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Abstract Accurate and scalable quantification of amyloid-β (Aβ) pathology is crucial for deeper disease phenotyping and furthering research in Alzheimer Disease (AD). This multidisciplinary study addresses the current limitations on neuropathology by leveraging a machine learning (ML) pipeline to perform a granular quantification of Aβ deposits and assess their distribution in the temporal lobe. Utilizing 131 whole-slide-images from consecutive autopsied cases at the University of California Davis Alzheimer Disease Research Center, our objectives were threefold: (1) Validate an automatic workflow for Aβ deposit quantification in white matter (WM) and gray matter (GM); (2) define the distributions of different Aβ deposit types in GM and WM, and (3) investigate correlates of Aβ deposits with dementia status and the presence of mixed pathology. Our methodology highlights the robustness and efficacy of the ML pipeline, demonstrating proficiency akin to experts’ evaluations. We provide comprehensive insights into the quantification and distribution of Aβ deposits in the temporal GM and WM revealing a progressive increase in tandem with the severity of established diagnostic criteria (NIA-AA). We also present correlations of Aβ load with clinical diagnosis as well as presence/absence of mixed pathology. This study introduces a reproducible workflow, showcasing the practical use of ML approaches in the field of neuropathology, and use of the output data for correlative analyses. Acknowledging limitations, such as potential biases in the ML model and current ML classifications, we propose avenues for future research to refine and expand the methodology. We hope to contribute to the broader landscape of neuropathology advancements, ML applications, and precision medicine, paving the way for deep phenotyping of AD brain cases and establishing a foundation for further advancements in neuropathological research.

Abstract Cerebrovascular and α-synuclein pathologies are frequently observed alongside Alzheimer disease (AD). The heterogeneity of AD necessitates comprehensive approaches to postmortem studies, including the representation of historically underrepresented ethnic groups. In this cohort study, we evaluated small vessel disease pathologies and α-synuclein deposits among Hispanic decedents (HD, n = 92) and non-Hispanic White decedents (NHWD, n = 184) from three Alzheimer’s Disease Research Centers: Columbia University, University of California San Diego, and University of California Davis. The study included cases with a pathological diagnosis of Intermediate/High AD based on the National Institute on Aging– Alzheimer’s Association (NIA-AA) and/or NIA-Reagan criteria. A 2:1 random comparison sample of NHWD was frequency-balanced and matched with HD by age and sex. An expert blinded to demographics and center origin evaluated arteriolosclerosis, cerebral amyloid angiopathy (CAA), and Lewy bodies/Lewy neurites (LBs/LNs) with a semi-quantitative approach using established criteria. There were many similarities and a few differences among groups. HD showed more severe Vonsattel grading of CAA in the cerebellum (p = 0.04), higher CAA density in the posterior hippocampus and cerebellum (ps = 0.01), and increased LBs/LNs density in the frontal (p = 0.01) and temporal cortices (p = 0.03), as determined by Wilcoxon’s test. Ordinal logistic regression adjusting for age, sex, and center confirmed these findings except for LBs/LNs in the temporal cortex. Results indicate HD with AD exhibit greater CAA and α-synuclein burdens in select neuroanatomic regions when compared to age- and sex-matched NHWD with AD. These findings aid in the generalizability of concurrent arteriolosclerosis, CAA, and LBs/LNs topography and severity within the setting of pathologically confirmed AD, particularly in persons of Hispanic descent, showing many similarities and a few differences to those of NHW descent and providing insights into precision medicine approaches.

BackgroundCardiovascular disease (CVD) risk factors are highly prevalent among Hispanic/Latino adults, while the prevalence of MRI infarcts is not well-documented. We, therefore, sought to examine the relationships between CVD risk factors and infarcts with brain structure among Hispanic/Latino individuals.MethodsParticipants included 1,886 Hispanic/Latino adults (50–85 years) who underwent magnetic resonance imaging (MRI) as part of the Study of Latinos—Investigation of Neurocognitive Aging-MRI (SOL-INCA-MRI) study. CVD risk was measured approximately 10.5 years before MRI using the Framingham cardiovascular risk score, a measure of 10-year CVD risk (low (

Introduction: HCHS/SOL is a representative study of Hispanic/Latinos living in the US. SOL-INCA examines cognition amongst those of HCHS/SOL over age 50 and SOL-INCA-MRI obtains quantitative MRI measures on a subgroup of these individuals. Prior research in SOL-INCA found that vascular risk factors summarized by the Framingham Cardiovascular Risk Score (Fram CVD) is associated with Mild Cognitive Impairment (MCI)1. We hypothesize that the extent of white matter hyperintensities (WMH) will partially mediate this impact of Fram CVD on MCI prevalence in this cohort. Methods: SOL-INCA-MRI consists of 2366 individuals of Hispanic/Latino Heritage from 4 centers across the US. Demographics of the cohort are summarized in the Table. High resolution MRI were acquired and WMH burden measured by previously reported methods2. WMH volumes were natural log transformed and corrected for scanner type using NeuroCombat. General linear models were used to test the associated between diagnosis (normal, questionable impairment and MCI) and Fram CVD and WMH adjusting for age, gender, education, heritage, and center. Casual mediation analysis was also performed to assess the extent to which WMH mediated the association between Fram CVD and diagnosis. Results: Subjects were 64.6 + 6.8 years of age at MRI, 68.5% were female, 16% had questionable impairment and 13% had MCI. Mean Fram CVD risk was 11.4 + 0.9%. Mean log WMH was -0.13 +1.55. Diagnosis was significantly associated with Fram CVD (beta= 780, p

Introduction: HCHS/SOL is a representative study of Hispanic/Latinos living in the US. HCHS/SOL participants have a high prevalence of diabetes (1) and diabetic associated cognitive decline (2). Previous studies of predominantly non-Hispanic White populations indicate that silent brain infarcts are associated with incident dementia (3). Given that diabetes is prevalent in HCHS/SOL and associated with cognitive decline, we examined whether diabetes may also lead to infarction on MRI. Methods: SOL-INCA-MRI consists of 2668 individuals of Hispanic/Latino Heritage from 4 centers across the US. Demographics of the cohort are summarized in the Table. The presence or absence of infarction on MRI was determined from review of high resolution T1, T2 and FLAIR imaging as well as gradient echo imaging by a neuroradiologist and a stroke neurologist with a high level of agreement (ICC >0.90). Desperate readings were adjudicated by a neurologist with experience in vascular brain injury. Diabetes based on American Diabetes Association definition of normal, prediabetic and diabetic. This definition used measures serum glucose levels adjusted for fasting time and, if available, post-OGTT glucose levels, A1C percentages, and scanned/transcribed anti- diabetic medication use. General linear model was used to test the associated between the presence or absence of MRI and diabetes adjusting for age, gender, heritage, and center. Results: Subjects were 62.3 + 9.2 years of age at MRI range [35-86], 67% were female and the prevalence of MRI infarction was 7% (Table). Pre-diabetes was present in 45% and diabetes in 22%. Stroke prevalence increased with age ranging from

Introduction: Identifying novel cerebral small vessel disease (cSVD) biomarkers in blood is critical to advance therapeutic and preventive strategies for Vascular Contributions to Cognitive Impairment and Dementia (VCID). This exploratory analysis aimed to identify novel blood-based markers of cSVD using proteomics in a Hispanic sample. This is critical, as most biomarker discovery has focused on non-Hispanic Whites, despite increased cardiometabolic risk and cognitive impairment among minoritized populations. Methods: We included 107 dementia-free Mexican American participants from the MarkVICD-1 San Antonio site (73% women, mean age 70 ±7 years, Table 1). Participants provided fasting blood samples and underwent a comprehensive clinical and neuroimaging evaluation. Serum aliquots were shipped to Olink for proteomic profiling with the Explore 3072 panel. Normalized protein levels were related to cSVD markers, including White Matter Hyperintensities (WMH), Peak-with of Skeletonized Mean Diffusivity (PSMD), and Free Water (FW), using linear regression models adjusting for age, age2, sex, and total intracranial volume. P-values were corrected using False Discovery Rate (FDR) to account for multiple testing. Results: We identified 36, and 128 proteins significantly associated with PSDM, and FW, respectively (Figure 1). The strongest associations were seen for elevated neurogranin with higher PSMD (Beta ±SE, 0.20 ±0.04, FDR=0.005) and FW (0.23 ±0.04, FDR=0.002), and Cartilage Intermediate Layer Protein (CILP) with higher PSMD (0.07 ±0.014, FDR=0.005). Neurogranin is a marker of synaptic dysfunction and CSF levels have shown potential as a predictor of cognitive decline. CILP may antagonize TGFB1 and IGF1 and has been related to mortality in patients with heart failure. Discussion: Our results uncovered multiple novel blood-based cSVD biomarkers for risk stratification in VCID studies, including neurogranin and CILP. Further studies are needed to confirm these findings in larger samples.

