distribution. A 2‐tail independent samples t‐test evaluated the difference in WMH volume between SuperAgers and EC. Additionally, we examined if there were differences within SA and EC groups by sex.Result46 SuperAgers (average age 86.3 ± 1.86; average years of education 16.9 ± 1.9) and 25 EC (average age 87.1 ± 5.17; average years of education 17.1 ± 3.1) met criteria and were included in the study. There was no significant difference in the volume of WMH between SuperAgers and ECs (t = 1.01, p = 0.32). Further, there was no significant difference in WMH by sex within the SuperAging group (t = 0.10, p = 0.92) or EC group (t = 0.82, p = 0.42).ConclusionResistance to the development of WMH, a cross‐sectional marker of cerebrovascular disease and demyelination, does not appear to be a strong discriminating factor in 80+ year‐olds with superior memory function compared to cognitively average agers. Future analyses will include longitudinal changes in WMH and examining regional WMH. Cognitive aging is associated with several underlying brain‐based etiologies, and SuperAging may be better explained by a more sophisticated multi‐modal approach.

Prevalence, incidence, and prognostic indicators of Dementia in patients with TIA and Stroke: the Population‐based Rotterdam Study

AbstractBackgroundRisk of dementia is high after major stroke, but reliable estimates of dementia by stroke severity, accounting for competing risk of mortality, in a population‐based setting are scarce. In this study, we aimed to determine risk and prognostic indicators of dementia before and after TIA and stroke.MethodWe included all individuals with first‐ever TIA or stroke from the population‐based Rotterdam Study, between 2002‐2018, and matched those on age (+/‐3 years) and sex, using an incidence density sampling approach. We determined prevalence of pre‐event dementia by event severity (TIA vs. minor stroke [NIHSS≤3] vs. major stroke [NIHSS&gt;3]). Similarly, we determined risk of dementia at 1‐,5‐ and 10‐year post‐event using the subdistribution hazards (SDH) cumulative incidence function (CIF), and explored prognostic indicators of dementia.ResultAmong 1352 participants with first‐ever TIA or stroke (mean age: 79.4 (9.5) years, 59.7% women), 466 had TIA, 392 minor stroke, 494 major stroke. Prevalence of pre‐event dementia was higher among patients with major stroke (90/494; 18.2%) than among those with minor stroke (29/392; 7.3%), TIA (37/466; 7.9%). During 10‐year follow‐up, dementia occurred in 784 of 4703 participants without pre‐event dementia or reference participants. Compared to TIA patients, dementia risk was higher after major stroke (HR 2.17; 95% CI 1.53–3.07) and minor stroke (HR 1.63; 95% CI 1.17–2.26). Dementia risk was associated with male sex (SDH‐ratio 1.23; 95% CI 1.04–1.45), age (per 1‐year increase: 1.07; 1.06–1.08), higher education (0.61; 0.44–0.86), diabetes mellitus (1.29; 1.06–1.56), atrial fibrillation (1.40; 1.14–.71), and premorbid cognition (MMSE≤24, 2.39; 1.87–3.05). Among 417 patients with available pre‐event brain MRI, white matter hyperintensity burden (per 1‐% increase of proportion of total brain volume: 1.24; 95% CI 1.07–1.45) and presence of microbleeds (2.14; 1.18–3.86) were indicators of post‐stroke dementia.ConclusionDementia incidence after TIA and stroke is increased in patients with major stroke and to a lesser extent minor stroke, but not TIA. Event severity in combination with clinical and imaging indicators may hold potential for the prediction of cognitive decline after stroke.

White matter hyperintensity burden does not differentiate SuperAgers from average cognitive agers

