Enhancing cognitive performance prediction by white matter hyperintensity connectivity assessment.

White matter hyperintensities of presumed vascular origin (WMH) are associated with cognitive impairment and are a key imaging marker in evaluating brain health. However, WMH volume alone does not fully account for the extent of cognitive deficits and the mechanisms linking WMH to these deficits remain unclear. Lesion network mapping (LNM) enables to infer if brain networks are connected to lesions and could be a promising technique for enhancing our understanding of the role of WMH in cognitive disorders. Our study employed LNM to test the following hypotheses: (1) LNM-informed markers surpass WMH volumes in predicting cognitive performance, and (2) WMH contributing to cognitive impairment map to specific brain networks. We analyzed cross-sectional data of 3,485 patients from 10 memory clinic cohorts within the Meta VCI Map Consortium, using harmonized test results in 4 cognitive domains and WMH segmentations. WMH segmentations were registered to a standard space and mapped onto existing normative structural and functional brain connectome data. We employed LNM to quantify WMH connectivity to 480 atlas-based gray and white matter regions of interest (ROI), resulting in ROI-level structural and functional LNM scores. We compared the capacity of total and regional WMH volumes and LNM scores in predicting cognitive function using ridge regression models in a nested cross-validation. LNM scores predicted performance in three cognitive domains (attention/executive function, information processing speed, and verbal memory) significantly better than WMH volumes. LNM scores did not improve prediction for language functions. ROI-level analysis revealed that higher LNM scores, representing greater connectivity to WMH, in gray and white matter regions of the dorsal and ventral attention networks were associated with lower cognitive performance. Measures of WMH-related brain network connectivity significantly improve the prediction of current cognitive performance in memory clinic patients compared to WMH volume as a traditional imaging marker of cerebrovascular disease. This highlights the crucial role of network integrity, particularly in attention-related brain regions, improving our understanding of vascular contributions to cognitive impairment. Moving forward, refining WMH information with connectivity data could contribute to patient-tailored therapeutic interventions and facilitate the identification of subgroups at risk of cognitive disorders.

Implementation and validation of face de-identification (de-facing) in ADNI4.

INTRODUCTION: Recent technological advances have increased the risk that de-identified brain images could be re-identified from face imagery. The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a leading source of publicly available de-identified brain imaging, who quickly acted to protect participants' privacy. METHODS: An independent expert committee evaluated 11 face-deidentification ("de-facing") methods and selected four for formal testing. RESULTS: Effects of de-facing on brain measurements were comparable across methods and sufficiently small to recommend de-facing in ADNI. The committee ultimately recommended mri_reface for advantages in reliability, and for some practical considerations. ADNI leadership approved the committee's recommendation, beginning in ADNI4. DISCUSSION: ADNI4 de-faces all applicable brain images before subsequent pre-processing, analyses, and public release. Trained analysts inspect de-faced images to confirm complete face removal and complete non-modification of brain. This paper details the history of the algorithm selection process and extensive validation, then describes the production workflows for de-facing in ADNI. HIGHLIGHTS: ADNI is implementing "de-facing" of MRI and PET beginning in ADNI4. "De-facing" alters face imagery in brain images to help protect privacy. Four algorithms were extensively compared for ADNI and mri_reface was chosen. Validation confirms mri_reface is robust and effective for ADNI sequences. Validation confirms mri_reface negligibly affects ADNI brain measurements.

Overview of ADNI MRI.

The magnetic resonance imaging (MRI) Core has been operating since Alzheimer's Disease Neuroimaging Initiative's (ADNI) inception, providing 20 years of data including reliable, multi-platform standardized protocols, carefully curated image data, and quantitative measures provided by expert investigators. The overarching purposes of the MRI Core include: (1) optimizing and standardizing MRI acquisition methods, which have been adopted by many multicenter studies and trials worldwide and (2) providing curated images and numeric summary values from relevant MRI sequences/contrasts to the scientific community. Over time, ADNI MRI has become increasingly complex. To remain technically current, the ADNI MRI protocol has changed substantially over the past two decades. The ADNI 4 protocol contains nine different imaging types (e.g., three dimensional [3D] T1-weighted and fluid-attenuated inversion recovery [FLAIR]). Our view is that the ADNI MRI data are a greatly underutilized resource. The purpose of this paper is to educate the scientific community on ADNI MRI methods and content to promote greater awareness, accessibility, and use. HIGHLIGHTS: The MRI Core provides multi-platform standardized protocols, carefully curated image data, and quantitative analysis by expert groups. The ADNI MRI protocol has undergone major changes over the past two decades to remain technically current. As of April 25, 2024, the following numbers of image series are available: 17,141 3D T1w; 6877 FLAIR; 3140 T2/PD; 6623 GRE; 3237 dMRI; 2846 ASL; 2968 TF-fMRI; and 2861 HighResHippo (see Table 1 for abbreviations). As of April 25, 2024, the following numbers of quantitative analyses are available: FreeSurfer 10,997; BSI 6120; tensor based morphometry (TBM) and TBM-SYN 12,019; WMH 9944; dMRI 1913; ASL 925; TF-fMRI NFQ 2992; and medial temporal subregion volumes 2726 (see Table 4 for abbreviations). ADNI MRI is an underutilized resource that could be more useful to the research community.

