scholarly journals Adolescent Brain Cognitive Development (ABCD) Community MRI Collection and Utilities

2021 ◽  
Author(s):  
Eric Feczko ◽  
Greg Conan ◽  
Scott Marek ◽  
Brenden Tervo-Clemens ◽  
Michaela Cordova ◽  
...  
Keyword(s):  
Mental Health ◽  
Cognitive Development ◽  
Data Storage ◽  
Data Privacy ◽  
Scientific Progress ◽  
Open Science ◽  
Population Based ◽  
Mental Health Research ◽  
Data Set ◽  
Neuroimaging Data

The Adolescent Brain Cognitive Development Study (ABCD), a 10 year longitudinal neuroimaging study of the largest population based and demographically distributed cohort of 9-10 year olds (N=11,877), was designed to overcome reproducibility limitations of prior child mental health studies. Besides the fantastic wealth of research opportunities, the extremely large size of the ABCD data set also creates enormous data storage, processing, and analysis challenges for researchers. To ensure data privacy and safety, researchers are not currently able to share neuroimaging data derivatives through the central repository at the National Data Archive (NDA). However, sharing derived data amongst researchers laterally can powerfully accelerate scientific progress, to ensure the maximum public benefit is derived from the ABCD study. To simultaneously promote collaboration and data safety, we developed the ABCD-BIDS Community Collection (ABCC), which includes both curated processed data and software utilities for further analyses. The ABCC also enables researchers to upload their own custom-processed versions of ABCD data and derivatives for sharing with the research community. This NeuroResource is meant to serve as the companion guide for the ABCC. In section we describe the ABCC. Section II highlights ABCC utilities that help researchers access, share, and analyze ABCD data, while section III provides two exemplar reproducibility analyses using ABCC utilities. We hope that adoption of the ABCC's data-safe, open-science framework will boost access and reproducibility, thus facilitating progress in child and adolescent mental health research.

scholarly journals Big data in mental health research – do the ns justify the means? Using large data-sets of electronic health records for mental health research

2017 ◽  
Vol 41 (3) ◽  
pp. 129-132 ◽  
Author(s):  
Peter Schofield
Keyword(s):  
Mental Health ◽  
Big Data ◽  
Data Storage ◽  
Health Research ◽  
Large Data ◽  
Population Data ◽  
Mental Health Research ◽  
Multiple Time ◽  
Short Account ◽  
Electronic Health

SummaryAdvances in information technology and data storage, so-called ‘big data’, have the potential to dramatically change the way we do research. We are presented with the possibility of whole-population data, collected over multiple time points and including detailed demographic information usually only available in expensive and labour-intensive surveys, but at a fraction of the cost and effort. Typically, accounts highlight the sheer volume of data available in terms of terabytes (1012) and petabytes (1015) of data while charting the exponential growth in computing power we can use to make sense of this. Presented with resources of such dizzying magnitude it is easy to lose sight of the potential limitations when the amount of data itself appears unlimited. In this short account I look at some recent advances in electronic health data that are relevant for mental health research while highlighting some of the potential pitfalls.

scholarly journals The Spectrum of Data sharing Policies in Neuroimaging Data Repositories

2021 ◽  
Author(s):  
Anita Jwa ◽  
Russell Poldrack
Keyword(s):  
Data Sharing ◽  
Data Privacy ◽  
Public Investment ◽  
Science Inquiry ◽  
Open Science ◽  
Machine Learning Techniques ◽  
Data Repositories ◽  
Learning Techniques ◽  
Neuroimaging Data ◽  
Regulatory Analysis

Sharing data is a scientific imperative that accelerates scientific discoveries, reinforces open science inquiry, and allows for efficient use of public investment and research resources. Considering these benefits, data sharing has been widely promoted in diverse fields and neuroscience has been no exception to this movement. For all its promise, however, the sharing of human neuroimaging data raises critical ethical and legal issues, such as data privacy. Recently, the heightened risks to data privacy posed by the exponential development in artificial intelligence and machine learning techniques has made data sharing more challenging; the regulatory landscape around data sharing has also been evolving rapidly. Here we present an in-depth ethical and regulatory analysis that will examine how neuroimaging data are currently shared against the backdrop of the relevant regulations and policies and how advanced software tools and algorithms might undermine subjects’ privacy in neuroimaging data sharing. This analysis will inform researchers on responsible practice of neuroimaging data sharing and shed light on a regulatory framework to provide adequate protection of neuroimaging data while maximizing the benefits of data sharing.

