Structural Brain Measures Research Articles

Mutant Huntingtin Drives Development of an Advantageous Brain Early in Life: Evidence in Support of Antagonistic Pleiotropy.

Huntington's disease (HD) is a neurodegenerative disease caused by a triplet repeat expansion within the gene huntingtin (HTT). Antagonistic pleiotropy is a theory of aging that posits that some genes, facilitating individual fitness early in life through adaptive evolutionary changes, also augment detrimental aging-related processes. Antagonistic pleiotropy theory may explain a positive evolutionary pressure toward functionally advantageous brain development that is vulnerable to rapid degeneration. The current study investigated antagonistic pleiotropy in HD using a years-to-onset paradigm in a unique sample of children and young adults at risk for HD. Cognitive, behavioral, motor, and brain structural measures from premanifest gene-expanded (n = 79) and gene nonexpanded (n = 112) participants (6-21 years) in the Kids-HD study were examined. All measures in the gene-expanded group were modeled using a mixed-effects regression approach to assess years-to-onset-based changes while controlling for normal growth. Simultaneously, structure-function associations were also examined. Decades from motor onset, gene-expanded participants showed significantly better cognitive, behavioral, and motor scores versus gene nonexpanded controls, along with larger cerebral volumes and cortical features. After this initial peak, a prolonged deterioration was observed in both functional and structural measures. Far from onset, brain measures were positively correlated with functional measures, supporting the view that functional advantages were mediated by structural differences. Mutant HTT may drive the development of a larger than normal brain that subserves superior early-life function. These findings support the antagonistic pleiotropy theory of HTT in HD, where this gene drives early advantage followed by accelerated aging processes. ANN NEUROL 2024;96:1006-1019.

Family history of dementia and brain health in childhood and middle age: a prospective community-based study.

We aimed to determine the association of family history of dementia with structural brain measures and cognitive performance in childhood and mid-life adulthood. We studied 1,259 parents (mean age: 47.3 years, range 31.9-67.4) and 866 of their children (mean age [range] at brain MRI: 9.9 years [8.8-11.9], and for cognition: 13.5 years [12.6-15.8]) of the population-based Generation R Study. Parents filled in a questionnaire on family history, and both parents and children underwent cognitive assessment and neuroimaging. Of all participants, 109 parents (8.6%) reported a parental family history of dementia and 73 children (8.4%) had a grandparental history of dementia with mean age of dementia diagnosis in those affected 75 years (± 7.3). We observed no associations of dementia family history with cognitive ability in either parents or their children, except for worse Purdue pegboard in parents with a parental history of dementia, compared to those without (mean difference [95%CI]: -1.23 [-2.15; -0.31], test range: 21-52). In parents and children, neuroimaging measures did not differ significantly by family history. Results did not depend on age, sex, and APOE genotype. Family history of dementia was associated with worse manual dexterity in mid-life adulthood, but not with any other measures of cognitive ability or subclinical brain health in childhood and mid-life. These findings suggest that the association of family history with dementia risk is due chiefly to neurodegenerative rather than neurodevelopmental processes, and might first present with reduced motor skills.

Childhood adversity, stress reactivity, and structural brain measures in stress-related/neurodevelopmental disorders, and their comorbidity: A large transdiagnostic cross-sectional study.

Childhood adversity (CA), including childhood adverse life events, increases the risk for development of psychiatric disorders later in life. Both CA and psychiatric disorders are associated with structural brain changes and dysfunctional hypothalamic-pituitary-adrenal-axis. However, many studies investigated single diagnostic and single regions of interest of the brain, and did not take stress reactivity into account. We investigated associations of CA and cortisol levels with gray matter volume and cortical thickness, in a whole-brain manner. Primary analysis constituted of a transdiagnostic approach, followed by a moderation analysis to investigate the influence of diagnosis. Patients with stress-related and/or neurodevelopmental disorders and matched healthy controls underwent an magnetic resonance imaging scan, next to assessing hair cortisol levels and CA/life events. CA was reported by 62-72% of the patients versus 33% of the controls. Primary transdiagnostic linear regression analyses revealed that CA was not associated with gray matter volume, while childhood life events were associated with lower right thalamic volume. Hair cortisol was not associated with any lobe volume. None of the associations were moderated by diagnosis. In conclusion, CA is a risk factor that needs to be taken into account when investigating psychiatric disorders. Yet the relationship with structural brain changes and stress reactivity is less clear than postulated on the basis of more seed-based studies.

