IMPORTANCE Amyloid-β (Aβ), tau, and cerebral small vessel disease (CSVD), which occasionally coexist, are the most common causes of cognitive impairments in older people. However, whether tau is observed in patients with subcortical vascular cognitive impairment (SVCI), as well as its associations with Aβ and CSVD, are not yet established. More importantly, the role of tau underlying cognitive impairments in SVCI is unknown. OBJECTIVE To investigate the extent and the role of tau in patients with SVCI using 18 F-AV1451, which is a new ligand to detect neurofibrillary tangles in vivo.
Our findings suggest that WMH-related cortical thinning as well as disrupted integrity of periventricular WM is linked to gait disturbances.
Background In a clinical setting, an individual subject classification model rather than a group analysis would be more informative. Specifically, the subtlety of cortical atrophy in some frontotemporal dementia (FTD) patients and overlapping patterns of atrophy among three FTD clinical syndromes including behavioral variant FTD (bvFTD), non-fluent/agrammatic variant primary progressive aphasia (nfvPPA), and semantic variant PPA (svPPA) give rise to the need for classification models at the individual level. In this study, we aimed to classify each individual subject into one of the diagnostic categories in a hierarchical manner by employing a machine learning-based classification method. Methods We recruited 143 patients with FTD, 50 patients with Alzheimer's disease (AD) dementia, and 146 cognitively normal subjects. All subjects underwent a three-dimensional volumetric brain magnetic resonance imaging (MRI) scan, and cortical thickness was measured using FreeSurfer. We applied the Laplace Beltrami operator to reduce noise in the cortical thickness data and to reduce the dimension of the feature vector. Classifiers were constructed by applying both principal component analysis and linear discriminant analysis to the cortical thickness data. For the hierarchical classification, we trained four classifiers using different pairs of groups: Step 1 - CN vs. FTD + AD, Step 2 - FTD vs. AD, Step 3 - bvFTD vs. PPA, Step 4 - svPPA vs. nfvPPA. To evaluate the classification performance for each step, we used a10-fold cross-validation approach, performed 1000 times for reliability. Results The classification accuracy of the entire hierarchical classification tree was 75.8%, which was higher than that of the non-hierarchical classifier (73.0%). The classification accuracies of steps 1–4 were 86.1%, 90.8%, 86.9%, and 92.1%, respectively. Changes in the right frontotemporal area were critical for discriminating behavioral variant FTD from PPA. The left frontal lobe discriminated nfvPPA from svPPA, while the bilateral anterior temporal regions were critical for identifying svPPA. Conclusions In the present study, our automated classifier successfully classified FTD clinical subtypes with good to excellent accuracy. Our classifier may help clinicians diagnose FTD subtypes with subtle cortical atrophy and facilitate appropriate specific interventions.
We demonstrated a downward spreading pattern of amyloid, suggesting that amyloid accumulates first in neocortex followed by subcortical structures. Furthermore, our new finding suggested that an amyloid staging scheme based on subcortical involvement might reveal how differential regional accumulation of amyloid affects cognitive decline through functional and structural changes of the brain.
Purpose We investigated the frequency and clinical significance of amyloid β (Aβ) positivity on PET in cerebral amyloid angiopathy (CAA) patients. MethodsWe recruited 65 patients who met the modified Boston criteria for probable CAA.All underwent amyloid PET, MRI, APOE genotyping and neuropsychological tests, and we obtained information of CAA and ischemic cerebral small vessel disease (CSVD) MRI markers. We investigated the CAA/ischemic CSVD burden and APOE genotypes by Aβ positivity and investigated the effect of Aβ positivity on longitudinal cognitive decline. Results Among 65 CAA patients, 43(66.2 %) showed Aβ PET positivity(+). Aβ+ CAA had more lobar microbleeds(9(2,41) vs. 3(2,8), p=0.045) and a higher frequency of cortical superficial siderosis(34.9 vs. 9.1%, p=0.025), while Aβ-CAA had more lacunes(1(0,2) vs. 0(0,1), p=0.029) and a higher frequency of severe white matter hyperintensities(45.5 vs. 20.9%, p=0.040). The frequency of ε4 carriers was higher in Aβ+(57.1%) than in Aβ-CAA(18.2%) (p=0.003) while the frequency of ε2 carriers did not differ between two groups.Finally, Aβ positivity was associated with faster decline in multiple cognitive domains including language (p<0.001), visuospatial function (p<0.001), and verbal memory (p<0.001) in linear mixed effects models.3 Conclusions Our findings suggest that a significant proportion of probable CAA patients in a memory clinic are Aβ PET negative. Aβ positivity in CAA patients is associated with a distinct pattern of CSVD biomarker expression, and a worse cognitive trajectory. Aβ positivity has clinical relevance in CAA and might represent either advanced CAA or additional Alzheimer's disease neuropathologic changes.
