1
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Gell M, Noble S, Laumann TO, Nelson SM, Tervo-Clemmens B. Psychiatric neuroimaging designs for individualised, cohort, and population studies. Neuropsychopharmacology 2024:10.1038/s41386-024-01918-y. [PMID: 39143320 DOI: 10.1038/s41386-024-01918-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 05/30/2024] [Accepted: 06/11/2024] [Indexed: 08/16/2024]
Abstract
Psychiatric neuroimaging faces challenges to rigour and reproducibility that prompt reconsideration of the relative strengths and limitations of study designs. Owing to high resource demands and varying inferential goals, current designs differentially emphasise sample size, measurement breadth, and longitudinal assessments. In this overview and perspective, we provide a guide to the current landscape of psychiatric neuroimaging study designs with respect to this balance of scientific goals and resource constraints. Through a heuristic data cube contrasting key design features, we discuss a resulting trade-off among small sample, precision longitudinal studies (e.g., individualised studies and cohorts) and large sample, minimally longitudinal, population studies. Precision studies support tests of within-person mechanisms, via intervention and tracking of longitudinal course. Population studies support tests of generalisation across multifaceted individual differences. A proposed reciprocal validation model (RVM) aims to recursively leverage these complementary designs in sequence to accumulate evidence, optimise relative strengths, and build towards improved long-term clinical utility.
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Affiliation(s)
- Martin Gell
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen University, Aachen, Germany.
- Institute of Neuroscience and Medicine (INM-7: Brain & Behaviour), Research Centre Jülich, Jülich, Germany.
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA.
| | - Stephanie Noble
- Psychology Department, Northeastern University, Boston, MA, USA
- Bioengineering Department, Northeastern University, Boston, MA, USA
- Center for Cognitive and Brain Health, Northeastern University, Boston, MA, USA
| | - Timothy O Laumann
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Steven M Nelson
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Brenden Tervo-Clemmens
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA.
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA.
- Institute for Translational Neuroscience, University of Minnesota, Minneapolis, MN, USA.
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2
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Herthum H, Hetzer S. Tensor denoising of quantitative multi-parameter mapping. Magn Reson Med 2024; 92:145-157. [PMID: 38368616 DOI: 10.1002/mrm.30050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 01/12/2024] [Accepted: 01/25/2024] [Indexed: 02/20/2024]
Abstract
PURPOSE Quantitative multi-parameter mapping (MPM) provides maps of physical quantities representing physiologically meaningful tissue characteristics, which allows to investigate microstructure-function relationships reflecting normal or pathologic processes in the brain. However, the achievable spatial resolution and stability of MPM for basic research or clinical applications is severely constrained by SNR limits of the MR acquisition process, resulting in relatively long acquisition times. To increase SNR, we denoise MPM acquisitions using principal component analysis along tensors exploiting the Marchenko-Pastur law (tMPPCA). METHODS tMPPCA denoising was applied to three sets of MPM raw data before the quantification of maps of proton density, magnetization transfer saturation, R1, and R2*. The regional SNR gain for high-resolution MPM was investigated as well as reproducibility gains for clinically optimized protocols with moderate and high acceleration factors at different image resolutions. RESULTS Substantial noise reduction in raw data was achieved, resulting in reduced noise for quantitative mapping up to sixfold without introducing bias of mean values (below 1%). Scan-rescan fluctuations were reduced up to threefold. Denoising allowed to decrease the voxel volume fourfold at the same scan time or reduce the scan time twofold at same voxel volume without loss of sensitivity. CONCLUSIONS tMPPCA denoising can (a) improve of fine spatial and temporal patterns, (b) considerably reduce scan time for clinical applications, or (c) increase resolution to potentially push cutting-edge MPM protocols from the upper to the lower limit of the mesoscopic scale.