Neurobiological correlates of comorbidity in disorders across the affective disorders-psychosis spectrum

Disorders across the affective disorders-psychosis spectrum such as major depressive disorder (MDD), bipolar disorder (BD), schizoaffective disorder (SCA), and schizophrenia (SCZ), have overlapping symptomatology and high comorbidity rates with other mental disorders. So far, however, it is largely unclear why some of the patients develop comorbidities. In particular, the specific genetic architecture of comorbidity and its relationship with brain structure remain poorly understood. Therefore, we performed systematic analyses of clinical, genetics and brain structural measures to gain further insights into the neurobiological correlates of mental disorder’s comorbidity. We investigated a sub-sample of the Marburg/Münster Cohort Study (MACS), comprising DSM-IV-TR diagnosed patients with a single disorder in the affective disorders-psychosis spectrum (SD, n=470, MDD; BD; SCA; SCZ), with additional mental disorder’s comorbidities (COM, n=310), and healthy controls (HC, n=649). We investigated group differences regarding a) the global severity index (based on SCL90-R), b) a cross-disorder polygenic risk score (PRS) calculated with PRS-continuous shrinkage (PRS-CS) using the summary statistics of a large genome-wide association study across mental disorders, and c) whole brain grey matter volume (GMV). The SCL90-R score significantly differed between groups (COM>SD>HC). While SD and COM did not differ in cross-disorder PRS and GMV, SD and COM versus HC displayed increased cross-disorder PRS and decreased GMV in the bilateral insula, the left middle temporal, the left inferior parietal, and several frontal gyri. Our results thus suggest that disorders in the affective disorders-psychosis spectrum with or without additional comorbidities differ in self-reported clinical data, but not on genetic or brain structural levels.

Quantifying brain development in the HEALthy Brain and Child Development (HBCD) Study: The magnetic resonance imaging and spectroscopy protocol

The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. The acquisition of multimodal magnetic resonance-based brain development data is central to the study’s core protocol. However, application of Magnetic Resonance Imaging (MRI) methods in this population is complicated by technical challenges and difficulties of imaging in early life. Overcoming these challenges requires an innovative and harmonized approach, combining age-appropriate acquisition protocols together with specialized pediatric neuroimaging strategies. The HBCD MRI Working Group aimed to establish a core acquisition protocol for all 27 HBCD Study recruitment sites to measure brain structure, function, microstructure, and metabolites. Acquisition parameters of individual modalities have been matched across MRI scanner platforms for harmonized acquisitions and state-of-the-art technologies are employed to enable faster and motion-robust imaging. Here, we provide an overview of the HBCD MRI protocol, including decisions of individual modalities and preliminary data. The result will be an unparalleled resource for examining early neurodevelopment which enables the larger scientific community to assess normative trajectories from birth through childhood and to examine the genetic, biological, and environmental factors that help shape the developing brain.

A - 1 Informant Report of Practical Judgment Is Associated with Mild Cognitive Impairment and Measures of Brain Structure in a Nondemented Sample of Older Adults

Abstract Objective Given the potential risks associated with compromised judgment in older adulthood, we examined whether informant-reported practical judgment abilities were associated with cognitive impairment and measures of brain structure. Method 97 community-dwelling older adults with informants (Mage = 74.64 ± 7.42, Medu = 15.76 ± 2.42, 74%female, 92%white) underwent neuropsychological evaluation, including the Test of Practical Judgment-Informant (TOP-J-INF), a 15-item informant-reported scale of judgment ability. Participants were diagnosed with mild cognitive impairment (MCI; n = 33) or as neuropsychologically unimpaired (n = 64). Participants completed an MRI study including T1-weighted image processed with FreeSurfer software (v 6.0.0) to obtain estimates of gray matter volume and cortical thickness (parcellated with the Desikan-Killiany atlas). Logistic and linear models analyzed the association of TOP-J-INF with MCI diagnosis and brain volume/thickness (in separate models). To better support the exploratory nature of this analysis of associations with structural brain variables, we did not apply correction for multiple comparisons. Results Each additional point on the TOP-J-INF increased the odds of MCI diagnosis by 16.8% (OR = 1.168, p = 0.004). Worse judgment was associated with reduced brain volume in the right parahippocampal gyrus (β = −11.688, p = 0.032), superior temporal gyrus (β = −51.330, p = 0.040), rostral anterior cingulate cortex (β = −25.704, p = 0.004); and reduced thickness of the left transverse temporal gyrus (β = −0.010, p = 0.041). Conclusions To our knowledge, this is the first study to investigate the relationship between informant report of judgment and measures of brain structure, providing support that informant concerns about a loved one’s compromised judgment is associated with volume/thickness reduction in key brain regions. Assessing practical judgment abilities via informants could provide critical clinical information when working with vulnerable older individuals.