To develop a new method for measuring Alzheimer’s disease (AD)-specific similarity of cortical atrophy patterns at the individual-level, we employed an individual-level machine learning algorithm. A total of 869 cognitively normal (CN) individuals and 473 patients with probable AD dementia who underwent high-resolution 3T brain MRI were included. We propose a machine learning-based method for measuring the similarity of an individual subject’s cortical atrophy pattern with that of a representative AD patient cohort. In addition, we validated this similarity measure in two longitudinal cohorts consisting of 79 patients with amnestic-mild cognitive impairment (aMCI) and 27 patients with probable AD dementia. Surface-based morphometry classifier for discriminating AD from CN showed sensitivity and specificity values of 87.1% and 93.3%, respectively. In the longitudinal validation study, aMCI-converts had higher atrophy similarity at both baseline (p < 0.001) and first year visits (p < 0.001) relative to non-converters. Similarly, AD patients with faster decline had higher atrophy similarity than slower decliners at baseline (p = 0.042), first year (p = 0.028), and third year visits (p = 0.027). The AD-specific atrophy similarity measure is a novel approach for the prediction of dementia risk and for the evaluation of AD trajectories on an individual subject level.
Although the association between apolipoprotein E (APOE) genotype and disease progression is well characterized in patients with Alzheimer’s disease, such a relationship is unknown in patients with subcortical vascular cognitive impairment. We evaluated whether APOE genotype is associated with disease progression in subcortical vascular mild cognitive impairment (svMCI) patients. We prospectively recruited 72 svMCI patients (19 APOE4 carriers, 42 APOE3 homozygotes, and 11 APOE2 carriers). Patients were annually followed-up with brain MRI and neuropsychological tests for three years and underwent a second Pittsburgh compound B (PiB)-PET at a mean interval of 32.3 months. Amyloid-ß burden was quantified by PiB standardized uptake value ratio (SUVR), and the amount of small vessel disease was quantified by number of lacune and small vessel disease score on MRI. We also measured cortical thickness. During the three years of follow-up, compared to the APOE3 homozygotes, there was less increase in PiB SUVR among APOE2 carriers (p = 0.023), while the APOE genotype did not show significant effects on small vessel disease progression. APOE2 carriers also showed less cortical thinning (p = 0.023) and a slower rate of cognitive decline (p = 0.009) compared to those with APOE3 homozygotes. Our findings suggest that, in svMCI patients, APOE2 has protective effects against amyloid-ß accumulation, cortical thinning, and cognitive decline.
Alzheimer’s disease dementia (ADD) and subcortical vascular dementia (SVaD) both show cortical thinning and white matter (WM) microstructural changes. We evaluated different patterns of correlation between gray matter (GM) and WM microstructural changes in pure ADD, pure SVaD, and mixed dementia. We enrolled 40 Pittsburgh compound B (PiB) positive ADD patients without WM hyperintensities (pure ADD), 32 PiB negative SVaD patients (pure SVaD), 23 PiB positive SVaD patients (mixed dementia), and 56 normal controls. WM microstructural integrity was quantified using fractional anisotropy (FA), axial diffusivity (DA), and radial diffusivity (DR) values. We used sparse canonical correlation analysis to show correlated regions of cortical thinning and WM microstructural changes. In pure ADD patients, lower FA in the frontoparietal area correlated with cortical thinning in the left inferior parietal lobule and bilateral paracentral lobules. In pure SVaD patients, lower FA and higher DR across extensive WM regions correlated with cortical thinning in bilateral fronto-temporo-parietal regions. In mixed dementia patients, DR and DA changes across extensive WM regions correlated with cortical thinning in the bilateral fronto-temporo-parietal regions. Our findings showed that the relationships between GM and WM degeneration are distinct in pure ADD, pure SVaD, and mixed dementia, suggesting that different pathomechanisms underlie their correlations.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.