Honeycomb: a template for reproducible psychophysiological tasks for clinic, laboratory, and home use.

OBJECTIVE: To improve the ability of psychiatry researchers to build, deploy, maintain, reproduce, and share their own psychophysiological tasks. Psychophysiological tasks are a useful tool for studying human behavior driven by mental processes such as cognitive control, reward evaluation, and learning. Neural mechanisms during behavioral tasks are often studied via simultaneous electrophysiological recordings. Popular online platforms such as Amazon Mechanical Turk (MTurk) and Prolific enable deployment of tasks to numerous participants simultaneously. However, there is currently no task-creation framework available for flexibly deploying tasks both online and during simultaneous electrophysiology. METHODS: We developed a task creation template, termed Honeycomb, that standardizes best practices for building jsPsych-based tasks. Honeycomb offers continuous deployment configurations for seamless transition between use in research settings and at home. Further, we have curated a public library, termed BeeHive, of ready-to-use tasks. RESULTS: We demonstrate the benefits of using Honeycomb tasks with a participant in an ongoing study of deep brain stimulation for obsessive compulsive disorder, who completed repeated tasks both in the clinic and at home. CONCLUSION: Honeycomb enables researchers to deploy tasks online, in clinic, and at home in more ecologically valid environments and during concurrent electrophysiology.

Feature extraction and machine learning techniques for identifying historic urban environmental hazards: New methods to locate lost fossil fuel infrastructure in US cities.

U.S. cities contain unknown numbers of undocumented "manufactured gas" sites, legacies of an industry that dominated energy production during the late-19th and early-20th centuries. While many of these unidentified sites likely contain significant levels of highly toxic and biologically persistent contamination, locating them remains a significant challenge. We propose a new method to identify manufactured gas production, storage, and distribution infrastructure in bulk by applying feature extraction and machine learning techniques to digitized historic Sanborn fire insurance maps. Our approach, which relies on a two-part neural network to classify candidate map regions, increases the rate of site identification 20-fold compared to unaided visual coding.

Lipopolysaccharide-induced neuroinflammation disrupts functional connectivity and community structure in primary cortical microtissues.

Three-dimensional (3D) neural microtissues are a powerful in vitro paradigm for studying brain development and disease under controlled conditions, while maintaining many key attributes of the in vivo environment. Here, we used primary cortical microtissues to study the effects of neuroinflammation on neural microcircuits. We demonstrated the use of a genetically encoded calcium indicator combined with a novel live-imaging platform to record spontaneous calcium transients in microtissues from day 14-34 in vitro. We implemented graph theory analysis of calcium activity to characterize underlying functional connectivity and community structure of microcircuits, which are capable of capturing subtle changes in network dynamics during early disease states. We found that microtissues cultured for 34 days displayed functional remodeling of microcircuits and that community structure strengthened over time. Lipopolysaccharide, a neuroinflammatory agent, significantly increased functional connectivity and disrupted community structure 5-9 days after exposure. These microcircuit-level changes have broad implications for the role of neuroinflammation in functional dysregulation of neural networks.

Productivity-driven physician scheduling in emergency departments.

The objective of this study is two-fold: to propose an alternative approach for computing the productivity of physicians in emergency departments (EDs); and, to allocate productivity-driven schedules to ED physicians so as to align physician productivity with demand (patient arrivals), without decreasing fairness between physicians, in order to improve patient wait times. Historical data between 2008 and 2017 from the Sacré-Coeur Montreal Hospital ED is analysed and used to predict the demand and to estimate the productivity of each physician. These estimates are incorporated into a mathematical programming model that identifies feasible schedules to physicians that minimise the difference between patients' demand and physicians' productivity, along with the violation of physicians' preferences and fairness in the distribution of shifts. Results on real-world-based data show that when physician productivity is included in the allocation of schedules, demand under-covering is reduced by 10.85% and the fairness between physicians is maintained. However, physicians' preferences (e.g., sum of the differences between the number of wanted shifts and the number of allocated shifts) deteriorates by 7.61%. By incorporating the productivity of physicians in the scheduling process, we see a reduction in EDs overcrowding and an improvement in the overall quality of health-care services.

Web-Based Visualization of MeSH-Based PubMed/MEDLINE Statistics.