Prediction of depressive symptoms severity based on sleep quality, anxiety, and gray matter volume: a generalizable machine learning approach across three datasets

Depressive symptoms are rising in the general population, but their associated factors are unclear. Although the link between sleep disturbances and depressive symptoms severity (DSS) is reported, the predictive role of sleep on DSS and the impact of anxiety and the brain on their relationship remained obscure. Using three population-based datasets (N=1813), we trained the machine learning models in the primary dataset (N=1101) to assess the predictive role of sleep quality, anxiety problems, and brain structural (and functional) measurements on DSS, then we tested our models' performance in two independent datasets (N=378, N=334) to test the generalizability of our findings. Furthermore, we applied our model to a smaller longitudinal subsample (N=66). In addition, we performed a mediation analysis to identify the role of anxiety and brain measurements on the sleep quality and DSS association. Sleep quality could predict individual DSS (r=0.43, R2=0.18, rMSE=2.73), and adding anxiety, contrary to brain measurements, strengthened its prediction performance (r=0.67, R2=0.45, rMSE=2.25). Importantly, out-of-cohort validations in other cross-sectional datasets and a longitudinal subsample provided robust similar results. Furthermore, anxiety scores, contrary to brain measurements, mediated the association between sleep quality and DSS. Poor sleep quality could predict DSS at the individual subject level across three datasets. Anxiety scores not only increased the predictive model's performance but also mediated the link between sleep quality and DSS. The study is supported by Helmholtz Imaging Platform grant (NimRLS, ZTI-PF-4-010), the Deutsche Forschungsgemeinschaft (DFG, GE 2835/2-1, GE 2835/4-1), the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)-Project-ID 431549029-SFB 1451, the programme "Profilbildung 2020" (grant no. PROFILNRW-2020-107-A), an initiative of the Ministry of Culture and Science of the State of Northrhine Westphalia.

Cerebrovascular hemodynamics association with brain structure and function in Multiple Sclerosis

BackgroundVascular risk factors seem to contribute to disease progression in Multiple Sclerosis (MS), but the mechanistic connection between vascular risk and MS is unknown. Understanding cerebrovascular hemodynamics (CVH) in MS may help advance our understanding of the link between vascular risk and MS. ObjectivesExamine the relationship between CVH [dynamic cerebral autoregulation (dCA) and vasoreactivity (VR)] and brain structure (MRI) and function (cognition, and gait) in individuals with MS. MethodsTranscranial Doppler ultrasound (TCD) was utilized to assess two key markers of CVH: dCA and VR. dCA (reported as phase and gain) is calculated from the spontaneous blood pressure and flow velocity oscillations. VR is calculated as the slope of change in cerebral blood flow velocity in response to end-tidal CO2. Global gray matter (GM), white matter (WM), WM hyperintensity (WMH) volumes and WM lesion counts were measured from brain MRI. All participants underwent detailed cognitive and gait assessments. ResultsEighty participants were included (age 44 ± 11, 26 % male); 75 had relapsing-remitting MS (94 %), with disease duration of 8 (11) years [median (IQR)] since MS diagnosis and an Expanded Disability Status Scale (EDSS) of 2.0 (4.0). Higher phase (better dCA) was associated with greater GM volume, lower WHM burden and higher cognitive scores in the memory and global cognitive domains (all P values <0.05). There was no relationship between CVH and gait speed in our study participants. There was no relationship between VR and any measures of brain structure and function. ConclusionsMore efficient cerebral autoregulation is associated with better brain structure (larger GM and lower WMH volumes) and function (cognition, but not gait) in patients with MS.