AbstractBackgroundThe amount of white matter hyperintensity (WMH) found on T2‐weighted imaging has shown a strong relationship with age and is thought to be a marker of ischemia‐associated demyelination or secondary axonal degeneration associated with neurodegenerative disease. Previous healthy older aging studies have shown increased WMH are associated with poorer episodic memory performance and decreased volume of medial temporal lobe regions. Here, we examine if lower WMH are associated with a phenotype of successful cognitive aging, SuperAging. SuperAgers are participants over the age 80 with episodic memory at least as good as that of average middle‐aged adults.MethodWe quantified the WMH volume from T2‐FLAIR imaging with a pre‐trained Bayesian 3D U‐Net convolutional neural network in SuperAgers and cognitively healthy elderly controls (EC) of a similar age and education. All participants underwent the ADNI‐3 FLAIR on the same 3T Prisma MRI. WMH volume was square root transformed to account for the skewed distribution. A 2‐tail independent samples t‐test evaluated the difference in WMH volume between SuperAgers and EC. Additionally, we examined if there were differences within SA and EC groups by sex.Result46 SuperAgers (average age 86.3 ± 1.86; average years of education 16.9 ± 1.9) and 25 EC (average age 87.1 ± 5.17; average years of education 17.1 ± 3.1) met criteria and were included in the study. There was no significant difference in the volume of WMH between SuperAgers and ECs (t = 1.01, p = 0.32). Further, there was no significant difference in WMH by sex within the SuperAging group (t = 0.10, p = 0.92) or EC group (t = 0.82, p = 0.42).ConclusionResistance to the development of WMH, a cross‐sectional marker of cerebrovascular disease and demyelination, does not appear to be a strong discriminating factor in 80+ year‐olds with superior memory function compared to cognitively average agers. Future analyses will include longitudinal changes in WMH and examining regional WMH. Cognitive aging is associated with several underlying brain‐based etiologies, and SuperAging may be better explained by a more sophisticated multi‐modal approach.

Genetic variants underlying the neuro‐anatomical signatures of hypertension measured on structural MRI

AbstractBackgroundHypertension (HTN) is associated with gray matter (GM) atrophy and increased white matter hyperintensity (WMH) burden, increasing their susceptibility to Alzheimer’s disease and related dementias. We developed a machine learning based model to quantify spatial patterns of abnormality recognizing HTN‐related brain changes (SPARE‐HTN) from structural magnetic resonance images (sMRI). We performed a genome wide association study (GWAS) to identify the genetic variants and associated biological traits that underlie the “expression” of HTN‐related brain changes, i.e., the individualized SPARE‐HTN index.MethodSPARE‐HTN model was trained on the large multi‐study iSTAGING dataset, with regional GM volumes from T1‐weighted images and lobar WMH volumes from T2‐weighted FLAIR images used as imaging features. GWAS was carried out in a subset of N = 7490 (46% Female, Age = 65.5±7 years) hypertensive participants from UK‐Biobank. A GWAS of SPARE‐HTN scores was run using linear regression with Plink, correcting for the top 40 genetic principal components, age, sex, and intracranial volume. Significant SNPs were identified and mapped to functional genes using the Functional Mapping and Annotation (FUMA) platform; and candidate SNPs were mapped to the GWAS catalog.ResultSPARE‐HTN was associated with regional atrophy most prominent in perisylvian and temporal cortex and increased WMH volumes, particularly in frontal white matter. SPARE‐HTN scores showed better discriminative power between normal and stage 1 participants (Cohen’s‐d effect size, d = 0.2) and stage 2 (d = 0.71) hypertensive participants when compared to total WMH volume (d = 0.04 and 0.38, respectively). GWAS identified N = 321 independently significant SNPs, of which a subset N = 46 candidate SNPs were mapped to 6 genes (ICA1L, CARF, WDR12, NBEAL1, KRT8P15, CYP20A1) in Chromosome 2 (Figure 2). These genes were previously reported to be associated with several cardiovascular conditions including coronary artery disease and stroke, and with increased WM damage on sMRI and diffusion MRI (Figure 3).ConclusionThe individualized imaging marker, SPARE‐HTN, was associated with genes relevant to HTN with traits including cardiovascular conditions and higher prevalence of WMH, and was more sensitive to HTN than total WMH volume. SPARE‐HTN is a useful tool to characterize HTN‐related brain changes and monitor treatment effects at an individual level.

