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Previous studies have identified effects of age and vascular risk factors on brain injury in elderly individuals. We aimed to establish whether the effects of high blood pressure in the brain are evident as early as the fifth decade of life. In an investigation of the third generation of the Framingham Heart Study, we approached all participants in 2009 to ask whether they would be willing to undergo MRI. Consenting patients underwent clinical assessment and cerebral MRI that included T1-weighted and diffusion tensor imaging to obtain estimates of fractional anisotropy, mean diffusivity, and grey-matter volumes. All images were coregistered to a common minimum deformation template for voxel-based linear regressions relating fractional anisotropy, mean diffusivity, and grey-matter volumes to age and systolic blood pressure, with adjustment for potential confounders. 579 (14·1%) of 4095 participants in the third-generation cohort (mean age 39·2 years, SD 8·4) underwent brain MRI between June, 2009 and June, 2010. Age was associated with decreased fractional anisotropy and increased mean diffusivity in almost all cerebral white-matter voxels. Age was also independently associated with reduced grey-matter volumes. Increased systolic blood pressure was linearly associated with decreased regional fractional anisotropy and increased mean diffusivity, especially in the anterior corpus callosum, the inferior fronto-occipital fasciculi, and the fibres that project from the thalamus to the superior frontal gyrus. It was also strongly associated with reduced grey-matter volumes, particularly in Brodmann's area 48 on the medial surface of the temporal lobe and Brodmann's area 21 of the middle temporal gyrus. Our results suggest that subtle vascular brain injury develops insidiously during life, with discernible effects even in young adults. These findings emphasise the need for early and optimum control of blood pressure. National Institutes of Health and National Heart, Lung, and Blood Institute; National Institute on Aging; and National Institute of Neurological Disorders and Stroke.

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

BackgroundWhite matter hyperintensities (WMH) have been implicated in the pathogenesis of neuropsychiatric symptoms of dementia but the functional significance of WMH in specific white matter (WM) tracts is unclear. We investigate whether WMH burden within major WM fibre classes and individual WM tracts are differentially associated with different neuropsychiatric syndromes in a large multicentre study.MethodNeuroimaging and neuropsychiatric data of seven memory clinic cohorts through the Meta VCI Map consortium were harmonised. Class-based analyses of major WM fibres (association, commissural and projection) and region-of-interest-based analyses on 11 individual WM tracts were used to evaluate associations of WMH volume with severity of hyperactivity, psychosis, affective and apathy syndromes.ResultsAmong 2935 patients (50.4% women; mean age=72.2 years; 19.8% subjective cognitive impairment, 39.8% mild cognitive impairment, and 40.4% dementia), larger WMH volume within projection fibres (B=0.24, SE=0.10, p=0.013) was associated with greater apathy. Larger WMH volume within association (B=0.31, SE=0.12, p=0.009), commissural (B=0.47, SE=0.17, p=0.006) and projection (B=0.39, SE=0.16, p=0.016) fibres was associated with greater hyperactivity, driven by the inferior fronto-occipital fasciculus (B=0.50, SE=0.18, p=0.006), forceps major (B=0.48, SE=0.18, p=0.009) and anterior thalamic radiation (B=0.49, SE=0.19, p=0.011), respectively. Larger WMH volume in the uncinate fasciculus (B=1.82, SE=0.67, p=0.005) and forceps minor (B=0.61, SE=0.19, p=0.001) were additionally associated with greater apathy. No associations with affective and psychosis were observed.ConclusionsTract-syndrome specificity of WMH burden with apathy and hyperactivity suggests that disruption of strategic neuronal pathways may be a potential mechanism through which small vessel disease affects emotional and behavioural regulation in memory clinic patients.