Association of hemoglobin with plasma neurofilament light and white matter hyperintensities in Alzheimer's disease continuum

ObjectiveThis study aimed to investigate the association of hemoglobin (Hb) with axonal injury marker plasma neurofilament light (PNFL) and brain structure measurements in the Alzheimer's disease (AD) continuum. MethodsThe data used in this study were collected from the Alzheimer's Disease Neuroimaging Initiative database. Participants with cognitively normal, mild cognitive impairment, and mild dementia were included in the data analyses. All participants had available data on blood tests, PNFL levels, neuropsychological assessments, brain structure measurements (including volumes of white matter hyperintensities [WMH], hippocampus, gray matter, and total brain), and Aβ positron emission tomography standardized uptake value ratio (SUVR) at baseline. Aβ-positive was defined as SUVR threshold value > 1.11. Linear regression, restricted cubic spline, and causal mediation analyses were conducted to investigate the association of Hb concentration with PNFL levels and brain structure measurements. Stratified analyses were also employed to evaluate the association between Hb concentration and PNFL levels across different APOE genotypes and sex. ResultsIn the Aβ-positive group, Hb concentration was associated with PNFL levels (β = −0.022, p = 0.002). Stratified analyses suggested an association between Hb concentration and PNFL in APOE ɛ4 carriers (β = −0.031, p < 0.001) and males (β = −0.030, p < 0.001) but not in non-carriers and females (p > 0.05). Hb concentration was also associated with WMH volume (β = −0.04, p = 0.028), especially in APOE ɛ4 carriers, with mediation analysis revealing that PNFL mediated the association between Hb concentration and WMH volume. The association of Hb concentration with other brain structure measurements was minimal. ConclusionIn the AD continuum, Hb was associated with axonal injury marker PNFL and WMH volume, particularly in APOE ɛ4 carriers.

Stacking models of brain dynamics to improve prediction of subject traits in fMRI

Abstract Beyond structural and time-averaged functional connectivity brain measures, modelling the way brain activity dynamically unfolds can add important information to our understanding and characterisation of individual cognitive traits. One approach to leveraging this information is to extract features from models of brain network dynamics to predict individual traits. However, these predictions are susceptible to variability due to factors such as variation in model estimation induced by the choice of hyperparameters. We suggest that, rather than merely being statistical noise, this variability may be useful in providing complementary information that can be leveraged to improve prediction accuracy. To leverage this variability, we propose the use of stacking, a prediction-driven approach for model selection. Specifically, we combine predictions developed from multiple hidden Markov models—a probabilistic generative model of network dynamics that identifies recurring patterns of brain activity—to demonstrate that stacking can slightly improve the accuracy and robustness of cognitive trait predictions. By comparing analysis from the Human Connectome Project and UK Biobank datasets, we show that stacking is relatively effective at improving prediction accuracy and robustness when there are enough subjects, and that the effectiveness of combining predictions from static and dynamic functional connectivity approaches depends on the length of scan per subject. We also show that the effectiveness of stacking predictions is driven by the accuracy and diversity in the underlying model estimations.

Non-linear development of brain morphometry in child and adolescent offspring of individuals with bipolar disorder or schizophrenia

Offspring of parents with severe mental illness (e.g., bipolar disorder or schizophrenia) are at increased risk of developing psychopathology. Structural brain alterations have been found in child and adolescent offspring of patients with bipolar disorder and schizophrenia, but the developmental trajectories of brain anatomy in this high-familial-risk population are still unclear. 300 T1-weighted scans were obtained of 187 offspring of at least one parent diagnosed with bipolar disorder (n=80) or schizophrenia (n=53) and offspring of parents without severe mental illness (n=54). The age range was 8 to 23 years old; 113 offspring underwent two scans. Global brain measures and regional cortical thickness and surface area were computed. A generalized additive mixed model was used to capture non-linear age trajectories. Offspring of parents with schizophrenia had smaller total brain volume than offspring of parents with bipolar disorder (d=-0.20, p=0.004) and control offspring (d=-0.22, p=0.005) and lower mean cortical thickness than control offspring (d=-0.23, p<0.001). Offspring of parents with schizophrenia showed differential age trajectories of mean cortical thickness and cerebral white matter volume compared with control offspring (both p’s=0.003). Regionally, offspring of parents with schizophrenia had a significantly different trajectory of cortical thickness in the middle temporal gyrus versus control offspring (p<0.001) and bipolar disorder offspring (p=0.001), which was no longer significant after correcting for mean cortical thickness. These findings suggest that particularly familial high risk of schizophrenia is related to reductions and deviating developmental trajectories of global brain structure measures, which were not driven by specific regions.

