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The degree to which genetic factors influence brain connectivity is beginning to be understood. Large-scale efforts are underway to map the profile of genetic effects in various brain regions. The NIH-funded Human Connectome Project (HCP) is providing data valuable for analyzing the degree of genetic influence underlying brain connectivity revealed by state-of-the-art neuroimaging methods. We calculated the heritability of the fractional anisotropy (FA) measure derived from diffusion tensor imaging (DTI) reconstruction in 481 HCP subjects (194/287 M/F) consisting of 57/60 pairs of mono- and dizygotic twins, and 246 siblings. FA measurements were derived using (Enhancing NeuroImaging Genetics through Meta-Analysis) ENIGMA DTI protocols and heritability estimates were calculated using the SOLAR-Eclipse imaging genetic analysis package. We compared heritability estimates derived from HCP data to those publicly available through the ENIGMA-DTI consortium, which were pooled together from five-family based studies across the US, Europe, and Australia. FA measurements from the HCP cohort for eleven major white matter tracts were highly heritable (h2=0.53–0.90, p<10−5), and were significantly correlated with the joint-analytical estimates from the ENIGMA cohort on the tract and voxel-wise levels. The similarity in regional heritability suggests that the additive genetic contribution to white matter microstructure is consistent across populations and imaging acquisition parameters. It also suggests that the overarching genetic influence provides an opportunity to define a common genetic search space for future gene-discovery studies. Uniquely, the measurements of additive genetic contribution performed in this study can be repeated using online genetic analysis tools provided by the HCP ConnectomeDB web application. •Data from 488 HCP subjects were processed using ENIGMA-DTI protocols.•Heritability in HCP sample was compared to ENIGMA-DTI joint-analytical estimates.•Estimates from HCP and ENIGMA-DTI were highly correlated.•Genetic contribution to white matter integrity is consistent across populations.•Defines common genetic search space for future gene-discovery studies

Choosing the appropriate neuroimaging phenotype is critical to successfully identify genes that influence brain structure or function. While neuroimaging methods provide numerous potential phenotypes, their role for imaging genetics studies is unclear. Here we examine the relationship between brain volume, grey matter volume, cortical thickness and surface area, from a genetic standpoint. Four hundred and eighty-six individuals from randomly ascertained extended pedigrees with high-quality T1-weighted neuroanatomic MRI images participated in the study. Surface-based and voxel-based representations of brain structure were derived, using automated methods, and these measurements were analysed using a variance-components method to identify the heritability of these traits and their genetic correlations. All neuroanatomic traits were significantly influenced by genetic factors. Cortical thickness and surface area measurements were found to be genetically and phenotypically independent. While both thickness and area influenced volume measurements of cortical grey matter, volume was more closely related to surface area than cortical thickness. This trend was observed for both the volume-based and surface-based techniques. The results suggest that surface area and cortical thickness measurements should be considered separately and preferred over gray matter volumes for imaging genetic studies.

Ten cosmonauts, who spent an average of 189 days in space, had changes in brain volumes — mainly decreased cortical volume and increased CSF subarachnoid and ventricular volume — with some changes persisting up to an average of 7 months after return to Earth.

