Search

There is a consensus that substantial heterogeneity underlies the neurobiology of autism spectrum disorder (ASD). As such, it has become increasingly clear that a dissection of variation at the molecular, cellular, and brain network domains is a prerequisite for identifying biomarkers. Neuroimaging has been widely used to characterize atypical brain patterns in ASD, although findings have varied across studies. This is due, at least in part, to a failure to account for neurobiological heterogeneity. Here, we summarize emerging data-driven efforts to delineate more homogeneous ASD subgroups at the level of brain structure and function—that is, neurosubtyping. We break this pursuit into key methodological steps: the selection of diagnostic samples, neuroimaging features, algorithms, and validation approaches. Although preliminary and methodologically diverse, current studies generally agree that at least 2 to 4 distinct ASD neurosubtypes may exist. Their identification improved symptom prediction and diagnostic label accuracy above and beyond group average comparisons. Yet, this nascent literature has shed light onto challenges and gaps. These include 1) the need for wider and more deeply transdiagnostic samples collected while minimizing artifacts (e.g., head motion), 2) quantitative and unbiased methods for feature selection and multimodal fusion, 3) greater emphasis on algorithms’ ability to capture hybrid dimensional and categorical models of ASD, and 4) systematic independent replications and validations that integrate different units of analyses across multiple scales. Solutions aimed to address these challenges and gaps are discussed for future avenues leading toward a comprehensive understanding of the mechanisms underlying ASD heterogeneity.

STRUCTURED ABSTRACTObjectivesTo expose the potential impact of residual confounding in common observational study designs investigating metformin using a type 2 diabetes cohort; to propose a more robust study design for future observational studies of metformin.DesignRetrospective cohort studies using a prevalent user design conducted in two distinct cohorts: individuals with type 2 diabetes and individuals with prediabetes.SettingInsurance claims database for Medicare Advantage beneficiaries in the United States, 2018-2019. An identical analysis of commercial insurance beneficiaries appears in the supplement.Participants404,765 individuals with type 2 diabetes, 81,791 individuals with prediabetes.Main outcome measuresTotal inpatient admission days in 2019, total medical spend (excluding prescription drugs) in 2019. Each of these measures is treated as a binary outcome: greater than zero inpatient days and top 10% medical spend.ResultsWe implement a common observational study design and observe a strong metformin effect estimate associated with reduced inpatient admissions and reduced medical expenditures; we also implement a more robust study design that suggests any estimated effect is attributable to residual confounding related to individuals’ overall health.ConclusionsCommon observational study designs examining metformin in a type 2 diabetes population are likely impacted by significant residual confounding. By additionally considering numerous negative control outcomes and a complementary prediabetes cohort, the study design proposed here demonstrates efficacy at exposing residual confounding related to overall health, nullifying the claim derived from a standard study design.Trial registrationPreregistration available at https://osf.io/qf49p.

ImportanceAlpha-1-adrenergic receptor antagonists (α1-blockers) can abrogate pro-inflammatory cytokines and may improve outcomes among patients with respiratory infections. Repurposing readily available drugs such as α1-blockers could augment the medical response to the COVID-19 pandemic.ObjectiveTo evaluate the association between α1-blocker exposure and COVID-19 mortalityDesignReal-world evidence studySettingPatient level data with 32,355 records tested for SARS-CoV-2 at the Mount Sinai Health System including 8,442 laboratory-confirmed cases extracted from five member hospitals in the New York City metropolitan area.Participants2,627 men aged 45 or older admitted with COVID-19 between February 24 and May 31, 2020Exposuresα1-blocker use as an outpatient or while admitted for COVID-19Main Outcomes and MeasuresIn-hospital mortalityResultsMen exposed to α1-blockers (N=436) were older (median age 73 vs. 64 years, P1-blockers was not associated with COVID-19 hospital outcomes, though there was a trend towards significance (OR 0.749, 95% CI 0.527-1.064; P=0.106). Conversely, inpatient use of α1-blockers was independently associated with improved in-hospital mortality in both multivariable logistic (OR 0.633, 95% CI 0.434-0.921; P=0.017) and Cox regression analyses (HR 0.721, 95% CI 0.572-0.908; P=0.006) adjusting for patient demographics, comorbidities, and baseline vitals and labs. Age-stratified analyses suggested greater benefit from inpatient α1-blocker use among younger age groups: Age 45-65 OR 0.384, 95% CI 0.164-0.896 (P=0.027); Age 55-75 OR 0.511, 95% CI 0.297-0.880 (P=0.015); Age 65-89 OR 0.810, 95% CI 0.509-1.289 (P=0.374).Conclusions and RelevanceInpatient α1-blocker use was independently associated with improved COVID-19 mortality among hospitalized men. Clinical trials to assess the therapeutic value of α1-blockers in COVID-19 are warranted.