INTRODUCTION White matter hyperintensities (WMH) are associated with key dementia etiologies, in particular arteriolosclerosis and amyloid pathology. We aimed to identify WMH locations associated with vascular risk or cerebral amyloid‐β1‐42 (Aβ42)‐positive status. METHODS Individual patient data (n = 3,132; mean age 71.5 ± 9 years; 49.3% female) from 11 memory clinic cohorts were harmonized. WMH volumes in 28 regions were related to a vascular risk compound score (VRCS) and Aß42 status (based on cerebrospinal fluid or amyloid positron emission tomography), correcting for age, sex, study site, and total WMH volume. RESULTS VRCS was associated with WMH in anterior/superior corona radiata (B = 0.034/0.038, p < 0.001), external capsule (B = 0.052, p < 0.001), and middle cerebellar peduncle (B = 0.067, p < 0.001), and Aß42‐positive status with WMH in posterior thalamic radiation (B = 0.097, p < 0.001) and splenium (B = 0.103, p < 0.001). DISCUSSION Vascular risk factors and Aß42 pathology have distinct signature WMH patterns. This regional vulnerability may incite future studies into how arteriolosclerosis and Aß42 pathology affect the brain's white matter. Highlights Key dementia etiologies may be associated with specific patterns of white matter hyperintensities (WMH). We related WMH locations to vascular risk and cerebral Aβ42 status in 11 memory clinic cohorts. Aβ42 positive status was associated with posterior WMH in splenium and posterior thalamic radiation. Vascular risk was associated with anterior and infratentorial WMH. Amyloid pathology and vascular risk have distinct signature WMH patterns.

We have longitudinally assessed normative brain growth patterns in naturalistically reared Macaca mulatta monkeys. Postnatal to early adulthood brain development in two cohorts of rhesus monkeys was analyzed using magnetic resonance imaging. Cohort A consisted of 24 rhesus monkeys (12 male, 12 female) and cohort B of 21 monkeys (11 male, 10 female). All subjects were scanned at 1, 4, 8, 13, 26, 39, and 52 weeks; cohort A had additional scans at 156 weeks (3 years) and 260 weeks (5 years). Age-specific segmentation templates were developed for automated volumetric analyses of the T1-weighted magnetic resonance imaging scans. Trajectories of total brain size as well as cerebral and subcortical subdivisions were evaluated over this period. Total brain volume was about 64 % of adult estimates in the 1-week-old monkey. Brain volume of the male subjects was always, on average, larger than the female subjects. While brain volume generally increased between any two imaging time points, there was a transient plateau of brain growth between 26 and 39 weeks in both cohorts of monkeys. The trajectory of enlargement differed across cortical regions with the occipital cortex demonstrating the most idiosyncratic pattern of maturation and the frontal and temporal lobes showing the greatest and most protracted growth. A variety of allometric measurements were also acquired and body weight gain was most closely associated with the rate of brain growth. These findings provide a valuable baseline for the effects of fetal and early postnatal manipulations on the pattern of abnormal brain growth related to neurodevelopmental disorders.

Studies suggest that bilingualism may be associated with better cognition, but the role of active bilingualism, the daily use of two languages, on cognitive trajectories remains unclear. One hypothesis is that frequent language switching may protect cognitive trajectories against effects of brain atrophy. Here, we examined interaction effects between language and brain variables on cognition among Hispanic participants at baseline (N = 153) and longitudinally (N = 84). Linguistic measures included self-reported active Spanish–English bilingualism or Spanish monolingualism. Brain measures included, at baseline, regions of gray matter (GM) thickness strongly correlated with cross-sectional episodic memory and executive function and longitudinally, tissue atrophy rates correlated with episodic memory and executive function change. Active Spanish–English bilinguals showed reduced association strength between cognition and gray matter thickness cross-sectionally, β =0.303, p < .01 but not longitudinally, β =0.024, p = 0.105. Thus, active bilingualism may support episodic memory and executive function despite GM atrophy cross-sectionally, but not longitudinally.

