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As acquiring bigger data becomes easier in experimental brain science, computational and statistical brain science must achieve similar advances to fully capitalize on these data. Tackling these problems will benefit from a more explicit and concerted effort to work together. Specifically, brain science can be further democratized by harnessing the power of community-driven tools, which both are built by and benefit from many different people with different backgrounds and expertise. This perspective can be applied across modalities and scales and enables collaborations across previously siloed communities.

Connectomics—the study of brain networks—provides a unique and valuable opportunity to study the brain. However, research in human connectomics, accomplished via Magnetic Resonance Imaging (MRI), is a resource-intensive practice: typical analysis routines require impactful decision making and significant computational capabilities. Mitigating these issues requires the development of low-resource, easy to use, and flexible pipelines which can be applied across data with variable collection parameters. In response to these challenges, we have developed the MRI to Graphs (m2g) pipeline. m2g leverages functional and diffusion datasets to estimate connectomes reliably. To illustrate, m2g was used to process MRI data from 35 different studies (≈6,000 scans) from 15 sites without any manual intervention or parameter tuning. Every single scan yielded an estimated connectome that followed established properties, such as stronger ipsilateral than contralateral connections in structural connectomes, and stronger homotopic than heterotopic correlations in functional connectomes. Moreover, the connectomes generated by m2g are more similar within individuals than between them, suggesting that m2g preserves biological variability. m2g is portable, and can run on a single CPU with 16 GB of RAM in less than a couple hours, or be deployed on the cloud using its docker container. All code is available on https://neurodata.io/mri/.

Energy statistics was proposed by Sz\' ekely in the 80's inspired by Newton's gravitational potential in classical mechanics and it provides a model-free hypothesis test for equality of distributions. In its original form, energy statistics was formulated in Euclidean spaces. More recently, it was generalized to metric spaces of negative type. In this paper, we consider a formulation for the clustering problem using a weighted version of energy statistics in spaces of negative type. We show that this approach leads to a quadratically constrained quadratic program in the associated kernel space, establishing connections with graph partitioning problems and kernel methods in machine learning. To find local solutions of such an optimization problem, we propose kernel k-groups, which is an extension of Hartigan's method to kernel spaces. Kernel k-groups is cheaper than spectral clustering and has the same computational cost as kernel k-means (which is based on Lloyd's heuristic) but our numerical results show an improved performance, especially in higher dimensions. Moreover, we verify the efficiency of kernel k-groups in community detection in sparse stochastic block models which has fascinating applications in several areas of science., several improvements; connections with community detection and stochastic block model. Matches published version

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.

Batch effects, undesirable sources of variance across multiple experiments, present a substantial hurdle for scientific and clinical discoveries. Specifically, the presence of batch effects can create both spurious discoveries and hide veridical signals, contributing to the ongoing reproducibility crisis. Typical approaches to dealing with batch effects conceptualize ‘batches’ as an associational effect, rather than a causal effect, despite the fact that the sources of variance that comprise the batch – potentially including experimental design and population demographics – causally impact downstream inferences. We therefore cast batch effects as a causal problem rather than an associational problem. This reformulation enables us to make explicit the assumptions and limitations of existing approaches for dealing with batch effects. We therefore develop causal batch effect strategies—CausalDcorr for discovery of batch effects and CausalComBat for mitigating batch effects – which build upon existing statistical associational methods by incorporating modern causal inference techniques. We apply these strategies to a large mega-study of human connectomes assembled by the Consortium for Reliability and Reproducibility, consisting of 24 batches including over 1700 individuals to illustrate that existing approaches create more spurious discoveries (false positives) and miss more veridical signals (true positives) than our proposed approaches. Our work therefore introduces a conceptual framing, as well as open source code, for combining multiple distinct datasets to increase confidence in claims of scientific and clinical discoveries.

Since the beginning of the COVID-19 pandemic, pharmaceutical treatment hypotheses have abounded, each requiring careful evaluation. A randomized controlled trial generally provides the most credible evaluation of a treatment, but the efficiency and effectiveness of the trial depend on the existing evidence supporting the treatment. The researcher must therefore compile a body of evidence justifying the use of time and resources to further investigate a treatment hypothesis in a trial. An observational study can provide this evidence, but the lack of randomized exposure and the researcher’s inability to control treatment administration and data collection introduce significant challenges. A proper analysis of observational health care data thus requires contributions from experts in a diverse set of topics ranging from epidemiology and causal analysis to relevant medical specialties and data sources. Here we summarize these contributions as 10 rules that serve as an end-to-end introduction to retrospective pharmacoepidemiological analyses of observational health care data using a running example of a hypothetical COVID-19 study. A detailed supplement presents a practical how-to guide for following each rule. When carefully designed and properly executed, a retrospective pharmacoepidemiological analysis framed around these rules will inform the decisions of whether and how to investigate a treatment hypothesis in a randomized controlled trial. This work has important implications for any future pandemic by prescribing what we can and should do while the world waits for global vaccine distribution.