Using brain atlases to localize regions of interest is a required for making neuroscientifically valid statistical inferences. These atlases, represented in volumetric or surface coordinate spaces, can describe brain topology from a variety of perspectives. Although many human brain atlases have circulated the field over the past fifty years, limited effort has been devoted to their standardization and specification. The purpose of standardization and specification is to ensure consistency and transparency with respect to orientation, resolution, labeling scheme, file storage format, and coordinate space designation. Consequently, researchers are often confronted with limited knowledge about a given atlas’s organization, which make analytic comparisons more difficult. To fill this gap, we consolidate an extensive selection of popular human brain atlases into a single, curated open-source library, where they are stored following a standardized protocol with accompanying metadata. We propose that this protocol serves as the basis for storing future atlases. To demonstrate the utility of storing and standardizing these atlases following a common protocol, we conduct an experiment using data from the Healthy Brain Network whereby we quantify the statistical dependence of each atlas label on several key phenotypic variables. The repository containing the atlases, the specification, as well as relevant transformation functions is available at https://neurodata.io/mri

Effective therapies for coronavirus disease 2019 (COVID-19) are urgently needed, and pre-clinical data suggest alpha-1 adrenergic receptor antagonists (α1-AR antagonists) may be effective in reducing mortality related to hyperinflammation independent of etiology. Using a retrospective cohort design with patients in the Department of Veterans Affairs healthcare system, we use doubly robust regression and matching to estimate the association between baseline use of α1-AR antagonists and likelihood of death due to COVID-19 during hospitalization. Having an active prescription for any α1-AR antagonist (tamsulosin, silodosin, prazosin, terazosin, doxazosin, or alfuzosin) at the time of admission had a significant negative association with in-hospital mortality (relative risk reduction 18%; odds ratio 0.73; 95% CI 0.63–0.85; p ≤ 0.001) and death within 28 days of admission (relative risk reduction 17%; odds ratio 0.74; 95% CI 0.65–0.84; p ≤ 0.001). In a subset of patients on doxazosin specifically, an inhibitor of all three alpha-1 adrenergic receptors, we observed a relative risk reduction for death of 74% (odds ratio 0.23; 95% CI 0.03–0.94; p = 0.028) compared to matched controls not on any α1-AR antagonist at the time of admission. These findings suggest that use of α1-AR antagonists may reduce mortality in COVID-19, supporting the need for randomized, placebo-controlled clinical trials in patients with early symptomatic infection.

Genome-wide association studies have demonstrated significant links between human brain structure and common DNA variants. Similar studies with rodents have been challenging because of smaller brain volumes. Using high field MRI (9.4 T) and compressed sensing, we have achieved microscopic resolution and sufficiently high throughput for rodent population studies. We generated whole brain structural MRI and diffusion connectomes for four diverse isogenic lines of mice (C57BL/6J, DBA/2J, CAST/EiJ, and BTBR) at spatial resolution 20,000 times higher than human connectomes. We measured narrow sense heritability (h2) I.e. the fraction of variance explained by strains in a simple ANOVA model for volumes and scalar diffusion metrics, and estimates of residual technical error for 166 regions in each hemisphere and connectivity between the regions. Volumes of discrete brain regions had the highest mean heritability (0.71 ± 0.23 SD, n = 332), followed by fractional anisotropy (0.54 ± 0.26), radial diffusivity (0.34 ± 0.022), and axial diffusivity (0.28 ± 0.19). Connection profiles were statistically different in 280 of 322 nodes across all four strains. Nearly 150 of the connection profiles were statistically different between the C57BL/6J, DBA/2J, and CAST/EiJ lines. Microscopic whole brain MRI/DTI has allowed us to identify significant heritable phenotypes in brain volume, scalar DTI metrics, and quantitative connectomes.

A common coordinate space enabling comparison across individuals is vital to understanding human brain organization and individual differences. By leveraging dimensionality reduction algorithms, high-dimensional fMRI data can be represented in a low-dimensional space to characterize individual features. Such a representative space encodes the functional architecture of individuals and enables the observation of functional changes across time. However, determining comparable functional features across individuals in resting-state fMRI in a way that simultaneously preserves individual-specific connectivity structure can be challenging. In this work we propose scalable joint embedding to simultaneously embed multiple individual brain connectomes within a common space that allows individual representations across datasets to be aligned. Using Human Connectome Project data, we evaluated the joint embedding approach by comparing it to the previously established orthonormal alignment model. Alignment using joint embedding substantially increased the similarity of functional representations across individuals while simultaneously capturing their distinct profiles, allowing individuals to be more discriminable from each other. Additionally, we demonstrated that the common space established using resting-state fMRI provides a better overlap of task-activation across participants. Finally, in a more challenging scenario - alignment across a lifespan cohort aged from 6 to 85 - joint embedding provided a better prediction of age (r2 = 0.65) than the prior alignment model. It facilitated the characterization of functional trajectories across lifespan. Overall, these analyses establish that joint embedding can simultaneously capture individual neural representations in a common connectivity space aligning functional data across participants and populations and preserve individual specificity.