Sex and sobriety: Human brain structure and function in AUD abstinence

Women are drinking alcohol as much as men for the first time in history. Women experience more health-related consequences from alcohol use disorder (AUD), like increased prevalence of alcohol-related cancers, faster progression of alcohol-related liver disease, and greater risk for relapse compared to men. Thus, sex differences in chronic alcohol use pose a substantial public health problem. Despite these evident sex differences, our understanding of how these differences present during alcohol abstinence is limited. Investigations of brain structure and function are therefore critical for disentangling factors that lead to sex differences in AUD abstinence. This review will discuss current human neuroimaging data on sex differences in alcohol abstinence, focusing on structural and functional brain measures. Current structural imaging literature reveals that abstinent men have smaller gray and white matter volume and weaker structural connectivity compared to control men. Interestingly, abstinent women do not show differences in brain structure when compared to controls; instead, abstinent women show a relation between alcohol use and decreased measures of brain structure. Current functional brain studies reveal that abstinent men exhibit greater brain activation and stronger task-based functional connectivity to aversive stimuli than control men, while abstinent women exhibit lesser brain activation and weaker task-based functional connectivity than control women. Together, the current literature suggests that sex differences persist well into alcohol abstinence and impact brain structure and function differently. Understanding how men and women differ during alcohol abstinence can improve our understanding of sex-specific effects of alcohol, which will be critical to augment treatment methods to better serve women.

Eye-brain connections revealed by multimodal retinal and brain imaging genetics

The retina, an anatomical extension of the brain, forms physiological connections with the visual cortex of the brain. Although retinal structures offer a unique opportunity to assess brain disorders, their relationship to brain structure and function is not well understood. In this study, we conducted a systematic cross-organ genetic architecture analysis of eye-brain connections using retinal and brain imaging endophenotypes. We identified novel phenotypic and genetic links between retinal imaging biomarkers and brain structure and function measures from multimodal magnetic resonance imaging (MRI), with many associations involving the primary visual cortex and visual pathways. Retinal imaging biomarkers shared genetic influences with brain diseases and complex traits in 65 genomic regions, with 18 showing genetic overlap with brain MRI traits. Mendelian randomization suggests bidirectional genetic causal links between retinal structures and neurological and neuropsychiatric disorders, such as Alzheimer’s disease. Overall, our findings reveal the genetic basis for eye-brain connections, suggesting that retinal images can help uncover genetic risk factors for brain disorders and disease-related changes in intracranial structure and function.

Multivariate analysis of multimodal brain structure predicts individual differences in risk and intertemporal preference.

Large changes to brain structure (e.g., from damage or disease) can explain alterations in behavior. It is therefore plausible that smaller structural differences in healthy samples can be used to better understand and predict individual differences in behavior. Despite the brain's multivariate and distributed structure-to-function mapping, most studies have used univariate analyses of individual structural brain measures. Here we used a multivariate approach in a multimodal data set composed of volumetric, surface-based, diffusion-based, and functional resting-state MRI measures to predict reliable individual differences in risk and intertemporal preferences. We show that combining twelve brain structure measures led to better predictions across tasks than using any individual measure, and by examining model coefficients, we visualize the relative contribution of different brain measures from different brain regions. Using a multivariate approach to brain structure-to-function mapping that combines across many brain structure properties, along with reliably measured behavior phenotypes, may increase out-of-sample prediction accuracies and insight into neural underpinnings. Furthermore, this methodological approach may be useful to improve predictions and neural insight across basic, translational, and clinical research fields.