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Affiliation(s)
- Helge Herthum
- Berlin Center for Advanced Neuroimaging, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Stefan Hetzer
- Berlin Center for Advanced Neuroimaging, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
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3
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Gonzalez-Castillo J, Spurney MA, Lam KC, Gephart IS, Pereira F, Handwerker DA, Kam J, Bandettini PA. In-Scanner Thoughts shape Resting-state Functional Connectivity: how participants "rest" matters. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.05.596482. [PMID: 38903114 PMCID: PMC11188111 DOI: 10.1101/2024.06.05.596482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/22/2024]
Abstract
Resting-state fMRI (rs-fMRI) scans-namely those lacking experimentally-controlled stimuli or cognitive demands-are often used to identify aberrant patterns of functional connectivity (FC) in clinical populations. To minimize interpretational uncertainty, researchers control for across-cohort disparities in age, gender, co-morbidities, and head motion. Yet, studies rarely, if ever, consider the possibility that systematic differences in inner experience (i.e., what subjects think and feel during the scan) may directly affect FC measures. Here we demonstrate that is the case using a rs-fMRI dataset comprising 471 scans annotated with experiential data. Wide-spread significant differences in FC are observed between scans that systematically differ in terms of reported in-scanner experience. Additionally, we show that FC can successfully predict specific aspects of in-scanner experience in a manner similar to how it predicts demographics, cognitive abilities, clinical outcomes and labels. Together, these results highlight the key role of in-scanner experience in shaping rs-fMRI estimates of FC.
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Affiliation(s)
| | - M A Spurney
- Section on Functional Imaging Methods, NIMH, NIH, Bethesda, Maryland, USA
| | - K C Lam
- Machine Learning Team, NIMH, NIH, Bethesda, Maryland, USA
| | - I S Gephart
- Section on Functional Imaging Methods, NIMH, NIH, Bethesda, Maryland, USA
| | - F Pereira
- Machine Learning Team, NIMH, NIH, Bethesda, Maryland, USA
| | - D A Handwerker
- Section on Functional Imaging Methods, NIMH, NIH, Bethesda, Maryland, USA
| | - Jwy Kam
- Department of Psychology, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - P A Bandettini
- Section on Functional Imaging Methods, NIMH, NIH, Bethesda, Maryland, USA
- Functional MRI Core, NIMH, NIH, Bethesda, Maryland, USA
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4
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Lu B, Chen X, Xavier Castellanos F, Thompson PM, Zuo XN, Zang YF, Yan CG. The power of many brains: Catalyzing neuropsychiatric discovery through open neuroimaging data and large-scale collaboration. Sci Bull (Beijing) 2024; 69:1536-1555. [PMID: 38519398 DOI: 10.1016/j.scib.2024.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 12/12/2023] [Accepted: 02/27/2024] [Indexed: 03/24/2024]
Abstract
Recent advances in open neuroimaging data are enhancing our comprehension of neuropsychiatric disorders. By pooling images from various cohorts, statistical power has increased, enabling the detection of subtle abnormalities and robust associations, and fostering new research methods. Global collaborations in imaging have furthered our knowledge of the neurobiological foundations of brain disorders and aided in imaging-based prediction for more targeted treatment. Large-scale magnetic resonance imaging initiatives are driving innovation in analytics and supporting generalizable psychiatric studies. We also emphasize the significant role of big data in understanding neural mechanisms and in the early identification and precise treatment of neuropsychiatric disorders. However, challenges such as data harmonization across different sites, privacy protection, and effective data sharing must be addressed. With proper governance and open science practices, we conclude with a projection of how large-scale imaging resources and collaborations could revolutionize diagnosis, treatment selection, and outcome prediction, contributing to optimal brain health.