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.

Quantifying terabyte-scale multi-modal human and animal imaging data requires scalable analysis tools. We developed CloudReg, an open-source, automatic, terabyte-scale, cloud-based image analysis pipeline that pre-processes and registers cross-modal volumetric datasets with artifacts via spatially-varying polynomial intensity transform. CloudReg accurately registers the following datasets to their respective atlases: in vivo human and ex vivo macaque brain magnetic resonance imaging, ex vivo mouse brain micro-computed tomography, and cleared murine brain light-sheet microscopy.

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.

This work presents a novel strategy for classifying neurons, represented by nodes of a directed graph, based on their circuitry (edge connectivity). We assume a stochastic block model (SBM) where neurons belong together if they connect to neurons of other groups according to the same probability distributions. Following adjacency spectral embedding (ASE) of the SBM graph, we derive the number of classes and assign each neuron to a class with a Gaussian mixture model-based expectation-maximization (EM) clustering algorithm. To improve accuracy, we introduce a simple variation using random hierarchical agglomerative clustering to initialize the EM algorithm and picking the best solution over multiple EM restarts. We test this procedure on a large (n ~ 212 − 215 neurons), sparse, biologically inspired connectome with eight neuron classes. The simulation results demonstrate that the proposed approach is broadly stable to the choice of dimensional embedding and scales extremely well as the number of neurons in the network increases. Clustering accuracy is robust to variations in model parameters and highly tolerant to simulated experimental noise, achieving perfect classifications with up to 40% of swapped edges. Thus, this approach may be useful to analyze and interpret large-scale brain connectomics data in terms of underlying cellular components.

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.

Recently, deep learning has unlocked unprecedented success in various domains, especially using images, text, and speech. However, deep learning is only beneficial if the data have nonlinear relationships and if they are exploitable at available sample sizes. We systematically profiled the performance of deep, kernel, and linear models as a function of sample size on UKBiobank brain images against established machine learning references. On MNIST and Zalando Fashion, prediction accuracy consistently improves when escalating from linear models to shallow-nonlinear models, and further improves with deep-nonlinear models. In contrast, using structural or functional brain scans, simple linear models perform on par with more complex, highly parameterized models in age/sex prediction across increasing sample sizes. In sum, linear models keep improving as the sample size approaches ~10,000 subjects. Yet, nonlinearities for predicting common phenotypes from typical brain scans remain largely inaccessible to the examined kernel and deep learning methods., Schulz et al. systematically benchmark performance scaling with increasingly sophisticated prediction algorithms and with increasing sample size in reference machine-learning and biomedical datasets. Complicated nonlinear intervariable relationships remain largely inaccessible for predicting key phenotypes from typical brain scans.

Compelling evidence suggests the need for more data per individual to reliably map the functional organization of the human connectome. As the notion that ‘more data is better’ emerges as a golden rule for functional connectomics, researchers find themselves grappling with the challenges of how to obtain the desired amounts of data per participant in a practical manner, particularly for retrospective data aggregation. Increasingly, the aggregation of data across all fMRI scans available for an individual is being viewed as a solution, regardless of scan condition (e.g., rest, task, movie). A number of open questions exist regarding the aggregation process and the impact of different decisions on the reliability of resultant aggregate data. We leveraged the availability of highly sampled test-retest datasets to systematically examine the impact of data aggregation strategies on the reliability of cortical functional connectomics. Specifically, we compared functional connectivity estimates derived after concatenating from: 1) multiple scans under the same state, 2) multiple scans under different states (i.e. hybrid or general functional connectivity), and 3) subsets of one long scan. We also varied connectivity processing (i.e. global signal regression, ICA-FIX, and task regression) and estimation procedures. When the total number of time points is equal, and the scan state held constant, concatenating multiple shorter scans had a clear advantage over a single long scan. However, this was not necessarily true when concatenating across different fMRI states (i.e. task conditions), where the reliability from the aggregate data varied across states. Concatenating fewer numbers of states that are more reliable tends to yield higher reliability. Our findings provide an overview of multiple dependencies of data concatenation that should be considered to optimize reliability in analysis of functional connectivity data.

