The genetic architecture of multimodal human brain age.

The complex biological mechanisms underlying human brain aging remain incompletely understood. This study investigated the genetic architecture of three brain age gaps (BAG) derived from gray matter volume (GM-BAG), white matter microstructure (WM-BAG), and functional connectivity (FC-BAG). We identified sixteen genomic loci that reached genome-wide significance (P-value < 5×10-8). A gene-drug-disease network highlighted genes linked to GM-BAG for treating neurodegenerative and neuropsychiatric disorders and WM-BAG genes for cancer therapy. GM-BAG displayed the most pronounced heritability enrichment in genetic variants within conserved regions. Oligodendrocytes and astrocytes, but not neurons, exhibited notable heritability enrichment in WM and FC-BAG, respectively. Mendelian randomization identified potential causal effects of several chronic diseases on brain aging, such as type 2 diabetes on GM-BAG and AD on WM-BAG. Our results provide insights into the genetics of human brain aging, with clinical implications for potential lifestyle and therapeutic interventions. All results are publicly available at https://labs.loni.usc.edu/medicine .

Genomic loci influence patterns of structural covariance in the human brain.

Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to patterns of structural covariance (PSC) across brain regions and individuals during brain aging and diseases. The genetic underpinnings of these patterns remain largely unknown. We apply a stochastic multivariate factorization method to a diverse population of 50,699 individuals (12 studies and 130 sites) and derive data-driven, multi-scale PSCs of regional brain size. PSCs were significantly correlated with 915 genomic loci in the discovery set, 617 of which are newly identified, and 72% were independently replicated. Key pathways influencing PSCs involve reelin signaling, apoptosis, neurogenesis, and appendage development, while pathways of breast cancer indicate potential interplays between brain metastasis and PSCs associated with neurodegeneration and dementia. Using support vector machines, multi-scale PSCs effectively derive imaging signatures of several brain diseases. Our results elucidate genetic and biological underpinnings that influence structural covariance patterns in the human brain.

The Genetic Architecture of Multimodal Human Brain Age.

The complex biological mechanisms underlying human brain aging remain incompletely understood, involving multiple body organs and chronic diseases. In this study, we used multimodal magnetic resonance imaging and artificial intelligence to examine the genetic architecture of the brain age gap (BAG) derived from gray matter volume (GM-BAG, N=31,557 European ancestry), white matter microstructure (WM-BAG, N=31,674), and functional connectivity (FC-BAG, N=32,017). We identified sixteen genomic loci that reached genome-wide significance (P-value<5×10-8). A gene-drug-disease network highlighted genes linked to GM-BAG for treating neurodegenerative and neuropsychiatric disorders and WM-BAG genes for cancer therapy. GM-BAG showed the highest heritability enrichment for genetic variants in conserved regions, whereas WM-BAG exhibited the highest heritability enrichment in the 5' untranslated regions; oligodendrocytes and astrocytes, but not neurons, showed significant heritability enrichment in WM and FC-BAG, respectively. Mendelian randomization identified potential causal effects of several exposure variables on brain aging, such as type 2 diabetes on GM-BAG (odds ratio=1.05 [1.01, 1.09], P-value=1.96×10-2) and AD on WM-BAG (odds ratio=1.04 [1.02, 1.05], P-value=7.18×10-5). Overall, our results provide valuable insights into the genetics of human brain aging, with clinical implications for potential lifestyle and therapeutic interventions. All results are publicly available at the MEDICINE knowledge portal: https://labs.loni.usc.edu/medicine.

Inflammatory cytokines and white matter microstructure in the acute phase of first-episode psychosis: A longitudinal study.

