Brain Imaging Phenotypes Research Articles

Association of Visual Health With Depressive Symptoms and Brain Imaging Phenotypes Among Middle-Aged and Older Adults

Vision loss and depression are common conditions with major health implications. However, mechanisms of the association of visual health (across the full acuity spectrum) with depression remain unclear. To characterize the association between visual health and depression and investigate the association between depression and brain microstructure and macrostructure in subgroups divided by visual acuity. In the UK Biobank Study cohort, 114 583 volunteers were included at baseline from March to June 2006 to July 2010. Habitual distance visual acuity was examined using the logarithm of the minimum angle of resolution (LogMAR) characters. Depression was identified based on Patient Health Questionnaire (PHQ) or through an interview-based psychiatric diagnosis. Subgroup participants completed multimodal magnetic resonance imaging (MRI) of the brain and PHQ evaluation during the imaging visit after 2014. Data were analyzed from May 5 to August 9, 2022. Depression, depressive symptoms, and imaging-derived phenotypes from T1-weighted and diffusion MRI. Of the 114 583 participants from the UK Biobank Study, 62 401 (54.5%) were women, and the mean (SD) age was 56.8 (8.1) years (range, 39-72 years). A 1-line worse visual acuity (0.1 LogMAR increase) was associated with 5% higher odds of depression (odds ratio, 1.05 [95% CI, 1.04-1.07]) after adjustment for age, sex, race and ethnicity, Townsend index, educational qualifications, smoking, alcohol consumption, obesity, physical activity, history of hypertension, diabetes, hyperlipidemia, and family history of depression. Of the 7844 participants eligible for MRI analysis, there were linear associations between PHQ score and the left volume of gray matter in supracalcarine cortex (coefficient, 7.61 [95% CI, 3.90-11.31]) and mean isotropic volume fraction (ISOVF) in the right fornix (cres) and/or stria terminalis (coefficient, 0.003 [95% CI, 0.001-0.004]) after correction for multiple comparison. In addition, their association could be moderated by visual acuity, whereby increased PHQ score was associated with higher ISOVF levels only among those with poorer visual acuity (P = .02 for interaction). This study suggests an association between visual health and depression and that the diffusion characteristic of ISOVF in the fornix (cres) and/or stria terminalis is associated with depressive symptoms in participants with poorer visual acuity.

Brain Proteome-Wide Association Study Identifies Candidate Genes that Regulate Protein Abundance Associated with Post-Traumatic Stress Disorder.

Although previous genome-wide association studies (GWASs) on post-traumatic stress disorder (PTSD) have identified multiple risk loci, how these loci confer risk of PTSD remains unclear. Through the FUSION pipeline, we integrated two human brain proteome reference datasets (ROS/MAP and Banner) with the PTSD GWAS dataset, respectively, to conduct a proteome-wide association study (PWAS) analysis. Then two transcriptome reference weights (Rnaseq and Splicing) were applied to a transcriptome-wide association study (TWAS) analysis. Finally, the PWAS and TWAS results were investigated through brain imaging analysis. In the PWAS analysis, 8 and 13 candidate genes were identified in the ROS/MAP and Banner reference weight groups, respectively. Examples included ADK (pPWAS-ROS/MAP = 3.00 × 10−5) and C3orf18 (pPWAS-Banner = 7.07 × 10−31). Moreover, the TWAS also detected multiple candidate genes associated with PTSD in two different reference weight groups, including RIMS2 (pTWAS-Splicing = 3.84 × 10−2), CHMP1A (pTWAS-Rnaseq = 5.09 × 10−4), and SIRT5 (pTWAS-Splicing = 4.81 × 10−3). Further comparison of the PWAS and TWAS results in different populations detected the overlapping genes: MADD (pPWAS-Banner = 4.90 × 10−2, pTWAS-Splicing = 1.23 × 10−2) in the total population and GLO1(pPWAS-Banner = 4.89 × 10−3, pTWAS-Rnaseq = 1.41 × 10−3) in females. Brain imaging analysis revealed several different brain imaging phenotypes associated with MADD and GLO1 genes. Our study identified multiple candidate genes associated with PTSD in the proteome and transcriptome levels, which may provide new clues to the pathogenesis of PTSD.

