A growing number of human disorders have been associated with expansions of a tract of a single amino acid. Recently, polyalanine (polyA) tract expansions in the Aristaless-related homeobox (ARX) protein have been identified in a subset of patients with infantile spasms and mental retardation. How alanine expansions in ARX, or any other transcription factor, cause disease have not been determined. We generated a series of polyA expansions in Arx and expressed these in cell culture and brain slices. Transfection of these constructs results in nuclear protein aggregation, filamentous nuclear inclusions, and an increase in cell death. These inclusions are ubiquitinated and recruit Hsp70. Coexpressing Hsp70 decreases the percentage of cells with nuclear inclusions. Finally, we show that expressing mutant Arx in mouse brains results in neuronal nuclear inclusion formation. Our data suggest expansions in one of the ARX polyA tracts results in nuclear protein aggregation and an increase in cell death; likely underlying the pathogenesis of the associated infantile spasms and mental retardation.

BACKGROUND: Brain perivascular spaces (PVS) are part of the glymphatic system and facilitate clearance of metabolic byproducts. Since enlarged PVS are associated with vascular health, we tested whether intensive systolic blood pressure (SBP) treatment affects PVS structure. METHODS: This is a secondary analysis of the Systolic PRessure INtervention Trial (SPRINT) MRI Substudy: a randomized trial of intensive SBP treatment to goal < 120 mm Hg vs < 140 mm Hg. Participants had increased cardiovascular risk, pre-treatment SBP 130-180, and no clinical stroke, dementia, or diabetes. Brain MRIs acquired at baseline and follow-up were used to automatically segment PVS in the supratentorial white matter and basal ganglia using a Frangi filtering method. PVS volumes were quantified as a fraction of the total tissue volume. The effects of SBP treatment group and major antihypertensive classes on PVS volume fraction were separately tested using linear mixed-effects models while covarying for MRI site, age, sex, Black race, baseline SBP, history of cardiovascular disease (CVD), chronic kidney disease, and white matter hyperintensities (WMH). RESULTS: For 610 participants with sufficient quality MRI at baseline (mean age 67 ± 8, 40 % female, 32 % Black), greater PVS volume fraction was associated with older age, male sex, non-Black race, concurrent CVD, WMH, and brain atrophy. For 381 participants with MRI at baseline and at follow-up (median ± IQR = 3.9 ± 0.4 years), intensive treatment was associated with decreased PVS volume fraction relative to standard treatment (interaction coefficient: -0.029 [-0.055 to -0.0029] p = 0.029). Reduced PVS volume fraction was also associated with exposure to calcium channel blockers (CCB). CONCLUSIONS: PVS enlargement was partially reversed in the intensive SBP treatment group. The association with CCB use suggests that improved vascular compliance may be partly responsible. Improved vascular health may facilitate glymphatic clearance. Clincaltrials.gov: NCT01206062.

We aimed to assess the reliability of cerebral blood flow (CBF) measured using arterial spin labeled (ASL) perfusion magnetic resonance imaging (MRI) from the periventricular white matter (PVWM) by computing its repeatability and comparing to [15O]-water Positron Emission Tomography (PET) as a reference. Simultaneous PET/MRI perfusion data were acquired twice in the same session, about 15 min apart, from 16 subjects (age: 41.4 ± 12.0 years, 9 female). ASL protocols used pseudocontinuous labeling (pCASL) with background-suppressed 3-dimensional readouts, and included both single and multiple post labeling delay (PLD) acquisitions, each acquired twice, with the latter providing both CBF and arterial transit time (ATT) maps. The reliability of ASL derived PVWM CBF was evaluated using intra-session repeatability assessed by the within-subject coefficient of variation (wsCV) of the PVWM CBF values obtained from the two scans, correlation with concurrently-acquired PET CBF values, and by comparing them with that measured in other commonly used regions of interest (ROIs) such as whole brain (WB), gray matter (GM) and white matter (WM). The wsCVs for PVWM CBF with single and multi-PLD acquisitions were 5.7 (95% CI: (3.4,7.7)) % and 6.1 (95% CI: (3.8,8.3))%, which were similar to those obtained from WB, GM and WM CBF even though the PVWM region is the most weakly perfused region of brain parenchyma. Correlations between relative PVWM CBF derived from ASL and from [15O]-water PET were also comparable to the other ROIs. Finally, the ATT of the PVWM region was found to be 1.27 ± 0.27s, which was not an outlier for the arterial circulation of the brain. These findings suggest that PVWM CBF can be reliably measured with the current state-of-the-art ASL methods.