Medications that target catecholamine-associated inflammation may prevent cytokine storm syndrome associated with COVID-19 and other diseases

The mortality of Coronavirus disease 2019 (COVID-19) appears to be driven by acute respiratory distress syndrome (ARDS) and a dysregulated immune response to SARS-CoV-2. Emerging evidence suggests that a subset of COVID-19 is characterized by the development of a cytokine storm syndrome (CSS), and interleukin (IL)-6 levels are predictors of COVID-19 severity and in-hospital mortality. Targeting hyper-inflammation in COVID-19 may be critical for reducing mortality. Catecholamines enhance inflammatory injury by augmenting the production of IL-6 and other cytokines through a self-amplifying feed-forward loop in immune cells that requires alpha-1 adrenergic receptor (α1-AR) signaling. Prophylactic inhibition of catecholamine synthesis with the α1-AR antagonist prazosin reduced catecholamines and cytokine responses in mice, and resulted in markedly increased survival following various hyper-inflammatory stimuli. These findings offer a rationale for studying α1-AR antagonists in the prophylaxis of patients with COVID-19-CSS and ARDS. As high infection rates threaten to overwhelm hospital capacity during this pandemic, preventative approaches that ameliorate COVID-19 severity and reduce excessive mortality are desperately needed. We hypothesize that treatment with prazosin of individuals who test positive for SARS-CoV-2 could reduce catecholamine surges, secondary cytokine dysregulation, and mortality. To investigate a potential role for α1-AR antagonists in preventing poor outcomes in ARDS, we conducted a retrospective analysis of hospitalized patients diagnosed with ARDS. Using data from the Truven Health MarketScan Research Database (2010-2017), we identified 13,125 men (age 45-64) with ARDS, of whom 655 patients (5.0%) were prescribed α1-AR antagonists in the previous year. Applying logistic regression models, we found that patients with prior use of α1-AR antagonists had lower odds of invasive mechanical ventilation compared to non-users (adjusted OR=0.75, 95% CI 0.59-0.95, p=0.019). Perhaps more importantly, those patients had a ~36% lower incidence of both being ventilated and dying in the hospital (adjusted OR=0.59, 95% CI 0.34-0.95, p=0.042). By contrast, prior use of beta-adrenergic receptor (β-AR) antagonists was not correlated with either outcome. We extended these analyses to patients admitted with pneumonia. Of 108,956 subjects in this cohort, 5,498 patients (5.0%) were taking α1-AR antagonist. Similar to ARDS, patients with pneumonia on α1-AR antagonists (but no β-AR antagonists) had a lower odds of mechanical ventilation (adjusted OR=0.83, 95% CI 0.75-0.92, p

We here describe a selected reaction monitoring (SRM)-based approach for the discovery and validation of peptide biomarkers for cancer. The first stage of this approach is the direct identification of candidate peptides through comparison of proteolytic peptides derived from the plasma of cancer patients or healthy individuals. Several hundred candidate peptides were identified through this method, providing challenges for choosing and validating the small number of peptides that might prove diagnostically useful. To accomplish this validation, we used 2D chromatography coupled with SRM of candidate peptides. We applied this approach, called sequential analysis of fractionated eluates by SRM (SAFE-SRM), to plasma from cancer patients and discovered two peptides encoded by the peptidyl-prolyl cis-trans isomerase A (PPIA) gene whose abundance was increased in the plasma of ovarian cancer patients. At optimal thresholds, elevated levels of at least one of these two peptides was detected in 43 (68.3%) of 63 women with ovarian cancer but in none of 50 healthy controls. In addition to providing a potential biomarker for ovarian cancer, this approach is generally applicable to the discovery of peptides characteristic of various disease states.

Investigating the dense meshwork of wires and synapses that form neuronal circuits is possible with the high resolution of serial-section electron microscopy (ssEM)1. However, the imaging scale required to comprehensively reconstruct axons and dendrites is more than 10 orders of magnitude smaller than the spatial extents occupied by networks of interconnected neurons2—some of which span nearly the entire brain. The difficulties in generating and handling data for relatively large volumes at nanoscale resolution has thus restricted all studies in vertebrates to neuron fragments, thereby hindering investigations of complete circuits. These efforts were transformed by recent advances in computing, sample handling, and imaging techniques1, but examining entire brains at high resolution remains a challenge. Here we present ssEM data for a complete 5.5 days post-fertilisation larval zebrafish brain. Our approach utilizes multiple rounds of targeted imaging at different scales to reduce acquisition time and data management. The resulting dataset can be analysed to reconstruct neuronal processes, allowing us to, for example, survey all the myelinated axons (the projectome). Further, our reconstructions enabled us to investigate the precise projections of neurons and their contralateral counterparts. In particular, we observed that myelinated axons of reticulospinal and lateral line afferent neurons exhibit remarkable bilateral symmetry. Additionally, we found that fasciculated reticulospinal axons maintain the same neighbour relations throughout the extent of their projections. Furthermore, we use the dataset to set the stage for whole-brain comparisons of structure and function by co-registering functional reference atlases and in vivo two-photon fluorescence microscopy data from the same specimen. We provide the complete dataset and reconstructions as an open-access resource for neurobiologists and others interested in the ultrastructure of the larval zebrafish.