As the United States’ (US) elderly population becomes increasingly diverse, it is imperative that research studies address cognitive health in diverse populations of older Americans. White Matter Hyperintensities (WMH) are useful imaging findings that can be studied in elderly individuals and have been linked to an increased risk of neurological conditions, such as stroke, cognitive impairment, and dementia. We performed a systematic review of literature using PubMed sources to compile all the studies that investigated the prevalence of ethnic and racial differences of WMH burden amongst diverse groups in the US. We identified 23 unique articles that utilized 16 distinct cohorts of which 94 % were prospective, longitudinal studies that included community-based and family-based populations. The overall results were heterogenous in all aspects of data collection and analysis, limiting our ability to run meta-analyses and draw definitive conclusions. General observations suggest increased vascular risk on African American populations, contributing to greater WMH burden in that population. Overall, the findings of this study indicate a need for a standardized approach to investigating WMH in efforts to measure its clinical impact on diverse populations.

Abstract Background Preclinical studies highlight the importance of endogenous cannabinoids (endocannabinoids; eCBs) in neurodegeneration. Yet, prior observational studies focused on limited outcome measures and assessed only few eCB compounds while largely ignoring the complexity of the eCB system. We examined the associations of multiple circulating eCBs and eCB-like molecules with early markers of neurodegeneration and neuro-injury and tested for effect modification by sex. Methods This exploratory cross-sectional study included a random sample of 237 dementia-free older participants from the Framingham Heart Study Offspring cohort who attended examination cycle 9 (2011–2014), were 65 years or older, and cognitively healthy. Forty-four eCB compounds were quantified in serum, via liquid chromatography high-resolution mass spectrometry. Linear regression models were used to examine the associations of eCB levels with brain MRI measures (i.e., total cerebral brain volume, gray matter volume, hippocampal volume, and white matter hyperintensities volume) and blood biomarkers of Alzheimer’s disease and neuro-injury (i.e., total tau, neurofilament light, glial fibrillary acidic protein and Ubiquitin C-terminal hydrolase L1). All models were adjusted for potential confounders and effect modification by sex was examined. Results Participants mean age was 73.3 ± 6.2 years, and 40% were men. After adjustment for potential confounders and correction for multiple comparisons, no statistically significant associations were observed between eCB levels and the study outcomes. However, we identified multiple sex-specific associations between eCB levels and the various study outcomes. For example, high linoleoyl ethanolamide (LEA) levels were related to decreased hippocampal volume among men and to increased hippocampal volume among women (β ± SE = − 0.12 ± 0.06, p = 0.034 and β ± SE = 0.08 ± 0.04, p = 0.026, respectively). Conclusions Circulating eCBs may play a role in neuro-injury and may explain sex differences in susceptibility to accelerated brain aging. Particularly, our results highlight the possible involvement of eCBs from the N-acyl amino acids and fatty acid ethanolamide classes and suggest specific novel fatty acid compounds that may be implicated in brain aging. Furthermore, investigation of the eCBs contribution to neurodegenerative disease such as Alzheimer’s disease in humans is warranted, especially with prospective study designs and among diverse populations, including premenopausal women.

Abstract A bidirectional communication exists between the brain and the gut, in which the gut microbiota influences cognitive function and vice-versa. Gut dysbiosis has been linked to several diseases, including Alzheimer's disease and related dementias (ADRD). However, the relationship between gut dysbiosis and markers of cerebral small vessel disease (cSVD), a major contributor to ADRD, is unknown. In this cross-sectional study, we examined the connection between the gut microbiome, cognitive, and neuroimaging markers of cSVD in the Framingham Heart Study (FHS). Markers of cSVD included white matter hyperintensities (WMH), peak width of skeletonized mean diffusivity (PSMD), and executive function (EF), estimated as the difference between the trail-making tests B and A. We included 972 FHS participants with MRI scans, neurocognitive measures, and stool samples and quantified the gut microbiota composition using 16S rRNA sequencing. We used multivariable association and differential abundance analyses adjusting for age, sex, BMI, and education level to estimate the association between gut microbiota and WMH, PSMD, and EF measures. Our results suggest an increased abundance of Pseudobutyrivibrio and Ruminococcus genera was associated with lower WMH and PSMD (p values

Abstract Precise, scalable, and quantitative evaluation of whole slide images is crucial in neuropathology. We release a deep learning model for rapid object detection and precise information on the identification, locality, and counts of cored plaques and cerebral amyloid angiopathy (CAA). We trained this object detector using a repurposed image-tile dataset without any human-drawn bounding boxes. We evaluated the detector on a new manually-annotated dataset of whole slide images (WSIs) from three institutions, four staining procedures, and four human experts. The detector matched the cohort of neuropathology experts, achieving 0.64 (model) vs. 0.64 (cohort) average precision (AP) for cored plaques and 0.75 vs. 0.51 AP for CAAs at a 0.5 IOU threshold. It provided count and locality predictions that approximately correlated with gold-standard human CERAD-like WSI scoring (p = 0.07 ± 0.10). The openly-available model can quickly score WSIs in minutes without a GPU on a standard workstation.

Abstract Despite the increasing demographic diversity of the United States’ aging population, there remain significant gaps in post-mortem research investigating the ethnoracial heterogeneity in the neuropathological landscape of Alzheimer Disease (AD). Most autopsy-based studies have focused on cohorts of non-Hispanic White decedents (NHWD), with few studies including Hispanic decedents (HD). We aimed to characterize the neuropathologic landscape of AD in NHWD (n = 185) and HD (n = 92) evaluated in research programs across three institutions: University of California San Diego, University of California Davis, and Columbia University. Only persons with a neuropathologic diagnosis of intermediate/high AD determined by NIA Reagan and/or NIA-AA criteria were included. A frequency-balanced random sample without replacement was drawn from the NHWD group using a 2:1 age and sex matching scheme with HD. Four brain areas were evaluated: posterior hippocampus, frontal, temporal, and parietal cortices. Sections were stained with antibodies against Aβ (4G8) and phosphorylated tau (AT8). We compared the distribution and semi-quantitative densities for neurofibrillary tangles (NFTs), neuropil threads, core, diffuse, and neuritic plaques. All evaluations were conducted by an expert blinded to demographics and group status. Wilcoxon’s two-sample test revealed higher levels of neuritic plaques in the frontal cortex (p = 0.02) and neuropil threads (p = 0.02) in HD, and higher levels of cored plaques in the temporal cortex in NHWD (p = 0.02). Results from ordinal logistic regression controlling for age, sex, and site of origin were similar. In other evaluated brain regions, semi-quantitative scores of plaques, tangles, and threads did not differ statistically between groups. Our results demonstrate HD may be disproportionately burdened by AD-related pathologies in select anatomic regions, particularly tau deposits. Further research is warranted to understand the contributions of demographic, genetic, and environmental factors to heterogeneous pathological presentations.

Abstract We studied the genetic associations of a previously developed Metabolomic Risk Score (MRS) for Mild Cognitive Impairment (MCI) and beta-aminoisobutyric acid metabolite (BAIBA)—the metabolite highlighted by results from a genome-wide association study (GWAS) of the MCI-MRS, and assessed their association with MCI in datasets of diverse race/ethnicities. We first performed a GWAS for the MCI-MRS and BAIBA, in Hispanic/Latino adults (n = 3890) from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). We identified ten independent genome-wide significant (p value

Abstract APOE-ɛ4 risk on Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD) differs between race/ethnic groups, presumably due to ancestral genomic background surrounding the APOE locus. We studied whether African and Amerindian ancestry-enriched genetic variants in the APOE region modify the effect of the APOE-ɛ4 alleles on Mild Cognitive Impairment (MCI) in Hispanics/Latinos. We defined African and Amerindian ancestry-enriched variants as those common in one Hispanic/Latino parental ancestry and rare in the other two. We identified such variants in the APOE region with a predicted moderate impact based on the SnpEff tool. We tested their interaction with APOE-ɛ4 on MCI in the Study of Latinos-Investigation of Neurocognitive Aging (SOL-INCA) population and African Americans from the Atherosclerosis Risk In Communities (ARIC) study. We identified 5 Amerindian and 14 African enriched variants with an expected moderate effect. A suggestive significant interaction (p-value = 0.01) was found for one African-enriched variant, rs8112679, located in the ZNF222 gene fourth exon. Our results suggest there are no ancestry-enriched variants with large effect sizes of interaction effects with APOE-ɛ4 on MCI in the APOE region in the Hispanic/Latino population. Further studies are needed in larger datasets to identify potential interactions with smaller effect sizes.