Identifying novel data-driven subgroups in congenital heart disease using multi-modal measures of brain structure

Individuals with congenital heart disease (CHD) have an increased risk of neurodevelopmental impairments. Given the hypothesized complexity linking genomics, atypical brain structure, cardiac diagnoses and their management, and neurodevelopmental outcomes, unsupervised methods may provide unique insight into neurodevelopmental variability in CHD. Using data from the Pediatric Cardiac Genomics Consortium Brain and Genes study, we identified data-driven subgroups of individuals with CHD from measures of brain structure. Using structural magnetic resonance imaging (MRI; N = 93; cortical thickness, cortical volume, and subcortical volume), we identified subgroups that differed primarily on cardiac anatomic lesion and language ability. In contrast, using diffusion MRI (N = 88; white matter connectivity strength), we identified subgroups that were characterized by differences in associations with rare genetic variants and visual-motor function. This work provides insight into the differential impacts of cardiac lesions and genomic variation on brain growth and architecture in patients with CHD, with potentially distinct effects on neurodevelopmental outcomes.

Mapping Individual Differences in Intermodal Coupling in Neurodevelopment.

Within-individual coupling between measures of brain structure and function evolves in development and may underlie differential risk for neuropsychiatric disorders. Despite increasing interest in the development of structure-function relationships, rigorous methods to quantify and test individual differences in coupling remain nascent. In this article, we explore and address gaps in approaches for testing and spatially localizing individual differences in intermodal coupling. We propose a new method, called CIDeR, which is designed to simultaneously perform hypothesis testing in a way that limits false positive results and improve detection of true positive results. Through a comparison across different approaches to testing individual differences in intermodal coupling, we delineate subtle differences in the hypotheses they test, which may ultimately lead researchers to arrive at different results. Finally, we illustrate the utility of CIDeR in two applications to brain development using data from the Philadelphia Neurodevelopmental Cohort.

The interplay between insomnia symptoms and Alzheimer's disease across three main brain networks.

Insomnia symptoms are prevalent along the trajectory of Alzheimer's disease (AD), but the neurobiological underpinning of their interaction is poorly understood. Here, we assessed structural and functional brain measures within and between the default mode network (DMN), salience network, and central executive network (CEN). We selected 320 participants from the ADNI database and divided them by their diagnosis: cognitively normal (CN), Mild Cognitive Impairment (MCI), and AD, with and without self-reported insomnia symptoms. We measured the gray matter volume (GMV), structural covariance (SC), degrees centrality (DC), and functional connectivity (FC), testing the effect and interaction of insomnia symptoms and diagnosis on each index. Subsequently, we performed a within-group linear regression across each network and ROI. Finally, we correlated observed abnormalities with changes in cognitive and affective scores. Insomnia symptoms were associated with FC alterations across all groups. The AD group also demonstrated an interaction between insomnia and diagnosis. Within-group analyses revealed that in CN and MCI, insomnia symptoms were characterized by within-network hyperconnectivity, while in AD, within- and between-network hypoconnectivity was ubiquitous. SC and GMV alterations were nonsignificant in the presence of insomnia symptoms, and DC indices only showed network-level alterations in the CEN of AD individuals. Abnormal FC within and between DMN and CEN hubs was additionally associated with reduced cognitive function across all groups, and increased depressive symptoms in AD. We conclude that patients with clinical AD present with a unique pattern of insomnia-related functional alterations, highlighting the profound interaction between both conditions.

Examining the neurostructural architecture of intelligence: The Lothian Birth Cohort 1936 study