SUMMARYGenome-wide association studies have demonstrated significant links between human brain structure and common DNA variants. Similar studies with rodents have been challenging because of smaller brain volumes. Using high field MRI (9.4T) and compressed sensing, we have achieved microscopic resolution and sufficiently high throughput for rodent population studies. We generated whole brain structural MRI and diffusion connectomes for four diverse isogenic lines of mice (C57BL/6J, DBA/2J, CAST/EiJ, and BTBR) at spatial resolution 20,000 times higher than human connectomes. We derived volumes, scalar diffusion metrics, and estimates of residual technical error for 166 regions in each hemisphere and connectivity between the regions. Volumes of discrete brain regions had the highest mean heritability (0.71 ± 0.23 SD, n = 332), followed by fractional anisotropy (0.54 ± 0.26), radial diffusivity (0.34 ± 0.022), and axial diffusivity (0.28 ± 0.19). Connection profiles were statistically different in 280 of 322 nodes across all four strains. Nearly 150 of the connection profiles were statistically different between the C57BL/6J, DBA/2J, and CAST/EiJ lines.

Wearable devices provide a means of tracking hand position in relation to the head, but have mostly relied on wrist-worn inertial measurement unit sensors and proximity sensors, which are inadequate for identifying specific locations. This limits their utility for accurate and precise monitoring of behaviors or providing feedback to guide behaviors. A potential clinical application is monitoring body-focused repetitive behaviors (BFRBs), recurrent, injurious behaviors directed toward the body, such as nail biting and hair pulling, which are often misdiagnosed and undertreated. Here, we demonstrate that including thermal sensors achieves higher accuracy in position tracking when compared against inertial measurement unit and proximity sensor data alone. Our Tingle device distinguished between behaviors from six locations on the head across 39 adult participants, with high AUROC values (best was back of the head: median (1.0), median absolute deviation (0.0); worst was on the cheek: median (0.93), median absolute deviation (0.09)). This study presents preliminary evidence of the advantage of including thermal sensors for position tracking and the Tingle wearable device’s potential use in a wide variety of settings, including BFRB diagnosis and management.

Key Points Question Is the receipt of α1–adrenergic receptor blocking agents (α1-blockers) associated with protective benefits against adverse outcomes, such as mortality and intensive care unit admission, among adult patients with severe respiratory tract infections? Findings In this cohort study of 528 467 Danish adults hospitalized with influenza or pneumonia, current receipt of α1-blockers was associated with a 14% reduction in the relative risk of 30-day mortality. Meaning This study’s findings suggest that the receipt of α1-blockers may have a clinically relevant association with protective benefits against adverse outcomes among patients with severe respiratory tract infections., Importance Alpha 1–adrenergic receptor blocking agents (α1-blockers) have been reported to have protective benefits against hyperinflammation and cytokine storm syndrome, conditions that are associated with mortality in patients with coronavirus disease 2019 and other severe respiratory tract infections. However, studies of the association of α1-blockers with outcomes among human participants with respiratory tract infections are scarce. Objective To examine the association between the receipt of α1-blockers and outcomes among adult patients hospitalized with influenza or pneumonia. Design, Setting, and Participants This population-based cohort study used data from Danish national registries to identify individuals 40 years and older who were hospitalized with influenza or pneumonia between January 1, 2005, and November 30, 2018, with follow-up through December 31, 2018. In the main analyses, patients currently receiving α1-blockers were compared with those not receiving α1-blockers (defined as patients with no prescription for an α1-blocker filled within 365 days before the index date) and those currently receiving 5α-reductase inhibitors. Propensity scores were used to address confounding factors and to compute weighted risks, absolute risk differences, and risk ratios. Data were analyzed from April 21 to December 21, 2020. Exposures Current receipt of α1-blockers compared with nonreceipt of α1-blockers and with current receipt of 5α-reductase inhibitors. Main Outcomes and Measures Death within 30 days of hospital admission and risk of intensive care unit (ICU) admission. Results A total of 528 467 adult patients (median age, 75.0 years; interquartile range, 64.4-83.6 years; 273 005 men [51.7%]) were hospitalized with influenza or pneumonia in Denmark between 2005 and 2018. Of those, 21 772 patients (4.1%) were currently receiving α1-blockers compared with a population of 22 117 patients not receiving α1-blockers who were weighted to the propensity score distribution of those receiving α1-blockers. In the propensity score–weighted analyses, patients receiving α1-blockers had lower 30-day mortality (15.9%) compared with patients not receiving α1-blockers (18.5%), with a corresponding risk difference of −2.7% (95% CI, −3.2% to −2.2%) and a risk ratio (RR) of 0.85 (95% CI, 0.83-0.88). The risk of ICU admission was 7.3% among patients receiving α1-blockers and 7.7% among those not receiving α1-blockers (risk difference, −0.4% [95% CI, −0.8% to 0%]; RR, 0.95 [95% CI, 0.90-1.00]). A comparison between 18 280 male patients currently receiving α1-blockers and 18 228 propensity score–weighted male patients currently receiving 5α-reductase inhibitors indicated that those receiving α1-blockers had lower 30-day mortality (risk difference, −2.0% [95% CI, −3.4% to −0.6%]; RR, 0.89 [95% CI, 0.82-0.96]) and a similar risk of ICU admission (risk difference, −0.3% [95% CI, −1.4% to 0.7%]; RR, 0.96 [95% CI, 0.83-1.10]). Conclusions and Relevance This cohort study’s findings suggest that the receipt of α1-blockers is associated with protective benefits among adult patients hospitalized with influenza or pneumonia., This cohort study uses data from national registries to examine the association between the receipt of α1–adrenergic receptor blockers and the risks of 30-day mortality and intensive care unit admission among adult patients hospitalized with influenza or pneumonia in Denmark between 2005 and 2018.