Abstract Objective We aimed to determine whether combining white matter hyperintensity (WMH) with neurofilament light chain (NfL) could provide additional information for cognition in older adults. Methods Utilizing data from the population‐based Chicago Health and Aging Project, we studied 701 individuals with both biomarkers and cognitive data during the follow‐up period. NfL was measured using an ultrasensitive immunoassay, single‐molecule array technology. MRI scans of the brain were acquired using 1.5‐T systems. Global cognitive function was created as a composite measure of four neuropsychological tests, standardized and averaged to z‐scores. Multivariable linear mixed‐effects models were used to evaluate the association of WMH and NfL with the rate of cognitive decline. Results Higher WMH and NfL were associated with a faster rate of cognitive decline during the follow‐up; β‐coefficients (95%CIs) were −0.011 (−0.02, −0.001) and −0.010 (−0.017, −0.003), respectively. In individuals with lower concentration of NfL (i.e., bottom tertile), a higher WMH volume was associated with a faster cognitive decline (β: −0.030; 95%CI −0.046, −0.014). Similarly, in individuals with lower volumes of WMH (i.e., bottom tertile), a higher concentrations of NfL was associated with a faster cognitive decline (β: −0.023; 95%CI −0.042, −0.005). When we combined WMH with NfL, we noted a graded association with increasing volumes of WMH, particularly in people with lower NfL values. Interpretation While both biomarkers, WMH and NfL, were similarly associated with the annual rate of cognitive decline, our study suggests that they provide different underlying mechanisms affecting cognition.

Introduction: The spatial distribution of white matter hyperintensities (WMH) on MRI is often considered in the diagnostic evaluation of patients with cognitive problems. In some patients, clinicians may classify WMH patterns as “unusual”, but this is largely based on expert opinion, because detailed quantitative information about WMH distribution frequencies in a memory clinic setting is lacking. Here we report voxel wise 3D WMH distribution frequencies in a large multicenter dataset and also aimed to identify individuals with unusual WMH patterns. Methods: Individual participant data (N = 3525, including 777 participants with subjective cognitive decline, 1389 participants with mild cognitive impairment and 1359 patients with dementia) from eleven memory clinic cohorts, recruited through the Meta VCI Map Consortium, were used. WMH segmentations were provided by participating centers or performed in Utrecht and registered to the Montreal Neurological Institute (MNI)-152 brain template for spatial normalization. To determine WMH distribution frequencies, we calculated WMH probability maps at voxel level. To identify individuals with unusual WMH patterns, region-of-interest (ROI) based WMH probability maps, rule-based scores, and a machine learning method (Local Outlier Factor (LOF)), were implemented. Results: WMH occurred in 82% of voxels from the white matter template with large variation between subjects. Only a small proportion of the white matter (1.7%), mainly in the periventricular areas, was affected by WMH in at least 20% of participants. A large portion of the total white matter was affected infrequently. Nevertheless, 93.8% of individual participants had lesions in voxels that were affected in less than 2% of the population, mainly located in subcortical areas. Only the machine learning method effectively identified individuals with unusual patterns, in particular subjects with asymmetric WMH distribution or with WMH at relatively rarely affected locations despite common locations not being affected. Discussion: Aggregating data from several memory clinic cohorts, we provide a detailed 3D map of WMH lesion distribution frequencies, that informs on common as well as rare localizations. The use of data-driven analysis with LOF can be used to identify unusual patterns, which might serve as an alert that rare causes of WMH should be considered.

Accurate measurement of Alzheimer's disease (AD) pathology in older adults without significant clinical impairment is critical to assessing intervention strategies aimed at slowing AD-related cognitive decline. The U.S. Study to Protect Brain Health Through Lifestyle Intervention to Reduce Risk (POINTER) is a 2-year randomized controlled trial to evaluate the effect of multicomponent risk reduction strategies in older adults (60-79 years) who are cognitively unimpaired but at increased risk for cognitive decline/dementia due to factors such as cardiovascular disease and family history. The POINTER Imaging ancillary study is collecting tau-PET ([18F]MK6240), beta-amyloid (Aβ)-PET ([18F]florbetaben [FBB]) and MRI data to evaluate neuroimaging biomarkers of AD and cerebrovascular pathophysiology in this at-risk sample. Here 481 participants (70.0±5.0; 66% F) with baseline MK6240, FBB and structural MRI scans were included. PET scans were coregistered to the structural MRI which was used to create FreeSurfer-defined reference regions and target regions of interest (ROIs). We also created off-target signal (OTS) ROIs to examine the magnitude and distribution of MK6240 OTS across the brain as well as relationships between OTS and age, sex, and race. OTS was unimodally distributed, highly correlated across OTS ROIs and related to younger age and sex but not race. Aiming to identify an optimal processing approach for MK6240 that would reduce the influence of OTS, we compared our previously validated MRI-guided standard PET processing and 6 alternative approaches. The alternate approaches included combinations of reference region erosion and meningeal OTS masking before spatial smoothing as well as partial volume correction. To compare processing approaches we examined relationships between target ROIs (entorhinal cortex (ERC), hippocampus or a temporal meta-ROI (MetaROI)) SUVR and age, sex, race, Aβ and a general cognitive status measure, the Modified Telephone Interview for Cognitive Status (TICSm). Overall, the processing approaches performed similarly, and none showed a meaningful improvement over standard processing. Across processing approaches we observed previously reported relationships with MK6240 target ROIs including positive associations with age, an Aβ+> Aβ- effect and negative associations with cognition. In sum, we demonstrated that different methods for minimizing effects of OTS, which is highly correlated across the brain within subject, produced no substantive change in our performance metrics. This is likely because OTS contaminates both reference and target regions and this contamination largely cancels out in SUVR data. Caution should be used when efforts to reduce OTS focus on target or reference regions in isolation as this may exacerbate OTS contamination in SUVR data.

As neurodegenerative disease pathological hallmarks have been reported in both grey matter (GM) and white matter (WM) with different density distributions, automating the segmentation process of GM/WM would be extremely advantageous for aiding in neuropathologic deep phenotyping. Standard segmentation methods typically involve manual annotations, where a trained researcher traces the delineation of GM/WM in ultra-high-resolution Whole Slide Images (WSIs). This method can be time-consuming and subjective, preventing a scalable analysis on pathology images. This paper proposes an automated segmentation pipeline (BrainSec) combining a Convolutional Neural Network (CNN) module for segmenting GM/WM regions and a post-processing module to remove artifacts/residues of tissues. The final output generates XML annotations that can be visualized via Aperio ImageScope. First, we investigate two baseline models for medical image segmentation: FCN, and U-Net. Then we propose a patch-based approach, BrainSec, to classify the GM/WM/background regions. We demonstrate BrainSec is robust and has reliable performance by testing it on over 180 WSIs that incorporate numerous unique cases as well as distinct neuroanatomic brain regions. We also apply gradient-weighted class activation mapping (Grad-CAM) to interpret the segmentation masks and provide relevant explanations and insights. In addition, we have integrated BrainSec with an existing Amyloid- $\beta $ pathology classification model into a unified framework (without incurring significant computation complexity) to identify pathologies, visualize their distributions, and quantify each type of pathologies in segmented GM/WM regions, respectively.

Perivascular spaces (PVS) visible on brain MRI signal cerebral small vessel disease (CSVD). The coexistence of PVS with other CSVD manifestations likely increases the risk of adverse neurological outcomes. We related PVS to other CSVD manifestations and brain volumes that are markers of vascular brain injury and neurodegeneration. Framingham Heart Study (FHS) participants with CSVD ratings on brain MRI were included. PVS were rated in the basal ganglia (BG) and centrum semiovale (CSO) into grades I–IV and a category reflecting high burden in single or mixed CSO-BG regions. We related PVS to covert brain infarcts (CBI), white matter hyperintensities (WMH), cerebral microbleeds (CMB), total brain, hippocampal, and cortical gray matter volumes using adjusted multivariable regression analyses. In 2454 participants (mean age 54 ± 12 years), we observed that higher PVS burden in both BG and CSO was related to CMB in lobar and deep brain regions and increased WMH. Greater CSO PVS burden was associated with decreased total cortical gray volumes. PVS are associated with ischemic markers of CSVD and neurodegeneration markers. Further studies should elucidate the causality between PVS and other CSVD manifestations.