Development of a machine learning algorithm to predict the residual cognitive reserve index.

Elucidating the mechanisms by which late-life neurodegeneration causes cognitive decline requires understanding why some individuals are more resilient than others to the effects of brain change on cognition (cognitive reserve). Currently, there is no way of measuring cognitive reserve that is valid (e.g. capable of moderating brain-cognition associations), widely accessible (e.g. does not require neuroimaging and large sample sizes), and able to provide insight into resilience-promoting mechanisms. To address these limitations, this study sought to determine whether a machine learning approach to combining standard clinical variables could (i) predict a residual-based cognitive reserve criterion standard and (ii) prospectively moderate brain-cognition associations. In a training sample combining data from the University of California (UC) Davis and the Alzheimer's Disease Neuroimaging Initiative-2 (ADNI-2) cohort (N = 1665), we operationalized cognitive reserve using an MRI-based residual approach. An eXtreme Gradient Boosting machine learning algorithm was trained to predict this residual reserve index (RRI) using three models: Minimal (basic clinical data, such as age, education, anthropometrics, and blood pressure), Extended (Minimal model plus cognitive screening, word reading, and depression measures), and Full [Extended model plus Clinical Dementia Rating (CDR) and Everyday Cognition (ECog) scale]. External validation was performed in an independent sample of ADNI 1/3/GO participants (N = 1640), which examined whether the effects of brain change on cognitive change were moderated by the machine learning models' cognitive reserve estimates. The three machine learning models differed in their accuracy and validity. The Minimal model did not correlate strongly with the criterion standard (r = 0.23) and did not moderate the effects of brain change on cognitive change. In contrast, the Extended and Full models were modestly correlated with the criterion standard (r = 0.49 and 0.54, respectively) and prospectively moderated longitudinal brain-cognition associations, outperforming other cognitive reserve proxies (education, word reading). The primary difference between the Minimal model-which did not perform well as a measure of cognitive reserve-and the Extended and Full models-which demonstrated good accuracy and validity-is the lack of cognitive performance and informant-report data in the Minimal model. This suggests that basic clinical variables like anthropometrics, vital signs, and demographics are not sufficient for estimating cognitive reserve. Rather, the most accurate and valid estimates of cognitive reserve were obtained when cognitive performance data-ideally augmented by informant-reported functioning-was used. These results indicate that a dynamic and accessible proxy for cognitive reserve can be generated for individuals without neuroimaging data and gives some insight into factors that may promote resilience.

Associations of Amyloid Burden, White Matter Hyperintensities, and Hippocampal Volume With Cognitive Trajectories in the 90+ Study.