Single-cell and single-nuclei RNA sequencing (scRNA-seq) has revolutionized the exploration of tissue biology and cellular heterogeneity by delivering transcriptomic data at the individual cell level. Yet, the logistical challenge of utilizing fresh material has hindered investigations, particularly on human samples. Here, we aimed to address this limitation by implementing and comparing two cryopreservation and scRNA-seq methods for human bronchoalveolar lavage fluid (BALF) cells based on droplet and microwell entrapment. Four BALF samples were collected from routine diagnostic procedures and each sample was divided and processed using both techniques. Although the droplet-based method initially required a greater number of cells for fixation and cryopreservation, cells recovered post-sequencing and quality filtering displayed significantly higher counts of transcripts and genes per cell. This was particularly evident for alveolar macrophages, epithelial cells, mast cells and T cells, while both methodologies were similarly able to capture transcripts from neutrophils. Of note, the microwell-based approach uniquely identified fragile eosinophils. We performed single cell regulatory network inference and clustering (SCENIC) analyses and found that the ability to predict the activities of key transcription factors implicated in the differentiation and identity of BALF immune cells populations correlated with the amounts of transcripts and genes per cell. Our results can serve as a resource for the design of large-scale translational and clinical projects involving scRNA-seq analyses.Single-cell and single-nuclei RNA sequencing (scRNA-seq) has revolutionized the exploration of tissue biology and cellular heterogeneity by delivering transcriptomic data at the individual cell level. Yet, the logistical challenge of utilizing fresh material has hindered investigations, particularly on human samples. Here, we aimed to address this limitation by implementing and comparing two cryopreservation and scRNA-seq methods for human bronchoalveolar lavage fluid (BALF) cells based on droplet and microwell entrapment. Four BALF samples were collected from routine diagnostic procedures and each sample was divided and processed using both techniques. Although the droplet-based method initially required a greater number of cells for fixation and cryopreservation, cells recovered post-sequencing and quality filtering displayed significantly higher counts of transcripts and genes per cell. This was particularly evident for alveolar macrophages, epithelial cells, mast cells and T cells, while both methodologies were similarly able to capture transcripts from neutrophils. Of note, the microwell-based approach uniquely identified fragile eosinophils. We performed single cell regulatory network inference and clustering (SCENIC) analyses and found that the ability to predict the activities of key transcription factors implicated in the differentiation and identity of BALF immune cells populations correlated with the amounts of transcripts and genes per cell. Our results can serve as a resource for the design of large-scale translational and clinical projects involving scRNA-seq analyses.

The hippocampal formation is a brain structure integrally involved in episodic memory, spatial navigation, cognition and stress responsiveness. Structural abnormalities in hippocampal volume and shape are found in several common neuropsychiatric disorders. To identify the genetic underpinnings of hippocampal structure here we perform a genome-wide association study (GWAS) of 33,536 individuals and discover six independent loci significantly associated with hippocampal volume, four of them novel. Of the novel loci, three lie within genes ( ASTN2 , DPP4 and MAST4 ) and one is found 200 kb upstream of SHH . A hippocampal subfield analysis shows that a locus within the MSRB3 gene shows evidence of a localized effect along the dentate gyrus, subiculum, CA1 and fissure. Further, we show that genetic variants associated with decreased hippocampal volume are also associated with increased risk for Alzheimer’s disease ( r g =−0.155). Our findings suggest novel biological pathways through which human genetic variation influences hippocampal volume and risk for neuropsychiatric illness. The hippocampus in mammalian brain varies in size across individuals. Here, Hibar and colleagues perform a genome-wide association meta-analysis to find six genetic loci with significant association to hippocampus volume.

Comprehensive understanding of the human protein-protein interaction (PPI) network, aka the human interactome, can provide important insights into the molecular mechanisms of complex biological processes and diseases. Despite the remarkable experimental efforts undertaken to date to determine the structure of the human interactome, many PPIs remain unmapped. Computational approaches, especially network-based methods, can facilitate the identification of previously uncharacterized PPIs. Many such methods have been proposed. Yet, a systematic evaluation of existing network-based methods in predicting PPIs is still lacking. Here, we report community efforts initiated by the International Network Medicine Consortium to benchmark the ability of 26 representative network-based methods to predict PPIs across six different interactomes of four different organisms: A. thaliana , C. elegans , S. cerevisiae , and H. sapiens . Through extensive computational and experimental validations, we found that advanced similarity-based methods, which leverage the underlying network characteristics of PPIs, show superior performance over other general link prediction methods in the interactomes we considered. Comprehensive understanding of the human protein-protein interaction network, aka the human interactome, can provide important insights into the molecular mechanisms of complex biological processes and diseases. Here the authors summarize the community efforts initiated by the International Network Medicine Consortium to benchmark the ability of 26 representative network-based methods to predict protein-protein interactions.