Background. As the US population becomes increasingly older and more diverse, the number of Americans with dementia is expected to rise substantially, particularly the number of those with concomitant vascular disease. Moreover, the impact of vascular disease on dementia risk may be exacerbated in African Americans and Hispanics, who are at greater risk for vascular disease and for whom vascular disease may play a larger role in clinical dementia. As public health awareness of dementia increases, it is becoming common to see individuals presenting for clinical assessment with minor cognitive complaints. Neuroimaging studies of these individuals frequently identify “incidental” white matter hyperintensities (WMH), usually ascribed to “microvascular disease” by radiologists, raising concerns in patients about their brain health and future risk for dementia. Methods. To date, however, we are not aware of any studies designed to examine the baseline and future impact of white matter (WM) injury in the clinical setting, particularly among diverse populations where age-related WMH volumes are known to be higher1 or comprehensively examined the impact of individual and combined magnetic resonance imaging (MRI) measures of white matter injury on cognitive performance among a diverse, non-demented, stroke-free population with cognitive complaints over an extended period of observation. For this presentation, I summarized the available evidence of the impact of WMH and cognition and reviewed the design of a new study to prospectively assess this outcome in a diverse population and develop a risk factor profile to assist in the diagnosis of those at risk for dementia. Results. There is substantial evidence that WMH are common, increase in amount and prevalence with age,2 impact cognition and dementia,3 and are a measure of vascular brain injury. The significance of studying WM injury is buoyed by the fact that dementia risk scales emphasize the role of vascular risk in dementia prediction4 and that institution of effective treatment could lessen the burden of dementia on population.5 Yet, a complete understanding of WMH as markers of vascular brain injury contributing to cognitive complaints and possibly vascular cognitive impairment (VCI) requires a comprehensive determination of the full spectrum of WM injury associated with vascular risk, potential mechanisms of WMH formation and progression, ethnoracial, other genetic influences and how WMH interact with neurodegenerative pathologies, in particular Alzheimer’s disease (AD). Evidence of the importance of “asymptomatic” vascular brain injury on present cognition and future cognitive decline led to the design of a new multi-site study of diverse individuals with cognitive complaints and WMH, that aims to develop a predictive risk factor score that can be widely used in future treatment trials called diverse VCID. Diverse VCID is a multi-site study of 2250 individuals of non-Hispanic Whites, Hispanic/Latinos, and Black African Americans who will be followed for approximately 3 years to deliver a clinical risk score that predicts dementia based on neuroimaging, plasma biomarkers, genetic and clinical data. Conclusions. Vascular diseases are now recognized to be a major contributor to cognitive impairment and dementia disproportionately affecting individuals from diverse backgrounds. Diverse VCID is designed to address this critical issue and to facilitate future treatment studies of vascular cognitive impairment.

BackgroundDysregulation of compensatory mechanisms to regulate blood pressure (BP) upon postural change is a phenotype of BP variability and an emerging risk factor for cardiovascular outcomes.Materials and methodsWe assessed postural change in BP (starting 2 min after standing from a supine position), carotid-femoral pulse wave velocity (cfPWV), and markers of hypertension-mediated organ damage (HMOD) in the heart, kidney, and brain in Framingham Third Generation, Omni-2, and New Offspring Spouse Cohort participants. We related vascular measures (postural change in BP measures and cfPWV) with HMOD in 3,495 participants (mean age 47 years, 53% women) using multivariable logistic and linear regression models.ResultsIn multivariable-adjusted models, we did not observe significant associations of vascular measures with presence of left ventricular hypertrophy, albuminuria, covert brain infarcts, or white matter hyperintensities (Bonferroni-adjusted P-values > 0.05/20 > 0.0025). In multivariable models, greater cfPWV (est. β = 0.11 ± 0.03; P < 0.001), but not postural change in BP measures (Bonferroni-adjusted P-values > 0.05/20 > 0.0025), was associated with higher white matter free water using brain magnetic resonance imaging. In multivariable models, greater postural change in pulse pressure was associated with higher urinary albumin-creatinine ratio (est. β = 0.07 ± 0.02; P < 0.001). No other postural change in BP measure was associated with urinary albumin-creatinine ratio (Bonferroni-adjusted P-values > 0.05/20 > 0.0025). In sex-specific analyses, higher cfPWV was associated with higher urinary albumin-creatinine ratio in men (est. β: 0.11 ± 0.04; P = 0.002) but not in women (est. β: 0.03 ± 0.03; P = 0.44). We also observed marginal to strong effect modification by above vs. at/below median postural change in BP for the association of cfPWV with urinary albumin−creatinine ratio (Bonferroni-adjusted interaction P < 0.001–0.01). Vascular measures were not related to left ventricular mass index or fractional anisotropy (Bonferroni-adjusted P-values > 0.05/20 > 0.0025).ConclusionBaroreflex dysfunction is associated with greater subclinical kidney damage. Additionally, relations of higher aortic stiffness with greater kidney damage may be modified by associated baroreflex dysregulation.

Summary: Background: It remains unclear whether persistent loneliness is related to brain structures that are associated with cognitive decline and development of Alzheimer's disease (AD). This study aimed to investigate the relationships between different loneliness types, cognitive functioning, and regional brain volumes. Methods: Loneliness was measured longitudinally, using the item from the Center for Epidemiologic Studies Depression Scale in the Framingham Heart Study, Generation 3, with participants’ average age of 46·3 ± 8·6 years. Robust regression models tested the association between different loneliness types with longitudinal neuropsychological performance (n = 2,609) and regional magnetic resonance imaging brain data (n = 1,829) (2002-2019). Results were stratified for sex, depression, and Apolipoprotein E4 (ApoE4). Findings: Persistent loneliness, but not transient loneliness, was strongly associated with cognitive decline, especially memory and executive function. Persistent loneliness was negatively associated with temporal lobe volume (β = −0.18, 95%CI [−0.32, −0.04], P = 0·01). Among women, persistent loneliness was associated with smaller frontal lobe (β = −0.19, 95%CI [−0.38, −0.01], P = 0·04), temporal lobe (β = −0.20, 95%CI [−0.37, −0.03], P = 0·02), and hippocampus volumes (β = −0.23, 95%CI [−0.40, −0.06], P = 0·007), and larger lateral ventricle volume (β = 0.15, 95%CI [0.02, 0.28], P = 0·03). The higher cumulative loneliness scores across three exams, the smaller parietal, temporal, and hippocampus volumes and larger lateral ventricle were evident, especially in the presence of ApoE4. Interpretation: Persistent loneliness in midlife was associated with atrophy in brain regions responsible for memory and executive dysfunction. Interventions to reduce the chronicity of loneliness may mitigate the risk of age-related cognitive decline and AD. Funding: US National Institute on Aging.

PurposeTo assess the pathological aging effect on caudate functional connectivity among mild cognitive impairment (MCI) participants and examine whether and how sex and amyloid contribute to this process.Materials and MethodsTwo hundred and seventy-seven functional magnetic resonance imaging (fMRI) sessions from 163 cognitive normal (CN) older adults and 309 sessions from 139 participants with MCI were included as the main sample in our analysis. Pearson's correlation was used to characterize the functional connectivity (FC) between caudate nuclei and each brain region, then caudate nodal strength was computed to quantify the overall caudate FC strength. Association analysis between caudate nodal strength and age was carried out in MCI and CN separately using linear mixed effect (LME) model with covariates (education, handedness, sex, Apolipoprotein E4, and intra-subject effect). Analysis of covariance was conducted to investigate sex, amyloid status, and their interaction effects on aging with the fMRI data subset having amyloid status available. LME model was applied to women and men separately within MCI group to evaluate aging effects on caudate nodal strength and each region's connectivity with caudate nuclei. We then evaluated the roles of sex and amyloid status in the associations of neuropsychological scores with age or caudate nodal strength. An independent cohort was used to validate the sex-dependent aging effects in MCI.ResultsThe MCI group had significantly stronger age-related increase of caudate nodal strength compared to the CN group. Analyzing women and men separately revealed that the aging effect on caudate nodal strength among MCI participants was significant only for women (left: P = 6.23 × 10−7, right: P = 3.37 × 10−8), but not for men (P > 0.3 for bilateral caudate nuclei). The aging effects on caudate nodal strength were not significantly mediated by brain amyloid burden. Caudate connectivity with ventral prefrontal cortex substantially contributed to the aging effect on caudate nodal strength in women with MCI. Higher caudate nodal strength is significantly related to worse cognitive performance in women but not in men with MCI.ConclusionSex modulates the pathological aging effects on caudate nodal strength in MCI regardless of amyloid status. Caudate nodal strength may be a sensitive biomarker of pathological aging in women with MCI.