OBJECTIVES: Schizophrenia-related psychosis is associated with abnormalities in white matter (WM) microstructure and structural brain dysconnectivity. However, the pathological process underlying such changes is unknown. We sought to investigate the potential association between peripheral cytokine levels and WM microstructure during the acute phase of first-episode psychosis (FEP) in a cohort of drug-naïve patients. METHODS: Twenty-five non-affective FEP patients and 69 healthy controls underwent MRI scanning and blood collection at study entry. After achieving clinical remission, 21 FEP were reassessed; 38 age and biological sex-matched controls also had a second assessment. We measured fractional anisotropy (FA) of selected WM regions-of-interest (ROIs) and plasma levels of four cytokines (IL-6, IL-10, IFN-γ, and TNF-α). RESULTS: At baseline (acute psychosis), the FEP group showed reduced FA relative to controls in half the examined ROIs. Within the FEP group, IL-6 levels were negatively correlated with FA values. Longitudinally, patients showed increments of FA in several ROIs affected at baseline, and such changes were associated with reductions in IL-6 levels. CONCLUSIONS: A state-dependent process involving an interplay between a pro-inflammatory cytokine and brain WM might be associated with the clinical manifestation of FEP. This association suggests a deleterious effect of IL-6 on WM tracts during the acute phase of psychosis.

The impact of perioperative stroke and delirium on outcomes after surgical aortic valve replacement.

OBJECTIVE: The effects of stroke and delirium on postdischarge cognition and patient-centered health outcomes after surgical aortic valve replacement (SAVR) are not well characterized. Here, we assess the impact of postoperative stroke and delirium on these health outcomes in SAVR patients at 90 days. METHODS: Patients (N = 383) undergoing SAVR (41% received concomitant coronary artery bypass graft) enrolled in a randomized trial of embolic protection devices underwent serial neurologic and delirium evaluations at postoperative days 1, 3, and 7 and magnetic resonance imaging at day 7. Outcomes included 90-day functional status, neurocognitive decline from presurgical baseline, and quality of life. RESULTS: By postoperative day 7, 25 (6.6%) patients experienced clinical stroke and 103 (28.5%) manifested delirium. During index hospitalization, time to discharge was longer in patients experiencing stroke (hazard ratio, 0.62; 95% confidence interval [CI], 0.42-0.94; P = .02) and patients experiencing delirium (hazard ratio, 0.68; 95% CI, 0.54-0.86; P = .001). At day 90, patients experiencing stroke were more likely to have a modified Rankin score >2 (odds ratio [OR], 5.9; 95% CI, 1.7-20.1; P = .01), depression (OR, 5.3; 95% CI, 1.6-17.3; P = .006), a lower 12-Item Short Form Survey physical health score (adjusted mean difference -3.3 ± 1.9; P = .08), and neurocognitive decline (OR, 7.8; 95% CI, 2.3-26.4; P = .001). Delirium was associated with depression (OR, 2.2; 95% CI, 0.9-5.3; P = .08), lower 12-Item Short Form Survey physical health (adjusted mean difference -2.3 ± 1.1; P = .03), and neurocognitive decline (OR, 2.2; 95% CI, 1.2-4.0; P = .01). CONCLUSIONS: Stroke and delirium occur more frequently after SAVR than is commonly recognized, and these events are associated with disability, depression, cognitive decline, and poorer quality of life at 90 days postoperatively. These findings support the need for new interventions to reduce these events and improve patient-centered outcomes.

Association of Brain Volumes and White Matter Injury With Race, Ethnicity, and Cardiovascular Risk Factors: The Multi-Ethnic Study of Atherosclerosis.

Background Cardiovascular risk factors are associated with cognitive decline and dementia. Magnetic resonance imaging provides sensitive measurement of brain morphology and vascular brain injury. However, associations of risk factors with brain magnetic resonance imaging findings have largely been studied in White participants. We investigated associations of race, ethnicity, and cardiovascular risk factors with brain morphology and white matter (WM) injury in a diverse population. Methods and Results In the Multi-Ethnic Study of Atherosclerosis, measures were made in 2018 to 2019 of total brain volume, gray matter and WM volume, and WM injury, including WM hyperintensity volume and WM fractional anisotropy. We assessed cross-sectional associations of race and ethnicity and of cardiovascular risk factors with magnetic resonance imaging measures. Magnetic resonance imaging data were complete in 1036 participants; 25% Black, 15% Chinese-American, 19% Hispanic, and 41% White. Mean (SD) age was 72 (8) years and 53% were women. Although WM injury was greater in Black than in White participants in a minimally adjusted model, additional adjustment for cardiovascular risk factors and socioeconomic status each attenuated this association, rendering it nonsignificant. Overall, greater average WM hyperintensity volume was associated with older age and current smoking (69% greater vs never smoking); lower fractional anisotropy was additionally associated with higher diastolic blood pressure, use of antihypertensive medication, and diabetes. Conclusions We found no statistically significant difference in measures of WM injury by race and ethnicity after adjustment for cardiovascular risk factors and socioeconomic status. In all racial and ethnic groups, older age, current smoking, hypertension, and diabetes were strongly associated with WM injury.