Environmental neuroscience linking exposome to brain structure and function underlying cognition and behavior.

Individual differences in human brain structure, function, and behavior can be attributed to genetic variations, environmental exposures, and their interactions. Although genome-wide association studies have identified many genetic variants associated with brain imaging phenotypes, environmental exposures associated with these phenotypes remain largely unknown. Here, we propose that environmental neuroscience should pay more attention on exploring the associations between lifetime environmental exposures (exposome) and brain imaging phenotypes and identifying both cumulative environmental effects and their vulnerable age windows during the life course. Exposome-neuroimaging association studies face several challenges including the accurate measurement of the totality of environmental exposures varied in space and time, the highly correlated structure of the exposome, and the lack of standardized approaches for exposome-wide association studies. By agnostically scanning the effects of environmental exposures on brain imaging phenotypes and their interactions with genomic variations, exposome-neuroimaging association analyses will improve our understanding of causal factors associated with individual differences in brain structure and function as well as their relations with cognitive abilities and neuropsychiatric disorders.

Enhanced neuroimaging genetics using multi-view non-negative matrix factorization with sparsity and prior knowledge.

Neuroimaging genetics is a powerful approach to jointly explore genetic features with rich brain imaging phenotypes for neurodegenerative diseases. Conventional imaging genetics approaches based on canonical correlation analysis cannot accommodate multimodal inputs effectively and have limited interpretability. We propose a novel imaging genetics approach based on non-negative matrix factorization (NMF). By leveraging the parsimonious property known as topic modeling in multi-view NMF, we add sparsity constraints and prior information to identify a sparse set of biologically related features across modalities. Thus, our approach incorporates prior knowledge and improves multimodal integration capabilities and interpretability. We applied our algorithm to simulated and real imaging genetics datasets of Parkinson's disease (PD) for performance evaluation. Our algorithm could identify important associated features mapped to interpretable distinct topics more robustly than other methods. It revealed promising features of single-nucleotide polymorphisms and brain regions related to a subset of PD-related clinical scores in a few topics using a real imaging genetic dataset. The proposed imaging genetics approach can reveal novel associations between genetic and neuroimaging features to improve understanding of various neurodegenerative diseases.

Predictive classification of Alzheimer\u2019s disease using brain imaging and genetic data

For now, Alzheimer’s disease (AD) is incurable. But if it can be diagnosed early, the correct treatment can be used to delay the disease. Most of the existing research methods use single or multi-modal imaging features for prediction, relatively few studies combine brain imaging with genetic features for disease diagnosis. In order to accurately identify AD, healthy control (HC) and the two stages of mild cognitive impairment (MCI: early MCI, late MCI) combined with brain imaging and genetic characteristics, we proposed an integrated Fisher score and multi-modal multi-task feature selection research method. We learned first genetic features with Fisher score to perform dimensionality reduction in order to solve the problem of the large difference between the feature scales of genetic and brain imaging. Then we learned the potential related features of brain imaging and genetic data, and multiplied the selected features with the learned weight coefficients. Through the feature selection program, five imaging and five genetic features were selected to achieve an average classification accuracy of 98% for HC and AD, 82% for HC and EMCI, 86% for HC and LMCI, 80% for EMCI and LMCI, 88% for EMCI and AD, and 72% for LMCI and AD. Compared with only using imaging features, the classification accuracy has been improved to a certain extent, and a set of interrelated features of brain imaging phenotypes and genetic factors were selected.