Abstract Background Low socioeconomic status (SES) in early and late life has been associated with lower late-life cognition. Less is known about how changes in SES from childhood to late life are associated with late-life cognition, especially among diverse populations of older adults. Methods In a multi-ethnic sample (n = 1353) of older adults, we used linear regression to test associations of change in comprehensive measures of SES (financial, cultural, and social domains) from childhood to late life with semantic memory, episodic memory, and executive function. We tested whether the association between SES trajectory and late-life cognition differed by populations who resided in the U.S. during childhood or immigrated to the U.S. as adults. Results Participants with low childhood/high late-life financial capital had better semantic memory (β = 0.18; 95% CI: 0.04, 0.32) versus those with low financial capital in both childhood and late life, regardless of childhood residence. We observed a significant interaction in the association of verbal episodic memory and cultural capital by childhood residence (p = 0.08). Participants with a foreign childhood residence had higher verbal episodic memory if they had low childhood/high late-life cultural capital (β = 0.32; 95% CI: 0.01, 0.63), but lower verbal episodic memory if they had high childhood/low late-life cultural capital (β = − 0.40; 95% CI: − 0.94, 0.13). Having high lifecourse social capital was associated with better verbal episodic memory scores among those with a U.S. childhood (β = 0.34; 95% CI: 0.14, 0.55), but lower verbal episodic memory among those with a foreign childhood (β = − 0.10; 95% CI: − 0.51, 0.31). Conclusions High financial and cultural capital in late life is associated with better cognition, regardless of early childhood SES or childhood residence.

BackgroundThe digital Clock Drawing Test (dCDT) has been recently used as a more objective tool to assess cognition. However, the association between digitally obtained clock drawing features and structural neuroimaging measures has not been assessed in large population-based studies. ObjectiveWe aimed to investigate the association between dCDT features and brain volume. MethodsThis study included participants from the Framingham Heart Study who had both a dCDT and magnetic resonance imaging (MRI) scan, and were free of dementia or stroke. Linear regression models were used to assess the association between 18 dCDT composite scores (derived from 105 dCDT raw features) and brain MRI measures, including total cerebral brain volume (TCBV), cerebral white matter volume, cerebral gray matter volume, hippocampal volume, and white matter hyperintensity (WMH) volume. Classification models were also built from clinical risk factors, dCDT composite scores, and MRI measures to distinguish people with mild cognitive impairment (MCI) from those whose cognition was intact. ResultsA total of 1656 participants were included in this study (mean age 61 years, SD 13 years; 50.9% women), with 23 participants diagnosed with MCI. All dCDT composite scores were associated with TCBV after adjusting for multiple testing (P value

White matter hyperintensities (WMH) are a common brain-imaging feature of cerebral small vessel disease. Here, the authors carry out a GWAS and followup analyses for WMH-volume, implicating several variants with potential for risk stratification and drug targeting.

Cortex morphology varies with age, cognitive function, and in neurological and psychiatric diseases. Here the authors report 160 genome-wide significant associations with thickness, surface area and volume of the total cortex and 34 cortical regions from a GWAS meta-analysis in 22,824 adults.

Abstract Objective Examine whether cognitive reserve moderates the association of 1) vascular risk factors and 2) white matter hyperintensity burden with risk of clinical progression and longitudinal cognitive decline. Methods BIOCARD Study participants were cognitively normal and primarily middle‐aged (M = 57 years) at baseline and have been followed with annual cognitive and clinical assessments (M = 13 years). Baseline cognitive reserve was indexed with a composite score combining education with reading and vocabulary scores. Baseline vascular risk (N = 229) was assessed with a composite risk score reflecting five vascular risk factors. Baseline white matter hyperintensity load (N = 271) was measured with FLAIR magnetic resonance imaging. Cox regression models assessed risk of progression from normal cognition to onset of clinical symptoms of Mild Cognitive Impairment. Longitudinal mixed effects models measured the relationship of these variables to cognitive decline, using a global composite score, and executive function and episodic memory sub‐scores. Results Both vascular risk and white matter hyperintensities were associated with cognitive decline, particularly in executive function. Higher vascular risk, but not white matter hyperintensity burden, was associated with an increased risk of progression to Mild Cognitive Impairment. Higher cognitive reserve was associated with a reduced risk of symptom onset and higher levels of baseline cognition but did not modify the associations between the vascular risk score and white matter hyperintensities with clinical progression or cognitive decline. Interpretation Although cognitive reserve has protective effects on clinical and cognitive outcomes, it does not mitigate the negative impact of vascular risk and small vessel cerebrovascular disease on these same outcomes.

Abstract Objective Cerebral microbleeds (CMBs) are associated with higher risk of stroke and dementia, predating clinical diagnosis by several years. CMB are considered markers of cerebral small vessel disease (CSVD): hypertensive (deep CMB) and cerebral amyloid angiopathy (lobar CMB). We related plasma β‐Amyloid (40, 42 and their ratio), clusterin, and tau levels to CMB to elucidate their role as biomarkers for the angiopathies represented by CMB. Methods Dementia, stroke, and other neurological disease‐free Framingham Heart Study participants with available CMB and biomarker measurements were included. We related biomarker levels (standardized for analyses) to CMB presence overall and stratified by brain topography (any, lobar, deep), using multivariable logistic regression analyses. Results CMB were observed in 208 (5.7%) participants (mean age 57 years, 54% women). After multivariable adjustment, Aβ1‐40 was associated with any CMB (OR (95%CI) 1.20 (0.99, 1.45) P = 0.062)) and lobar CMB (OR (95%CI) 1.33 (1.05, 1.68) P = 0.019), but not with deep CMB. Log‐Aβ1‐42 levels were not associated with CMB overall. Clusterin was related to mixed CMB (1.70 [1.05, 2.74], P = 0.031). Tau levels were associated with any CMB (OR (95%CI) 1.26 (1.07, 1.49) P = 0.006), lobar CMB (OR (95%CI) 1.26 (1.05, 1.52) P = 0.013), and with deep CMB (OR (95% CI) 1.46 (1.13, 1.89) P = 0.004). Interpretation We found that plasma Aβ1‐40 and Tau are associated with CMB but further studies are needed to confirm their role in hemorrhage prone CSVD represented by CMB and as indicators of ongoing subclinical neuronal injury.

Abstract Background We determined the association between ratios of plasma ceramide species of differing fatty‐acyl chain lengths and incident dementia and Alzheimer’s disease (AD) dementia in a large, community‐based sample. Methods We measured plasma ceramide levels in 1892 [54% women, mean age 70.1 (SD 6.9) yr.] dementia‐free Framingham Offspring Study cohort participants between 2005 and 2008. We related ratios of very long‐chain (C24:0, C22:0) to long‐chain (C16:0) ceramides to subsequent risk of incident dementia and AD dementia. Structural MRI brain measures were included as secondary outcomes. Results During a median 6.5 year follow‐up, 81 participants developed dementia, of whom 60 were diagnosed with AD dementia. In multivariable Cox‐proportional hazards analyses, each standard deviation (SD) increment in the ratio of ceramides C24:0/C16:0 was associated with a 27% reduction in the risk of dementia (HR 0.73, 95% CI 0.56–0.96) and AD dementia (HR 0.73, 95% CI 0.53–1.00). The ratio of ceramides C22:0/C16:0 was also inversely associated with incident dementia (HR per SD 0.75, 95% CI 0.57–0.98), and approached statistical significance for AD (HR 0.73, 95% CI 0.53–1.01, P = 0.056). Higher ratios of ceramides C24:0/C16:0 and C22:0/C16:0 were also cross‐sectionally associated with lower white matter hyperintensity burden on MRI (−0.05 ± 0.02, P = 0.02; −0.06 ± 0.02, P = 0.003; respectively per SD increase), but not with other MRI brain measures. Conclusions Higher plasma ratios of very long‐chain to long‐chain ceramides are associated with a reduced risk of incident dementia and AD dementia in our community‐based sample. Circulating ceramide ratios may serve as potential biomarkers for predicting dementia risk in cognitively healthy adults.

Abstract Introduction Many consequences of cerebrovascular disease are identifiable by magnetic resonance imaging (MRI), but variation in methods limits multicenter studies and pooling of data. The European Union Joint Program on Neurodegenerative Diseases (EU JPND) funded the HARmoNizing Brain Imaging MEthodS for VaScular Contributions to Neurodegeneration (HARNESS) initiative, with a focus on cerebral small vessel disease. Methods Surveys, teleconferences, and an in‐person workshop were used to identify gaps in knowledge and to develop tools for harmonizing imaging and analysis. Results A framework for neuroimaging biomarker development was developed based on validating repeatability and reproducibility, biological principles, and feasibility of implementation. The status of current MRI biomarkers was reviewed. A website was created at www.harness‐neuroimaging.org with acquisition protocols, a software database, rating scales and case report forms, and a deidentified MRI repository. Conclusions The HARNESS initiative provides resources to reduce variability in measurement in MRI studies of cerebral small vessel disease.