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

Positron emission tomography (PET) is the current gold standard for assessing amyloid burden in vivo and is often quantified using standardized uptake value ratios (SUVRs). We evaluated the performance of four SUVR calculation methods for predicting amyloid deposition at autopsy in a group of oldest-old participants. We analyzed data from 165 participants from The 90 + Study with both florbetapir PET and postmortem assessments. PET scans were re-aligned to an older age template using a custom MRI-free pipeline. SUVRs were computed from two target regions—combined posterior cingulate and precuneus (PC 2 ) and an established cortical summary region—and two reference regions—white matter (WM) and cerebellar gray matter. Their predictive performance for amyloid beta positivity (Thal phase ≥ 3) and neuritic plaque positivity (moderate or frequent CERAD score) was evaluated using receiver operating characteristic analyses, including DeLong’s test for comparisons. Participants had a mean (± standard deviation) age at PET of 94 ± 3 years, and age at death of 97 ± 4 years. Most participants had amyloid at autopsy: 113 (68%) were amyloid beta positive, and 104 (63%) were neuritic plaque positive. The best performing SUVR was the PC 2 +WM, yielding an area under the curve (AUC) of 0.84 (95% CI 0.78–0.90) for amyloid beta and 0.82 (95% CI 0.76–0.89) for neuritic plaque, with an optimal cutoff of 0.77 for both outcomes. Reference and target region selection was more consequential for predicting amyloid beta positivity, with the WM consistently outperforming the cerebellar gray matter as a reference region for both target regions, while the PC 2 significantly outperformed the cortical summary region for the cerebellar gray matter reference ( P  < 0.001) but only trended for significance for the WM reference ( P  = 0.069, DeLong’s test). Our findings suggest that using a WM reference and a limited cortical target region may improve PET-neuropathology correspondence, especially in older cohorts.

INTRODUCTION The apolipoprotein E (ApoE) ε4 allele is a key genetic risk for Alzheimer's disease, but its effects may differ by heritage. We examined how ApoE4 influences hippocampal volume, episodic memory, and clinical syndrome across race and ethnicity (ethnoracial) groups. METHODS We analyzed 946 participants with the ApoE genotype, clinical diagnosis, and magnetic resonance imaging scans. Regression and structural equation modeling tested ApoE4's impact on cognition and whether an ethnoracial group consisting of 486 White, 234 Black/African American, or 226 Hispanic/Latino individuals moderated these effects. RESULTS ApoE4 prevalence increased with the clinical syndrome (odds ratio [OR] = 3.0, p < 0.0001). It correlated with lower hippocampal volume (ß = −0.14, p < 0.001) and weaker memory performance (ß = −0.21, p < 0.001). The indirect effect of ApoE4 via hippocampal volume was largest among White participants. DISCUSSION ApoE4's influence on hippocampal volume, episodic memory and clinical syndrome varies by ethnoracial group, with the strongest effects among White individuals. Our findings necessitate further AD biomarker research, as standard markers may not apply universally. Highlights For Whites, the ApoE4 genotype, hippocampal atrophy, and memory performance were associated. For Hispanic/Latinos, the ApoE4 genotype had no significant effect on hippocampal atrophy. For Black/African Americans, the ApoE4 genotype had no significant effect on cognition. Pathological processes associated with cognition differs by race/ethnicity in this cohort.

: To analyze the relationship between blood pressure (BP) variables, including circadian pattern, and cognition in 90+ year-olds. : Twenty-four hour ambulatory BP monitoring was completed on 121 participants drawn from a longitudinal study of aging and dementia in the oldest-old. Various measures of BP and its variability, including nocturnal dipping, were calculated. Each person was given both a neuropsychological test battery covering different cognitive domains and a neurological examination to determine cognitive status. Seventy-one participants had a brain magnetic resonance imaging (MRI) scan. : Participants ranged in age from 90 to 102 years (mean = 93), about two-thirds were female, and nearly 80% had at least some college education. Mean nocturnal dips differed significantly between cognitively normal ( = 97) and impaired individuals ( = 24), with cognitively normal participants having on average greater nocturnal dips [6.6% vs. 1.3%, = 0.006 for systolic BP (SBP); 11% vs. 4.4%, = 0.002 for diastolic BP (DBP)]. Nocturnal dips were also related to performance on select cognitive test scores (especially those related to language, recent memory and visual-spatial ability), with individuals who performed below previously established median norms having significantly smaller nocturnal dips (both SBP and DBP) than those above the median. DBP reverse dippers had larger mean white matter hyperintensities (WMH as percent of total brain volume; 1.7% vs. 1.2%, 1.1% and 1.0% in extreme dippers, dippers, non-dippers) and a greater proportion had lobar cerebral microbleeds (CMBs; 44% vs. 0%, 7%, 16%, < 0.05). Impaired participants had higher mean WMH than those with normal cognition (1.6% vs. 1.0% = 0.03) and more tended to have CMB (31% vs. 20%, p = n.s.). : These findings suggest that cognitive dysfunction is associated with dysregulation in the normal circadian BP pattern. Further study is warranted of the potential role of WHM and CMB as mediators of this association.