Developing methods that increase the reproducibility and reliability of neuroimaging measurement is an important challenge in clinical and cognitive neuroscience neuroimaging. One particular area of importance is estimation of functional areal organization, often studied through functional parcellation of the brain. Functional areal organization shows substantial variance across individuals, and creating more reproducible and reliable functional areal parcellations would allow for more generalizable estimates of brain organization. We apply bootstrap aggregation, or bagging, to the problem of improving reproducibility in functional parcellation. We use two test-retest datasets, one of 30 young adults scanned 10 times for ten minutes per scan, and another of 300 young adults scanned twice for six minutes per scan, to demonstrate that bagging provides functional parcellations with higher reproducibility and reliability compared to non-bagged functional parcellation. While increasing scan length and sample size have been regarded as the main methods of improving robustness of estimating functional organization, our results demonstrate that bagging can be used to boost the robustness of functional parcellation with as little as five minutes of scan time in as few as thirty subjects. These results imply bagging can be used to improve the robustness in acquisitions with a short scan time, which is commonplace in many established and ongoing studies and open source datasets. By testing an array of different reproducibility metrics, datasets, cluster levels, and acquisition lengths, we show where bagging can improve the reproducibility and reliability of functional parcellations. Overall, it seems that the use of this approach is beneficial in creating more reproducible clusters, and bagging should be applied when reproducibility of functional parcellations are under consideration.

Background: Surgical removal of malignancies may enhance the risk of recurrence but mechanisms remain poorly understood. Neutrophils, first line responders after surgical stress, can facilitate cancer progression. Neutrophils can expel nuclear DNA studded with proteins to form web-like structures known as Neutrophil Extracellular Traps (NETs) in response to stress. Here we aim to characterize the role of NETs in promoting metastatic cancer growth after surgical stress that is currently entirely unknown. Methods: We used a standardized mouse model of partial hepatic ischemia reperfusion, to stimulate surgical stress, directly or five days after splenic injection of MC38 colon carcinoma cells. Results: We found that NET formation is significantly increased in the immediate post-operative period in humans undergoing curative liver resection for colorectal cancer metastases. The risk of liver metastases recurrence was 4.22 times higher in patients with increased post-operative NET formation. In the murine model, we demonstrated increased NET deposition after surgical stress and this was associated with both increased formation of metastases and growth of already established micrometastatic disease. In vitro, NETs increase growth, invasion, migration and adhesion in multiple cancer cell lines. All the above effects were abrogated by NET inhibition with DNAse. NETs exert their pro-tumorigenic effects by releasing HMGB1 and activating TLR-9 dependent pathways in the cancer cells. In addition, as metastatic tumors continue to grow, hypoxia within the tumor microenvironment induces further neutrophil recruitment and NET formation that further accelerates tumor growth. Conclusions: These findings implicate NETs in the process of cancer metastasis progression and recurrence after surgery and identifies NETs as potential therapeutic targets. Citation Format: Samer Tohme, Hai Huang, Alexis Chidi, Richard Simmons, Allan Tsung. The role of neutrophil extracellular traps in promoting metastatic cancer following surgical stress. [abstract]. In: Proceedings of the AACR Special Conference on Tumor Metastasis; 2015 Nov 30-Dec 3; Austin, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(7 Suppl):Abstract nr A30.

Understanding the relationships between different properties of data, such as whether a genome or connectome has information about disease status, is increasingly important. While existing approaches can test whether two properties are related, they may require unfeasibly large sample sizes and often are not interpretable. Our approach, ‘Multiscale Graph Correlation’ (MGC), is a dependence test that juxtaposes disparate data science techniques, including k-nearest neighbors, kernel methods, and multiscale analysis. Other methods may require double or triple the number of samples to achieve the same statistical power as MGC in a benchmark suite including high-dimensional and nonlinear relationships, with dimensionality ranging from 1 to 1000. Moreover, MGC uniquely characterizes the latent geometry underlying the relationship, while maintaining computational efficiency. In real data, including brain imaging and cancer genetics, MGC detects the presence of a dependency and provides guidance for the next experiments to conduct., eLife digest If you want to estimate whether height is related to weight in humans, what would you do? You could measure the height and weight of a large number of people, and then run a statistical test. Such ‘independence tests’ can be thought of as a screening procedure: if the two properties (height and weight) are not related, then there is no point in proceeding with further analyses. In the last 100 years different independence tests have been developed. However, classical approaches often fail to accurately discern relationships in the large, complex datasets typical of modern biomedical research. For example, connectomics datasets include tens or hundreds of thousands of connections between neurons that collectively underlie how the brain performs certain tasks. Discovering and deciphering relationships from these data is currently the largest barrier to progress in these fields. Another drawback to currently used methods of independence testing is that they act as a ‘black box’, giving an answer without making it clear how it was calculated. This can make it difficult for researchers to reproduce their findings – a key part of confirming a scientific discovery. Vogelstein et al. therefore sought to develop a method of performing independence tests on large datasets that can easily be both applied and interpreted by practicing scientists. The method developed by Vogelstein et al., called Multiscale Graph Correlation (MGC, pronounced ‘magic’), combines recent developments in hypothesis testing, machine learning, and data science. The result is that MGC typically requires between one half to one third as big a sample size as previously proposed methods for analyzing large, complex datasets. Moreover, MGC also indicates the nature of the relationship between different properties; for example, whether it is a linear relationship or not. Testing MGC on real biological data, including a cancer dataset and a human brain imaging dataset, revealed that it is more effective at finding possible relationships than other commonly used independence methods. MGC was also the only method that explained how it found those relationships. MGC will enable relationships to be found in data across many fields of inquiry – and not only in biology. Scientists, policy analysts, data journalists, and corporate data scientists could all use MGC to learn about the relationships present in their data. To that extent, Vogelstein et al. have made the code open source in MATLAB, R, and Python.

