A Monte Carlo Evaluation of Weighted Community Detection Algorithms

The past decade has been marked with a proliferation of community detection algorithms that aim to organize nodes (e.g., individuals, brain regions, variables) into modular structures that indicate subgroups, clusters, or communities. Motivated by the emergence of big data across many fields of inquiry, these methodological developments have primarily focused on the detection of communities of nodes from matrices that are very large. However, it remains unknown if the algorithms can reliably detect communities in smaller graph sizes (i.e., 1000 nodes and fewer) which are commonly used in brain research. More importantly, these algorithms have predominantly been tested only on binary or sparse count matrices and it remains unclear the degree to which the algorithms can recover community structure for different types of matrices, such as the often used cross-correlation matrices representing functional connectivity across predefined brain regions. Of the publicly available approaches for weighted graphs that can detect communities in graph sizes of at least 1000, prior research has demonstrated that Newman's spectral approach (i.e., Leading Eigenvalue), Walktrap, Fast Modularity, the Louvain method (i.e., multilevel community method), Label Propagation, and Infomap all recover communities exceptionally well in certain circumstances. The purpose of the present Monte Carlo simulation study is to test these methods across a large number of conditions, including varied graph sizes and types of matrix (sparse count, correlation, and reflected Euclidean distance), to identify which algorithm is optimal for specific types of data matrices. The results indicate that when the data are in the form of sparse count networks (such as those seen in diffusion tensor imaging), Label Propagation and Walktrap surfaced as the most reliable methods for community detection. For dense, weighted networks such as correlation matrices capturing functional connectivity, Walktrap consistently outperformed the other approaches for recovering communities

State space modeling of time-varying contemporaneous and lagged relations in connectivity maps

Most connectivity mapping techniques for neuroimaging data assume stationarity (i.e., network parameters are constant across time), but this assumption does not always hold true. The authors provide a description of a new approach for simultaneously detecting time-varying (or dynamic) contemporaneous and lagged relations in brain connectivity maps. Specifically, they use a novel raw data likelihood estimation technique (involving a second-order extended Kalman filter/smoother embedded in a nonlinear optimizer) to determine the variances of the random walks associated with state space model parameters and their autoregressive components. The authors illustrate their approach with simulated and blood oxygen level-dependent functional magnetic resonance imaging data from 30 daily cigarette smokers performing a verbal working memory task, focusing on seven regions of interest (ROIs). Twelve participants had dynamic directed functional connectivity maps: Eleven had one or more time-varying contemporaneous ROI state loadings, and one had a time-varying autoregressive parameter. Compared to smokers without dynamic maps, smokers with dynamic maps performed the task with greater accuracy. Thus, accurate detection of dynamic brain processes is meaningfully related to behavior in a clinical sample

Examining the Dynamic Structure of Daily Internalizing and Externalizing Behavior at Multiple Levels of Analysis

Psychiatric diagnostic covariation suggests that the underlying structure of psychopathology is not one of circumscribed disorders. Quantitative modeling of individual differences in diagnostic patterns has uncovered several broad domains of mental disorder liability, of which the Internalizing and Externalizing spectra have garnered the greatest support. These dimensions have generally been estimated from lifetime or past-year comorbidity patters, which are distal from the covariation of symptoms and maladaptive behavior that ebb and flow in daily life. In this study, structural models are applied to daily diary data (Median = 94 days) of maladaptive behaviors collected from a sample (N = 101) of individuals diagnosed with personality disorders. Using multilevel and unified structural equation modeling, between-person, within-person, and person-specific structures were estimated from 16 behaviors that are encompassed by the Internalizing and Externalizing spectra. At the between-person level (i.e., individual differences in average endorsement across days) we found support for a two-factor Internalizing-Externalizing model, which exhibits significant associations with corresponding diagnostic spectra. At the within-person level (i.e., dynamic covariation among daily behavior pooled across individuals) we found support for a more differentiated, four-factor, Negative Affect-Detachment-Hostility-Impulsivity structure. Finally, we demonstrate that the person-specific structures of associations between these four domains are highly idiosyncratic

Examining the Dynamic Structure of Daily Internalizing and Externalizing Behavior at Multiple Levels of Analysis

Psychiatric diagnostic covariation suggests that the underlying structure of psychopathology is not one of circumscribed disorders. Quantitative modeling of individual differences in diagnostic patterns has uncovered several broad domains of mental disorder liability, of which the Internalizing and Externalizing spectra have garnered the greatest support. These dimensions have generally been estimated from lifetime or past-year comorbidity patters, which are distal from the covariation of symptoms and maladaptive behavior that ebb and flow in daily life. In this study, structural models are applied to daily diary data (Median = 94 days) of maladaptive behaviors collected from a sample (N = 101) of individuals diagnosed with personality disorders. Using multilevel and unified structural equation modeling, between-person, within-person, and person-specific structures were estimated from 16 behaviors that are encompassed by the Internalizing and Externalizing spectra. At the between-person level (i.e., individual differences in average endorsement across days) we found support for a two-factor Internalizing-Externalizing model, which exhibits significant associations with corresponding diagnostic spectra. At the within-person level (i.e., dynamic covariation among daily behavior pooled across individuals) we found support for a more differentiated, four-factor, Negative Affect-Detachment-Hostility-Impulsivity structure. Finally, we demonstrate that the person-specific structures of associations between these four domains are highly idiosyncratic