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

We present modern machine learning, focusing on the state-of-the-art classification methods of decision forests and deep networks, as partition and vote schemes. This illustrative presentation allows for both a unified basic understanding of how these methods work from the perspective of classical statistical pattern recognition as well as useful basic insight into their relationship with each other … and potentially with brain functioning.

Despite myriad demonstrations of feasibility, the high dimensionality of fMRI data remains a critical barrier to its utility for reproducible biomarker discovery. Recent studies applying dimensionality reduction techniques to resting-state fMRI (R-fMRI) have unveiled neurocognitively meaningful connectivity gradients that are present in both human and primate brains, and appear to differ meaningfully among individuals and clinical populations. Here, we provide a critical assessment of the suitability of connectivity gradients for biomarker discovery. Using the Human Connectome Project (discovery subsample=209; two replication subsamples= 209×2) and the Midnight scan club (n=9), we tested the following key biomarker traits – reliability, reproducibility and predictive validity – of functional gradients. In doing so, we systematically assessed the effects of three analytical settings, including i) dimensionality reduction algorithms (i.e., linear vs. non-linear methods), ii) input data types (i.e., raw time series, [un-]thresholded functional connectivity), and iii) amount of the data (R-fMRI time-series lengths). We found that the reproducibility of functional gradients across algorithms and subsamples is generally higher for those explaining more variances of whole-brain connectivity data, as well as those having higher reliability. Notably, among different analytical settings, a linear dimensionality reduction (principal component analysis in our study), more conservatively thresholded functional connectivity (e.g., 95-97%) and longer time-series data (at least ≥20mins) was found to be preferential conditions to obtain higher reliability. Those gradients with higher reliability were able to predict unseen phenotypic scores with a higher accuracy, highlighting reliability as a critical prerequisite for validity. Importantly, prediction accuracy with connectivity gradients exceeded that observed with more traditional edge-based connectivity measures, suggesting the added value of a low-dimensional gradient approach. Finally, the present work highlights the importance and benefits of systematically exploring the parameter space for new imaging methods before widespread deployment.Highlights- There is a growing need to identify benchmark parameters in advancing functional connectivity gradients into a reliable biomarker.- Here, we explored multidimensional parameter space in calculating functional gradients to improve their reproducibility, reliability and predictive validity.- We demonstrated that more reproducible and reliable gradient markers tend to have higher predictive power for unseen phenotypic scores across various cognitive domains.- We showed that the low-dimensional connectivity gradient approach could outperform raw edge-based analyses in terms of predicting phenotypic scores.- We highlight the necessity of optimizing parameters for new imaging methods before their widespread deployment.

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

Replicability, the ability to replicate scientific findings, is a prerequisite for scientific discovery and clinical utility. Troublingly, we are in the midst of a replicability crisis. A key to replicability is that multiple measurements of the same item (e.g., experimental sample or clinical participant) under fixed experimental constraints are relatively similar to one another. Thus, statistics that quantify the relative contributions of accidental deviations—such as measurement error—as compared to systematic deviations—such as individual differences—are critical. We demonstrate that existing replicability statistics, such as intra-class correlation coefficient and fingerprinting, fail to adequately differentiate between accidental and systematic deviations in very simple settings. We therefore propose a novel statistic, discriminability, which quantifies the degree to which an individual’s samples are relatively similar to one another, without restricting the data to be univariate, Gaussian, or even Euclidean. Using this statistic, we introduce the possibility of optimizing experimental design via increasing discriminability and prove that optimizing discriminability improves performance bounds in subsequent inference tasks. In extensive simulated and real datasets (focusing on brain imaging and demonstrating on genomics), only optimizing data discriminability improves performance on all subsequent inference tasks for each dataset. We therefore suggest that designing experiments and analyses to optimize discriminability may be a crucial step in solving the replicability crisis, and more generally, mitigating accidental measurement error., Author summary In recent decades, the size and complexity of data has grown exponentially. Unfortunately, the increased scale of modern datasets brings many new challenges. At present, we are in the midst of a replicability crisis, in which scientific discoveries fail to replicate to new datasets. Difficulties in the measurement procedure and measurement processing pipelines coupled with the influx of complex high-resolution measurements, we believe, are at the core of the replicability crisis. If measurements themselves are not replicable, what hope can we have that we will be able to use the measurements for replicable scientific findings? We introduce the “discriminability” statistic, which quantifies how discriminable measurements are from one another, without limitations on the structure of the underlying measurements. We prove that discriminable strategies tend to be strategies which provide better accuracy on downstream scientific questions. We demonstrate the utility of discriminability over competing approaches in this context on two disparate datasets from both neuroimaging and genomics. Together, we believe these results suggest the value of designing experimental protocols and analysis procedures which optimize the discriminability.