Epigenome-wide association study of global cortical volumes in generation Scotland: Scottish family health study

ABSTRACT A complex interplay of genetic and environmental risk factors influence global brain structural alterations associated with brain health and disease. Epigenome-wide association studies (EWAS) of global brain imaging phenotypes have the potential to reveal the mechanisms of brain health and disease and can lead to better predictive analytics through the development of risk scores. We perform an EWAS of global brain volumes in Generation Scotland using peripherally measured whole blood DNA methylation (DNAm) from two assessments, (i) at baseline recruitment, ~6 years prior to MRI assessment (N = 672) and (ii) concurrent with MRI assessment (N=565). Four CpGs at baseline were associated with global cerebral white matter, total grey matter, and whole-brain volume (Bonferroni p≤7.41×10−8, βrange = −1.46x10−6 to 9.59 × 10−7). These CpGs were annotated to genes implicated in brain-related traits, including psychiatric disorders, development, and ageing. We did not find significant associations in the meta-analysis of the EWAS of the two sets concurrent with imaging at the corrected level. These findings reveal global brain structural changes associated with DNAm measured ~6 years previously, indicating a potential role of early DNAm modifications in brain structure. Although concurrent DNAm was not associated with global brain structure, the nominally significant findings identified here present a rationale for future investigation of associations between DNA methylation and structural brain phenotypes in larger population-based samples.

Associating brain imaging phenotypes and genetic in Alzheimer's disease via JSCCA approach with autocorrelation constraints.

Imaging genetics research can explore the potential correlation between imaging and genomics. Most association analysis methods cannot effectively use the prior knowledge of the original data. In this respect, we add the prior knowledge of each original data to mine more effective biomarkers. The study of imaging genetics based on the sparse canonical correlation analysis (SCCA) is helpful to mine the potential biomarkers of neurological diseases. To improve the performance and interpretability of SCCA, we proposed a penalty method based on the autocorrelation matrix for discovering the possible biological mechanism between single nucleotide polymorphisms (SNP) variations and brain regions changes of Alzheimer's disease (AD). The addition of the penalty allows the proposed algorithm to analyze the correlation between different modal features. The proposed algorithm obtains more biologically interpretable ROIs and SNPs that are significantly related to AD, which has better anti-noise performance. Compared with other SCCA-based algorithms (JCB-SCCA, JSNMNMF), the proposed algorithm can still maintain a stronger correlation with ground truth even when the noise is larger. Then, we put the regions of interest (ROI) selected by the three algorithms into the SVM classifier. The proposed algorithm has higher classification accuracy. Also, we use ridge regression with SNPs selected by three algorithms and four AD risk ROIs. The proposed algorithm has a smaller root mean square error (RMSE). It shows that proposed algorithm has a good ability in association recognition and feature selection. Furthermore, it selects important features more stably, improving the clinical diagnosis of new potential biomarkers.

Exome sequencing and analysis of 454,787 UK Biobank participants

A major goal in human genetics is to use natural variation to understand the phenotypic consequences of altering each protein-coding gene in the genome. Here we used exome sequencing1 to explore protein-altering variants and their consequences in 454,787 participants in the UK Biobank study2. We identified 12 million coding variants, including around 1 million loss-of-function and around 1.8 million deleterious missense variants. When these were tested for association with 3,994 health-related traits, we found 564 genes with trait associations at P ≤ 2.18 × 10−11. Rare variant associations were enriched in loci from genome-wide association studies (GWAS), but most (91%) were independent of common variant signals. We discovered several risk-increasing associations with traits related to liver disease, eye disease and cancer, among others, as well as risk-lowering associations for hypertension (SLC9A3R2), diabetes (MAP3K15, FAM234A) and asthma (SLC27A3). Six genes were associated with brain imaging phenotypes, including two involved in neural development (GBE1, PLD1). Of the signals available and powered for replication in an independent cohort, 81% were confirmed; furthermore, association signals were generally consistent across individuals of European, Asian and African ancestry. We illustrate the ability of exome sequencing to identify gene–trait associations, elucidate gene function and pinpoint effector genes that underlie GWAS signals at scale.