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Affiliation(s)
- Bin Lu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100101, China
| | - Xiao Chen
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100101, China
| | - Francisco Xavier Castellanos
- Department of Child and Adolescent Psychiatry, NYU Grossman School of Medicine, New York 10016, USA; Nathan Kline Institute for Psychiatric Research, Orangeburg 10962, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles 90033, USA
| | - Xi-Nian Zuo
- Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; National Basic Science Data Center, Beijing 100190, China
| | - Yu-Feng Zang
- Centre for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou 310004, China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou 310030, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairment, Hangzhou 311121, China
| | - Chao-Gan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100101, China; International Big-Data Center for Depression Research, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China.
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5
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Thapaliya B, Ohib R, Geenjar E, Liu J, Calhoun V, Plis S. Efficient Federated Learning for distributed NeuroImaging Data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.14.594167. [PMID: 38798689 PMCID: PMC11118301 DOI: 10.1101/2024.05.14.594167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Recent advancements in neuroimaging have led to greater data sharing among the scientific community. However, institutions frequently maintain control over their data, citing concerns related to research culture, privacy, and accountability. This creates a demand for innovative tools capable of analyzing amalgamated datasets without the need to transfer actual data between entities. To address this challenge, we propose a decentralized sparse federated learning (FL) strategy. This approach emphasizes local training of sparse models to facilitate efficient communication within such frameworks. By capitalizing on model sparsity and selectively sharing parameters between client sites during the training phase, our method significantly lowers communication overheads. This advantage becomes increasingly pronounced when dealing with larger models and accommodating the diverse resource capabilities of various sites. We demonstrate the effectiveness of our approach through the application to the Adolescent Brain Cognitive Development (ABCD) dataset.
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6
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Uddin LQ. Accessible computing platforms democratize neuroimaging data analysis. Nat Methods 2024; 21:754-755. [PMID: 38605112 DOI: 10.1038/s41592-024-02238-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/13/2024]
Affiliation(s)
- Lucina Q Uddin
- Department of Psychiatry and Biobehavioral Science, University of California Los Angeles David Geffen School of Medicine, Los Angeles, CA, USA.
- Department of Psychology, University of California Los Angeles, Los Angeles, CA, USA.
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7
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Cardenas-Iniguez C, Gonzalez MR. Recommendations for the responsible use and communication of race and ethnicity in neuroimaging research. Nat Neurosci 2024; 27:615-628. [PMID: 38519749 DOI: 10.1038/s41593-024-01608-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 02/16/2024] [Indexed: 03/25/2024]
Abstract
The growing availability of large-population human biomedical datasets provides researchers with unique opportunities to conduct rigorous and impactful studies on brain and behavioral development, allowing for a more comprehensive understanding of neurodevelopment in diverse populations. However, the patterns observed in these datasets are more likely to be influenced by upstream structural inequities (that is, structural racism), which can lead to health disparities based on race, ethnicity and social class. This paper addresses the need for guidance and self-reflection in biomedical research on conceptualizing, contextualizing and communicating issues related to race and ethnicity. We provide recommendations as a starting point for researchers to rethink race and ethnicity choices in study design, model specification, statistical analysis and communication of results, implement practices to avoid the further stigmatization of historically minoritized groups, and engage in research practices that counteract existing harmful biases.
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Affiliation(s)
- Carlos Cardenas-Iniguez
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA.
