Molecular anatomy of adult mouse leptomeninges.

Leptomeninges, consisting of the pia mater and arachnoid, form a connective tissue investment and barrier enclosure of the brain. The exact nature of leptomeningeal cells has long been debated. In this study, we identify five molecularly distinct fibroblast-like transcriptomes in cerebral leptomeninges; link them to anatomically distinct cell types of the pia, inner arachnoid, outer arachnoid barrier, and dural border layer; and contrast them to a sixth fibroblast-like transcriptome present in the choroid plexus and median eminence. Newly identified transcriptional markers enabled molecular characterization of cell types responsible for adherence of arachnoid layers to one another and for the arachnoid barrier. These markers also proved useful in identifying the molecular features of leptomeningeal development, injury, and repair that were preserved or changed after traumatic brain injury. Together, the findings highlight the value of identifying fibroblast transcriptional subsets and their cellular locations toward advancing the understanding of leptomeningeal physiology and pathology

Cerebral small vessel disease genomics and its implications across the lifespan

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.Peer reviewe

Analysis of shared heritability in common disorders of the brain

ience, this issue p. eaap8757 Structured Abstract INTRODUCTION Brain disorders may exhibit shared symptoms and substantial epidemiological comorbidity, inciting debate about their etiologic overlap. However, detailed study of phenotypes with different ages of onset, severity, and presentation poses a considerable challenge. Recently developed heritability methods allow us to accurately measure correlation of genome-wide common variant risk between two phenotypes from pools of different individuals and assess how connected they, or at least their genetic risks, are on the genomic level. We used genome-wide association data for 265,218 patients and 784,643 control participants, as well as 17 phenotypes from a total of 1,191,588 individuals, to quantify the degree of overlap for genetic risk factors of 25 common brain disorders. RATIONALE Over the past century, the classification of brain disorders has evolved to reflect the medical and scientific communities' assessments of the presumed root causes of clinical phenomena such as behavioral change, loss of motor function, or alterations of consciousness. Directly observable phenomena (such as the presence of emboli, protein tangles, or unusual electrical activity patterns) generally define and separate neurological disorders from psychiatric disorders. Understanding the genetic underpinnings and categorical distinctions for brain disorders and related phenotypes may inform the search for their biological mechanisms. RESULTS Common variant risk for psychiatric disorders was shown to correlate significantly, especially among attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder (MDD), and schizophrenia. By contrast, neurological disorders appear more distinct from one another and from the psychiatric disorders, except for migraine, which was significantly correlated to ADHD, MDD, and Tourette syndrome. We demonstrate that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine. We also identify significant genetic sharing between disorders and early life cognitive measures (e.g., years of education and college attainment) in the general population, demonstrating positive correlation with several psychiatric disorders (e.g., anorexia nervosa and bipolar disorder) and negative correlation with several neurological phenotypes (e.g., Alzheimer's disease and ischemic stroke), even though the latter are considered to result from specific processes that occur later in life. Extensive simulations were also performed to inform how statistical power, diagnostic misclassification, and phenotypic heterogeneity influence genetic correlations. CONCLUSION The high degree of genetic correlation among many of the psychiatric disorders adds further evidence that their current clinical boundaries do not reflect distinct underlying pathogenic processes, at least on the genetic level. This suggests a deeply interconnected nature for psychiatric disorders, in contrast to neurological disorders, and underscores the need to refine psychiatric diagnostics. Genetically informed analyses may provide important "scaffolding" to support such restructuring of psychiatric nosology, which likely requires incorporating many levels of information. By contrast, we find limited evidence for widespread common genetic risk sharing among neurological disorders or across neurological and psychiatric disorders. We show that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures. Further study is needed to evaluate whether overlapping genetic contributions to psychiatric pathology may influence treatment choices. Ultimately, such developments may pave the way toward reduced heterogeneity and improved diagnosis and treatment of psychiatric disorders

Meta-analysis of 375,000 individuals identifies 38 susceptibility loci for migraine

Migraine is a debilitating neurological disorder affecting around one in seven people worldwide, but its molecular mechanisms remain poorly understood. There is some debate about whether migraine is a disease of vascular dysfunction or a result of neuronal dysfunction with secondary vascular changes. Genome-wide association (GWA) studies have thus far identified 13 independent loci associated with migraine. To identify new susceptibility loci, we carried out a genetic study of migraine on 59,674 affected subjects and 316,078 controls from 22 GWA studies. We identified 44 independent single-nucleotide polymorphisms (SNPs) significantly associated with migraine risk (P < 5 × 10−8) that mapped to 38 distinct genomic loci, including 28 loci not previously reported and a locus that to our knowledge is the first to be identified on chromosome X. In subsequent computational analyses, the identified loci showed enrichment for genes expressed in vascular and smooth muscle tissues, consistent with a predominant theory of migraine that highlights vascular etiologies

Effect of analysis strategy on reproducibility of detected fMRI activations

this paper is as follows [3]: First, as an initialization step the classification is initialized using Eq. (1). After the initialization, the voxels are re-classified. In the reclassification, a voxel is considered as active if z i + (u i 2) &gt;T (2) and otherwise as non-active. Constant N n is the number of neighbors under consideration. By defining the neighborhood to consist of 26 closest voxels, N n =26. Variable u i is the number of currently active neighborhood voxels of voxel i . The parameter # determines the weighting of neighborhood information, and when positive, encourages neighbors to be of like class. One way to set the # is to write # = s . (3) Parameter s is a user specified parameter and may have any real positive value. Intuitively, s can be understood as a required excess of activated voxels (= u i 2) in the neighborhood of voxel i to transform a value of zero to the level of T . Classification rule (2) is repeated until convergence or oscillation between states occurs. During each iteration the classification and u i &apos;s are updated. It is easy to see that as s approaches the contextual clustering approaches the voxel-by-voxel thresholding technique. As s approaches zero, the contextual clustering approaches a recursive majority vote classificatio

A contextual analysis method for multi-subject fMRI data

In this article, we present a novel approach to create single subject fMRI (functional magnetic resonance imaging) activation maps by utilizing the group data of several subjects on the individual level. Classification of a voxel as either activated or non-activated is based on its z-value and classification in the neighborhood. We defined the neighborhood of a voxel to consist of corresponding and neighboring voxels in two subjects in addition to the neighbourhood voxels within the subject itself. Determination of the two neighboring subjects was based on kappa-statistics between single subject activation maps calculated without data from other subjects. This approach was taken using multi-subject contextual clustering. Both fully simulated and real subject null data with simulated activations were used. ROC (receiver operator characteristics) analysis showed increased classification accuracy of activated and non-activated voxels when using the described approach.</p

Quality of life in rheumatological patients: The impact of personality disorders

PMID = 2593073