The National Institute of Mental Health Research Domain Criteria

2020 ◽  
pp. 62-72
Author(s):  
Charles A. Sanislow ◽  
Sarah E. Morris ◽  
Jennifer Pacheco ◽  
Bruce N. Cuthbert
Keyword(s):  
Mental Health ◽  
Clinical Symptoms ◽  
Scientific Progress ◽  
The United States ◽  
Mental Health Research ◽  
Research Domain Criteria ◽  
Behavioural Science ◽  
Integrative Research ◽  
Clinically Significant ◽  
Research Domain

The United States National Institute of Mental Health (NIMH) Research Domain Criteria (RDoC) initiative offers a framework to facilitate integrative research to clarify core mechanisms of human mental distress and dysfunction. The RDoC was developed to provide an alternative to research, designed around clinical syndromes based on descriptive diagnosis. Rather than beginning with a syndrome and then working ‘down’ to clarify mechanisms, the aim of the RDoC is to guide research that begins with disruptions in neurobiological and behavioural mechanisms, and then works across systems to clarify connections among such disruptions and clinical symptoms. The RDoC also departs from widely accepted categorical diagnoses, instead advocating a dimensional account of clinically significant variance in disrupted mechanisms and symptoms. The need for the RDoC stemmed from the realization that psychopathology research was not keeping pace with advances in clinical neuroscience and behavioural science, and the recognition that the cycle of scientific progress has been hampered by the instantiation of DSM diagnoses as the starting point of psychiatric research design. This chapter details the rationale and development of the RDoC and describes their structure. Some practical considerations and theoretical matters for implementing the RDoC alternative are considered.

scholarly journals Psychiatric research platform within SAIL - Linkage of prospectively ascertained phenotypically rich and genetic data to routinely collected anonymised records.

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Joanna McGregor ◽  
Ann John ◽  
Keith Lloyd
Keyword(s):  
Mental Health ◽  
Health Research ◽  
Large Population ◽  
Genetic Data ◽  
Population Based ◽  
Mental Health Research ◽  
Response To Treatment ◽  
Psychiatric Research ◽  
Illness Onset ◽  
Polygenic Scores

ABSTRACT ObjectivesWe have conducted a feasibility study linking clinically rich survey data to routine data to create a platform for psychosis research in Wales: K Lloyd et al (2015), A national population-based e-cohort of people with psychosis (PsyCymru) linking prospectively ascertained phenotypically rich and genetic data to routinely collected records: overview, recruitment and linkage, Schizophrenia Research. Now we expand upon this through the linkage of large clinically rich cohorts with a range of mental health diagnoses along with genetic data to conduct validation exercises, develop novel methodologies, assess genetic and environment interactions and outcomes and address hypothesis-driven research questions. ApproachThrough collaborations between the Farr Institute, Cardiff University based MRC centre for Neuropsychiatric Genetics and Genomics and the National Centre for Mental Health (NCMH) clinically rich data and genetic (CNVs, SNPs & polygenic scores) data from around 6000+ participants recruited from a variety of mental health research studies including ‘PsyCymru’, ‘Genetic susceptibility to cognitive deficits study and NCMH amongst others will be loaded and linked to the datasets within SAIL. The analysis plan would firstly include validation exercises to compare the data between sources. Methodologies would be developed using this data to determine illness onset, relapse, chronicity, severity and response to treatment applied to large population-based mental health e-cohorts. ResultsBy pooling together health service data, genetic variants, environmental and lifestyle factors, phenotypic and endo-phenotypic (cognitive scores) along with the ability to ascertain temporal relationships afforded by the longitudinal perspective available in SAIL we may be able to evaluate potential risk factors, assess the complex GxE interactions that lead to disease progression, and assess outcomes such as prognosis, remission, relapse and premature mortality. The on-going routine updates provide us with the opportunity to follow-up these individuals across multiple health care settings in a cost effective and in-obtrusive manner and to carry out health services utilization/benefit and treatment surveillance in a naturalistic setting. This resource will continue to expand over the coming years in size, breadth and depth of data, with continued recruitment and additional measures planned. ConclusionTo advance mental health research by developing our understanding of the causes, course and outcomes of mental illness that may lead to the development of better diagnostic classification, predictive, preventative strategies and therapeutic approaches.

scholarly journals A perceptual decision making EEG/fMRI data set

2018 ◽  
Author(s):  
Yasmin K. Georgie ◽  
Camillo Porcaro ◽  
Stephen D. Mayhew ◽  
Andrew P. Bagshaw ◽  
Dirk Ostwald
Keyword(s):  
Decision Making ◽  
Human Subjects ◽  
Open Science ◽  
Fmri Data ◽  
Imaging Data ◽  
Perceptual Decision Making ◽  
Data Set ◽  
Perceptual Decision ◽  
Neuroimaging Data ◽  
Science Framework

AbstractWe present a neuroimaging data set comprising behavioural, electroencephalographic (EEG), and functional magnetic resonance imaging (fMRI) data that were acquired from human subjects performing a perceptual decision making task. EEG data were acquired both independently and simultaneously with fMRI data. Potential data usages include the validation of biocomputational accounts of human perceptual decision making or the empirical validation of simultaneous EEG/fMRI data processing algorithms. The dataset is available from the Open Science Framework and organized according to the Brain Imaging Data Structure standard.

scholarly journals Calibrating well-being, quality of life and common mental disorder items: Psychometric epidemiology in public mental health research

2016 ◽  
Vol 209 (2) ◽  
pp. 162-168 ◽  
Author(s):  
Jan R. Böhnke ◽  
Tim J. Croudace
Keyword(s):  
Mental Health ◽  
Health Research ◽  
General Health ◽  
Common Mental Disorder ◽  
General Health Questionnaire ◽  
Public Mental Health ◽  
Well Being ◽  
Population Based ◽  
Mental Health Research ◽  
Health And Well Being