Promoting physical activity among cancer survivors: meta-analysis and meta-cart analysis of randomized controlled trials

Objective: We conducted a meta-analysis of physical activity interventions among cancer survivors in order to (a) quantify the magnitude of intervention effects on physical activity, and (b) determine what combination of intervention strategies maximizes behavior change. Methods: Out of 32,626 records that were located using computerized searches, 138 independent tests (N = 13,050) met the inclusion criteria for the review. We developed a bespoke taxonomy of 34 categories of techniques designed to promote psychological change, and categorized sample, intervention, and methodological characteristics. Random effects meta-analysis and meta-regressions were conducted; effect size data were also submitted to Meta-CART analysis. Results: The sample-weighted average effect size for physical activity interventions was d+ = .35, equivalent to an increase of 1,149 steps per day. Effect sizes exhibited both publication bias and small sample bias but remained significantly different from zero, albeit of smaller magnitude (d+ ≥ .20), after correction for bias. Meta-CART analysis indicated that the major difference in effectiveness was attributable to supervised versus unsupervised programs (d+ = .49 vs. .26). Greater contact time was associated with larger effects in supervised programs. For unsupervised programs, establishing outcome expectations, greater contact time, and targeting overweight or sedentary participants each predicted greater program effectiveness, whereas prompting barrier identification and providing workbooks were associated with smaller effect sizes. Conclusion: The present review indicates that interventions have a small but significant effect on physical activity among cancer survivors, and offers insights into how the effectiveness of future interventions might be improved

BRAIN Initiative: Cutting-Edge Tools and Resources for the Community.

The overarching goal of the NIH BRAIN (Brain Research through Advancing Innovative Neurotechnologies) Initiative is to advance the understanding of healthy and diseased brain circuit function through technological innovation. Core principles for this goal include the validation and dissemination of the myriad innovative technologies, tools, methods, and resources emerging from BRAIN-funded research. Innovators, BRAIN funding agencies, and non-Federal partners are working together to develop strategies for making these products usable, available, and accessible to the scientific community. Here, we describe several early strategies for supporting the dissemination of BRAIN technologies. We aim to invigorate a dialogue with the neuroscience research and funding community, interdisciplinary collaborators, and trainees about the existing and future opportunities for cultivating groundbreaking research products into mature, integrated, and adaptable research systems. Along with the accompanying Society for Neuroscience 2019 Mini-Symposium, "BRAIN Initiative: Cutting-Edge Tools and Resources for the Community," we spotlight the work of several BRAIN investigator teams who are making progress toward providing tools, technologies, and services for the neuroscience community. These tools access neural circuits at multiple levels of analysis, from subcellular composition to brain-wide network connectivity, including the following: integrated systems for EM- and florescence-based connectomics, advances in immunolabeling capabilities, and resources for recording and analyzing functional connectivity. Investigators describe how the resources they provide to the community will contribute to achieving the goals of the NIH BRAIN Initiative. Finally, in addition to celebrating the contributions of these BRAIN-funded investigators, the Mini-Symposium will illustrate the broader diversity of BRAIN Initiative investments in cutting-edge technologies and resources

Initial evaluation of the Robert Wood Johnson Foundation Nurse Faculty Scholars program

The Robert Wood Johnson Foundation Nurse Faculty Scholars (RWJF NFS) program was developed to enhance the career trajectory of young nursing faculty and to train the next generation of nurse scholars. Although there are publications that describe the RWJF NFS, no evaluative reports have been published. The purpose of this study was to evaluate the first three cohorts (n = 42 scholars) of the RWJF NFS program

Electron-Induced Radiolysis of Astrochemically Relevant Ammonia Ices

We elucidate mechanisms of electron-induced radiolysis in cosmic (interstellar, planetary, and cometary) ice analogs of ammonia (NH3), likely the most abundant nitrogen-containing compound in the interstellar medium (ISM). Astrochemical processes were simulated under ultrahigh vacuum conditions by high-energy (1 keV) and low-energy (7 eV) electron-irradiation of nanoscale thin films of ammonia deposited on cryogenically cooled metal substrates. Irradiated films were analyzed by temperature-programmed desorption (TPD). Experiments with ammonia isotopologues provide convincing evidence for the electron-induced formation of hydrazine (N2H4) and diazene (N2H2) from condensed NH3. To understand the dynamics of ammonia radiolysis, the dependence of hydrazine and diazene yields on incident electron energy, electron flux, electron fluence, film thickness, and ice temperature were investigated. Radiolysis yield measurements versus (1) irradiation time and (2) film thickness are semiquantitatively consistent with a reaction mechanism that involves a bimolecular step for the formation of hydrazine and diazene from the dimerization of amidogen (NH2) and imine (NH) radicals, respectively. The apparent decrease in radiolysis yield of hydrazine and diazene with decreasing electron flux at constant fluence may be due to the competing desorption of these radicals at 90 K under low incident electron flux conditions. The production of hydrazine at electron energies as low as 7 eV and an ice temperature of 22 K is consistent with condensed phase radiolysis being mediated by low-energy secondary electrons produced by the interaction of high-energy radiation with matter. These results provide a basis from which we can begin to understand the mechanisms by which ammonia can form more complex species in cosmic ices