Genetic variants underlying the neuro‐anatomical signatures of hypertension measured on structural MRI

AbstractBackgroundHypertension (HTN) is associated with gray matter (GM) atrophy and increased white matter hyperintensity (WMH) burden, increasing their susceptibility to Alzheimer’s disease and related dementias. We developed a machine learning based model to quantify spatial patterns of abnormality recognizing HTN‐related brain changes (SPARE‐HTN) from structural magnetic resonance images (sMRI). We performed a genome wide association study (GWAS) to identify the genetic variants and associated biological traits that underlie the “expression” of HTN‐related brain changes, i.e., the individualized SPARE‐HTN index.MethodSPARE‐HTN model was trained on the large multi‐study iSTAGING dataset, with regional GM volumes from T1‐weighted images and lobar WMH volumes from T2‐weighted FLAIR images used as imaging features. GWAS was carried out in a subset of N = 7490 (46% Female, Age = 65.5±7 years) hypertensive participants from UK‐Biobank. A GWAS of SPARE‐HTN scores was run using linear regression with Plink, correcting for the top 40 genetic principal components, age, sex, and intracranial volume. Significant SNPs were identified and mapped to functional genes using the Functional Mapping and Annotation (FUMA) platform; and candidate SNPs were mapped to the GWAS catalog.ResultSPARE‐HTN was associated with regional atrophy most prominent in perisylvian and temporal cortex and increased WMH volumes, particularly in frontal white matter. SPARE‐HTN scores showed better discriminative power between normal and stage 1 participants (Cohen’s‐d effect size, d = 0.2) and stage 2 (d = 0.71) hypertensive participants when compared to total WMH volume (d = 0.04 and 0.38, respectively). GWAS identified N = 321 independently significant SNPs, of which a subset N = 46 candidate SNPs were mapped to 6 genes (ICA1L, CARF, WDR12, NBEAL1, KRT8P15, CYP20A1) in Chromosome 2 (Figure 2). These genes were previously reported to be associated with several cardiovascular conditions including coronary artery disease and stroke, and with increased WM damage on sMRI and diffusion MRI (Figure 3).ConclusionThe individualized imaging marker, SPARE‐HTN, was associated with genes relevant to HTN with traits including cardiovascular conditions and higher prevalence of WMH, and was more sensitive to HTN than total WMH volume. SPARE‐HTN is a useful tool to characterize HTN‐related brain changes and monitor treatment effects at an individual level.

Higher systolic and diastolic blood pressures in recently postmenopausal women are associated with greater WMH more than a decade later in the KEEPS Continuation

AbstractBackgroundHigher blood pressure (BP) is associated with greater white matter hyperintensity (WMH) volume, but research is limited in recently menopausal and postmenopausal women. Postmenopausal women have a greater burden of WMH than men and premenopausal women, but whether hormone therapy (HT) modifies the association between systolic and diastolic BP (SBP, DBP) and WMH is unknown. We investigated the association of SBP and DBP in recently postmenopausal women who participated in an HT trial with WMH volumes measured 14 years later and explored whether HT modified the results.MethodWomen with good cardiovascular (CV) health, who were previously enrolled in a randomized placebo‐controlled HT trial, the Kronos Early Estrogen Prevention Study (KEEPS), participated in the current observational KEEPS continuation (n = 212; median age = 67; range 58‐72). SBP and DBP were measured at KEEPS baseline (median age = 54 years; range 44‐59) and continuation. Antihypertensive users were excluded (baseline, n = 34; continuation, n = 61). Approximately 10 years after the end of KEEPS HT versus placebo 4‐year interventions, automated WMH volumes were measured from 3D‐FLAIR MRI. Associations of KEEPS baseline and continuation SBP and DBP with log WMH volume at KEEPS continuation were tested using linear regression analyses adjusting for covariates in model 1 (age, total intracranial volume, study site) and model 2 (model 1 plus cardiovascular risk factors (CVRF)).ResultHigher KEEPS baseline SBP and DBP were associated with greater WMH volume measured 14 years later, and after adjusting for CVRF (p = 0.01). Similarly, in KEEPS continuation, higher SBP and DBP were associated with greater WMH volume measured concurrently and after adjusting for CVRF (p&lt;0.001). We did not find statistically significant evidence that HT versus placebo modified these associations. Topographically, higher SBP and DBP were associated with greater periventricular WMH in the frontal and parietal lobes after adjusting for age (Figure).ConclusionHigher SBP and DBP in recently menopausal women with good CV health were associated with greater WMH volumes more than a decade later after adjusting for CVRF, independent of randomization to HT. Our results show the importance of blood pressure control in recently menopausal women, that may reduce the white matter injury later in life.