Abstract Advances in technology enable increasing amounts of data collection from individuals for biomedical research. Such technologies, for example, in genetics and medical imaging, have also led to important scientific discoveries about health and disease. The combination of multiple types of high‐throughput data for complex analyses, however, has been limited by analytical and logistic resources to handle high‐dimensional data sets. In our previous EU Joint Programme–Neurodegenerative Disease Research (JPND) Working Group, called HD‐READY, we developed methods that allowed successful combination of omics data with neuroimaging. Still, several issues remained to fully leverage high‐dimensional multimodality data. For instance, high‐dimensional features, such as voxels and vertices, which are common in neuroimaging, remain difficult to harmonize. In this Full‐HD Working Group, we focused on such harmonization of high‐dimensional neuroimaging phenotypes in combination with other omics data and how to make the resulting ultra‐high‐dimensional data easily accessible in neurodegeneration research.

Cognitive decline is common in chronic kidney disease (CKD). While the evidence of vascular cognitive impairment in this population is robust, the role of Alzheimer's pathology is unknown. We evaluated serum cystatin C-estimated glomerular filtration rate (eGFR), brain amyloid-β positron emission tomography (PET) imaging, and cognitive function in 166 participants from The 90+ Study. Mean age was 93 years (range 90-107) and 101 (61%) were women; 107 participants had normal cognitive status while 59 participants had cognitive impairment no dementia (CIND) or dementia. Mean ± standard deviation cystatin C was 1.59 ± 0.54 mg/L with eGFR 40.7 ± 18.7 ml/min/1.73m2. Higher amyloid-β burden was associated with dementia, but not with age, diabetes, hypertension, or cardiovascular disease. We found no association between brain amyloid-β burden and cystatin C eGFR. We previously reported that kidney function was associated with cognition and cerebral microbleeds in the same cohort of oldest-old adults (90+ years old). Collectively, these findings suggest that microvascular rather than Alzheimer's pathology drives CKD-associated cognitive dysfunction in this population.

Purpose: To quantitatively investigate the microstructural properties of the optic nerve (ON) in vivo using diffusion magnetic resonance imaging (dMRI) tractography in an elderly population and to determine the differences between the ON diffusion properties stratified by basic demographics.Methods: We measured fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) of the intraorbital ON in cognitively normal controls selected from the Alzheimer's Disease Neuroimaging Initiative 3 database (n =104; mean age = 73. 8 ± 8.1 years) using dMRI probabilistic tractography and evaluated the correlation between diffusion parameters and demographic factors. Diffusion parameters were measured in 20 equidistant nodes along the tract, and the data from proximal 70% (14 nodes) of the intraorbital ON were averaged.Results: The mean FA of the intraorbital ON was 0.392 ± 0.063, and the mean MD was 1.163 ± 0.165 μm2/s. The mean RD was 0.882 ± 0.152 μm2/s, and the mean AD was 1.693 ± 0.183 μm2/s. The multiple linear regression model showed a negative correlation between FA and age. FA in females was significantly higher than males, whereas RD in female was significantly lower.Conclusions: We measured the diffusion properties of the intraorbital ON using dMRI tractography in an elderly cognitively normal population. The diffusion properties detected by dMRI tractography may substantially reflect the microstructure of the ON.

Alzheimer’s disease (AD) is characterized by the presence of β-amyloid (Aβ) and tau, and subcortical vascular cognitive impairment (SVCI) is characterized by cerebral small vessel disease (CSVD). They are the most common causes of cognitive impairment in the elderly population. Concurrent CSVD burden is more commonly observed in AD-type dementia than in other neurodegenerative diseases. Recent developments in Aβ and tau positron emission tomography (PET) have enabled the investigation of the relationship between AD biomarkers and CSVD in vivo. In this review, we focus on the interaction between AD and CSVD markers and the clinical effects of these two markers based on molecular imaging studies. First, we cover the frequency of AD imaging markers, including Aβ and tau, in patients with SVCI. Second, we discuss the relationship between AD and CSVD markers and the potential distinct pathobiology of AD markers in SVCI compared to AD-type dementia. Next, we discuss the clinical effects of AD and CSVD markers in SVCI, and hemorrhagic markers in cerebral amyloid angiopathy. Finally, this review provides both the current challenges and future perspectives for SVCI.

Deep learning implementations using convolutional neural nets have recently demonstrated promise in many areas of medical imaging. In this article we lay out the methods by which we have achieved consistently high quality, high throughput computation of intra-cranial segmentation from whole head magnetic resonance images, an essential but typically time-consuming bottleneck for brain image analysis. We refer to this output as “production-level” because it is suitable for routine use in processing pipelines. Training and testing with an extremely large archive of structural images, our segmentation algorithm performs uniformly well over a wide variety of separate national imaging cohorts, giving Dice metric scores exceeding those of other recent deep learning brain extractions. We describe the components involved to achieve this performance, including size, variety and quality of ground truth, and appropriate neural net architecture. We demonstrate the crucial role of appropriately large and varied datasets, suggesting a less prominent role for algorithm development beyond a threshold of capability.

Convolutional neural networks have been applied to various areas of medical imaging and histology. Here the authors develop an automated approach using interpretable neural networks to determine Alzheimer’s disease plaque and cerebral amyloid angiopathy burden in post-mortem human brain tissue.

Background: Amyloid β (Aβ) is thought to initiate a cascade of pathology culminating in Alzheimer’s disease-related cognitive decline. Aβ accumulation in brain tissues may begin one to two decades prior to clinical diagnosis of Alzheimer’s disease. Prior studies have demonstrated that Aβ detected in vivo with positron emission tomography with amyloid ligands (amyloid-PET) predicts contemporaneously measured cognition and future cognitive trajectories. Prior studies have not evaluated the added value of Aβ measures in predicting future cognition when repeated past cognitive measures are available. We evaluated the extent to which amyloid-PET improves prediction of future cognitive changes over and above predictions based only on sociodemographics and past cognitive measures. Methods: We used data from participants in the University of California Davis Alzheimer’s Disease Research cohort who were cognitively normal at baseline, participated in amyloid-PET imaging, and completed at least three cognitive assessments prior to amyloid-PET imaging (N = 132 for memory andN = 135 for executive function). We used sociodemographic and cognitive measures taken prior to amyloid-PET imaging to predict cognitive trajectory after amyloid-PET imaging and assessed whether measures of amyloid burden improved predictions of subsequent cognitive change. Improvements in prediction were characterized as percent reduction in the mean squared error (MSE) in predicted cognition post amyloid-PET and increase in percent variance explained. Results: The base model using only sociodemographics and past cognitive performance explained the majority of variance in both predicted memory measures (55.6%) and executive function measures (74.5%) following amyloid-PET. Adding amyloid positivity to the model reduced the MSE for memory by 0.2%, 95% CI: (0%, 2.6%), p = 0.48 and for executive function by 3.4%, 95% CI: (0.6%, 10.2%), p = 0.002. This corresponded to an increase in the percent variance explained of 0.1%, 95% CI: (0%, 1.2%) for memory and 0.9%, 95% CI: (0.1%, 2.8%) for executive function. Similar results were obtained using a continuous measure of amyloid burden. Conclusion: In this cohort, the addition of amyloid burden slightly improved predictions of executive function compared to models based only on past cognitive assessments and sociodemographics. When repeated cognitive assessments are available, the additional utility of amyloid-PET in predicting future cognitive impairment may be limited.

Abstract Introduction The Kaiser Healthy Aging and Diverse Life Experiences (KHANDLE) study enrolled Asian, Black, Latino, and White adults ages 65+ without prior dementia diagnosis (N = 1709). We evaluated the prevalence of cognitive impairment (mild cognitive impairment or dementia) accounting for potential biases. Methods A random subgroup (N = 541) received clinical evaluation and others were evaluated if they failed a cognitive screen. Diagnoses were made under two conditions: (1) demographics‐blind, based on clinical exam and demographically adjusted neuropsychological test scores; and (2) all available information (clinical exam, demographics, and adjusted and unadjusted test scores). Results Cognitive impairment prevalence was 28% for blinded‐adjusted diagnosis and 25% using all available information. Black participants had higher impairment rates than White (both conditions) and Latino (blinded‐adjusted diagnosis) participants. Incomplete assessments negatively biased prevalence estimates for White participants. Discussion Racial/ethnic disparities in cognitive impairment were amplified by attrition bias in White participants but were unaffected by type of test norms and diagnosticians’ knowledge of demographics.