Statistical analysis of Medical Subject Headings (MeSH) descriptors to improve biomedical literature search is an active research area. Existing tools have limited interactive visualizations that are accessible to researchers investigating how their hypotheses compare to trends in the research literature. We present a web application that computes and provides an interactive visualization of basic frequencies and co-occurrence statistics of MeSH descriptors associated with a PubMed query.

Decoding task engagement from distributed network electrophysiology in humans.

OBJECTIVE: Here, our objective was to develop a binary decoder to detect task engagement in humans during two distinct, conflict-based behavioral tasks. Effortful, goal-directed decision-making requires the coordinated action of multiple cognitive processes, including attention, working memory and action selection. That type of mental effort is often dysfunctional in mental disorders, e.g. when a patient attempts to overcome a depression or anxiety-driven habit but feels unable. If the onset of engagement in this type of focused mental activity could be reliably detected, decisional function might be augmented, e.g. through neurostimulation. However, there are no known algorithms for detecting task engagement with rapid time resolution. APPROACH: We defined a new network measure, fixed canonical correlation (FCCA), specifically suited for neural decoding applications. We extracted FCCA features from local field potential recordings in human volunteers to give a temporally continuous estimate of mental effort, defined by engagement in experimental conflict tasks. MAIN RESULTS: Using a small number of features per participant, we accurately decoded and distinguished task engagement from other mental activities. Further, the decoder distinguished between engagement in two different conflict-based tasks within seconds of their onset. SIGNIFICANCE: These results demonstrate that network-level brain activity can detect specific types of mental efforts. This could form the basis of a responsive intervention strategy for decision-making deficits.

Attention-deficit/hyperactivity disorder and comorbid disruptive behavior disorders: evidence of pleiotropy and new susceptibility loci.

BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) comorbid with oppositional defiant disorder (ODD) or conduct disorder (CD) and substance abuse/dependence seems to represent a specific subset within the phenotypic ADHD spectrum. METHODS: We applied complex segregation and linkage analyses in a set of multigenerational families densely segregating ADHD comorbid with ODD, CD, alcohol abuse/dependence, and nicotine dependence. RESULTS: Our data suggest that ADHD cosegregates with disruptive behaviors as a unique, phenotypically variable trait as evidenced by highly significant pair-wise linkages among: ADHD and ODD (logarithm of odds [LOD]=14.19), ADHD and CD (LOD=5.34), ODD and CD (LOD=6.68), and CD and alcohol abuse/dependence (LOD=3.98). In addition to previously reported ADHD susceptibility loci, we found evidence of linkage for comorbid ADHD phenotypes to loci at 8q24, 2p21-22.3, 5p13.1-p13.3, 12p11.23-13.3, 8q15, and 14q21.1-22.2. These results were replicated with an affected status phenotype derived from latent class clusters. CONCLUSIONS: Patterns of cosegregation of ADHD with comorbidities can inform our understanding of the inheritance patterns not only of ADHD but also of disruptive behavioral disorders and alcohol abuse/dependence. Refining the comorbid ADHD phenotype by determining the cosegregation profile of specific comorbidities might be a powerful tool for defining significant regions of linkage.

Honeycomb: a template for reproducible psychophysiological tasks for clinic, laboratory, and home use

Objective: To improve the ability of psychiatry researchers to build, deploy, maintain, reproduce, and share their own psychophysiological tasks. Psychophysiological tasks are a useful tool for studying human behavior driven by mental processes such as cognitive control, reward evaluation, and learning. Neural mechanisms during behavioral tasks are often studied via simultaneous electrophysiological recordings. Popular online platforms such as Amazon Mechanical Turk (MTurk) and Prolific enable deployment of tasks to numerous participants simultaneously. However, there is currently no task-creation framework available for flexibly deploying tasks both online and during simultaneous electrophysiology. Methods: We developed a task creation template, termed Honeycomb, that standardizes best practices for building jsPsych-based tasks. Honeycomb offers continuous deployment configurations for seamless transition between use in research settings and at home. Further, we have curated a public library, termed BeeHive, of ready-to-use tasks. Results: We demonstrate the benefits of using Honeycomb tasks with a participant in an ongoing study of deep brain stimulation for obsessive compulsive disorder, who completed repeated tasks both in the clinic and at home. Conclusion: Honeycomb enables researchers to deploy tasks online, in clinic, and at home in more ecologically valid environments and during concurrent electrophysiology.