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Cerebral small vessel disease genomics and its implications across the lifespan Muralidharan Sargurupremraj et al. # White matter hyperintensities (WMH) are the most common brain-imaging feature of cerebral small vessel disease (SVD), hypertension being the main known risk factor. Here, we identify 27 genome-wide loci for WMH-volume in a cohort of 50,970 older individuals, accounting for modification/confounding by hypertension. Aggregated WMH risk variants were associated with altered white matter integrity (p = 2.5×10-7) in brain images from 1,738 young healthy adults, providing insight into the lifetime impact of SVD genetic risk. Mendelian randomization suggested causal association of increasing WMH-volume with stroke, Alzheimer-type dementia, and of increasing blood pressure (BP) with larger WMH-volume, notably also in persons without clinical hypertension. Transcriptome-wide colocalization analyses showed association of WMH-volume with expression of 39 genes, of which four encode known drug targets. Finally, we provide insight into BP-independent biological pathways underlying SVD and suggest potential for genetic stratification of high-risk individuals and for genetically-informed prioritization of drug targets for prevention trials.

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MR Tractography, which is based on MRI measures of water diffusivity, is currently the only method available for noninvasive reconstruction of fiber pathways in the brain. However, it has several fundamental limitations that call into question its accuracy in many applications. Therefore, there has been intense interest in defining and mitigating the intrinsic limitations of the method. Recent studies have reported that tractography is inherently limited in its ability to accurately reconstruct the connections of the brain, when based on voxel-averaged estimates of local fiber orientation alone. Several validation studies have confirmed that tractography techniques are plagued by both false positive and false negative connections. However, these validation studies which quantify sensitivity and specificity, particularly in animal models, have not utilized prior anatomical knowledge, as is done in the human literature, for virtual dissection of white matter pathways, instead assessing tractography implemented in a relatively unconstrained manner. Thus, they represent a worse-case scenario for bundle segmentation techniques and may not be indicative of the anatomical accuracy in the process of bundle-segmentation, where streamline filtering using inclusion and exclusion regions of interest is common. With this in mind, the aim of the current study is to investigate and quantify the upper bounds of accuracy using current tractography methods. Making use of the same dataset utilized in two seminal validation papers, we show that prior anatomical knowledge in the form of manually-placed or template-driven constraints can significantly improve the anatomical accuracy of estimated brain connections. Thus, we show that it is possible to achieve a high sensitivity and high specificity simultaneously, and conclude that current tractography algorithms, in combination with anatomically-driven constraints, can result in reconstructions which very accurately reflect the ground truth white matter connections.

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Diffusion magnetic resonance imaging is a noninvasive imaging modality that has been extensively used in the literature to study the neuronal architecture of the brain in a wide range of neurological conditions using tractography. However, recent studies highlighted that the anatomical accuracy of the reconstructions is inherently limited and challenged its appropriateness. Several solutions have been proposed to tackle this issue, but none of them proved effective to overcome this fundamental limitation. In this work, we present a novel processing framework to inject into the reconstruction problem basic prior knowledge about brain anatomy and its organization and evaluate its effectiveness using both simulated and real human brain data. Our results indicate that our proposed method dramatically increases the accuracy of the estimated brain networks and, thus, represents a major step forward for the study of connectivity.

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Parameters of water diffusion in white matter derived from diffusion-weighted imaging (DWI), such as fractional anisotropy (FA), mean, axial, and radial diffusivity (MD, AD, and RD), and more recently, peak width of skeletonized mean diffusivity (PSMD), have been proposed as potential markers of normal and pathological brain ageing. However, their relative evolution over the entire adult lifespan in healthy individuals remains partly unknown during early and late adulthood, and particularly for the PSMD index. Here, we gathered and analyzed cross-sectional diffusion tensor imaging (DTI) data from 10 population-based cohort studies in order to establish the time course of white matter water diffusion phenotypes from post-adolescence to late adulthood. DTI data were obtained from a total of 20,005 individuals aged 18.1 to 92.6 years and analyzed with the same pipeline for computing skeletonized DTI metrics from DTI maps. For each individual, MD, AD, RD, and FA mean values were computed over their FA volume skeleton, PSMD being calculated as the 90% peak width of the MD values distribution across the FA skeleton. Mean values of each DTI metric were found to strongly vary across cohorts, most likely due to major differences in DWI acquisition protocols as well as pre-processing and DTI model fitting. However, age effects on each DTI metric were found to be highly consistent across cohorts. RD, MD, and AD variations with age exhibited the same U-shape pattern, first slowly decreasing during post-adolescence until the age of 30, 40, and 50 years, respectively, then progressively increasing until late life. FA showed a reverse profile, initially increasing then continuously decreasing, slowly until the 70s, then sharply declining thereafter. By contrast, PSMD constantly increased, first slowly until the 60s, then more sharply. These results demonstrate that, in the general population, age affects PSMD in a manner different from that of other DTI metrics. The constant increase in PSMD throughout the entire adult life, including during post-adolescence, indicates that PSMD could be an early marker of the ageing process.