Phenotypic and genetic associations between anhedonia and brain structure in UK Biobank

Anhedonia is a core symptom of multiple psychiatric disorders and has been associated with alterations in brain structure. Genome-wide association studies suggest that anhedonia is heritable, with a polygenic architecture, but few studies have explored the association between genetic loading for anhedonia—indexed by polygenic risk scores for anhedonia (PRS-anhedonia)—and structural brain imaging phenotypes. Here, we investigated how anhedonia and PRS-anhedonia were associated with brain structure within the UK Biobank cohort. Brain measures (including total grey/white matter volumes, subcortical volumes, cortical thickness (CT) and white matter integrity) were analysed using linear mixed models in relation to anhedonia and PRS-anhedonia in 19,592 participants (9225 males; mean age = 62.6 years, SD = 7.44). We found that state anhedonia was significantly associated with reduced total grey matter volume (GMV); increased total white matter volume (WMV); smaller volumes in thalamus and nucleus accumbens; reduced CT within the paracentral cortex, the opercular part of inferior frontal gyrus, precentral cortex, insula and rostral anterior cingulate cortex; and poorer integrity of many white matter tracts. PRS-anhedonia was associated with reduced total GMV; increased total WMV; reduced white matter integrity; and reduced CT within the parahippocampal cortex, superior temporal gyrus and insula. Overall, both state anhedonia and PRS-anhedonia were associated with individual differences in multiple brain structures, including within reward-related circuits. These associations may represent vulnerability markers for psychopathology relevant to a range of psychiatric disorders.

Detecting genetic associations with brain imaging phenotypes in Alzheimer's disease via a novel structured KCCA approach.

Neuroimaging genetics has become an important research topic since it can reveal complex associations between genetic variants (i.e. single nucleotide polymorphisms (SNPs) and the structures or functions of the human brain. However, existing kernel mapping is difficult to directly use the sparse representation method in the kernel feature space, which makes it difficult for most existing sparse canonical correlation analysis (SCCA) methods to be directly promoted in the kernel feature space. To bridge this gap, we adopt a novel alternating projected gradient approach, gradient KCCA (gradKCCA) model to develop a powerful model for exploring the intrinsic associations among genetic markers, imaging quantitative traits (QTs) of interest. Specifically, this model solves kernel canonical correlation (KCCA) with an additional constraint that projection directions have pre-images in the original data space, a sparsity-inducing variant of the model is achieved through controlling the [Formula: see text]-norm of the preimages of the projection directions. We evaluate this model using Alzheimer's disease Neuroimaging Initiative (ADNI) cohort to discover the relationships among SNPs from Alzheimer's disease (AD) risk gene APOE, imaging QTs extracted from structural magnetic resonance imaging (MRI) scans. Our results show that the algorithm not only outperforms the traditional KCCA method in terms of Root Mean Square Error (RMSE) and Correlation Coefficient (CC) but also identify the meaningful and relevant biomarkers of SNPs (e.g. rs157594 and rs405697), which are positively related to right Postcentral and right SupraMarginal brain regions in this study. Empirical results indicate its promising capability in revealing biologically meaningful neuroimaging genetics associations and improving the disease-related mechanistic understanding of AD.

An expanded set of genome-wide association studies of brain imaging phenotypes in UK Biobank.

UK Biobank is a major prospective epidemiological study, including multimodal brain imaging, genetics and ongoing health outcomes. Previously, we published genome-wide associations of 3,144 brain imaging-derived phenotypes, with a discovery sample of 8,428 subjects. Here we present a new open resource of GWAS summary statistics, using the 2020 data release, almost tripling the discovery sample size. We now include the X chromosome, and new classes of image derived phenotypes (subcortical volumes and tissue contrast). Previously we had found 148 replicated clusters of associations between genetic variants and imaging phenotypes; here we find 692, including 12 on the X chromosome. We describe some of the newly found associations, focussing on the X chromosome and autosomal associations involving the new classes of imaging-derived phenotypes. Our novel associations implicate e.g. pathways involved in the rare X-linked syndrome STAR (syndactyly, telecanthus and anogenital and renal malformations), Alzheimer’s disease and mitochondrial disorders.