Mutations in the aristaless-related homeobox (ARX) gene are associated with multiple neurologic disorders in humans. Studies in mice indicate Arx plays a role in neuronal progenitor proliferation and development of the cerebral cortex, thalamus, hippocampus, striatum, and olfactory bulbs. Specific defects associated with Arx loss of function include abnormal interneuron migration and subtype differentiation. How disruptions in ARX result in human disease and how loss of Arx in mice results in these phenotypes remains poorly understood. To gain insight into the biological functions of Arx, we performed a genome-wide expression screen to identify transcriptional changes within the subpallium in the absence of Arx. We have identified 84 genes whose expression was dysregulated in the absence of Arx. This population was enriched in genes involved in cell migration, axonal guidance, neurogenesis, and regulation of transcription and includes genes implicated in autism, epilepsy, and mental retardation; all features recognized in patients with ARX mutations. Additionally, we found Arx directly repressed three of the identified transcription factors: Lmo1, Ebf3 and Shox2. To further understand how the identified genes are involved in neural development, we used gene set enrichment algorithms to compare the Arx gene regulatory network (GRN) to the Dlx1/2 GRN and interneuron transcriptome. These analyses identified a subset of genes in the Arx GRN that are shared with that of the Dlx1/2 GRN and that are enriched in the interneuron transcriptome. These data indicate Arx plays multiple roles in forebrain development, both dependent and independent of Dlx1/2, and thus provides further insights into the understanding of the mechanisms underlying the pathology of mental retardation and epilepsy phenotypes resulting from ARX mutations.

Objective

In line with cognitive reserve theory, higher occupational cognitive complexity is associated with reduced cognitive decline in older adulthood. How and when occupational cognitive complexity first exerts protective effects during the life span remains unclear. We investigated associations between occupational cognitive complexity during early to midadulthood and brain structure and cognition in midlife.

Method

Participants were 669 adults from the Coronary Artery Risk Development in Young Adults study (aged 18-30 years at baseline, 52% female, 38% Black). We calculated scores reflecting occupational cognitive complexity using Census Occupation Codes (years 10 and 15) and Occupational Information Network (O*NET) data. At year 25, participants had structural brain magnetic resonance imaging, diffusion tensor imaging, and cognitive testing (Rey Auditory Verbal Learning Test, Digit Symbol Substitution Test, Stroop). In adjusted mixed models, we examined associations between occupational cognitive complexity during early to midadulthood and midlife brain structure, specifically gray matter volume and white matter fractional anisotropy, and cognition in midlife (all outcomes converted to z-scores).

Results

Higher occupational cognitive complexity was associated with greater white matter fractional anisotropy (estimate = 0.10, p = .01) but not gray matter volume. Higher occupational cognitive complexity was associated with better Digit Symbol Substitution Test (estimate = 0.13, p < .001) and Stroop (estimate = 0.09, p = .01) performance but not Rey Auditory Verbal Learning Test performance.

Conclusions

Occupational cognitive complexity earlier in adulthood is associated with better white matter integrity, processing speed, and executive function in midlife. These associations may capture how occupational cognitive complexity contributes to cognitive reserve. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

BACKGROUND: High blood pressure (BP) is a risk factor for late-life brain health; however, the association of elevated BP with brain health in mid-life is unclear. METHODS: We identified 661 participants from the Coronary Artery Risk Development in Young Adults Study (age 18-30 at baseline) with 30 years of follow-up and brain magnetic resonance imaging at year 30. Cumulative exposure of BP was estimated by time-weighted averages (TWA). Ideal cardiovascular health was defined as systolic BP < 120 mm Hg, diastolic BP < 80 mm Hg. Brain age was calculated using previously validated high dimensional machine learning pattern analyses. RESULTS: Every 5 mmHg increment in TWA systolic BP was associated with approximately 1-year greater brain age (95% confidence interval [CI]: 0.50-1.36) Participants with TWA systolic or diastolic BP over the recommended guidelines for ideal cardiovascular health, had on average 3-year greater brain age (95% CI: 1.00-4.67; 95% CI: 1.45-5.13, respectively). CONCLUSION: Elevated BP from early to mid adulthood, even below clinical cut-offs, is associated with advanced brain aging in mid-life.

Study objectives

Sleep duration has been associated with risk of dementia and stroke, but few studies have investigated the relationship between sleep duration and brain MRI measures, particularly in middle age.

Methods

In a prospective cohort of 613 black and white adults (mean age = 45.4 years) enrolled in the Coronary Artery Risk Development in Young Adults (CARDIA) study, participants reported typical sleep duration, dichotomized into moderate sleep duration (> 6 to ≤ 8 h) and short sleep duration (≤ 6 h) at baseline (2005-2006). Five years later, we obtained brain MRI markers of white matter including fractional anisotropy, mean diffusivity, and white matter hyperintensities.

Results

Compared to moderate sleepers, short sleepers had an elevated ratio of white matter hyperintensities to normal tissue in the parietal region (OR = 2.31, 95% CI: 1.47, 3.61) adjusted for age, race/sex, education, hypertension, stroke/TIA, depression, smoking status, and physical activity. White matter diffusivity was also higher, approximately a 0.2 standard deviation difference, in frontal, parietal, and temporal white matter regions, among those reporting shorter sleep duration in (P < 0.05 for all).