SEEK and you may find cancer earlier Many cancers can be cured by surgery and/or systemic therapies when detected before they have metastasized. This clinical reality, coupled with the growing appreciation that cancer's rapid genetic evolution limits its response to drugs, have fueled interest in methodologies for earlier detection of the disease. Cohen et al. developed a noninvasive blood test, called CancerSEEK that can detect eight common human cancer types (see the Perspective by Kalinich and Haber). The test assesses eight circulating protein biomarkers and tumor-specific mutations in circulating DNA. In a study of 1000 patients previously diagnosed with cancer and 850 healthy control individuals, CancerSEEK detected cancer with a sensitivity of 69 to 98% (depending on cancer type) and 99% specificity. Science , this issue p. 926 ; see also p. 866

Optogenetic Insights Mapping functional neural circuits for many behaviors has been almost impossible, so Vogelstein et al. (p. 386 , published online 27 March; see the Perspective by O'Leary and Marder ) developed a broadly applicable optogenetic method for neuron-behavior mapping and used it to phenotype larval Drosophila and thus developed a reference atlas. As optogenetic experiments become routine in certain fields of neuroscience research, creating even more specialized tools is imperative (see the Perspective by Hayashi ). By engineering channelrhodopsin, Wietek et al. (p. 409 , published online 27 March) and Berndt et al. (p. 420 ) created two different light-gated anion channels to block action potential generation during synaptic stimulation or depolarizing current injections. These new tools not only improve understanding of channelrhodopsins but also provide a way to silence cells.

At macroscopic scales, the human connectome is composed of anatomically distinct brain areas, the structural pathways connecting them, and their functional interactions. Successful annotation of phenotypic associations with variation in the connectome and cataloguing of neurophenotypes promise to transform our understanding of the human brain. This review provides a survey of magnetic resonance imaging-based measurements of functional and structural connectivity. We highlight emerging areas of development and inquiry, and emphasize the importance of integrating structural and functional perspectives on brain architecture.

The CLARITY method renders brains optically transparent to enable high-resolution imaging in the structurally intact brain. Anatomically annotating CLARITY brains is necessary for discovering which regions contain signals of interest. Manually annotating whole-brain, terabyte CLARITY images is difficult, time-consuming, subjective, and error-prone. Automatically registering CLARITY images to a pre-annotated brain atlas offers a solution, but is difficult for several reasons. Removal of the brain from the skull and subsequent storage and processing cause variable non-rigid deformations, thus compounding inter-subject anatomical variability. Additionally, the signal in CLARITY images arises from various biochemical contrast agents which only sparsely label brain structures. This sparse labeling challenges the most commonly used registration algorithms that need to match image histogram statistics to the more densely labeled histological brain atlases. The standard method is a multiscale Mutual Information B-spline algorithm that dynamically generates an average template as an intermediate registration target. We determined that this method performs poorly when registering CLARITY brains to the Allen Institute's Mouse Reference Atlas (ARA), because the image histogram statistics are poorly matched. Therefore, we developed a method (Mask-LDDMM) for registering CLARITY images, that automatically finds the brain boundary and learns the optimal deformation between the brain and atlas masks. Using Mask-LDDMM without an average template provided better results than the standard approach when registering CLARITY brains to the ARA. The LDDMM pipelines developed here provide a fast automated way to anatomically annotate CLARITY images; our code is available as open source software at http://NeuroData.io.

Neuronal responses during sensory processing are influenced by both the organization of intracortical connections and the statistical features of sensory stimuli. How these intrinsic and extrinsic factors govern the activity of excitatory and inhibitory populations is unclear. Using two-photon calcium imaging in vivo and intracellular recordings in vitro, we investigated the dependencies between synaptic connectivity, feature selectivity and network activity in pyramidal cells and fast-spiking parvalbumin-expressing (PV) interneurons in mouse visual cortex. In pyramidal cell populations, patterns of neuronal correlations were largely stimulus-dependent, indicating that their responses were not strongly dominated by functionally biased recurrent connectivity. By contrast, visual stimulation only weakly modified co-activation patterns of fast-spiking PV cells, consistent with the observation that these broadly tuned interneurons received very dense and strong synaptic input from nearby pyramidal cells with diverse feature selectivities. Therefore, feedforward and recurrent network influences determine the activity of excitatory and inhibitory ensembles in fundamentally different ways.

