The Brain Imaging Data Structure, a Format for Organizing and Describing Outputs of Neuroimaging Experiments

The development of magnetic resonance imaging (MRI) techniques has defined modern neuroimaging. Since its inception, tens of thousands of studies using techniques such as functional MRI and diffusion weighted imaging have allowed for the non-invasive study of the brain. Despite the fact that MRI is routinely used to obtain data for neuroscience research, there has been no widely adopted standard for organizing and describing the data collected in an imaging experiment. This renders sharing and reusing data (within or between labs) difficult if not impossible and unnecessarily complicates the application of automatic pipelines and quality assurance protocols. To solve this problem, we have developed the Brain Imaging Data Structure (BIDS), a standard for organizing and describing MRI datasets. The BIDS standard uses file formats compatible with existing software, unifies the majority of practices already common in the field, and captures the metadata necessary for most common data processing operations

Liquid Phase Hydrodechlorination of Dieldrin and DDT over Pd/C and Raney-Ni

Selectivity and product distribution of hydrodechlorination (HDCl) of dieldrin and DDT are studied in different liquid phase systems, namely in: (1) in ethanol; and (2) in the supported ionic liquid heterogeneous catalytic system (multiphase system), composed by the organic phase and aqueous KOH, a quaternary ammonium ionic liquid promoter (Aliquat 336), and a metal catalyst, e.g. 5% Pd/C, 5% Pt/C, or Raney-Ni. At 50 8C and atmospheric pressure of hydrogen, a quantitative hydrodechlorination of DDT in the biphasic system with ionic liquid layer is achieved in 40 min and in 4 h with Raney-Ni and Pd/C, respectively, while the reaction on Pt/C or on Pd/C without Aliquat 336 is slow. Dieldrin undergoes partial dechlorination, with high selectivity achievable only for its mono- and bi-dechlorination products. Dechlorination pathways and reactivity of different types of organic chlorine atoms versus the catalyst nature and other conditions are discussed

Improving Prediction of Real-Time Loneliness and Companionship Type Using Geosocial Features of Personal Smartphone Data

Loneliness is a widely affecting mental health symptom and can be mediated by and co-vary with patterns of social exposure. Using momentary survey and smartphone sensing data collected from 129 Android-using college student participants over three weeks, we (1) investigate and uncover the relations between momentary loneliness experience and companionship type and (2) propose and validate novel geosocial features of smartphone-based Bluetooth and GPS data for predicting loneliness and companionship type in real time. We base our features on intuitions characterizing the quantity and spatiotemporal predictability of an individual's Bluetooth encounters and GPS location clusters to capture personal significance of social exposure scenarios conditional on their temporal distribution and geographic patterns. We examine our features' statistical correlation with momentary loneliness through regression analyses and evaluate their predictive power using a sliding window prediction procedure. Our features achieved significant performance improvement compared to baseline for predicting both momentary loneliness and companionship type, with the effect stronger for the loneliness prediction task. As such we recommend incorporation and further evaluation of our geosocial features proposed in this study in future mental health sensing and context-aware computing applications.This work was supported by Whole Communities—Whole Health, a research grand challenge at the University of Texas at AustinOffice of the VP for Researc

Patterns of thought : Population variation in the associations between large-scale network organisation and self-reported experiences at rest

Contemporary cognitive neuroscience recognises unconstrained processing varies across individuals, describing variation in meaningful attributes, such as intelligence. It may also have links to patterns of on-going experience. This study examined whether dimensions of population variation in different modes of unconstrained processing can be described by the associations between patterns of neural activity and self-reports of experience during the same period. We selected 258 individuals from a publicly available data set who had measures of resting-state functional magnetic resonance imaging, and self-reports of experience during the scan. We used machine learning to determine patterns of association between the neural and self-reported data, finding variation along four dimensions. ‘Purposeful’ experiences were associated with lower connectivity - in particular default mode and limbic networks were less correlated with attention and sensorimotor networks. ‘Emotional’ experiences were associated with higher connectivity, especially between limbic and ventral attention networks. Experiences focused on themes of ‘personal importance’ were associated with reduced functional connectivity within attention and control systems. Finally, visual experiences were associated with stronger connectivity between visual and other networks, in particular the limbic system. Some of these patterns had contrasting links with cognitive function as assessed in a separate laboratory session - purposeful thinking was linked to greater intelligence and better abstract reasoning, while a focus on personal importance had the opposite relationship. Together these findings are consistent with an emerging literature on unconstrained states and also underlines that these states are heterogeneous, with distinct modes of population variation reflecting the interplay of different large-scale networks