Conclusions

Short sleep duration was associated with worse markers of white matter integrity in midlife. These mid-life differences in white matter may underlie the link between poor sleep and risk of dementia and stroke.

Alzheimer's disease (AD) is defined by amyloid (A) and tau (T) pathologies, with T better correlated to neurodegeneration (N). However, T and N have complex regional relationships in part related to non-AD factors that influence N. With machine learning, we assessed heterogeneity in ¹⁸F-flortaucipir vs. ¹⁸F-fluorodeoxyglucose positron emission tomography as markers of T and neuronal hypometabolism (N_M) in 289 symptomatic patients from the Alzheimer's Disease Neuroimaging Initiative. We identified six T/N_M clusters with differing limbic and cortical patterns. The canonical group was defined as the T/N_M pattern with lowest regression residuals. Groups resilient to T had less hypometabolism than expected relative to T and displayed better cognition than the canonical group. Groups susceptible to T had more hypometabolism than expected given T and exhibited worse cognitive decline, with imaging and clinical measures concordant with non-AD copathologies. Together, T/N_M mismatch reveals distinct imaging signatures with pathobiological and prognostic implications for AD.

Objective

We examined imaging surrogates of white matter microstructural abnormalities which may precede white matter lesions (WML) and represent a relevant marker of cerebrovascular injury in adults in midlife.

Methods

In 698 community-dwelling adults (mean age 50 years ±3.5 SD) from the Coronary Artery Risk Development in Young Adults (CARDIA) Brain MRI sub-study, WML were identified on structural MR and fractional anisotropy (FA), representing WM microstructural integrity, was derived using Diffusion Tensor Imaging. FA and WML maps were overlaid on a parcellated T1-template, based on an expert-delineated brain atlas, which included 42 WM tract ROIs. Analyses occurred in stages: 1) WML were quantified for the different tracts (i.e., frequency, volume, volume relative to tract size); 2) the interdependence of FA in normal appearing WM (NAWM) and WML was examined across tracts; 3) associations of NAWM FA and hypertension status were assessed controlling for WML volume. In the latter analysis, both overall hypertension (i.e. hypertension vs. normotension and prehypertension vs. normotension) and hypertension categorized by antihypertensive treatment status (yes/no) and blood pressure control (e.g., diastolic <90 mmHg, systolic <140 mmHg), were assessed.

Results

WML were widely distributed across different WM tracts, however, WML volume was small. Mean NAWM FA was lower in participants with vs. participants without WML in given tracts. Hypertension was significantly associated with lower mean NAWM FA globally across tracts, both before and after adjustment for WML volume. Moreover, the magnitude of this association differed by treatment status and the level of control of the hypertension.

Conclusions

In middle-aged adults, NAWM FA could represent a relevant marker of cerebrovascular injury when WML are minimally present.

Background Although artificial intelligence (AI) shows promise across many aspects of radiology, the use of AI to create differential diagnoses for rare and common diseases at brain MRI has not been demonstrated. Purpose To evaluate an AI system for generation of differential diagnoses at brain MRI compared with radiologists. Materials and Methods This retrospective study tested performance of an AI system for probabilistic diagnosis in patients with 19 common and rare diagnoses at brain MRI acquired between January 2008 and January 2018. The AI system combines data-driven and domain-expertise methodologies, including deep learning and Bayesian networks. First, lesions were detected by using deep learning. Then, 18 quantitative imaging features were extracted by using atlas-based coregistration and segmentation. Third, these image features were combined with five clinical features by using Bayesian inference to develop probability-ranked differential diagnoses. Quantitative feature extraction algorithms and conditional probabilities were fine-tuned on a training set of 86 patients (mean age, 49 years ± 16 [standard deviation]; 53 women). Accuracy was compared with radiology residents, general radiologists, neuroradiology fellows, and academic neuroradiologists by using accuracy of top one, top two, and top three differential diagnoses in 92 independent test set patients (mean age, 47 years ± 18; 52 women). Results For accuracy of top three differential diagnoses, the AI system (91% correct) performed similarly to academic neuroradiologists (86% correct; P = .20), and better than radiology residents (56%; P < .001), general radiologists (57%; P < .001), and neuroradiology fellows (77%; P = .003). The performance of the AI system was not affected by disease prevalence (93% accuracy for common vs 85% for rare diseases; P = .26). Radiologists were more accurate at diagnosing common versus rare diagnoses (78% vs 47% across all radiologists; P < .001). Conclusion An artificial intelligence system for brain MRI approached overall top one, top two, and top three differential diagnoses accuracy of neuroradiologists and exceeded that of less-specialized radiologists. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Zaharchuk in this issue.