Alzheimer's disease (AD), including its mild cognitive impairment (MCI) phase that may or may not progress into the AD, is the most ordinary form of dementia. It is extremely important to correctly identify patients during the MCI stage because this is the phase where AD may or may not develop. Thus, it is crucial to predict outcomes during this phase. Thus far, many researchers have worked on only using a single modality of a biomarker for the diagnosis of AD or MCI. Although recent studies show that a combination of one or more different biomarkers may provide complementary information for the diagnosis, it also increases the classification accuracy distinguishing between different groups. In this paper, we propose a novel machine learning-based framework to discriminate subjects with AD or MCI utilizing a combination of four different biomarkers: fluorodeoxyglucose positron emission tomography (FDG-PET), structural magnetic resonance imaging (sMRI), cerebrospinal fluid (CSF) protein levels, and Apolipoprotein-E (APOE) genotype. The Alzheimer's Disease Neuroimaging Initiative (ADNI) baseline dataset was used in this study. In total, there were 158 subjects for whom all four modalities of biomarker were available. Of the 158 subjects, 38 subjects were in the AD group, 82 subjects were in MCI groups (including 46 in MCIc [MCI converted; conversion to AD within 24 months of time period], and 36 in MCIs [MCI stable; no conversion to AD within 24 months of time period]), and the remaining 38 subjects were in the healthy control (HC) group. For each image, we extracted 246 regions of interest (as features) using the Brainnetome template image and NiftyReg toolbox, and later we combined these features with three CSF and two APOE genotype features obtained from the ADNI website for each subject using early fusion technique. Here, a different kernel-based multiclass support vector machine (SVM) classifier with a grid-search method was applied. Before passing the obtained features to the classifier, we have used truncated singular value decomposition (Truncated SVD) dimensionality reduction technique to reduce high dimensional features into a lower-dimensional feature. As a result, our combined method achieved an area under the receiver operating characteristic (AU-ROC) curve of 98.33, 93.59, 96.83, 94.64, 96.43, and 95.24% for AD vs. HC, MCIs vs. MCIc, AD vs. MCIs, AD vs. MCIc, HC vs. MCIc, and HC vs. MCIs subjects which are high relative to single modality results and other state-of-the-art approaches. Moreover, combined multimodal methods have improved the classification performance over the unimodal classification.

Background GDF15 (growth differentiation factor 15) and NT‐proBNP (N‐terminal pro‐B‐type natriuretic peptide) may offer promise as biomarkers for cognitive outcomes, including dementia. We determined the association of these biomarkers with cognitive outcomes in a community‐based cohort. Methods and Results Plasma GDF15 (n=1603) and NT‐proBNP levels (n=1590) (53% women; mean age, 68.7 years) were measured in dementia‐free Framingham Offspring cohort participants at examination 7 (1998–2001). Participants were followed up for incident dementia. Secondary outcomes included Alzheimer disease dementia, magnetic resonance imaging structural brain measures, and neurocognitive performance. During a median 11.8‐year follow‐up, 131 participants developed dementia. On multivariable Cox proportional‐hazards analysis, higher circulating GDF15 was associated with an increased risk of incident all‐cause and Alzheimer disease dementia (hazard ratio [HR] per SD increment in natural log‐transformed biomarker value, 1.54 [95% CI, 1.22–1.95] and 1.37 [95% CI, 1.03–1.81], respectively), whereas higher plasma NT‐proBNP was also associated with an increased risk of all‐cause dementia (HR, 1.32; 95% CI, 1.05–1.65). Elevated GDF15 was associated with lower total brain and hippocampal volumes, greater white matter hyperintensity volume, and poorer cognitive performance. Elevated NT‐proBNP was associated with greater white matter hyperintensity volume and poorer cognitive performance. Addition of both biomarkers to a conventional risk factor model improved dementia risk classification (net reclassification improvement index, 0.25; 95% CI, 0.05–0.45). Conclusions Elevated plasma GDF15 and NT‐proBNP were associated with vascular brain injury on magnetic resonance imaging, poorer neurocognitive performance, and increased risk of incident dementia in individuals aged >60 years. Both biomarkers improved dementia risk classification beyond that of traditional clinical risk factors, indicating their potential value in predicting incident dementia.

Abstract Introduction We studied the association of carotid intima‐media thickness (CIMT) with hippocampal volume (HV) in community dwelling individuals, testing the hypothesis that persons with carotid atherosclerosis progression would have lower HV. Methods We studied 1376 Framingham Offspring participants with two carotid ultrasounds and brain magnetic resonance imaging (MRIs). We used multivariable linear regression analyses to relate CIMT progression and HV and total brain volume. Regression models were adjusted for demographics and vascular risk factors, time interval between imaging examinations, and baseline CIMT. We assessed effect modification by hypertension treatment (HRx). Results Participants with higher ICA IMT progression had significantly lower HV after adjustment for vascular risk factors and baseline IMT (standardized beta ± standard error: −0.067 ± 0.027, P = .01). We observed weaker association between ICA IMT change and HV among subjects treated for hypertension (β = −0.047, P = .19 vs β = −0.096, P = .026). Discussion Cumulative vascular risk factor exposure, reflected by CIMT progression, may increase the risk of neurodegeneration.

An increase in the volume of the brain lateral ventricles is a sign of normal aging, but can also be associated with neurological and psychiatric disorders. Here, Vojinovic et al. identify seven genetic loci in a GWA study for ventricular volume in 23,500 individuals and find correlation with thalamus volume.

Abstract Introduction Current models posit a sequence of amyloid β (Aβ), tau, atrophy, and cognitive change leading to Alzheimer's disease, but ambiguities remain. We examined these sequences via serial mediations. Methods We studied normal controls, early mild cognitive impairment, and late mild cognitive impairment individuals from the Alzheimer's Disease Neuroimaging Initiative 2 database for the mediation of baseline cerebrospinal fluid Aβ effects on 2‐year cognitive change via regional longitudinal atrophy rate (AR) alone or AR and tau. Results In normal controls, Aβ correlated directly with regional ARs and memory loss, with no mediations. In early mild cognitive impairment, tau and lateral temporal ARs serially mediated the influence of Aβ on memory while Aβ affected memory via hippocampal AR. Late mild cognitive impairment consistently showed serial mediations of tau followed by atrophy. However, Aβ effects on memory also continued to be specifically mediated by medial temporal ARs without intermediate tau. Discussion Biomarker sequences vary by region and disease state, suggesting the need to refine current cascade models.

Mild cognitive impairment (MCI) is a transitional state between the cognitive changes in normal aging and Alzheimer’s disease (AD), which induces abnormalities in specific brain regions. Previous studies showed that paired helical filaments Tau (PHF-Tau) protein is a potential pathogenic protein which may cause abnormal brain function and structure in MCI and AD patients. However, the understanding of the PHF-Tau protein network in MCI patients is limited. In this study, 225 subjects with PHF-Tau Positron Emission Tomography (PET) images were divided into four groups based on whether they carried Apolipoprotein E ε4 (APOE 4) or abnormal cerebrospinal fluid Total-Tau (CSF T-Tau). They are two important pathogenic factors that might cause cognitive function impairment. The four groups were: individuals harboring CSF T-Tau pathology but no APOE 4 (APOE 4−T+); APOE 4 carriers with normal CSF T-Tau (APOE 4+T−); APOE 4 carriers with abnormal CSF T-Tau (APOE 4+T+); and APOE 4 noncarriers with abnormal CSF T-Tau (APOE 4−T−). We explored the topological organization of PHF-Tau networks in these four groups and calculated five kinds of network properties: clustering coefficient, shortest path length, Q value of modularity, nodal centrality and degree. Our findings showed that compared with APOE 4−T− group, the other three groups showed different alterations in the clustering coefficient, shortest path length, Q value of modularity, nodal centrality and degree. Simultaneously, voxel-level analysis was conducted and the results showed that compared with APOE 4−T− group, the other three groups were found increased PHF-Tau distribution in some brain regions. For APOE 4+T+ group, positive correlation was found between the value of PHF-Tau distribution in altered regions and Functional Assessment Questionnaire (FAQ) score. Our results indicated that the effects of APOE 4 and abnormal CSF T-Tau may induce abnormalities of PHF-Tau protein and APOE 4 has a greater impact on PHF-Tau than abnormal CSF T-Tau. Our results may be particularly helpful in uncovering the pathophysiology underlying the cognitive dysfunction in MCI patients.