Hyper-graph based Adaptive Sparse Multi-view Canonical Correlation Analysis with Application to Neuroimaging Genetics Study of Alzheimer’s Disease

Neuroimaging genetics has gained more and more attention on account of detecting the linkage between the brain imaging phenotypes (i.e., regional volumetric measures) and the genetic variants (i.e., Single Nucleotide Polymorphism (SNP) in Alzheimer’s disease (AD)). To overcome the problem of sparse multi-view canonical correlation (SMCCA) ‘unfair combination of pairwise convariance’, introducing adaptive weights when combining pairwise covariances, a novel formulation of SMCCA, named adaptive SMCCA. In this paper, we integrate multi-modal genomic data from postmortem AD brain and proposed a hyper-graph based sparse multi-view canonical correlation analysis (HGSMCCA) method to extract the most correlated multi-modal biomarkers. Specifically, we utilized the adaptive sparse multi-view canonical correlation analysis (AdsSMCCA) framework, consider the benefit of hyper-graph-based regularization term into consideration that will contribute to the selection of more discriminative biomarkers. We propose a hyper-graph optimization strategy based on the adaptive SMCCA model, and we apply it to neuroimaging genetics data. All these results demonstrate the capability of HGSMCCA in identifying diagnostically genotype-phenotype patterns.

Cross‐sex shifts in two brain imaging phenotypes and their relation to polygenic scores for same‐sex sexual behavior: A study of 18,645 individuals from the UK Biobank

Genetic and hormonal factors have been suggested to influence human sexual orientation. Previous studied proposed brain differences related to sexual orientation and that these follow cross‐sex shifted patterns. However, the neurobiological correlates of sexual orientation and how genetic factors relate to brain structural variation remains largely unexplored. Using the largest neuroimaging‐genetics dataset available on same‐sex sexual behavior (SSB) (n = 18,645), we employed a data‐driven multivariate classification algorithm (PLS) on magnetic resonance imaging data from two imaging modalities to extract brain covariance patterns related to sex. Through analyses of latent variables, we tested for SSB‐related cross‐sex shifts in such patterns. Using genotype data, polygenic scores reflecting the genetic predisposition for SSB were computed and tested for associations with neuroimaging outcomes. Patterns important for classifying between males and females were less pronounced in non‐heterosexuals. Predominantly in non‐heterosexual females, multivariate brain patterns as represented by latent variables were shifted toward the opposite sex. Complementary univariate analyses revealed region specific SSB‐related differences in both males and females. Polygenic scores for SSB were associated with volume of lateral occipital and temporo‐occipital cortices. The present large‐scale study demonstrates multivariate neuroanatomical correlates of SSB, and tentatively suggests that genetic factors related to SSB may contribute to structural variation in certain brain structures. These findings support a neurobiological basis to the differences in human sexuality.

Multivariate genome-wide analysis of education, socioeconomic status and brain phenome.

Socioeconomic status (SES) and education (EDU) are phenotypically associated with psychiatric disorders and behaviors. It remains unclear how these associations influence genetic risk for psychopathology, psychosocial factors, and EDU/SES individually. Using information from >1 million individuals, we conditioned the genetic risk for psychiatric disorders, personality traits, brain imaging phenotypes, and externalizing behaviors with genome-wide data for EDU/SES. Accounting for EDU/SES significantly affected the observed heritability of psychiatric traits ranging from 2.44% h2 decrease for bipolar disorder to 14.2% h2 decrease for Tourette syndrome. Neuroticism h2 significantly increased by 20.23% after conditioning with SES. After EDU/SES conditioning, neuronal cell-types were identified for risky behavior (excitatory), major depression (inhibitory), schizophrenia (excitatory and GABAergic), and bipolar disorder (excitatory). Conditioning with EDU/SES also revealed unidirectional causality between brain morphology, psychopathology, and psychosocial factors. Our results indicate that genetic discoveries related to psychopathology and psychosocial factors may be limited by genetic overlap with EDU/SES.

Structural Brain Imaging Phenotypes of Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD) Found by Hierarchical Clustering.