Brain extraction on MRI scans in presence of diffuse glioma: Multi-institutional performance evaluation of deep learning methods and robust modality-agnostic training.

Brain extraction, or skull-stripping, is an essential pre-processing step in neuro-imaging that has a direct impact on the quality of all subsequent processing and analyses steps. It is also a key requirement in multi-institutional collaborations to comply with privacy-preserving regulations. Existing automated methods, including Deep Learning (DL) based methods that have obtained state-of-the-art results in recent years, have primarily targeted brain extraction without considering pathologically-affected brains. Accordingly, they perform sub-optimally when applied on magnetic resonance imaging (MRI) brain scans with apparent pathologies such as brain tumors. Furthermore, existing methods focus on using only T1-weighted MRI scans, even though multi-parametric MRI (mpMRI) scans are routinely acquired for patients with suspected brain tumors. In this study, we present a comprehensive performance evaluation of recent deep learning architectures for brain extraction, training models on mpMRI scans of pathologically-affected brains, with a particular focus on seeking a practically-applicable, low computational footprint approach, generalizable across multiple institutions, further facilitating collaborations. We identified a large retrospective multi-institutional dataset of n=3340 mpMRI brain tumor scans, with manually-inspected and approved gold-standard segmentations, acquired during standard clinical practice under varying acquisition protocols, both from private institutional data and public (TCIA) collections. To facilitate optimal utilization of rich mpMRI data, we further introduce and evaluate a novel ''modality-agnostic training'' technique that can be applied using any available modality, without need for model retraining. Our results indicate that the modality-agnostic approach1 obtains accurate results, providing a generic and practical tool for brain extraction on scans with brain tumors.

Overall survival prediction in glioblastoma patients using structural magnetic resonance imaging (MRI): advanced radiomic features may compensate for lack of advanced MRI modalities.

Purpose: Glioblastoma, the most common and aggressive adult brain tumor, is considered noncurative at diagnosis, with 14 to 16 months median survival following treatment. There is increasing evidence that noninvasive integrative analysis of radiomic features can predict overall and progression-free survival, using advanced multiparametric magnetic resonance imaging (Adv-mpMRI). If successfully applicable, such noninvasive markers can considerably influence patient management. However, most patients prior to initiation of therapy typically undergo only basic structural mpMRI (Bas-mpMRI, i.e., T1, T1-Gd, T2, and T2-fluid-attenuated inversion recovery) preoperatively, rather than Adv-mpMRI that provides additional vascularization (dynamic susceptibility contrast-MRI) and cell-density (diffusion tensor imaging) related information. Approach: We assess a retrospective cohort of 101 glioblastoma patients with available Adv-mpMRI from a previous study, which has shown that an initial feature panel (IFP, i.e., intensity, volume, location, and growth model parameters) extracted from Adv-mpMRI can yield accurate overall survival stratification. We focus on demonstrating that equally accurate prediction models can be constructed using augmented radiomic feature panels (ARFPs, i.e., integrating morphology and textural descriptors) extracted solely from widely available Bas-mpMRI, obviating the need for using Adv-mpMRI. We extracted 1612 radiomic features from distinct tumor subregions to build multivariate models that stratified patients as long-, intermediate-, or short-survivors. Results: The classification accuracy of the model utilizing Adv-mpMRI protocols and the IFP was 72.77% and degraded to 60.89% when using only Bas-mpMRI. However, utilizing the ARFP on Bas-mpMRI improved the accuracy to 74.26%. Furthermore, Kaplan-Meier analysis demonstrated superior classification of subjects into short-, intermediate-, and long-survivor classes when using ARFP extracted from Bas-mpMRI. Conclusions: This quantitative evaluation indicates that accurate survival prediction in glioblastoma patients is feasible using solely Bas-mpMRI and integrative advanced radiomic features, which can compensate for the lack of Adv-mpMRI. Our finding holds promise for generalization across multiple institutions that may not have access to Adv-mpMRI and to better inform clinical decision-making about aggressive interventions and clinical trials.