Examining underlying neurostructural correlates of specific cognitive abilities is practically and theoretically complicated by the existence of the positive manifold (all cognitive tests positively correlate): if a brain structure is associated with a cognitive task, how much of this is uniquely related to the cognitive domain, and how much is due to covariance with all other tests across domains (captured by general cognitive functioning, also known as general intelligence, or ‘g’)? We quantitatively address this question by examining associations between brain structural and diffusion MRI measures (global tissue volumes, white matter hyperintensities, global white matter diffusion fractional anisotropy and mean diffusivity, and FreeSurfer processed vertex-wise cortical volumes, smoothed at 20mm fwhm) with g and cognitive domains (processing speed, crystallised ability, memory, visuospatial ability). The cognitive domains were modelled using confirmatory factor analysis to derive both hierarchical and bifactor solutions using 13 cognitive tests in 697 participants from the Lothian Birth Cohort 1936 study (mean age 72.5 years; SD = .7). Associations between the extracted cognitive factor scores for each domain and g were computed for each brain measure covarying for age, sex and intracranial volume, and corrected for false discovery rate.There were a range of significant associations between cognitive domains and global MRI brain structural measures (r range .008 to .269, p < .05). Regions implicated by vertex-wise regional cortical volume included a widespread number of medial and lateral areas of the frontal, temporal and parietal lobes. However, at both global and regional level, much of the domain-MRI associations were shared (statistically accounted for by g). Removing g-related variance from cognitive domains attenuated association magnitudes with global brain MRI measures by 27.9–59.7% (M = 46.2%), with only processing speed retaining all significant associations. At the regional cortical level, g appeared to account for the majority (range 22.1–88.4%; M = 52.8% across cognitive domains) of regional domain-specific associations. Crystallised and memory domains had almost no unique cortical correlates, whereas processing speed and visuospatial ability retained limited cortical volumetric associations. The greatest spatial overlaps across cognitive domains (as denoted by g) were present in the medial and lateral temporal, lateral parietal and lateral frontal areas.

Investigating the relationships between motor skills, cognitive status, and area deprivation index in Arizona: a pilot study.

Previous studies highlight the negative impact of adverse socioeconomic conditions throughout life on motor skills and cognitive health. Factors such as cognitive activity, physical activity, lifestyle, and socioeconomic position significantly affect general health status and brain health. This pilot study investigates the relationships among the Area Deprivation Index (ADI)-a measure of neighborhood-level socioeconomic deprivation, brain structure (cortical volume and thickness), and cognitive status in adults in Arizona. Identifying measures sensitive to ADI could elucidate mechanisms driving cognitive decline. The study included 22 adults(mean age = 56.2 ± 15.2) in Arizona, residing in the area for over 10 years(mean = 42.7 ± 15.8). We assessed specific cognitive domains using the NeuroTrax™ cognitive screening test, which evaluates memory, executive function, visual-spatial processing, attention, information processing speed, and motor function. We also measured cortical thickness and volume in 10 cortical regions using FreeSurfer 7.2. Linear regression tests were conducted to examine the relationships between ADI metrics, cognitive status, and brain health measures. Results indicated a significant inverse relationship between ADI metrics and memory scores, explaining 25% of the variance. Both national and state ADI metrics negatively correlated with motor skills and global cognition (r's < -0.40, p's < 0.05). In contrast, ADI metrics generally positively correlated with motor-related volumetric and cortical thickness measures (r's > 0.40, p's < 0.05). The findings suggest that neighborhood-level social deprivation might influence memory and motor status, primarily through its impact on motor brain health.

Interactions between age, sex and visceral adipose tissue on brain ageing.

To examine the associations between visceral adipose tissue (VAT) and brain structural measures at midlife and explore how these associations may be affected by age, sex and cardiometabolic factors. We used abdominal and brain magnetic resonance imaging data from a population-based cohort of people at midlife in the UK Biobank. Regression modelling was applied to study associations of VAT volume with total brain volume (TBV), grey matter volume (GMV), white matter volume, white matter hyperintensity volume (WMHV) and total hippocampal volume (THV), and whether these associations were altered by age, sex or cardiometabolic factors. Complete data were available for 17 377 participants (mean age 63 years, standard deviation = 12, 53% female). Greater VAT was associated with lower TBV, GMV and THV (P < .001). We found an interaction between VAT and sex on TBV (P < .001), such that the negative association of VAT with TBV was greater in men (β = -2.89, 95% confidence interval [CI] -2.32 to -10.15) than in women (β = -1.32, 95% CI -0.49 to -3.14), with similar findings for GMV. We also found an interaction between VAT and age (but not sex) on WMHV (P < .001). The addition of other cardiometabolic factors or measures of physical activity resulted in little change to the models. VAT volume is associated with poorer brain health in midlife and this relationship is greatest in men and those at younger ages.