BACKGROUND AND OBJECTIVES: Amyloid pathology, vascular disease pathology, and pathologies affecting the medial temporal lobe are associated with cognitive trajectories in older adults. However, only limited evidence exists on how these pathologies influence cognition in the oldest old. We evaluated whether amyloid burden, white matter hyperintensity (WMH) volume, and hippocampal volume (HV) are associated with cognitive level and decline in the oldest old. METHODS: This was a longitudinal, observational community-based cohort study. We included participants with 18F-florbetapir PET and MRI data from the 90+ Study. Amyloid load was measured using the standardized uptake value ratio in the precuneus/posterior cingulate with eroded white matter mask as reference. WMH volume was log-transformed. All imaging measures were standardized using sample means and SDs. HV and log-WMH volume were normalized by total intracranial volume using the residual approach. Global cognitive performance was measured by the Mini-Mental State Examination (MMSE) and modified MMSE (3MS) tests, repeated every 6 months. We used linear mixed-effects models with random intercepts; random slopes; and interaction between time, time squared, and imaging variables to estimate the associations of imaging variables with cognitive level and cognitive decline. Models were adjusted for demographics, APOE genotype, and health behaviors. RESULTS: The sample included 192 participants. The mean age was 92.9 years, 125 (65.1%) were female, 71 (37.0%) achieved a degree beyond college, and the median follow-up time was 3.0 years. A higher amyloid load was associated with a lower cognitive level (ßMMSE = -0.82, 95% CI -1.17 to -0.46; ß3MS = -2.77, 95% CI -3.69 to -1.84). A 1-SD decrease in HV was associated with a 0.70-point decrease in the MMSE score (95% CI -1.14 to -0.27) and a 2.27-point decrease in the 3MS score (95% CI -3.40 to -1.14). Clear nonlinear cognitive trajectories were detected. A higher amyloid burden and smaller HV were associated with faster cognitive decline. WMH volume was not significantly associated with cognitive level or decline. DISCUSSION: Amyloid burden and hippocampal atrophy are associated with both cognitive level and cognitive decline in the oldest old. Our findings shed light on how different pathologies contributed to driving cognitive function in the oldest old.

Enhancing Cognitive Performance Prediction through White Matter Hyperintensity Connectivity Assessment: A Multicenter Lesion Network Mapping Analysis of 3,485 Memory Clinic Patients.

Introduction: White matter hyperintensities of presumed vascular origin (WMH) are associated with cognitive impairment and are a key imaging marker in evaluating cognitive health. However, WMH volume alone does not fully account for the extent of cognitive deficits and the mechanisms linking WMH to these deficits remain unclear. We propose that lesion network mapping (LNM), enables to infer if brain networks are connected to lesions, and could be a promising technique for enhancing our understanding of the role of WMH in cognitive disorders. Our study employed this approach to test the following hypotheses: (1) LNM-informed markers surpass WMH volumes in predicting cognitive performance, and (2) WMH contributing to cognitive impairment map to specific brain networks. Methods & results: We analyzed cross-sectional data of 3,485 patients from 10 memory clinic cohorts within the Meta VCI Map Consortium, using harmonized test results in 4 cognitive domains and WMH segmentations. WMH segmentations were registered to a standard space and mapped onto existing normative structural and functional brain connectome data. We employed LNM to quantify WMH connectivity across 480 atlas-based gray and white matter regions of interest (ROI), resulting in ROI-level structural and functional LNM scores. The capacity of total and regional WMH volumes and LNM scores in predicting cognitive function was compared using ridge regression models in a nested cross-validation. LNM scores predicted performance in three cognitive domains (attention and executive function, information processing speed, and verbal memory) significantly better than WMH volumes. LNM scores did not improve prediction for language functions. ROI-level analysis revealed that higher LNM scores, representing greater disruptive effects of WMH on regional connectivity, in gray and white matter regions of the dorsal and ventral attention networks were associated with lower cognitive performance. Conclusion: Measures of WMH-related brain network connectivity significantly improve the prediction of current cognitive performance in memory clinic patients compared to WMH volume as a traditional imaging marker of cerebrovascular disease. This highlights the crucial role of network effects, particularly in attentionrelated brain regions, improving our understanding of vascular contributions to cognitive impairment. Moving forward, refining WMH information with connectivity data could contribute to patient-tailored therapeutic interventions and facilitate the identification of subgroups at risk of cognitive disorders.

Non-invasive quantification of 18F-florbetaben with total-body EXPLORER PET.