Integrative radiomic analysis for pre-surgical prognostic stratification of glioblastoma patients: from advanced to basic MRI protocols.

Glioblastoma, the most common and aggressive adult brain tumor, is considered non-curative at diagnosis. Current literature shows promise on imaging-based overall survival prediction for patients with glioblastoma while integrating advanced (structural, perfusion, and diffusion) multipara metric magnetic resonance imaging (Adv-mpMRI). However, most patients prior to initiation of therapy typically undergo only basic structural mpMRI (Bas-mpMRI, i.e., T1,T1-Gd,T2,T2-FLAIR) pre-operatively, rather than Adv-mpMRI. Here we assess a retrospective cohort of 101 glioblastoma patients with available Adv-mpMRI from a previous study, which has shown that an initial feature panel (IFP) extracted from Adv-mpMRI can yield accurate overall survival stratification. We further focus on demonstrating that equally accurate prediction models can be constructed using augmented feature panels (AFP) extracted solely from Bas-mpMRI, obviating the need for using Adv-mpMRI. The classification accuracy of the model utilizing Adv-mpMRI protocols and the IFP was 72.77%, and improved to 74.26% when utilizing the AFP on Bas-mpMRI. Furthermore, Kaplan-Meier analysis demonstrated superior classification of subjects into short-, intermediate-, and long-survivor classes when using AFPon Basic-mpMRI. This quantitative evaluation indicates that accurate survival prediction in glioblastoma patients is feasible by using solely Bas-mpMRI and integrative radiomic analysis can compensate for the lack of Adv-mpMRI. Our finding holds promise for predicting overall survival based on commonly-acquired Bas-mpMRI, and hence for potential generalization across multiple institutions that may not have access to Adv-mpMRI, facilitating better patient selection.

Association of Midlife Hearing Impairment With Late-Life Temporal Lobe Volume Loss.

IMPORTANCE: Hearing impairment (HI) in midlife (45-65 years of age) may be associated with longitudinal neurodegeneration of temporal lobe structures, a biomarker of early Alzheimer disease. OBJECTIVE: To evaluate the association of midlife HI with brain volume trajectories in later life (≥65 years of age). DESIGN, SETTING, AND PARTICIPANTS: This prospective cohort study used data from the Baltimore Longitudinal Study of Aging to evaluate hearing from November 5, 1990, to October 3, 1994, and late-life volume change from July 10, 2008, to January 29, 2015, using magnetic resonance imaging (MRI) (mean follow-up time, 19.3 years). Data analysis was performed from September 22, 2017, to August 27, 2018. A total of 194 community-dwelling older adults who had midlife measures of peripheral hearing at a mean age of 54.5 years and late-life volume change of up to 6 years between the first and most recent MRI assessment were studied. Excluded were those with baseline cognitive impairment, stroke, head injuries, Parkinson disease, and bipolar disorder. EXPOSURES: Hearing as measured with pure tone audiometry in each ear from November 5, 1990, to October 3, 1994, and late-life temporal lobe volume change measured by MRI. MAIN OUTCOMES AND MEASURES: Linear mixed-effects models with random intercepts were used to examine the association of midlife hearing (pure tone average of 0.5-4 kHz tones in the better ear and each ear separately) with longitudinal late-life MRI-based measures of temporal lobe structures (hippocampus, entorhinal cortex, parahippocampal gyrus, and superior, middle, and inferior temporal gyri) in the left and right hemispheres, in addition to global and lobar regions, adjusting for baseline demographic characteristics (age, sex, subsequent cognitive impairment status, and educational level) and intracranial volume. RESULTS: A total of 194 patients (mean [SD] age at hearing assessment, 54.5 [10.0] years; 106 [54.6%] female; 169 [87.1%] white) participated in the study. After Bonferroni correction, poorer midlife hearing in the better ear was associated with steeper late-life volumetric declines in the right temporal gray matter (ß = -0.113; 95% CI, -0.182 to -0.044), right hippocampus (ß = -0.008; 95% CI, -0.012 to -0.004), and left entorhinal cortex (ß = -0.009; 95% CI, -0.015 to -0.003). Poorer midlife hearing in the right ear was associated with steeper late-life volumetric declines in the right temporal gray matter (ß = -0.136; 95% CI, -0.197 to -0.075), right hippocampus (ß = -0.008; 95% CI, -0.012 to -0.004), and left entorhinal cortex (ß = -0.009; 95% CI, -0.015 to -0.003), whereas there were no associations between poorer midlife hearing in the left ear with late-life volume loss. CONCLUSIONS AND RELEVANCE: The findings suggest that midlife HI is a risk factor for temporal lobe volume loss. Poorer midlife hearing, particularly in the right ear, was associated with declines in hippocampus and entorhinal cortex.