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.

Biological and social systems consist of myriad interacting units. The interactions can be represented in the form of a graph or network. Measurements of these graphs can reveal the underlying structure of these interactions, which provides insight into the systems that generated the graphs. Moreover, in applications such as connectomics, social networks, and genomics, graph data are accompanied by contextualizing measures on each node. We utilize these node covariates to help uncover latent communities in a graph, using a modification of spectral clustering. Statistical guarantees are provided under a joint mixture model that we call the node-contextualized stochastic blockmodel, including a bound on the mis-clustering rate. The bound is used to derive conditions for achieving perfect clustering. For most simulated cases, covariate-assisted spectral clustering yields results superior to regularized spectral clustering without node covariates and to an adaptation of canonical correlation analysis. We apply our clustering method to large brain graphs derived from diffusion MRI data, using the node locations or neurological region membership as covariates. In both cases, covariate-assisted spectral clustering yields clusters that are easier to interpret neurologically., Comment: 28 pages, 4 figures, includes substantial changes to theoretical results

In recent years, deep learning has unlocked unprecedented success in various domains, especially in image, text, and speech processing. These breakthroughs may hold promise for neuroscience and especially for brain-imaging investigators who start to analyze thousands of participants. However, deep learning is only beneficial if the data have nonlinear relationships and if they are exploitable at currently available sample sizes.We systematically profiled the performance of deep models, kernel models, and linear models as a function of sample size on UK Biobank brain images against established machine learning references. On MNIST and Zalando Fashion, prediction accuracy consistently improved when escalating from linear models to shallow-nonlinear models, and further improved when switching to deep-nonlinear models. The more observations were available for model training, the greater the performance gain we saw. In contrast, using structural or functional brain scans, simple linear models performed on par with more complex, highly parameterized models in age/sex prediction across increasing samplesizes. In fact, linear models kept improving as the sample size approached ~10,000 participants. Our results indicate that the increase in performance of linear models with additional data does not saturate at the limit of current feasibility. Yet, nonlinearities of common brain scans remain largely inaccessible to both kernel and deep learning methods at any axemined scale

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.

Much recent attention has been paid to quantifying anatomic and functional neuroimaging on the individual subject level. For optimal individual subject characterization, specific acquisition and analysis features need to be identified that maximize interindividual variability while concomitantly minimizing intra-subject variability. We delineate the effect of various acquisition parameters (length of acquisition, sampling frequency) and analysis methods (time course extraction, region of interest parcellation, and thresholding of connectivity-derived network graphs) on characterizing individual subject differentiation. We utilize a non-parametric statistical metric that quantifies the degree to which a parameter set allows this individual subject differentiation by both maximizing interindividual variance and minimizing intra-individual variance. We apply this metric to analysis of four publicly available test-retest resting-state fMRI (rs-fMRI) data sets. We find that for the question of maximizing individual differentiation, (i) for increasing sampling, there is a relative tradeoff between increased sampling frequency and increased acquisition time; (ii) for the sizes of the interrogated data sets, only 3-4 min of acquisition time was sufficient to maximally differentiate each subject with an algorithm that utilized no a priori information regarding subject identification; and (iii) brain regions that most contribute to this individual subject characterization lie in the default mode, attention, and executive control networks. These findings may guide optimal rs-fMRI experiment design and may elucidate the neural bases for subject-to-subject differences. Hum Brain Mapp 37:1986-1997, 2016. © 2016 Wiley Periodicals, Inc.

How much has a network changed since yesterday? How different is the wiring of Bob’s brain (a left-handed male) and Alice’s brain (a right-handed female), and how is it different? Graph similarity with given node correspondence, i.e., the detection of changes in the connectivity of graphs, arises in numerous settings. In this work, we formally state the axioms and desired properties of the graph similarity functions, and evaluate when state-of-the-art methods fail to detect crucial connectivity changes in graphs. We propose D elta C on , a principled, intuitive, and scalable algorithm that assesses the similarity between two graphs on the same nodes (e.g., employees of a company, customers of a mobile carrier). In conjunction, we propose D elta C on -A ttr , a related approach that enables attribution of change or dissimilarity to responsible nodes and edges. Experiments on various synthetic and real graphs showcase the advantages of our method over existing similarity measures. Finally, we employ D elta C on and D elta C on -A ttr on real applications: (a) we classify people to groups of high and low creativity based on their brain connectivity graphs, (b) do temporal anomaly detection in the who-emails-whom Enron graph and find the top culprits for the changes in the temporal corporate email graph, and (c) recover pairs of test-retest large brain scans ( ∼17M edges, up to 90M edges) for 21 subjects.