A hierarchical clustering algorithm was applied to magnetic resonance images (MRI) of a cohort of 751 subjects having a mild cognitive impairment (MCI), 282 subjects having received Alzheimer's disease (AD) diagnosis, and 428 normal controls (NC). MRIs were preprocessed to gray matter density maps and registered to a stereotactic space. By first rendering the gray matter density maps comparable by regressing out age, gender, and years of education, and then performing the hierarchical clustering, we found clusters displaying structural features of typical AD, cortically-driven atypical AD, limbic-predominant AD, and early-onset AD (EOAD). Among these clusters, EOAD subjects displayed marked cortical gray matter atrophy and atrophy of the precuneus. Furthermore, EOAD subjects had the highest progression rates as measured with ADAS slopes during the longitudinal follow-up of 36 months. Striking heterogeneities in brain atrophy patterns were observed with MCI subjects. We found clusters of stable MCI, clusters of diffuse brain atrophy with fast progression, and MCI subjects displaying similar atrophy patterns as the typical or atypical AD subjects. Bidirectional differences in structural phenotypes were found with MCI subjects involving the anterior cerebellum and the frontal cortex. The diversity of the MCI subjects suggests that the structural phenotypes of MCI subjects would deserve a more detailed investigation with a significantly larger cohort. Our results demonstrate that the hierarchical agglomerative clustering method is an efficient tool in dividing a cohort of subjects with gray matter atrophy into coherent clusters manifesting different structural phenotypes.

Associating Multi-Modal Brain Imaging Phenotypes and Genetic Risk Factors via a Dirty Multi-Task Learning Method

Brain imaging genetics becomes more and more important in brain science, which integrates genetic variations and brain structures or functions to study the genetic basis of brain disorders. The multi-modal imaging data collected by different technologies, measuring the same brain distinctly, might carry complementary information. Unfortunately, we do not know the extent to which the phenotypic variance is shared among multiple imaging modalities, which further might trace back to the complex genetic mechanism. In this paper, we propose a novel dirty multi-task sparse canonical correlation analysis (SCCA) to study imaging genetic problems with multi-modal brain imaging quantitative traits (QTs) involved. The proposed method takes advantages of the multi-task learning and parameter decomposition. It can not only identify the shared imaging QTs and genetic loci across multiple modalities, but also identify the modality-specific imaging QTs and genetic loci, exhibiting a flexible capability of identifying complex multi-SNP-multi-QT associations. Using the state-of-the-art multi-view SCCA and multi-task SCCA, the proposed method shows better or comparable canonical correlation coefficients and canonical weights on both synthetic and real neuroimaging genetic data. In addition, the identified modality-consistent biomarkers, as well as the modality-specific biomarkers, provide meaningful and interesting information, demonstrating the dirty multi-task SCCA could be a powerful alternative method in multi-modal brain imaging genetics.

Discovering correlates of age-related decline in a healthy late-midlife male birth cohort.

Studies exploring age-related brain and cognitive change have identified substantial heterogeneity among individuals, but the underlying reasons for the differential trajectories remain largely unknown. We investigated cross-sectional and longitudinal associations between brain-imaging phenotypes (IDPs) and cognitive ability, and how these relations may be modified by common risk and protective factors. Participants were recruited from the 1953 Danish Male Birth Cohort (N=123), a longitudinal study of cognitive and brain ageing. Childhood IQ and socio-demographic factors are available for these participants who have been assessed regularly on multiple IDPs and behavioural factors in midlife. Using Pearson correlations and canonical correlation analysis (CCA), we explored the relation between 454 IDPs and 114 behavioural variables. CCA identified a single mode of population covariation coupling cross-subject longitudinal changes in brain structure to changes in cognitive performance and to a range of age-related covariates (r=0.92, Pcorrected < 0.001). Specifically, this CCA-mode indicated that; decreases in IQ and speed assessed tasks, higher rates of familial myocardial infarct, less physical activity, and poorer mental health are associated with larger decreases in whole brain grey matter and white matter. We found no evidence supporting the role of baseline scores as predictors of impending brain and behavioural change in late-midlife.

Multi-trait Genome-Wide Analyses of the Brain Imaging Phenotypes in UK Biobank.