BackgroundNon-invasive brain stimulation (NIBS) has been widely used to treat mild cognitive impairment (MCI). However, there exists no consensus on the best stimulation sites.ObjectiveTo explore potential stimulation locations for NIBS treatment in patients with MCI, combining meta- and resting state functional connectivity (rsFC) analyses.MethodsThe meta-analysis was conducted to identify brain regions associated with MCI. Regions of interest (ROIs) were extracted based on this meta-analysis. The rsFC analysis was applied to 45 MCI patients to determine brain surface regions that are functionally connected with the above ROIs.ResultsWe found that the dorsolateral prefrontal cortex (DLPFC) and inferior frontal gyrus (IFG) were the overlapping brain regions between our results and those of previous studies. In addition, we recommend that the temporoparietal junction (including the angular gyrus), which was found in both the meta- and rsFC analysis, should be considered in NIBS treatment of MCI. Furthermore, the bilateral orbital prefrontal gyrus, inferior temporal gyrus, medial superior frontal gyrus, and right inferior occipital gyrus may be potential brain stimulation sites for NIBS treatment of MCI.ConclusionOur results provide several potential sites for NIBS, such as the DLFPC and IFG, and may shed light on the locations of NIBS sites in the treatment of patients with MCI.

Collecting multiple modalities of neuroimaging data on the same subject is increasingly becoming the norm in clinical practice and research. Fusing multiple modalities to find related patterns is a challenge in neuroimaging analysis. Canonical correlation analysis (CCA) is commonly used as a symmetric data fusion technique to find related patterns among multiple modalities. In CCA-based data fusion, principal component analysis (PCA) is frequently applied as a preprocessing step to reduce data dimension followed by CCA on dimension-reduced data. PCA, however, does not differentiate between informative voxels from non-informative voxels in the dimension reduction step. Sparse PCA (sPCA) extends traditional PCA by adding sparse regularization that assigns zero weights to non-informative voxels. In this study, sPCA is incorporated into CCA-based fusion analysis and applied on neuroimaging data. A cross-validation method is developed and validated to optimize the parameters in sPCA. Different simulations are carried out to evaluate the improvement by introducing sparsity constraint to PCA. Four fusion methods including sPCA+CCA, PCA+CCA, parallel ICA and sparse CCA were applied on structural and functional magnetic resonance imaging data of mild cognitive impairment subjects and normal controls. Our results indicate that sPCA significantly can reduce the impact of non-informative voxels and lead to improved statistical power in uncovering disease-related patterns by a fusion analysis.

Recent developments of tau Positron Emission Tomography (PET) allows assessment of regional neurofibrillary tangles (NFTs) deposition in human brain. Among the tau PET molecular probes, 18F-AV1451 is characterized by high selectivity for pathologic tau aggregates over amyloid plaques, limited non-specific binding in white and gray matter, and confined off-target binding. The objectives of the study are (1) to quantitatively characterize regional brain tau deposition measured by 18F-AV1451 PET in cognitively normal older adults (CN), mild cognitive impairment (MCI), and AD participants; (2) to evaluate the correlations between cerebrospinal fluid (CSF) biomarkers or Mini-Mental State Examination (MMSE) and 18F-AV1451 PET standardized uptake value ratio (SUVR); and (3) to evaluate the partial volume effects on 18F-AV1451 brain uptake.Methods: The study included total 115 participants (CN = 49, MCI = 58, and AD = 8) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Preprocessed 18F-AV1451 PET images, structural MRIs, and demographic and clinical assessments were downloaded from the ADNI database. A reblurred Van Cittertiteration method was used for voxelwise partial volume correction (PVC) on PET images. Structural MRIs were used for PET spatial normalization and region of interest (ROI) definition in standard space. The parametric images of 18F-AV1451 SUVR relative to cerebellum were calculated. The ROI SUVR measurements from PVC and non-PVC SUVR images were compared. The correlation between ROI 18F-AV1451 SUVR and the measurements of MMSE, CSF total tau (t-tau), and phosphorylated tau (p-tau) were also assessed.Results:18F-AV1451 prominently specific binding was found in the amygdala, entorhinal cortex, parahippocampus, fusiform, posterior cingulate, temporal, parietal, and frontal brain regions. Most regional SUVRs showed significantly higher uptake of 18F-AV1451 in AD than MCI and CN participants. SUVRs of small regions like amygdala, entorhinal cortex and parahippocampus were statistically improved by PVC in all groups (p < 0.01). Although there was an increasing tendency of 18F-AV-1451 SUVRs in MCI group compared with CN group, no significant difference of 18F-AV1451 deposition was found between CN and MCI brains with or without PVC (p > 0.05). Declined MMSE score was observed with increasing 18F-AV1451 binding in amygdala, entorhinal cortex, parahippocampus, and fusiform. CSF p-tau was positively correlated with 18F-AV1451 deposition. PVC improved the results of 18F-AV-1451 tau deposition and correlation studies in small brain regions.Conclusion: The typical deposition of 18F-AV1451 tau PET imaging in AD brain was found in amygdala, entorhinal cortex, fusiform and parahippocampus, and these regions were strongly associated with cognitive impairment and CSF biomarkers. Although more deposition was observed in MCI group, the 18F-AV-1451 PET imaging could not differentiate the MCI patients from CN population. More tau deposition related to decreased MMSE score and increased level of CSF p-tau, especially in ROIs of amygdala, entorhinal cortex and parahippocampus. PVC did improve the results of tau deposition and correlation studies in small brain regions and suggest to be routinely used in 18F-AV1451 tau PET quantification.

Introduction: White matter hyperintensities (WMHs) are areas of abnormal signal on magnetic resonance images (MRIs) that characterize various types of histopathological lesions. The load and location of WMHs are important clinical measures that may indicate the presence of small vessel disease in aging and Alzheimer's disease (AD) patients. Manually segmenting WMHs is time consuming and prone to inter-rater and intra-rater variabilities. Automated tools that can accurately and robustly detect these lesions can be used to measure the vascular burden in individuals with AD or the elderly population in general. Many WMH segmentation techniques use a classifier in combination with a set of intensity and location features to segment WMHs, however, the optimal choice of classifier is unknown. Methods: We compare 10 different linear and nonlinear classification techniques to identify WMHs from MRI data. Each classifier is trained and optimized based on a set of features obtained from co-registered MR images containing spatial location and intensity information. We further assess the performance of the classifiers using different combinations of MRI contrast information. The performances of the different classifiers were compared on three heterogeneous multi-site datasets, including images acquired with different scanners and different scan-parameters. These included data from the ADC study from University of California Davis, the NACC database and the ADNI study. The classifiers (naïve Bayes, logistic regression, decision trees, random forests, support vector machines, k-nearest neighbors, bagging, and boosting) were evaluated using a variety of voxel-wise and volumetric similarity measures such as Dice Kappa similarity index (SI), Intra-Class Correlation (ICC), and sensitivity as well as computational burden and processing times. These investigations enable meaningful comparisons between the performances of different classifiers to determine the most suitable classifiers for segmentation of WMHs. In the spirit of open-source science, we also make available a fully automated tool for segmentation of WMHs with pre-trained classifiers for all these techniques. Results: Random Forests yielded the best performance among all classifiers with mean Dice Kappa (SI) of 0.66±0.17 and ICC=0.99 for the ADC dataset (using T1w, T2w, PD, and FLAIR scans), SI=0.72±0.10, ICC=0.93 for the NACC dataset (using T1w and FLAIR scans), SI=0.66±0.23, ICC=0.94 for ADNI1 dataset (using T1w, T2w, and PD scans) and SI=0.72±0.19, ICC=0.96 for ADNI2/GO dataset (using T1w and FLAIR scans). Not using the T2w/PD information did not change the performance of the Random Forest classifier (SI=0.66±0.17, ICC=0.99). However, not using FLAIR information in the ADC dataset significantly decreased the Dice Kappa, but the volumetric correlation did not drastically change (SI=0.47±0.21, ICC=0.95). Conclusion: Our investigations showed that with appropriate features, most off-the-shelf classifiers are able to accurately detect WMHs in presence of FLAIR scan information, while Random Forests had the best performance across all datasets. However, we observed that the performances of most linear classifiers and some nonlinear classifiers drastically decline in absence of FLAIR information, with Random Forest still retaining the best performance.

Introduction The Resource Centers for Minority Aging Research (RCMAR) program was launched in 1997. Its goal is to build infrastructure to improve the well-being of older racial/ethnic minorities by identifying mechanisms to reduce health disparities. Methods Its primary objectives are to mentor faculty in research addressing the health of minority elders and to enhance the diversity of the workforce that conducts elder health research by prioritizing the mentorship of underrepresented diverse scholars. Results Through 2015, 12 centers received RCMAR awards and provided pilot research funding and mentorship to 361 scholars, 70% of whom were from underrepresented racial/ethnic groups. A large majority (85%) of RCMAR scholars from longstanding centers continue in academic research. Another 5% address aging and other health disparities through nonacademic research and leadership roles in public health agencies. Conclusions Longitudinal, team-based mentoring, cross-center scholar engagement, and community involvement in scholar development are important contributors to RCMAR’s success.