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.

We describe NDStore, a scalable multi-hierarchical data storage deployment for spatial analysis of neuroscience data on the AWS cloud. The system design is inspired by the requirement to maintain high I/O throughput for workloads that build neural connectivity maps of the brain from peta-scale imaging data using computer vision algorithms. We store all our data on the AWS object store S3 to limit our deployment costs. S3 serves as our base-tier of storage. Redis, an in-memory key-value engine, is used as our caching tier. The data is dynamically moved between the different storage tiers based on user access. All programming interfaces to this system are RESTful web-services. We include a performance evaluation that shows that our production system provides good performance for a variety of workloads by combining the assets of multiple cloud services.

R is one of the most popular programming languages for statistics and machine learning, but it is slow and unable to scale to large datasets. The general approach for having an efficient algorithm in R is to implement it in C or FORTRAN and provide an R wrapper. FlashR accelerates and scales existing R code by parallelizing a large number of matrix functions in the R base package and scaling them beyond memory capacity with solid-state drives (SSDs). FlashR performs memory hierarchy aware execution to speed up parallelized R code by (i) evaluating matrix operations lazily, (ii) performing all operations in a DAG in a single execution and with only one pass over data to increase the ratio of computation to I/O, (iii) performing two levels of matrix partitioning and reordering computation on matrix partitions to reduce data movement in the memory hierarchy. We evaluate FlashR on various machine learning and statistics algorithms on inputs of up to four billion data points. Despite the huge performance gap between SSDs and RAM, FlashR on SSDs closely tracks the performance of FlashR in memory for many algorithms. The R implementations in FlashR outperforms H 2 O and Spark MLlib by a factor of 3 -- 20.

Understanding the functional organization the brain is a centrally important theme of human neuroscience. Ideally, these organizational maps uncover the underlying structure of the brain's functional architecture, and group-level maps are accurate representations of the individuals in the sample. Using simulated fMRI data, we demonstrate that bagging improves the ability of clustering to uncover the data's underlying structure. We show that the group-level maps become more correlated to the individual-level maps with more bootstrap aggregates, suggesting bagging improves the representativeness of the group-level solution. Using a test-retest dataset of 30 young adults, we confirm these findings. More specifically, we see bagging improves the test-retest correlation between cluster maps, and increases correlation between group-level and individual-level cluster maps, and these effects are robust to number of clusters and length of scan used. These results suggest bagging is an important method for increasing reliability and validity of functional parcellation approaches.

Significance Spectral graph clustering—clustering the vertices of a graph based on their spectral embedding—is of significant current interest, finding applications throughout the sciences. But as with clustering in general, what a particular methodology identifies as “clusters” is defined (explicitly, or, more often, implicitly) by the clustering algorithm itself. We provide a clear and concise demonstration of a “two-truths” phenomenon for spectral graph clustering in which the first step—spectral embedding—is either Laplacian spectral embedding, wherein one decomposes the normalized Laplacian of the adjacency matrix, or adjacency spectral embedding given by a decomposition of the adjacency matrix itself. The two resulting clustering methods identify fundamentally different (true and meaningful) structure., Clustering is concerned with coherently grouping observations without any explicit concept of true groupings. Spectral graph clustering—clustering the vertices of a graph based on their spectral embedding—is commonly approached via K-means (or, more generally, Gaussian mixture model) clustering composed with either Laplacian spectral embedding (LSE) or adjacency spectral embedding (ASE). Recent theoretical results provide deeper understanding of the problem and solutions and lead us to a “two-truths” LSE vs. ASE spectral graph clustering phenomenon convincingly illustrated here via a diffusion MRI connectome dataset: The different embedding methods yield different clustering results, with LSE capturing left hemisphere/right hemisphere affinity structure and ASE capturing gray matter/white matter core–periphery structure.

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

Feature extraction and dimension reduction for networks is critical in a wide variety of domains. Efficiently and accurately learning features for multiple graphs has important applications in statistical inference on graphs. We propose a method to jointly embed multiple undirected graphs. Given a set of graphs, the joint embedding method identifies a linear subspace spanned by rank one symmetric matrices and projects adjacency matrices of graphs into this subspace. The projection coefficients can be treated as features of the graphs, while the embedding components can represent vertex features. We also propose a random graph model for multiple graphs that generalizes other classical models for graphs. We show through theory and numerical experiments that under the model, the joint embedding method produces estimates of parameters with small errors. Via simulation experiments, we demonstrate that the joint embedding method produces features which lead to state of the art performance in classifying graphs. Applying the joint embedding method to human brain graphs, we find it extracts interpretable features with good prediction accuracy in different tasks.