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Investigative studies of white matter (WM) brain structures using diffusion MRI (dMRI) tractography frequently require manual WM bundle segmentation, often called "virtual dissection." Human errors and personal decisions make these manual segmentations hard to reproduce, which have not yet been quantified by the dMRI community. It is our opinion that if the field of dMRI tractography wants to be taken seriously as a widespread clinical tool, it is imperative to harmonize WM bundle

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The Edinburgh Handedness Inventory (EHI) and the Waterloo Footedness Questionnaire (WFQ) are two of the most widely used questionnaires to assess lateralized everyday behavior in human participants. However, it is unclear to what extent the specific behavior assessed in these questionnaires elicit lateralized neural activity when performed in real-life situations. To illuminate this unresolved issue, we assessed EEG alpha and beta asymmetries during real-life performance of the behaviors assessed in the EHI and WFQ using a mobile EEG system. This methodology provides high ecological validity for studying neural correlates of motor behavior under more naturalistic conditions. Our results indicate that behavioral performance of items of both the EHI and WFQ differentiate between left- and right-handers and left- and right-footers on the neural level, especially in the alpha frequency band. These results were unaffected by movement parameters. Furthermore, we could demonstrate that neural activity elicited specifically during left-sided task performance provides predictive power for the EHI or WFQ score of the participants. Overall, our results show that these prominent questionnaires not only distinguish between different motor preferences on the behavioral level, but also on the neurophysiological level. Furthermore, we could show that mobile EEG systems are a powerful tool to investigate motor asymmetries in ecologically valid situations outside of the laboratory setting. Future research should focus on other lateralized behavioral phenotypes in real-life settings to provide more insights into lateralized motor functions.

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Supraspan verbal list-learning tests, such as the Rey Auditory Verbal Learning Test (RAVLT), are classic neuropsychological tests for assessing verbal memory. In this study, we investigated the impact of the meaning of the words to be learned on three memory stages [short-term recall (STR), learning, and delayed recall (DR)] in a cohort of 447 healthy adults. First, we compared scores obtained from the RAVLT (word condition) to those of an alternative version of this test using phonologically similar but meaningless items (pseudoword condition) and observed how each score varied as a function of age and sex. Then, we collected the participants' self-reported strategies to retain the word and pseudoword lists and examined if these strategies mediated the age and sex effects on memory scores. The word condition resulted in higher memory scores than pseudoword condition at each memory stage and even canceled out, for the learning stage, the detrimental effect of age that was observed for the short-term and DR. When taking sex into account, the word advantage was observed only in women for STR. The self-reported strategies, which were similar for words and pseudowords, were based on the position of the item on the list (word: 53%, pseudoword: 37%) or the meaning of the item (word: 64%, pseudoword: 58%) and were used alone or in combination. The best memory performance was associated with the meaning strategy in the word condition and with the combination of the meaning and position strategies in the pseudoword condition. Finally, we found that the word advantage observed in women for STR was mediated by the use of the meaning strategy. The RAVLT scores were thus highly dependent on word meaning, notably because it allowed efficient semantic knowledge-based strategies. Within the framework of Tulving's declarative memory model, these results are at odds with the depiction of the RAVLT as a verbal episodic memory test as it is increasingly referred to in the literature.

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Cortical thickness, surface area and volumes vary with age and cognitive function, and in neurological and psychiatric diseases. Here we report heritability, genetic correlations and genome-wide associations of these cortical measures across the whole cortex, and in 34 anatomically predefined regions. Our discovery sample comprises 22,824 individuals from 20 cohorts within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank. We identify genetic heterogeneity between cortical measures and brain regions, and 160 genome-wide significant associations pointing to wnt/β-catenin, TGF-β and sonic hedgehog pathways. There is enrichment for genes involved in anthropometric traits, hindbrain development, vascular and neurodegenerative disease and psychiatric conditions. These data are a rich resource for studies of the biological mechanisms behind cortical development and aging.

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