BackgroundThe assessment of ‘general health and well-being’ in public mental health research stimulates debates around relative merits of questionnaire instruments and their items. Little evidence regarding alignment or differential advantages of instruments or items has appeared to date.AimsPopulation-based psychometric study of items employed in public mental health narratives.MethodMultidimensional item response theory was applied to General Health Questionnaire (GHQ-12), Warwick-Edinburgh Mental Well-being Scale (WEMWBS) and EQ-5D items (Health Survey for England, 2010–2012; n = 19 290).ResultsA bifactor model provided the best account of the data and showed that the GHQ-12 and WEMWBS items assess mainly the same construct. Only one item of the EQ-5D showed relevant overlap with this dimension (anxiety/depression). Findings were corroborated by comparisons with alternative models and cross-validation analyses.ConclusionsThe consequences of this lack of differentiation (GHQ-12 v. WEMWBS) for mental health and well-being narratives deserves discussion to enrich debates on priorities in public mental health and its assessment.

Pseudonyms for Cancer Registries

1996 ◽  
Vol 35 (02) ◽  
pp. 112-121 ◽  
Author(s):  
M. Miller ◽  
I. Schmidtmann ◽  
J. Michaelis ◽  
K. Pommerening
Keyword(s):  
Pilot Study ◽  
Data Storage ◽  
Data Privacy ◽  
Cancer Registries ◽  
Population Based ◽  
Error Rates ◽  
Cancer Registration ◽  
Privacy And Security ◽  
Permanent Storage ◽  
German Legislation

AbstractIn order to conform to the rigid German legislation on data privacy and security we developed a new concept of data flow and data storage for population-based cancer registries. A special trusted office generates a pseudonym for each case by a cryptographic procedure. This office also handles the notification of cases and communicates with the reporting physicians. It passes pseudonymous records to the registration office for permanent storage. The registration office links the records according to the pseudonyms. Starting from a requirements analysis we show how to construct the pseudonyms; we then show that they meet the requirements. We discuss how the pseudonyms have to be protected by cryptographic and organizational means. A pilot study showed that the proposed procedure gives acceptable synonym and homonym error rates. The methods described are not restricted to cancer registration and may serve as a model for comparable applications in medical informatics.

scholarly journals An Iterative and Collaborative End-to-End Methodology Applied to Digital Mental Health

2021 ◽  
Vol 12 ◽  
Author(s):  
Laura Joy Boulos ◽  
Alexandre Mendes ◽  
Alexandra Delmas ◽  
Ikram Chraibi Kaadoud
Keyword(s):  
Mental Health ◽  
Data Storage ◽  
Health Sector ◽  
Digital Technologies ◽  
Mental Health Research ◽  
Ethical Challenges ◽  
Psychiatric Illnesses ◽  
Global Pandemic ◽  
Health Studies ◽  
End To End

Artificial intelligence (AI) algorithms together with advances in data storage have recently made it possible to better characterize, predict, prevent, and treat a range of psychiatric illnesses. Amid the rapidly growing number of biological devices and the exponential accumulation of data in the mental health sector, the upcoming years are facing a need to homogenize research and development processes in academia as well as in the private sector and to centralize data into federalizing platforms. This has become even more important in light of the current global pandemic. Here, we propose an end-to-end methodology that optimizes and homogenizes digital research processes. Each step of the process is elaborated from project conception to knowledge extraction, with a focus on data analysis. The methodology is based on iterative processes, thus allowing an adaptation to the rate at which digital technologies evolve. The methodology also advocates for interdisciplinary (from mathematics to psychology) and intersectoral (from academia to the industry) collaborations to merge the gap between fundamental and applied research. We also pinpoint the ethical challenges and technical and human biases (from data recorded to the end user) associated with digital mental health. In conclusion, our work provides guidelines for upcoming digital mental health studies, which will accompany the translation of fundamental mental health research to digital technologies.

scholarly journals The Neurobiology of Panic: A Chronic Stress Disorder

2017 ◽  
Vol 1 ◽  
pp. 247054701773603 ◽  
Author(s):  
Andrew W. Goddard
Keyword(s):  
Mental Health ◽  
Risk Factors ◽  
Panic Disorder ◽  
Chronic Stress ◽  
Stress Disorder ◽  
Mental Health Research ◽  
Research Domain Criteria ◽  
Neuroimaging Data ◽  
Potential Impact ◽  
Research Domain

Panic disorder is an often chronic and impairing human anxiety syndrome, which frequently results in serious psychiatric and medical comorbidities. Although, to date, there have been many advances in the diagnosis and treatment of panic disorder, its pathophysiology still remains to be elucidated. In this review, recent evidence for a neurobiological basis of panic disorder is reviewed with particular attention to risk factors such as genetic vulnerability, chronic stress, and temperament. In addition, neuroimaging data are reviewed which provides support for the concept of panic disorder as a fear network disorder. The potential impact of the National Institute of Mental Health Research Domain Criteria constructs of acute and chronic threats responses and their implications for the neurobiology of panic disorder are also discussed.

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