The proposed model generalizes the traditional linear dynamical system model.An efficient algorithm is designed for parameter estimation.The model works very well on high-dimensional neuro-imaging data.It is also a standard tool for big time-series data analysis in many domains. High-dimensional time-series data from a wide variety of domains, such as neuroscience, are being generated every day. Fitting statistical models to such data, to enable parameter estimation and time-series prediction, is an important computational primitive. Existing methods, however, are unable to cope with the high-dimensional nature of these data, due to both computational and statistical reasons. We mitigate both kinds of issues by proposing an M-estimator for Reduced-rank System IDentification (Mr. Sid). A combination of low-rank approximations, 1 and 2 penalties, and some numerical linear algebra tricks, yields an estimator that is computationally efficient and numerically stable. Simulations and real data examples demonstrate the usefulness of this approach in a variety of problems. In particular, we demonstrate that Mr. Sidcan accurately estimate spatial filters, connectivity graphs, and time-courses from native resolution functional magnetic resonance imaging data. Mr. Sidtherefore enables big time-series data to be analyzed using standard methods, readying the field for further generalizations including nonlinear and non-Gaussian state-space models.

CLARITY is a method for converting biological tissues into translucent and porous hydrogel-tissue hybrids. This facilitates interrogation with light sheet microscopy and penetration of molecular probes while avoiding physical slicing. In this work, we develop a pipeline for registering CLARIfied mouse brains to an annotated brain atlas. Due to the novelty of this microscopy technique it is impractical to use absolute intensity values to align these images to existing standard atlases. Thus we adopt a large deformation diffeomorphic approach for registering images via mutual information matching. Furthermore we show how a cascaded multi-resolution approach can improve registration quality while reducing algorithm run time. As acquired image volumes were over a terabyte in size, they were far too large for work on personal computers. Therefore the NeuroData computational infrastructure was deployed for multi-resolution storage and visualization of these images and aligned annotations on the web.

The analysis of data requires computation: originally by hand and more recently by computers. Different models of computing are designed and optimized for different kinds of data. In data-intensive science, the scale and complexity of data exceeds the comfort zone of local data stores on scientific workstations. Thus, cloud computing emerges as the preeminent model, utilizing data centers and high-performance clusters, enabling remote users to access and query subsets of the data efficiently. We examine how data-intensive computational systems originally built for cosmology, the Sloan Digital Sky Survey (SDSS), are now being used in connectomics, at the Open Connectome Project. We list lessons learned and outline the top challenges we expect to face. Success in computational connectomics would drastically reduce the time between idea and discovery, as SDSS did in cosmology.

This manuscript considers the following "graph classification" question: Given a collection of graphs and associated classes, how can one predict the class of a newly observed graph? To address this question, we propose a statistical model for graph/class pairs. This model naturally leads to a set of estimators to identify the class-conditional signal, or "signal-subgraph," defined as the collection of edges that are probabilistically different between the classes. The estimators admit classifiers which are asymptotically optimal and efficient, but which differ by their assumption about the "coherency" of the signal-subgraph (coherency is the extent to which the signal-edges "stick together" around a common subset of vertices). Via simulation, the best estimator is shown to be not just a function of the coherency of the model, but also the number of training samples. These estimators are employed to address a contemporary neuroscience question: Can we classify "connectomes" (brain-graphs) according to sex? The answer is yes, and significantly better than all benchmark algorithms considered. Synthetic data analysis demonstrates that even when the model is correct, given the relatively small number of training samples, the estimated signal-subgraph should be taken with a grain of salt. We conclude by discussing several possible extensions.

Modern technologies are enabling scientists to collect extraordinary amounts of complex and sophisticated data across a huge range of scales like never before. With this onslaught of data, we can allow the focal point to shift from data collection to data analysis. Unfortunately, lack of standardized sharing mechanisms and practices often make reproducing or extending scientific results very difficult. With the creation of data organization structures and tools that drastically improve code portability, we now have the opportunity to design such a framework for communicating extensible scientific discoveries. Our proposed solution leverages these existing technologies and standards, and provides an accessible and extensible model for reproducible research, called ‘science in the cloud’ (SIC). Exploiting scientific containers, cloud computing, and cloud data services, we show the capability to compute in the cloud and run a web service that enables intimate interaction with the tools and data presented. We hope this model will inspire the community to produce reproducible and, importantly, extensible results that will enable us to collectively accelerate the rate at which scientific breakthroughs are discovered, replicated, and extended.