Resurgent interest in synaptic circuitry and plasticity has emphasized the importance of 3D reconstruction from serial section electron microscopy (3DEM). Three volumes of hippocampal CA1 neuropil from adult rat were imaged at X-Y resolution of ~2 nm on serial sections of ~50–60 nm thickness. These are the first densely reconstructed hippocampal volumes. All axons, dendrites, glia, and synapses were reconstructed in a cube (~10 μm3) surrounding a large dendritic spine, a cylinder (~43 μm3) surrounding an oblique dendritic segment (3.4 μm long), and a parallelepiped (~178 μm3) surrounding an apical dendritic segment (4.9 μm long). The data provide standards for identifying ultrastructural objects in 3DEM, realistic reconstructions for modeling biophysical properties of synaptic transmission, and a test bed for enhancing reconstruction tools. Representative synapses are quantified from varying section planes, and microtubules, polyribosomes, smooth endoplasmic reticulum, and endosomes are identified and reconstructed in a subset of dendrites. The original images, traces, and Reconstruct software and files are freely available and visualized at the Open Connectome Project (Data Citation 1).

Visual Abstract, Methods for resolving the three-dimensional (3D) microstructure of the brain typically start by thinly slicing and staining the brain, followed by imaging numerous individual sections with visible light photons or electrons. In contrast, X-rays can be used to image thick samples, providing a rapid approach for producing large 3D brain maps without sectioning. Here we demonstrate the use of synchrotron X-ray microtomography (µCT) for producing mesoscale (∼1 µm 3 resolution) brain maps from millimeter-scale volumes of mouse brain. We introduce a pipeline for µCT-based brain mapping that develops and integrates methods for sample preparation, imaging, and automated segmentation of cells, blood vessels, and myelinated axons, in addition to statistical analyses of these brain structures. Our results demonstrate that X-ray tomography achieves rapid quantification of large brain volumes, complementing other brain mapping and connectomics efforts.

Currently, connectomes (e.g., functional or structural brain graphs) can be estimated in humans at $\approx 1~mm^3$ scale using a combination of diffusion weighted magnetic resonance imaging, functional magnetic resonance imaging and structural magnetic resonance imaging scans. This manuscript summarizes a novel, scalable implementation of open-source algorithms to rapidly estimate magnetic resonance connectomes, using both anatomical regions of interest (ROIs) and voxel-size vertices. To assess the reliability of our pipeline, we develop a novel nonparametric non-Euclidean reliability metric. Here we provide an overview of the methods used, demonstrate our implementation, and discuss available user extensions. We conclude with results showing the efficacy and reliability of the pipeline over previous state-of-the-art., Published as part of 2013 IEEE GlobalSIP conference

The human brain and the neuronal networks comprising it are of immense interest to the scientific community. In this work, we focus on the structural connectivity of human brains, investigating sex differences across male and female connectomes (brain-graphs) for the knowledge discovery problem "Which brain regions exert differences in connectivity across the two sexes?". One of our main findings discloses the statistical difference at the pars orbitalis of the connectome between sexes, which has been shown to function in language production. Moreover, we use these discriminative regions for the related learning problem "Can we classify a given human connectome to belong to one of the sexes just by analyzing its connectivity structure?" . We show that we can learn decision tree as well as support vector machine classification models for this task. We show that our models achieve up to 79% prediction accuracy with only a handful of brain regions as discriminating factors. Importantly, our results are consistent across two data sets, collected at two different centers, with two different scanning sequences, and two different age groups (children and elderly). This is highly suggestive that we have discovered scientifically meaningful sex differences.

New DNA sequencing methods will soon make it possible to identify all germline variants in any individual at a reasonable cost. However, the ability of whole-genome sequencing to predict predisposition to common diseases in the general population is unknown. To estimate this predictive capacity, we use the concept of a "genometype." A specific genometype represents the genomes in the population conferring a specific level of genetic risk for a specified disease. Using this concept, we estimated the maximum capacity of whole-genome sequencing to identify individuals at clinically significant risk for 24 different diseases. Our estimates were derived from the analysis of large numbers of monozygotic twin pairs; twins of a pair share the same genometype and therefore identical genetic risk factors. Our analyses indicate that (i) for 23 of the 24 diseases, most of the individuals will receive negative test results; (ii) these negative test results will, in general, not be very informative, because the risk of developing 19 of the 24 diseases in those who test negative will still be, at minimum, 50 to 80% of that in the general population; and (iii) on the positive side, in the best-case scenario, more than 90% of tested individuals might be alerted to a clinically significant predisposition to at least one disease. These results have important implications for the valuation of genetic testing by industry, health insurance companies, public policy-makers, and consumers.

