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

Individual-specific and subgroup level associations between stress and psychopathology in daily life:A temporal network investigation

INTRODUCTION: Stress is a risk factor for developing psychopathology. Emerging evidence suggests that daily experiences of stress may also predict symptoms during the day. It is unclear to what extent the influence of stress on psychopathology during the day is the same across individuals (including across diagnostic boundaries), and which effects are individual-specific OBJECTIVES: This study aims to reveal how stress and symptoms are interrelated in a cross-diagnostic context by modeling individual level temporal networks, and examining subgroups with similar dynamics. METHODS: Hundred twenty two young adults (43.4% women) with a wide range of psychopathology in terms of severity and type of problems completed a six-month daily diary study. We used a temporal network approach (i.e., group iterative multiple model estimation) to model how stress and ten specific symptoms (e.g., feeling down, paranoia, restlessness) were related across time at the individual-specific, subgroup, and group level. RESULTS: After controlling for the lagged influence of stress on itself, stress level predicted the level of restlessness, worrying, nervousness, and feeling down during the same day for >70% of individuals. We observed three larger subgroups with each over 20 individuals, whose temporal networks showed different dynamic patterns involving specific symptoms. Effects of stress on other specific symptoms differed across individuals, and these were not subgroup-specific. CONCLUSIONS: This study showed important overlap between individuals in terms of impact of stress on psychopathology in daily life. Subtle differences between individuals were also observed. Possibly, such differences are relevant for examining individual-specific vulnerability for future psychopathology. This requires further investigation. DISCLOSURE: No significant relationships

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

Ecological role of an offshore industry artificial structure

Decommissioning of oil and gas infrastructure globally has focused attention on its importance as hard substratum on continental shelf and slope habitats. Observational studies are needed to improve understanding of faunal assemblages supported by offshore infrastructure and better predict the effect of removal. Here, we present results from visual inspection and physical sampling of a small oil and gas industry structure decommissioned from an oil field in the North East Atlantic. This is supported by observations of similar structures nearby and by photographs of the surrounding seabed from environmental baseline surveys. The structure supported a reasonably high biomass and diversity of invertebrates (>10 kg and >39 macrofaunal and 17 megafaunal species) and fishes (>20 kg biomass and >4 species). The invertebrate megafaunal species present on the structure were a sub-set of the hard substratum fauna observed on surrounding seabed. Porifera were absent from the structure. Biological succession in the first 2 years occurred as follows. Sparse colonies of the hydroid Obelia sp. stet were early colonisers then subsequent development of thick hydroid turf (Obelia sp. stet. and Halecium sp. stet.) supported an invertebrate assemblage (2654 individuals kg wet mass–1) dominated by saddle oysters [Pododesmus squama (Gmelin, 1791) and Heteranomia sp. stet.)] and scale worms (Harmothoe spp.). Percentage cover of hydroid turf varied significantly over the structure, with most growth on sections exposed to strongest currents. Commercially important fish species present around the structure included Gadus morhua (Atlantic cod), Pollachius virens (saithe) and Lophius piscatorius (monkfish). Studies of artificial structures such as this provide much needed data to understand their role in the ecology of seafloor habitats and inform environmental decision making on all stages of industry from exploration to decommissioning. We show that the ecological role of the decommissioned three-dimensional structures was to enhance the biomass of a sub-set of epifaunal invertebrates found in the area. This supported diverse associated macrofaunal organisms, providing a food source for motile invertebrates and fishes in an area where background hard substratum can be lost through the impacts of drilling

Changes in Alcohol-Related Brain Networks Across the First Year of College: A Prospective Pilot Study Using fMRI Effective Connectivity Mapping

The upsurge in alcohol use that often occurs during the first year of college has been convincingly linked to a number of negative psychosocial consequences and may negatively affect brain development. In this longitudinal functional magnetic resonance imaging (fMRI) pilot study, we examined changes in neural responses to alcohol cues across the first year of college in a normative sample of late adolescents. Participants (N=11) were scanned three times across their first year of college (summer, first semester, second semester), while completing a go/no-go task in which images of alcoholic and non-alcoholic beverages were the response cues. A state-of-the-art effective connectivity mapping technique was used to capture spatiotemporal relations among brain regions of interest (ROIs) at the level of the group and the individual. Effective connections among ROIs implicated in cognitive control were greatest at the second assessment (when negative consequences of alcohol use increased), and effective connections among ROIs implicated in emotion processing were lower (and response times were slower) when participants were instructed to respond to alcohol cues compared to non-alcohol cues. These preliminary findings demonstrate the value of a prospective effective connectivity approach for understanding adolescent changes in alcohol-related neural processes.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/123049/1/Changes in Alcohol-Related Brain Networks Across the First Year of College_A Prospective Pilot Study Using fMRI Effective Connectivity Mapping.pd