Precision diagnostics based on machine learning-derived imaging signatures.

The complexity of modern multi-parametric MRI has increasingly challenged conventional interpretations of such images. Machine learning has emerged as a powerful approach to integrating diverse and complex imaging data into signatures of diagnostic and predictive value. It has also allowed us to progress from group comparisons to imaging biomarkers that offer value on an individual basis. We review several directions of research around this topic, emphasizing the use of machine learning in personalized predictions of clinical outcome, in breaking down broad umbrella diagnostic categories into more detailed and precise subtypes, and in non-invasively estimating cancer molecular characteristics. These methods and studies contribute to the field of precision medicine, by introducing more specific diagnostic and predictive biomarkers of clinical outcome, therefore pointing to better matching of treatments to patients.

Sex differences in the association between amyloid and longitudinal brain volume change in cognitively normal older adults.

OBJECTIVE: Amyloid positivity is a biomarker of AD pathology, yet the associations between amyloid positivity and brain volumetric changes, especially in the hippocampus, are inconsistent. We hypothesize that sex differences in associations may contribute to inconsistent findings among cognitively normal older adults. METHODS: Using linear mixed effects models, we examined the association of amyloid positivity with prospective volumetric changes (mean = 3.3 visits) of parahippocampal gyrus (phg), hippocampus, entorhinal cortex (erc), precuneus, and fusiform gyrus among 171 Baltimore Longitudinal Study of Aging participants aged ≥55 years. Amyloid positivity was defined by a mean 11C-Pittsburgh Compound B (PiB) distribution volume ratio (DVR) cut-off of 1.062. All analyses included age, race, sex, education, APOE e4 carrier status, and two-way interactions of these covariates with time. Two-way interaction between sex and PiB+/- status and three-way interaction of sex and PiB+/- status with time were added to assess whether sex modified associations. RESULTS: PiB+ status was associated with greater volumetric declines in the phg (ß = -0.036, SE = 0.011, p = 0.001) and erc (ß = -0.019, SE = 0.009, p = 0.045). Sex modified the association of PiB+ status and rates of volumetric declines in fusiform (ß = -0.117, SE = 0.049, p = 0.019). PiB+ males had steeper rates of volumetric declines in phg (ß = -0.051, SE = 0.013, p < 0.001) and erc (ß = -0.029, SE = 0.012, p = 0.014) than PiB- males, while there was no difference in rates of volumetric change between PiB+ and PiB- females. CONCLUSIONS: Amyloidosis is a marker of entorhinal and parahippocampal volume loss. Amyloid positivity is a predictor of volume loss in brain regions affected by early AD pathology in men, but not women.

Predictors of neurodegeneration differ between cognitively normal and subsequently impaired older adults.

Effects of Alzheimer's disease (AD) risk factors on brain volume changes may partly explain what happens during the preclinical AD stage in people who develop subsequent cognitive impairment (SI). We investigated predictors of neurodegeneration, measured by MRI-based volume loss, in older adults before diagnosis of cognitive impairment. There were 623 cognitively normal and 65 SI Baltimore Longitudinal Study of Aging participants (age 55-92 years) enrolled in the neuroimaging substudy from 1994 to 2015. Mixed-effects regression was used to assess the associations of AD risk factors (age, APOE e4 carrier status, diabetes, hypertension, obesity, current smoking, and elevated cholesterol) with brain regional volume change among the overall sample and by diagnostic status. Older age, APOE e4 carrier status, hypertension, and HDL cholesterol were predictors of volumetric change. Among SI participants only, hypertension, obesity, and APOE e4 carrier status were associated with greater declines in selected brain regions. SI individuals in the preclinical AD stage are vulnerable to risk factors that have either a protective or null effect in cognitively normal individuals.