BACKGROUND: Kinetic modeling of 18F-florbetaben provides important quantification of brain amyloid deposition in research and clinical settings but its use is limited by the requirement of arterial blood data for quantitative PET. The total-body EXPLORER PET scanner supports the dynamic acquisition of a full human body simultaneously and permits noninvasive image-derived input functions (IDIFs) as an alternative to arterial blood sampling. This study quantified brain amyloid burden with kinetic modeling, leveraging dynamic 18F-florbetaben PET in aorta IDIFs and the brain in an elderly cohort. METHODS: 18F-florbetaben dynamic PET imaging was performed on the EXPLORER system with tracer injection (300 MBq) in 3 individuals with Alzheimer's disease (AD), 3 with mild cognitive impairment, and 9 healthy controls. Image-derived input functions were extracted from the descending aorta with manual regions of interest based on the first 30 s after injection. Dynamic time-activity curves (TACs) for 110 min were fitted to the two-tissue compartment model (2TCM) using population-based metabolite corrected IDIFs to calculate total and specific distribution volumes (VT, Vs) in key brain regions with early amyloid accumulation. Non-displaceable binding potential ([Formula: see text] was also calculated from the multi-reference tissue model (MRTM). RESULTS: Amyloid-positive (AD) patients showed the highest VT and VS in anterior cingulate, posterior cingulate, and precuneus, consistent with [Formula: see text] analysis. [Formula: see text]and VT from kinetic models were correlated (r² = 0.46, P < 2[Formula: see text] with a stronger positive correlation observed in amyloid-positive participants, indicating reliable model fits with the IDIFs. VT from 2TCM was highly correlated ([Formula: see text]= 0.65, P < 2[Formula: see text]) with Logan graphical VT estimation. CONCLUSION: Non-invasive quantification of amyloid binding from total-body 18F-florbetaben PET data is feasible using aorta IDIFs with high agreement between kinetic distribution volume parameters compared to [Formula: see text]in amyloid-positive and amyloid-negative older individuals.

Unsupervised deep representation learning enables phenotype discovery for genetic association studies of brain imaging.

Understanding the genetic architecture of brain structure is challenging, partly due to difficulties in designing robust, non-biased descriptors of brain morphology. Until recently, brain measures for genome-wide association studies (GWAS) consisted of traditionally expert-defined or software-derived image-derived phenotypes (IDPs) that are often based on theoretical preconceptions or computed from limited amounts of data. Here, we present an approach to derive brain imaging phenotypes using unsupervised deep representation learning. We train a 3-D convolutional autoencoder model with reconstruction loss on 6130 UK Biobank (UKBB) participants' T1 or T2-FLAIR (T2) brain MRIs to create a 128-dimensional representation known as Unsupervised Deep learning derived Imaging Phenotypes (UDIPs). GWAS of these UDIPs in held-out UKBB subjects (n = 22,880 discovery and n = 12,359/11,265 replication cohorts for T1/T2) identified 9457 significant SNPs organized into 97 independent genetic loci of which 60 loci were replicated. Twenty-six loci were not reported in earlier T1 and T2 IDP-based UK Biobank GWAS. We developed a perturbation-based decoder interpretation approach to show that these loci are associated with UDIPs mapped to multiple relevant brain regions. Our results established unsupervised deep learning can derive robust, unbiased, heritable, and interpretable brain imaging phenotypes.

Trends in Intracranial and Cerebral Volumes of Framingham Heart Study Participants Born 1930 to 1970.

Importance: Human brain development and maintenance is under both genetic and environmental influences that likely affect later-life dementia risk. Objective: To examine environmental influences by testing whether time-dependent secular differences occurred in cranial and brain volumes and cortical thickness over birth decades spanning 1930 to 1970. Design, Setting, and Participants: This cross-sectional study used data from the community-based Framingham Heart Study cohort for participants born in the decades 1930 to 1970. Participants did not have dementia or history of stroke and had magnetic resonance imaging (MRI) obtained from March 18, 1999, to November 15, 2019. The final analysis dataset was created in October 2023. Exposure: Years of birth ranging from 1925 to 1968. Main Measures: Cross-sectional analysis of intracranial, cortical gray matter, white matter, and hippocampal volumes as well as cortical surface area and cortical thickness. The secular measure was the decade in which the participant was born. Covariates included age at MRI and sex. Results: The main study cohort consisted of 3226 participants with a mean (SD) age of 57.7 (7.8) years at the time of their MRI. A total of 1706 participants were female (53%) and 1520 (47%) were male. The birth decades ranged from the 1930s to 1970s. Significant trends for larger intracranial, hippocampal, and white matter volumes and cortical surface area were associated with progressive birth decades. Comparing the 1930s birth decade to the 1970s accounted for a 6.6% greater volume (1234 mL; 95% CI, 1220-1248, vs 1321 mL; 95% CI, 1301-1341) for ICV, 7.7% greater volume (441.9 mL; 95% CI, 435.2-448.5, vs 476.3 mL; 95% CI, 467.0-485.7) for white matter, 5.7% greater value (6.51 mL; 95% CI, 6.42-6.60, vs 6.89 mL; 95% CI, 6.77-7.02) for hippocampal volume, and a 14.9% greater value (1933 cm2; 95% CI, 1908-1959, vs 2222 cm2; 95% CI, 2186-2259) for cortical surface area. Repeat analysis applied to a subgroup of 1145 individuals of similar age range born in the 1940s (mean [SD] age, 60.0 [2.8] years) and 1950s (mean [SD] age, 59.0 [2.8] years) resulted in similar findings. Conclusion and Relevance: In this study, secular trends for larger brain volumes suggested improved brain development among individuals born between 1930 and 1970. Early life environmental influences may explain these results and contribute to the declining dementia incidence previously reported in the Framingham Heart Study cohort.