Deeper exploration of the brain’s vast synaptic networks will require new tools for high-throughput structural and molecular profiling of the diverse populations of synapses that compose those networks. Fluorescence microscopy (FM) and electron microscopy (EM) offer complementary advantages and disadvantages for single-synapse analysis. FM combines exquisite molecular discrimination capacities with high speed and low cost, but rigorous discrimination between synaptic and non-synaptic fluorescence signals is challenging. In contrast, EM remains the gold standard for reliable identification of a synapse, but offers only limited molecular discrimination and is slow and costly. To develop and test single-synapse image analysis methods, we have used datasets from conjugate array tomography (cAT), which provides voxel-conjugate FM and EM (annotated) images of the same individual synapses. We report a novel unsupervised probabilistic method for detection of synapses from multiplex FM (muxFM) image data, and evaluate this method both by comparison to EM gold standard annotated data and by examining its capacity to reproduce known important features of cortical synapse distributions. The proposed probabilistic model-based synapse detector accepts molecular-morphological synapse models as user queries, and delivers a volumetric map of the probability that each voxel represents part of a synapse. Taking human annotation of cAT EM data as ground truth, we show that our algorithm detects synapses from muxFM data alone as successfully as human annotators seeing only the muxFM data, and accurately reproduces known architectural features of cortical synapse distributions. This approach opens the door to data-driven discovery of new synapse types and their density. We suggest that our probabilistic synapse detector will also be useful for analysis of standard confocal and super-resolution FM images, where EM cross-validation is not practical., Author summary Brain function results from communication between neurons connected by complex synaptic networks. Synapses are themselves highly complex and diverse signaling machines, containing protein products of hundreds of different genes, some in hundreds of copies, precisely arranged at each individual synapse. Synapses are fundamental not only to synaptic network function but also to network development, adaptation, and memory. In addition, abnormalities of synapse numbers or their molecular components have been implicated in a variety of mental and neurological disorders. Despite their obvious importance, mammalian synapse populations have so far resisted detailed quantitative study. In human brains and most animal nervous systems, synapses are very small and very densely packed: there are approximately 1 billion synapses per cubic millimeter of human cortex. This volumetric density poses very substantial challenges to proteometric analysis at the critical level of the individual synapse. The present work describes new probabilistic image analysis methods suitable for single-synapse analysis of synapse populations in both animal and human brains, in health and disorder.

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.

It is widely believed that human brain is a complicated network and many neurological disorders such as Alzheimer's disease (AD) are related to abnormal changes of the brain network architecture. In this work, we present a kernel-based method to establish a network for each subject using mean cortical thickness, which we refer to hereafter as the individual's network. We construct individual networks for 83 subjects, including AD patients and normal controls (NC), which are taken from the Open Access Series of Imaging Studies database. The network edge features are used to make prediction of AD/NC through the sophisticated machine learning technology. As the number of edge features is much more than that of samples, feature selection is applied to avoid the adverse impact of high-dimensional data on the performance of classifier. We use a hybrid feature selection that combines filter and wrapper methods, and compare the performance of six different combinations of them. Finally, support vector machines are trained using the selected features. To obtain an unbiased evaluation of our method, we use a nested cross validation framework to choose the optimal hyper-parameters of classifier and evaluate the generalization of the method. We report the best accuracy of 90.4 % using the proposed method in the leave-one-out analysis, outperforming that using the raw cortical thickness data by more than 10 %.

For random graphs distributed according to stochastic blockmodels, a special case of latent position graphs, adjacency spectral embedding followed by appropriate vertex classification is asymptotically Bayes optimal; but this approach requires knowledge of and critically depends on the model dimension. In this paper, we propose a sparse representation vertex classifier which does not require information about the model dimension. This classifier represents a test vertex as a sparse combination of the vertices in the training set and uses the recovered coefficients to classify the test vertex. We prove consistency of our proposed classifier for stochastic blockmodels, and demonstrate that the sparse representation classifier can predict vertex labels with higher accuracy than adjacency spectral embedding approaches via both simulation studies and real data experiments. Our results demonstrate the robustness and effectiveness of our proposed vertex classifier when the model dimension is unknown.