This article presents a novel, tightly integrated pipeline for estimating a connectome. The pipeline utilizes magnetic resonance (MR) imaging (MRI) data to produce a high-level estimate of the structural connectivity in the human brain. The MR connectome automated pipeline (MRCAP) is efficient, and its modular construction allows researchers to modify algorithms to meet their specific requirements. The pipeline has been validated, and more than 200 connectomes have been processed and analyzed to date.

Although it has been over a century since neuroscientists first conjectured that networks of neurons comprise the brain, technology has limited high-throughput investigations of neural circuitry until very recently. In the last couple of decades, several experimental paradigms have arisen that are poised to finally begin studying neuroanatomy in a high-throughput fashion. In 2005, the term connectome was coined independently by Patric Hagmann and Olaf Sporns, to describe the complete set of neural connections in a brain. Interestingly, both usages seemed to be referring to using Magnetic Resonance Imaging (MRI) to study human brain networks. Shortly thereafter, Narayanan "Bobby" Kasthuri and Jeff Lichtman published an article suggesting that "connectome" should refer to connections between neurons, which one can infer using Electron Microscopy (EM) and fluorescence microscopy (e.g., brainbow animals). "Projectome", they suggested, is more appropriate for MRI based studies. Yet, the word connectome stuck, and now refers to essentially any neuroscientific investigation of the relationship between (collections of) neurons, be they functional or structural.

Deducing the structure of neural circuits is one of the central problems of modern neuroscience. Recently-introduced calcium fluorescent imaging methods permit experimentalists to observe network activity in large populations of neurons, but these techniques provide only indirect observations of neural spike trains, with limited time resolution and signal quality. In this work we present a Bayesian approach for inferring neural circuitry given this type of imaging data. We model the network activity in terms of a collection of coupled hidden Markov chains, with each chain corresponding to a single neuron in the network and the coupling between the chains reflecting the network's connectivity matrix. We derive a Monte Carlo Expectation--Maximization algorithm for fitting the model parameters; to obtain the sufficient statistics in a computationally-efficient manner, we introduce a specialized blockwise-Gibbs algorithm for sampling from the joint activity of all observed neurons given the observed fluorescence data. We perform large-scale simulations of randomly connected neuronal networks with biophysically realistic parameters and find that the proposed methods can accurately infer the connectivity in these networks given reasonable experimental and computational constraints. In addition, the estimation accuracy may be improved significantly by incorporating prior knowledge about the sparseness of connectivity in the network, via standard L$_1$ penalization methods., Published in at http://dx.doi.org/10.1214/09-AOAS303 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

A low-cost, consumer-grade, EEG-based individual authentication system is proposed in this work. While EEG signals are recorded, the subject performs four mental imagery tasks consisting of a baseline measurement, referential limb movement, counting, and rotation for 150 seconds each. The 150 seconds of data are divided into one second segments, from which features are obtained. Three sets of features are extracted from each electrode: 6th order autoregressive (AR) coefficients, power spectral density, and total power in five frequency bands. Two additional sets of features are extracted from interhemispheric data: interhemispheric power differences and interhemispheric linear complexity. These feature sets are combined into a feature vector that is then used by a linear support vector machine (SVM) with cross validation for classification. The goal was to minimize both false accept rates (FARs) and false reject rates (FRRs). Using voting rules across groups of ten segments, we were able to achieve 100% classification accuracy for each subject in each task. Though more work must be done with a larger subject pool as well as across multiple sessions, these results show that low-cost EEG authentication systems may be viable.

A mixed-signal very large scale integration (VLSI) chip for large scale emulation of spiking neural networks is presented. The chip contains 2400 silicon neurons with fully programmable and reconfigurable synaptic connectivity. Each neuron implements a discrete-time model of a single-compartment cell. The model allows for analog membrane dynamics and an arbitrary number of synaptic connections, each with tunable conductance and reversal potential. The array of silicon neurons functions as an address-event (AE) transceiver, with incoming and outgoing spikes communicated over an asynchronous event-driven digital bus. Address encoding and conflict resolution of spiking events are implemented via a randomized arbitration scheme that ensures balanced servicing of event requests across the array. Routing of events is implemented externally using dynamically programmable random-access memory that stores a postsynaptic address, the conductance, and the reversal potential of each synaptic connection. Here, we describe the silicon neuron circuits, present experimental data characterizing the 3 mm times 3 mm chip fabricated in 0.5-mum complementary metal-oxide-semiconductor (CMOS) technology, and demonstrate its utility by configuring the hardware to emulate a model of attractor dynamics and waves of neural activity during sleep in rat hippocampus

SummaryWe describe automated technologies to probe the structure of neural tissue at nanometer resolution and use them to generate a saturated reconstruction of a sub-volume of mouse neocortex in which all cellular objects (axons, dendrites, and glia) and many sub-cellular components (synapses, synaptic vesicles, spines, spine apparati, postsynaptic densities, and mitochondria) are rendered and itemized in a database. We explore these data to study physical properties of brain tissue. For example, by tracing the trajectories of all excitatory axons and noting their juxtapositions, both synaptic and non-synaptic, with every dendritic spine we refute the idea that physical proximity is sufficient to predict synaptic connectivity (the so-called Peters’ rule). This online minable database provides general access to the intrinsic complexity of the neocortex and enables further data-driven inquiries.Video Abstract