Amyloid pathology and vascular risk are associated with distinct patterns of cerebral white matter hyperintensities: A multicenter study in 3132 memory clinic patients.

INTRODUCTION: White matter hyperintensities (WMH) are associated with key dementia etiologies, in particular arteriolosclerosis and amyloid pathology. We aimed to identify WMH locations associated with vascular risk or cerebral amyloid-ß1-42 (Aß42)-positive status. METHODS: Individual patient data (n = 3,132; mean age 71.5 ± 9 years; 49.3% female) from 11 memory clinic cohorts were harmonized. WMH volumes in 28 regions were related to a vascular risk compound score (VRCS) and Aß42 status (based on cerebrospinal fluid or amyloid positron emission tomography), correcting for age, sex, study site, and total WMH volume. RESULTS: VRCS was associated with WMH in anterior/superior corona radiata (B = 0.034/0.038, p < 0.001), external capsule (B = 0.052, p < 0.001), and middle cerebellar peduncle (B = 0.067, p < 0.001), and Aß42-positive status with WMH in posterior thalamic radiation (B = 0.097, p < 0.001) and splenium (B = 0.103, p < 0.001). DISCUSSION: Vascular risk factors and Aß42 pathology have distinct signature WMH patterns. This regional vulnerability may incite future studies into how arteriolosclerosis and Aß42 pathology affect the brain's white matter. HIGHLIGHTS: Key dementia etiologies may be associated with specific patterns of white matter hyperintensities (WMH). We related WMH locations to vascular risk and cerebral Aß42 status in 11 memory clinic cohorts. Aß42 positive status was associated with posterior WMH in splenium and posterior thalamic radiation. Vascular risk was associated with anterior and infratentorial WMH. Amyloid pathology and vascular risk have distinct signature WMH patterns.

Racial and ethnic differences in the association between depressive symptoms and cognitive outcomes in older adults: Findings from KHANDLE and STAR.

INTRODUCTION: Depressive symptoms are associated with higher risk of dementia, but how they impact cognition in diverse populations is unclear. METHODS: Asian, Black, Latino, or White participants (n = 2227) in the Kaiser Healthy Aging and Diverse Life Experiences (age 65+) and the Study of Healthy Aging in African Americans (age 50+) underwent up to three waves of cognitive assessments over 4 years. Multilevel models stratified by race/ethnicity were used to examine whether depressive symptoms were associated with cognition or cognitive decline and whether associations differed by race/ethnicity. RESULTS: Higher depressive symptoms were associated with lower baseline verbal episodic memory scores (-0.06, 95% CI: -0.12, -0.01; -0.15, 95% CI: -0.25, -0.04), and faster decline annually in semantic memory (-0.04, 95% CI: -0.07, -0.01; -0.10, 95% CI: -0.15, -0.05) for Black and Latino participants. Depressive symptoms were associated with lower baseline but not decline in executive function. DISCUSSION: Depressive symptoms were associated with worse cognitive outcomes, with some evidence of heterogeneity across racial/ethnic groups. HIGHLIGHTS: We examined whether baseline depressive symptoms were differentially associated with domain-specific cognition or cognitive decline by race/ethnicity. Depressive symptoms were associated with worse cognitive scores for all racial/ethnic groups across different domains examined. Higher depressive symptoms were associated with faster cognitive decline for semantic memory for Black and Latino participants. The results suggest a particularly harmful association between depressive symptoms and cognition in certain racial/ethnic groups.

Associations between personality and psychological characteristics and cognitive outcomes among older adults.