Multi-Modal Data Collection for Measuring Health, Behavior, and Living Environment of Large-Scale Participant Cohorts: Conceptual Framework and Findings from Deployments

As mobile technologies become ever more sensor-rich, portable, and ubiquitous, data captured by smart devices are lending rich insights into users' daily lives with unprecedented comprehensiveness, unobtrusiveness, and ecological validity. A number of human-subject studies have been conducted in the past decade to examine the use of mobile sensing to uncover individual behavioral patterns and health outcomes. While understanding health and behavior is the focus for most of these studies, we find that minimal attention has been placed on measuring personal environments, especially together with other human-centric data modalities. Moreover, the participant cohort size in most existing studies falls well below a few hundred, leaving questions open about the reliability of findings on the relations between mobile sensing signals and human outcomes. To address these limitations, we developed a home environment sensor kit for continuous indoor air quality tracking and deployed it in conjunction with established mobile sensing and experience sampling techniques in a cohort study of up to 1584 student participants per data type for 3 weeks at a major research university in the United States. In this paper, we begin by proposing a conceptual framework that systematically organizes human-centric data modalities by their temporal coverage and spatial freedom. Then we report our study design and procedure, technologies and methods deployed, descriptive statistics of the collected data, and results from our extensive exploratory analyses. Our novel data, conceptual development, and analytical findings provide important guidance for data collection and hypothesis generation in future human-centric sensing studies.This work was supported by Whole Communities—Whole Health, a research grand challenge at the University of Texas at Austin, and National Science Foundation Award SES-1758835.Office of the VP for Researc

The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain–behavior relationships after stroke

The goal of the Enhancing Neuroimaging Genetics through Meta‐Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well‐powered meta‐ and mega‐analytic approaches. ENIGMA Stroke Recovery has data from over 2,100 stroke patients collected across 39 research studies and 10 countries around the world, comprising the largest multisite retrospective stroke data collaboration to date. This article outlines the efforts taken by the ENIGMA Stroke Recovery working group to develop neuroinformatics protocols and methods to manage multisite stroke brain magnetic resonance imaging, behavioral and demographics data. Specifically, the processes for scalable data intake and preprocessing, multisite data harmonization, and large‐scale stroke lesion analysis are described, and challenges unique to this type of big data collaboration in stroke research are discussed. Finally, future directions and limitations, as well as recommendations for improved data harmonization through prospective data collection and data management, are provided

The Healthy Brain Network Serial Scanning Initiative: a resource for evaluating inter-individual differences and their reliabilities across scan conditions and sessions

Background: Although typically measured during the resting state, a growing literature is illustrating the ability to map intrinsic connectivity with functional MRI during task and naturalistic viewing conditions. These paradigms are drawing excitement due to their greater tolerability in clinical and developing populations and because they enable a wider range of analyses (e.g., inter-subject correlations). To be clinically useful, the test-retest reliability of connectivity measured during these paradigms needs to be established. This resource provides data for evaluating test-retest reliability for full-brain connectivity patterns detected during each of four scan conditions that differ with respect to level of engagement (rest, abstract animations, movie clips, flanker task). Data are provided for 13 participants, each scanned in 12 sessions with 10 minutes for each scan of the four conditions. Diffusion kurtosis imaging data was also obtained at each session. Findings: Technical validation and demonstrative reliability analyses were carried out at the connection-level using the Intraclass Correlation Coefficient and at network-level representations of the data using the Image Intraclass Correlation Coefficient. Variation in intrinsic functional connectivity across sessions was generally found to be greater than that attributable to scan condition. Between-condition reliability was generally high, particularly for the frontoparietal and default networks. Between-session reliabilities obtained separately for the different scan conditions were comparable, though notably lower than between-condition reliabilities