Skull-Stripping of Glioblastoma MRI Scans Using 3D Deep Learning.

Skull-stripping is an essential pre-processing step in computational neuro-imaging directly impacting subsequent analyses. Existing skull-stripping methods have primarily targeted non-pathologicallyaffected brains. Accordingly, they may perform suboptimally when applied on brain Magnetic Resonance Imaging (MRI) scans that have clearly discernible pathologies, such as brain tumors. Furthermore, existing methods focus on using only T1-weighted MRI scans, even though multi-parametric MRI (mpMRI) scans are routinely acquired for patients with suspected brain tumors. Here we present a performance evaluation of publicly available implementations of established 3D Deep Learning architectures for semantic segmentation (namely DeepMedic, 3D U-Net, FCN), with a particular focus on identifying a skull-stripping approach that performs well on brain tumor scans, and also has a low computational footprint. We have identified a retrospective dataset of 1,796 mpMRI brain tumor scans, with corresponding manually-inspected and verified gold-standard brain tissue segmentations, acquired during standard clinical practice under varying acquisition protocols at the Hospital of the University of Pennsylvania. Our quantitative evaluation identified DeepMedic as the best performing method (Dice = 97.9, Hausdorf f 95 = 2.68). We release this pre-trained model through the Cancer Imaging Phenomics Toolkit (CaPTk) platform.

What Makes New Ischemic Lesions Symptomatic after Aortic Valve Replacement?

BACKGROUND: Acute cerebral infarctions on diffusion-weighted magnetic resonance imaging (MRI) are common after cardiothoracic surgery. However, most are asymptomatic and we aimed to identify features associated with clinical stroke symptoms. METHODS: Patients over 65 years of age undergoing surgical aortic valve replacement (AVR) for calcific stenosis were prospectively recruited (N = 196). All patients underwent neurological evaluation preoperatively and on postoperative days 1, 3, and 7, and MRI on planned postoperative day 5. Among those with new postoperative DWI lesions, we performed univariate and multivariable analyses to identify clinical, demographic, surgical, and imaging factors associated with clinical stroke symptoms. RESULTS: Of the 129 patients who completed a postsurgical MRI, 79 (61%) had DWI lesions and 17 (21.5%) of these had new stroke symptoms concordant with the infarct distribution. In an exploratory multivariable analysis, focal neurological symptoms were associated with increased age, a longer bypass duration, and a larger pre-existing lesion burden on fluid-attenuated inversion recovery. Limiting the analysis to the 61 patients with analyzable volume and location data, logistic regression failed to identify any location-related determinant of symptomatic lesions. CONCLUSIONS: New DWI lesions are common after AVR, but most are asymptomatic. Patients are more likely to have symptoms with longer bypass durations, increasing age, and larger pre-existing lesion burdens.

White matter hyperintensities and imaging patterns of brain ageing in the general population.

White matter hyperintensities are associated with increased risk of dementia and cognitive decline. The current study investigates the relationship between white matter hyperintensities burden and patterns of brain atrophy associated with brain ageing and Alzheimer's disease in a large populatison-based sample (n = 2367) encompassing a wide age range (20-90 years), from the Study of Health in Pomerania. We quantified white matter hyperintensities using automated segmentation and summarized atrophy patterns using machine learning methods resulting in two indices: the SPARE-BA index (capturing age-related brain atrophy), and the SPARE-AD index (previously developed to capture patterns of atrophy found in patients with Alzheimer's disease). A characteristic pattern of age-related accumulation of white matter hyperintensities in both periventricular and deep white matter areas was found. Individuals with high white matter hyperintensities burden showed significantly (P < 0.0001) lower SPARE-BA and higher SPARE-AD values compared to those with low white matter hyperintensities burden, indicating that the former had more patterns of atrophy in brain regions typically affected by ageing and Alzheimer's disease dementia. To investigate a possibly causal role of white matter hyperintensities, structural equation modelling was used to quantify the effect of Framingham cardiovascular disease risk score and white matter hyperintensities burden on SPARE-BA, revealing a statistically significant (P < 0.0001) causal relationship between them. Structural equation modelling showed that the age effect on SPARE-BA was mediated by white matter hyperintensities and cardiovascular risk score each explaining 10.4% and 21.6% of the variance, respectively. The direct age effect explained 70.2% of the SPARE-BA variance. Only white matter hyperintensities significantly mediated the age effect on SPARE-AD explaining 32.8% of the variance. The direct age effect explained 66.0% of the SPARE-AD variance. Multivariable regression showed significant relationship between white matter hyperintensities volume and hypertension (P = 0.001), diabetes mellitus (P = 0.023), smoking (P = 0.002) and education level (P = 0.003). The only significant association with cognitive tests was with the immediate recall of the California verbal and learning memory test. No significant association was present with the APOE genotype. These results support the hypothesis that white matter hyperintensities contribute to patterns of brain atrophy found in beyond-normal brain ageing in the general population. White matter hyperintensities also contribute to brain atrophy patterns in regions related to Alzheimer's disease dementia, in agreement with their known additive role to the likelihood of dementia. Preventive strategies reducing the odds to develop cardiovascular disease and white matter hyperintensities could decrease the incidence or delay the onset of dementia.