Prior research has shown that some personality traits are associated with cognitive outcomes and may confirm risk or protection against cognitive decline. The present study expands on previous work to examine the association between a more comprehensive set of psychological characteristics and cognitive performance in a diverse cohort of older adults. We also examine whether controlling for brain atrophy influences the association between psychological characteristics and cognitive function. A total of 157 older adults completed a battery of psychological questionnaires (Openness to Experience, Conscientiousness, Agreeableness, Neuroticism, Extraversion, positive affect, negative affect-sadness, negative affect-anger, sense of purpose, loneliness, grit, and self-efficacy). Cognitive outcomes were measured across multiple domains: episodic memory, semantic memory, executive function, and spatial ability. Baseline brain (MRI) variables included gray matter, hippocampus, and total white matter hyperintensity volume. Parallel process, multilevel models yielded intercept (individual cognitive domain scores) and linear slope (global cognitive change) random effects for the cognitive outcomes. Positive affect (ß = 0.013, SE = 0.005, p = .004) and Openness (ß = 0.018, SE = 0.007, p = .009) were associated with less cognitive change, independent of baseline brain variables and covariates. Greater sadness predicted more cognitive decline when controlling for covariates, but not brain atrophy. A variety of psychological characteristics were associated with the cross-sectional measures of cognition. This study highlights the important impact of positive and negative affect on reducing or enhancing the risk of longitudinal cognitive decline. Such findings are especially important, given the available efficacious interventions that can improve affect. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Self-reported mid- to late-life physical and recreational activities: Associations with late-life cognition.

OBJECTIVE: Physical and recreational activities are behaviors that may modify risk of late-life cognitive decline. We sought to examine the role of retrospectively self-reported midlife (age 40) physical and recreational activity engagement - and self-reported change in these activities from age 40 to initial study visit - in predicting late-life cognition. METHOD: Data were obtained from 898 participants in a longitudinal study of cognitive aging in demographically and cognitively diverse older adults (Age: range = 49-93 years, M = 75, SD = 7.19). Self-reported physical and recreational activity participation at age 40 and at the initial study visit were quantified using the Life Experiences Assessment Form. Change in activities was modeled using latent change scores. Cognitive outcomes were obtained annually (range = 2-17 years) using the Spanish and English Neuropsychological Assessment Scales, which measure verbal episodic memory, semantic memory, visuospatial processing, and executive functioning. RESULTS: Physical activity engagement at age 40 was strongly associated with cognitive performance in all four domains at the initial visit and with global cognitive slope. However, change in physical activities after age 40 was not associated with cognitive outcomes. In contrast, recreational activity engagement - both at age 40 and change after 40 - was predictive of cognitive intercepts and slope. CONCLUSIONS: Retrospectively self-reported midlife physical and recreational activity engagement were strongly associated with late-life cognition - both level of performance and rate of future decline. However, the data suggest that maintenance of recreational activity engagement (e.g., writing, taking classes, reading) after age 40 is more strongly associated with late-life cognition than continued maintenance of physical activity levels.

A novel neuroimaging signature for ADRD risk stratification in the community.

INTRODUCTION: Early risk stratification for clinical dementia could lead to preventive therapies. We identified and validated a magnetic resonance imaging (MRI) signature for Alzheimer's disease (AD) and related dementias (ARDR). METHODS: An MRI ADRD signature was derived from cortical thickness maps in Framingham Heart Study (FHS) participants with AD dementia and matched controls. The signature was related to the risk of ADRD and cognitive function in FHS. Results were replicated in the University of California Davis Alzheimer's Disease Research Center (UCD-ADRC) cohort. RESULTS: Participants in the bottom quartile of the signature had more than three times increased risk for ADRD compared to those in the upper three quartiles (P < 0.001). Greater thickness in the signature was related to better general cognition (P < 0.01) and episodic memory (P = 0.01). Results replicated in UCD-ADRC. DISCUSSION: We identified a robust neuroimaging biomarker for persons at increased risk of ADRD. Other cohorts will further test the validity of this biomarker.

Spatial distributions of white matter hyperintensities on brain MRI: A pooled analysis of individual participant data from 11 memory clinic cohorts.

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.

Racial and Ethnic Differences in the Association between Depressive Symptoms and Cognitive Outcomes in Older Adults: Findings from KHANDLE and STAR.

INTRODUCTION: Depressive symptoms are associated with higher risk of dementia but how they impact cognition in diverse populations is unclear. METHODS: Asian, Black, LatinX, or White participants (n=2,227) in the Kaiser Healthy Aging and Diverse Life Experiences (age 65+) and the Study of Healthy Aging in African Americans (age 50+) underwent up to three waves of cognitive assessments over four years. Multilevel models stratified by race/ethnicity were used to examine whether depressive symptoms were associated with cognition or cognitive decline and whether associations differed by race/ethnicity. RESULTS: Higher depressive symptoms were associated with lower baseline verbal episodic memory scores (-0.06, 95%CI: -0.12, -0.01; -0.15, 95%CI: -0.25, -0.04), and faster decline annually in semantic memory (-0.04, 95%CI: -0.07, -0.01; -0.10, 95%CI: -0.15, -0.05) for Black and LatinX participants. Depressive symptoms were associated with lower baseline but not decline in executive function. DISCUSSION: Depressive symptoms were associated with worse cognitive domains, with some evidence of heterogeneity across racial/ethnic groups.