Enhancing studies of the connectome in autism using the autism brain imaging data exchange II

The second iteration of the Autism Brain Imaging Data Exchange (ABIDE II) aims to enhance the scope of brain connectomics research in Autism Spectrum Disorder (ASD). Consistent with the initial ABIDE effort (ABIDE I), that released 1112 datasets in 2012, this new multisite open-data resource is an aggregate of resting state functional magnetic resonance imaging (MRI) and corresponding structural MRI and phenotypic datasets. ABIDE II includes datasets from an additional 487 individuals with ASD and 557 controls previously collected across 16 international institutions. The combination of ABIDE I and ABIDE II provides investigators with 2156 unique cross-sectional datasets allowing selection of samples for discovery and/or replication. This sample size can also facilitate the identification of neurobiological subgroups, as well as preliminary examinations of sex differences in ASD. Additionally, ABIDE II includes a range of psychiatric variables to inform our understanding of the neural correlates of co-occurring psychopathology; 284 diffusion imaging datasets are also included. It is anticipated that these enhancements will contribute to unraveling key sources of ASD heterogeneity

Brainhack: a collaborative workshop for the open neuroscience community

International audienceBrainhack events offer a novel workshop format with participant-generated content that caters to the rapidly growing open neuroscience community. Including components from hackathons and unconferences, as well as parallel educational sessions, Brainhack fosters novel collaborations around the interests of its attendees. Here we provide an overview of its structure, past events, and example projects. Additionally, we outline current innovations such as regional events and post-conference publications. Through introducing Brainhack to the wider neuroscience community, we hope to provide a unique conference format that promotes the features of collaborative, open science

Sulfur isotope measurement of sulfate and sulfide by high-resolution MC-ICP-MS

Author Posting. © Elsevier B.V. , 2008. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Chemical Geology 253 (2008): 102-113, doi:10.1016/j.chemgeo.2008.04.017.We have developed a technique for the accurate and precise determination of 34S/32S isotope ratios (δ34S) in sulfur-bearing minerals using solution and laser ablation multiple-collector inductively coupled plasma mass spectrometry (MC-ICP-MS). We have examined and determined rigorous corrections for analytical difficulties such as instrumental mass bias, unresolved isobaric interferences, blanks, and laser ablation- and matrix-induced isotopic fractionation. Use of high resolution sector-field mass spectrometry removes major isobaric interferences from O2+. Standard–sample bracketing is used to correct for the instrumental mass bias of unknown samples. Blanks on sulfur masses arising from memory effects and residual oxygen-tailing are typically minor (< 0.2‰, within analytical error), and are mathematically removed by on-peak zero subtraction and by bracketing of samples with standards determined at the same signal intensity (within 20%). Matrix effects are significant (up to 0.7‰) for matrix compositions relevant to many natural sulfur-bearing minerals. For solution analysis, sulfur isotope compositions are best determined using purified (matrix-clean) sulfur standards and sample solutions using the chemical purification protocol we present. For in situ analysis, where the complex matrix cannot be removed prior to analysis, appropriately matrix-matching standards and samples removes matrix artifacts and yields sulfur isotope ratios consistent with conventional techniques using matrix-clean analytes. Our method enables solid samples to be calibrated against aqueous standards; a consideration that is important when certified, isotopically-homogeneous and appropriately matrix-matched solid standards do not exist. Further, bulk and in situ analyses can be performed interchangeably in a single analytical session because the instrumental setup is identical for both. We validated the robustness of our analytical method through multiple isotope analyses of a range of reference materials and have compared these with isotope ratios determined using independent techniques. Long-term reproducibility of S isotope compositions is typically 0.20‰ and 0.45‰ (2σ) for solution and laser analysis, respectively. Our method affords the opportunity to make accurate and relatively precise S isotope measurement for a wide range of sulfur-bearing materials, and is particularly appropriate for geologic samples with complex matrix and for which high-resolution in situ analysis is critical.Support was provided by National Science Foundations grants OCE-0327448 to P.R.C. and W.B. and OCE-0622982 to O.J.R. Support for L.A.B. was provided by the Woods Hole Oceanographic Institution Plasma Facility Development Grant (NSF-EAR/IF-0318137)