Reconstructing a map of neuronal connectivity is a critical challenge in contemporary neuroscience. Recent advances in high-throughput serial section electron microscopy (EM) have produced massive 3D image volumes of nanoscale brain tissue for the first time. The resolution of EM allows for individual neurons and their synaptic connections to be directly observed. Recovering neuronal networks by manually tracing each neuronal process at this scale is unmanageable, and therefore researchers are developing automated image processing modules. Thus far, state-of-the-art algorithms focus only on the solution to a particular task (e.g., neuron segmentation or synapse identification). In this manuscript we present the first fully automated images-to-graphs pipeline (i.e., a pipeline that begins with an imaged volume of neural tissue and produces a brain graph without any human interaction). To evaluate overall performance and select the best parameters and methods, we also develop a metric to assess the quality of the output graphs. We evaluate a set of algorithms and parameters, searching possible operating points to identify the best available brain graph for our assessment metric. Finally, we deploy a reference end-to-end version of the pipeline on a large, publicly available data set. This provides a baseline result and framework for community analysis and future algorithm development and testing. All code and data derivatives have been made publicly available toward eventually unlocking new biofidelic computational primitives and understanding of neuropathologies., 13 pages, first two authors contributed equally V2: Added additional experiments and clarifications; added information on infrastructure and pipeline environment

A vertical asymmetry in memory-guided saccadic eye movements has been previously demonstrated in humans and in rhesus monkeys. In the upright orientation, saccades generally land several degrees above the target. The origin of this asymmetry has remained unknown. In this study, we investigated whether the asymmetry in memory saccades is dependent on body orientation in space. Thus animals performed memory saccades in four different body orientations: upright, left-side-down (LSD), right-side-down (RSD), and supine. Data in all three rhesus monkeys confirm previous observations regarding a significant upward vertical asymmetry. Saccade errors made from LSD and RSD postures were partitioned into components made along the axis of gravity and along the vertical body axis. Up/down asymmetry persisted only in body coordinates but not in gravity coordinates. However, this asymmetry was generally reduced in tilted positions. Therefore the upward bias seen in memory saccades is egocentric although orientation in space might play a modulatory role.

Graph matching---aligning a pair of graphs to minimize their edge disagreements---has received wide-spread attention from both theoretical and applied communities over the past several decades, including combinatorics, computer vision, and connectomics. Its attention can be partially attributed to its computational difficulty. Although many heuristics have previously been proposed in the literature to approximately solve graph matching, very few have any theoretical support for their performance. A common technique is to relax the discrete problem to a continuous problem, therefore enabling practitioners to bring gradient-descent-type algorithms to bear. We prove that an indefinite relaxation (when solved exactly) almost always discovers the optimal permutation, while a common convex relaxation almost always fails to discover the optimal permutation. These theoretical results suggest that initializing the indefinite algorithm with the convex optimum might yield improved practical performance. Indeed, experimental results illuminate and corroborate these theoretical findings, demonstrating that excellent results are achieved in both benchmark and real data problems by amalgamating the two approaches., 14 pages, 11 figures, 3 tables

Matching datasets of multiple modalities has become an important task in data analysis. Existing methods often rely on the embedding and transformation of each single modality without utilizing any correspondence information, which often results in sub-optimal matching performance. In this paper, we propose a nonlinear manifold matching algorithm using shortest-path distance and joint neighborhood selection. Specifically, a joint nearest-neighbor graph is built for all modalities. Then the shortest-path distance within each modality is calculated from the joint neighborhood graph, followed by embedding into and matching in a common low-dimensional Euclidean space. Compared to existing algorithms, our approach exhibits superior performance for matching disparate datasets of multiple modalities., Comment: 13 pages, 8 figures, 2 tables

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 preservation of our cultural heritage is of paramount importance. Thanks to recent developments in digital acquisition techniques, powerful image analysis algorithms are developed which can be useful non-invasive tools to assist in the restoration and preservation of art. In this paper we propose a semi-supervised crack detection method that can be used for high-dimensional acquisitions of paintings coming from different modalities. Our dataset consists of a recently acquired collection of images of the Ghent Altarpiece (1432), one of Northern Europe's most important art masterpieces. Our goal is to build a classifier that is able to discern crack pixels from the background consisting of non-crack pixels, making optimal use of the information that is provided by each modality. To accomplish this we employ a recently developed non-parametric Bayesian classifier, that uses tensor factorizations to characterize any conditional probability. A prior is placed on the parameters of the factorization such that every possible interaction between predictors is allowed while still identifying a sparse subset among these predictors. The proposed Bayesian classifier, which we will refer to as conditional Bayesian tensor factorization or CBTF, is assessed by visually comparing classification results with the Random Forest (RF) algorithm., 8 pages, double column