Multifactorial Modeling of Cognitive Trajectories Using an Advanced Regression Technique: Improving Our Understanding of Biomarkers and Modifiable Variables that Support Cognition.

Current research trends emphasize complex models of cognitive outcomes, with multiple, interacting predictors, including factors amenable to interventions toward sustaining healthy cognitive aging. Such models often require advanced analysis techniques. The article by Stark et al., 'Partial least squares regression analysis of Alzheimer's disease biomarkers, modifiable health variables, and cognitive change in older adults with mild cognitive impairment', uses partial least squares regression to examine the associations to memory and executive change of 29 biomarker and demographic variables. This commentary discusses the significance of their results and methods within the context of current research foci.

Vascular Risk Predicts Plasma Amyloid ß 42/40 Through Cerebral Amyloid Burden in Apolipoprotein E ε4 Carriers.

BACKGROUND: Understanding the neurobiological underpinnings between established multimodal dementia risk factors and noninvasive blood-based biomarkers may lead to greater precision and earlier identification of older adults at risk of accelerated decline and dementia. We examined whether key vascular and genetic risk impact the association between cerebral amyloid burden and plasma aß (amyloid ß) 42/40 in nondemented older adults. METHODS: We used nondemented older adults from the UCD-ADRC (University of California, Davis-Alzheimer's Disease Research Center) study (n=96) and Alzheimer's Disease Neuroimaging Initiative (n=104). Alzheimer's Disease Neuroimaging Initiative was examined as confirmatory study cohort. We followed a cross-sectional design and examined linear regression followed by mediation analyses. Vascular risk score was obtained as the sum of hypertension, diabetes, hyperlipidemia, coronary artery disease, and cerebrovascular disease. Apolipoprotein E (APOE) ε4+ risk was genotyped, and plasma aß42 and aß40 were assayed. Cerebral amyloid burden was quantified using Florbetapir-PET scans. Baseline age was included as a covariate in all models. RESULTS: Vascular risk significantly predicted cerebral amyloid burden in Alzheimer's Disease Neuroimaging Initiative but not in the UCD-ADRC cohort. Cerebral amyloid burden was associated with plasma aß 42/40 in both cohorts. Higher vascular risk increased cerebral amyloid burden was indirectly associated with reduced plasma aß 42/40 in Alzheimer's Disease Neuroimaging Initiative but not in UCD-ADRC cohort. However, when stratified by APOE ε4+ risk, we consistently observed this indirect relationship only in APOE ε4+ carriers across both cohorts. CONCLUSIONS: Vascular risk is indirectly associated with the level of plasma aß 42/40 via cerebral amyloid burden only in APOE ε4+ carriers. Nondemented older adults with genetic vulnerability to dementia and accelerated decline may benefit from careful monitoring of vascular risk factors directly associated with cerebral amyloid burden and indirectly with plasma aß 42/40.

Association of Early Adulthood Hypertension and Blood Pressure Change With Late-Life Neuroimaging Biomarkers.