BackgroundSynovial tissue research has become widely developed in several rheumatology centres, however, large discrepancies exist in the way synovial tissue is handled and, more specifically, how data pertaining to biopsy procedure, quality check and experimental results are reported in the literature. This heterogeneity hampers the progress of research in this rapidly expanding field. In that context, under the umbrella of European Alliance of Associations for Rheumatology, we aimed at proposing points to consider (PtC) for minimal reporting requirements in synovial tissue research.MethodsTwenty-five members from 10 countries across Europe and USA met virtually to define the key areas needing evaluation and formulating the research questions to inform a systematic literature review (SLR). The results were presented during a second virtual meeting where PtC were formulated and agreed.ResultsStudy design, biopsy procedures, tissue handling, tissue quality control and tissue outcomes (imaging, DNA/RNA analysis and disaggregation) were identified as important aspects for the quality of synovial tissue research. The SLR interrogated four databases, retrieved 7654 abstracts and included 26 manuscripts. Three OPs and nine PtC were formulated covering the following areas: description of biopsy procedure, overarching clinical design, patient characteristics, tissue handling and processing, quality control, histopathology, transcriptomic analyses and single-cell technologies.ConclusionsThese PtC provide guidance on how research involving synovial tissue should be reported to ensure a better evaluation of results by readers, reviewers and the broader scientific community. We anticipate that these PtC will enable the field to progress in a robust and transparent manner over the coming years.

Firing rate is an important means of encoding information in the nervous system. To reliably encode a wide range of signals, neurons need to achieve a broad range of firing frequencies and to move smoothly between low and high firing rates. This can be achieved with specific ionic currents, such as A-type potassium currents, which can linearize the frequency-input current curve. By applying recently developed mathematical tools to a number of biophysical neuron models, we show how currents that are classically thought to permit low firing rates can paradoxically cause a jump to a high minimum firing rate when expressed at higher levels. Consequently, achieving and maintaining a low firing rate is surprisingly difficult and fragile in a biological context. This difficulty can be overcome via interactions between multiple currents, implying a need for ion channel degeneracy in the tuning of neuronal properties.

Structural analysis of MRI data on the cortical surface usually focuses on cortical thickness. Cortical surface area, when considered, has been measured only over gross regions or approached indirectly via comparisons with a standard brain. Here we demonstrate that direct measurement and comparison of the surface area of the cerebral cortex at a fine scale is possible using mass conservative interpolation methods. We present a framework for analyses of the cortical surface area, as well as for any other measurement distributed across the cortex that is areal by nature. The method consists of the construction of a mesh representation of the cortex, registration to a common coordinate system and, crucially, interpolation using a pycnophylactic method. Statistical analysis of surface area is done with power-transformed data to address lognormality, and inference is done with permutation methods. We introduce the concept of facewise analysis, discuss its interpretation and potential applications. ► Introduces the concept of facewise analysis. ► Emphasizes the difference between point and area measurements in the cerebral cortex. ► Presents a method for analysis of cortical surface area and other areal quantities. ► Demonstrates that the local surface area is log-normally distributed. ► Discusses interpretation and potential applications.

Identifying genetic variants influencing human brain structures may reveal new biological mechanisms underlying cognition and neuropsychiatric illness. The volume of the hippocampus is a biomarker of incipient Alzheimer's disease and is reduced in schizophrenia, major depression and mesial temporal lobe epilepsy. Whereas many brain imaging phenotypes are highly heritable, identifying and replicating genetic influences has been difficult, as small effects and the high costs of magnetic resonance imaging (MRI) have led to underpowered studies. Here we report genome-wide association meta-analyses and replication for mean bilateral hippocampal, total brain and intracranial volumes from a large multinational consortium. The intergenic variant rs7294919 was associated with hippocampal volume (12q24.22; N = 21,151; P = 6.70 × 10(-16)) and the expression levels of the positional candidate gene TESC in brain tissue. Additionally, rs10784502, located within HMGA2, was associated with intracranial volume (12q14.3; N = 15,782; P = 1.12 × 10(-12)). We also identified a suggestive association with total brain volume at rs10494373 within DDR2 (1q23.3; N = 6,500; P = 5.81 × 10(-7)).