Many genetic variants identified in genome-wide association studies (GWAS) are associated with multiple, sometimes seemingly unrelated, traits. This motivates multi-trait association analyses, which have successfully identified novel associated loci for many complex diseases. While appealing, most existing methods focus on analyzing a relatively small number of traits, and may yield inflated Type1 error rates when a large number of traits need to be analyzed jointly. As deep phenotyping data are becoming rapidly available, we develop a novel method, referred to as aMAT (adaptive multi-trait association test), for multi-trait analysis of any number of traits. We applied aMAT to GWAS summary statistics for a set of 58 volumetric imaging derived phenotypes from the UK Biobank. aMAT had a genomic inflation factor of 1.04, indicating the Type1 error rate was well controlled. More important, aMAT identified 24 distinct risk loci, 13 of which were ignored by standard GWAS. In comparison, the competing methods either had a suspicious genomic inflation factor or identified much fewer risk loci. Finally, four additional sets of traits have been analyzed and provided similar conclusions.

Association of SBP and BMI with cognitive and structural brain phenotypes in UK Biobank.

To test for associations between SBP and BMI, with domain-specific cognitive abilities and examine which brain structural phenotypes mediate those associations. Using cross-sectional UK Biobank data (final N = 28 412), we examined SBP/BMI vs. cognitive test scores of pairs-matching, matrix completion, trail making test A/B, digit symbol substitution, verbal-numerical reasoning, tower rearranging and simple reaction time. We adjusted for potential confounders of age, sex, deprivation, medication, apolipoprotein e4 genotype, smoking, population stratification and genotypic array. We tested for mediation via multiple structural brain imaging phenotypes and corrected for multiple testing with false discovery rate. We found positive associations for higher BMI with worse reaction time, reasoning, tower rearranging and matrix completion tasks by 0.024-0.067 SDs per BMI SD (all P < 0.001). Higher SBP was associated with worse reasoning (0.034 SDs) and matrix completion scores (-0.024 SDs; both P < 0.001). Both BMI and SBP were associated with multiple brain structural metrics including total grey/white matter volumes, frontal lobe volumes, white matter tract integrity and white matter hyperintensity volumes: specific metrics mediated around one-third of the associations with cognition. Our findings add to the body of evidence that addressing cardiovascular risk factors may also preserve cognitive function, via specific aspects of brain structure.

Eating a Weekly Serving of Walnuts Relates to Beneficial Brain Imaging Phenotypes in a Cohort at Increased Risk of Alzheimer’s Disease

Abstract Objectives There is increasing evidence on the brain benefits of nut consumption. Magnetic resonance imaging (MRI) enables the detection of brain changes associated with neurodegenerative and vascular diseases. In middle-aged cognitively unimpaired subjects at increased risk of Alzheimer's disease (AD), we searched for cross-sectional associations between nut consumption and MRI-assessed brain phenotypes, including white matter hyperintensities (WMH, a marker of cerebral small vessel disease that confers increased risk of AD and stroke) and topographic patterns of gray matter volume (GMv). Methods We performed high-resolution structural MRI in 382 participants from the ALFA study (ALzheimer and FAmilies) cohort, which is enriched by family history of sporadic AD and APOE-ε4 carriership, the most prevalent genetic risk factor for AD. We assessed nut consumption by a food-frequency questionnaire containing five items related to nuts. WMH volume was normalized by intracranial volume (TIV) and rank-transformed. For WMH, we conducted univariate models with two factors: nut consumption (&amp;lt;1 and ≥1 serving/week) and being APOE-ε4 homozygote (yes/no), and their interaction, adjusting for age, gender, hypertension, hypercholesterolemia, and adherence to Mediterranean Diet. We also explored whether nut consumption related to differences in GMv using a voxel-based morphometry analysis corrected by age, gender, number of APOE-ε4 alleles, hypertension, hypercholesterolemia, adherence to Mediterranean Diet, and TIV. Results 187 participants reported nut consumption of ≥1 serving/week, 148 of whom disclosed walnut consumption. Nut (or walnut) consumption of ≥1 serving/week related to a significantly lower WMH volume (P ≤ 0.035, both). We found no statistically significant nut × APOE-ε4 interactions. Participants reporting consumption of ≥1 walnut serving/week showed significantly greater GMv in areas including the anterior/middle cingulate cortex, which is relevant for cognition and has been associated with successful aging. Conclusions Nut (in particular walnut) consumption relates to beneficial phenotypes of both cerebral vasculature and regional GMv. Funding Sources Instituto de Salud Carlos III, Spain; “la Caixa” Foundation; California Walnut Commission.