Importance: The association between hypertension developed before midlife and late-life brain health is understudied and, because of the cardioprotective benefits of estrogen before menopause, may differ by sex. Objective: To assess the association of early adulthood hypertension and blood pressure (BP) change with late-life neuroimaging biomarkers and examine potential sex differences. Design, Setting, and Participants: This cohort study used data from the Study of Healthy Aging in African Americans (STAR) and Kaiser Healthy Aging and Diverse Life Experiences (KHANDLE) study, which were harmonized longitudinal cohorts of racially and ethnically diverse adults aged 50 years and older from the San Francisco Bay area and Sacramento Valley in California. The STAR was conducted from November 6, 2017, to November 5, 2021, and the KHANDLE study was conducted from April 27, 2017, to June 15, 2021. The current study included 427 participants from the KHANDLE and STAR studies who received health assessments between June 1, 1964, and March 31, 1985. Regional brain volumes and white matter (WM) integrity were measured via magnetic resonance imaging between June 1, 2017, and March 1, 2022. Exposures: Hypertension status (normotension, transition to hypertension, and hypertension) and BP change (last measure minus first measure) were assessed at 2 multiphasic health checkups (MHCs; 1964-1985) in early adulthood (ages 30-40 years). Main Outcomes and Measures: Regional brain volumes and WM integrity were measured using 3T magnetic resonance imaging and z standardized. General linear models adjusted for potential confounders (demographic characteristics and study [KHANDLE or STAR]) were used to assess the association of hypertension and BP change with neuroimaging biomarkers. Sex interactions were tested. Results: Among 427 participants, median (SD) ages were 28.9 (7.3) years at the first MHC, 40.3 (9.4) years at the last MHC, and 74.8 (8.0) years at neuroimaging. A total of 263 participants (61.6%) were female and 231 (54.1%) were Black. Overall, 191 participants (44.7%) had normotension, 68 (15.9%) transitioned to hypertension, and 168 (39.3%) had hypertension. Compared with participants who had normotension, those who had hypertension and those who transitioned to hypertension had smaller cerebral volumes (hypertension: ß = -0.26 [95% CI, -0.41 to -0.10]; transition to hypertension: ß = -0.23 [95% CI, -0.44 to -0.23]), with similar differences in cerebral gray matter volume (hypertension: ß = -0.32 [95% CI, -0.52 to -0.13]; transition to hypertension: ß = -0.30 [95% CI, -0.56 to -0.05]), frontal cortex volume (hypertension: ß = -0.43 [95% CI, -0.63 to -0.23]; transition to hypertension: ß = -0.27 [95% CI, -0.53 to 0]), and parietal cortex volume (hypertension: ß = -0.22 [95% CI, -0.42 to -0.02]; transition to hypertension: ß = -0.29 [95% CI, -0.56 to -0.02]). Participants with hypertension also had smaller hippocampal volume (ß = -0.22; 95% CI, -0.42 to -0.02), greater ventricular volumes (lateral ventricle: ß = 0.44 [95% CI, 0.25-0.63]; third ventricle: ß = 0.20 [95% CI, 0.01-0.39]), larger free water volume (ß = 0.35; 95% CI, 0.18-0.52), and lower fractional anisotropy (ß = -0.26; 95% CI, -0.45 to -0.08) than those who had normotension. Holding hypertension status constant, a 5-mm Hg increase in systolic BP was associated with smaller temporal cortex volume (ß = -0.03; 95% CI, -0.06 to -0.01), while a 5-mm Hg increase in diastolic BP was associated with smaller parietal cortex volume (ß = -0.06; 95% CI, -0.10 to -0.02). The negative association of hypertension and BP change with regional brain volumes appeared stronger in men than women for some regions. Conclusions and Relevance: In this cohort study, early adulthood hypertension and BP change were associated with late-life volumetric and WM differences implicated in neurodegeneration and dementia. Sex differences were observed for some brain regions whereby hypertension and increasing BP appeared more detrimental for men. These findings suggest that prevention and treatment of hypertension in early adulthood is important for late-life brain health, particularly among men.

Toward a statistical validation of brain signatures as robust measures of behavioral substrates.

The "brain signature of cognition" concept has garnered interest as a data-driven, exploratory approach to better understand key brain regions involved in specific cognitive functions, with the potential to maximally characterize brain substrates of behavioral outcomes. Previously we presented a method for computing signatures of episodic memory. However, to be a robust brain measure, the signature approach requires a rigorous validation of model performance across a variety of cohorts. Here we report validation results and provide an example of extending it to a second behavioral domain. In each of two discovery data cohorts, we derived regional brain gray matter thickness associations for two domains: neuropsychological and everyday cognition memory. We computed regional association to outcome in 40 randomly selected discovery subsets of size 400 in each cohort. We generated spatial overlap frequency maps and defined high-frequency regions as "consensus" signature masks. Using separate validation datasets, we evaluated replicability of cohort-based consensus model fits and explanatory power by comparing signature model fits with each other and with competing theory-based models. Spatial replications produced convergent consensus signature regions. Consensus signature model fits were highly correlated in 50 random subsets of each validation cohort, indicating high replicability. In comparisons over each full cohort, signature models outperformed other models. In this validation study, we produced signature models that replicated model fits to outcome and outperformed other commonly used measures. Signatures in two memory domains suggested strongly shared brain substrates. Robust brain signatures may therefore be achievable, yielding reliable and useful measures for modeling substrates of behavioral domains.