Cardiorespiratory fitness and brain volume and white matter integrity: The CARDIA Study.

OBJECTIVE: We hypothesized that greater cardiorespiratory fitness is associated with lower odds of having unfavorable brain MRI findings. METHODS: We studied 565 healthy, middle-aged, black and white men and women in the CARDIA (Coronary Artery Risk Development in Young Adults) Study. The fitness measure was symptom-limited maximal treadmill test duration (Maxdur); brain MRI was measured 5 years later. Brain MRI measures were analyzed as means and as proportions below the 15th percentile (above the 85th percentile for white matter abnormal tissue volume). RESULTS: Per 1-minute-higher Maxdur, the odds ratio for having less whole brain volume was 0.85 (p = 0.04) and for having low white matter integrity was 0.80 (p = 0.02), adjusted for age, race, sex, clinic, body mass index, smoking, alcohol, diet, physical activity, education, blood pressure, diabetes, total cholesterol, and lung function (plus intracranial volume for white matter integrity). No significant associations were observed between Maxdur and abnormal tissue volume or blood flow in white matter. Findings were similar for associations with continuous brain MRI measures. CONCLUSIONS: Greater physical fitness was associated with more brain volume and greater white matter integrity measured 5 years later in middle-aged adults.

Vascular risk factors, cerebrovascular reactivity, and the default-mode brain network.

Cumulating evidence from epidemiologic studies implicates cardiovascular health and cerebrovascular function in several brain diseases in late life. We examined vascular risk factors with respect to a cerebrovascular measure of brain functioning in subjects in mid-life, which could represent a marker of brain changes in later life. Breath-hold functional MRI (fMRI) was performed in 541 women and men (mean age 50.4 years) from the Coronary Artery Risk Development in Young Adults (CARDIA) Brain MRI sub-study. Cerebrovascular reactivity (CVR) was quantified as percentage change in blood-oxygen level dependent (BOLD) signal in activated voxels, which was mapped to a common brain template and log-transformed. Mean CVR was calculated for anatomic regions underlying the default-mode network (DMN) - a network implicated in AD and other brain disorders - in addition to areas considered to be relatively spared in the disease (e.g. occipital lobe), which were utilized as reference regions. Mean CVR was significantly reduced in the posterior cingulate/precuneus (ß=-0.063, 95% CI: -0.106, -0.020), anterior cingulate (ß=-0.055, 95% CI: -0.101, -0.010), and medial frontal lobe (ß=-0.050, 95% CI: -0.092, -0.008) relative to mean CVR in the occipital lobe, after adjustment for age, sex, race, education, and smoking status, in subjects with pre-hypertension/hypertension compared to normotensive subjects. By contrast, mean CVR was lower, but not significantly, in the inferior parietal lobe (ß=-0.024, 95% CI: -0.062, 0.014) and the hippocampus (ß=-0.006, 95% CI: -0.062, 0.050) relative to mean CVR in the occipital lobe. Similar results were observed in subjects with diabetes and dyslipidemia compared to those without these conditions, though the differences were non-significant. Reduced CVR may represent diminished vascular functionality for the DMN for individuals with prehypertension/hypertension in mid-life, and may serve as a preclinical marker for brain dysfunction in later life.