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8
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Rastegarnia S, St-Laurent M, DuPre E, Pinsard B, Bellec P. Brain decoding of the Human Connectome Project tasks in a dense individual fMRI dataset. Neuroimage 2023; 283:120395. [PMID: 37832707 DOI: 10.1016/j.neuroimage.2023.120395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 09/21/2023] [Accepted: 09/27/2023] [Indexed: 10/15/2023] Open
Abstract
Brain decoding aims to infer cognitive states from patterns of brain activity. Substantial inter-individual variations in functional brain organization challenge accurate decoding performed at the group level. In this paper, we tested whether accurate brain decoding models can be trained entirely at the individual level. We trained several classifiers on a dense individual functional magnetic resonance imaging (fMRI) dataset for which six participants completed the entire Human Connectome Project (HCP) task battery >13 times over ten separate fMRI sessions. We evaluated nine decoding methods, from Support Vector Machines (SVM) and Multi-Layer Perceptron (MLP) to Graph Convolutional Neural Networks (GCN). All decoders were trained to classify single fMRI volumes into 21 experimental conditions simultaneously, using ∼7 h of fMRI data per participant. The best prediction accuracies were achieved with GCN and MLP models, whose performance (57-67 % accuracy) approached state-of-the-art accuracy (76 %) with models trained at the group level on >1 K hours of data from the original HCP sample. Our SVM model also performed very well (54-62 % accuracy). Feature importance maps derived from MLP -our best-performing model- revealed informative features in regions relevant to particular cognitive domains, notably in the motor cortex. We also observed that inter-subject classification achieved substantially lower accuracy than subject-specific models, indicating that our decoders learned individual-specific features. This work demonstrates that densely-sampled neuroimaging datasets can be used to train accurate brain decoding models at the individual level. We expect this work to become a useful benchmark for techniques that improve model generalization across multiple subjects and acquisition conditions.
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Affiliation(s)
- Shima Rastegarnia
- Université de Montréal, Montréal, QC, Canada; Centre de Recherche de L'Institut Universitaire de Gériatrie de Montréal, Montréal, Canada.
| | - Marie St-Laurent
- Centre de Recherche de L'Institut Universitaire de Gériatrie de Montréal, Montréal, Canada
| | | | - Basile Pinsard
- Centre de Recherche de L'Institut Universitaire de Gériatrie de Montréal, Montréal, Canada
| | - Pierre Bellec
- Université de Montréal, Montréal, QC, Canada; Centre de Recherche de L'Institut Universitaire de Gériatrie de Montréal, Montréal, Canada
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9
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Lewis A, Young MJ, Rohaut B, Jox RJ, Claassen J, Creutzfeldt CJ, Illes J, Kirschen M, Trevick S, Fins JJ. Ethics Along the Continuum of Research Involving Persons with Disorders of Consciousness. Neurocrit Care 2023; 39:565-577. [PMID: 36977963 PMCID: PMC11023737 DOI: 10.1007/s12028-023-01708-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 02/23/2023] [Indexed: 03/30/2023]
Abstract
Interest in disorders of consciousness (DoC) has grown substantially over the past decade and has illuminated the importance of improving understanding of DoC biology; care needs (use of monitoring, performance of interventions, and provision of emotional support); treatment options to promote recovery; and outcome prediction. Exploration of these topics requires awareness of numerous ethics considerations related to rights and resources. The Curing Coma Campaign Ethics Working Group used its expertise in neurocritical care, neuropalliative care, neuroethics, neuroscience, philosophy, and research to formulate an informal review of ethics considerations along the continuum of research involving persons with DoC related to the following: (1) study design; (2) comparison of risks versus benefits; (3) selection of inclusion and exclusion criteria; (4) screening, recruitment, and enrollment; (5) consent; (6) data protection; (7) disclosure of results to surrogates and/or legally authorized representatives; (8) translation of research into practice; (9) identification and management of conflicts of interest; (10) equity and resource availability; and (11) inclusion of minors with DoC in research. Awareness of these ethics considerations when planning and performing research involving persons with DoC will ensure that the participant rights are respected while maximizing the impact and meaningfulness of the research, interpretation of outcomes, and communication of results.
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Affiliation(s)
- Ariane Lewis
- NYU Langone Medical Center, 530 First Avenue, Skirball-7R, New York, NY, 10016, USA.
| | - Michael J Young
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Benjamin Rohaut
- Inserm, CNRS, APHP - Hôpital de la Pitié Salpêtrière, Paris Brain Institute - ICM, DMU Neuroscience, Sorbonne University, Paris, France
| | - Ralf J Jox
- Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Jan Claassen
- New York Presbyterian Hospital, Columbia University, New York, NY, USA
| | - Claire J Creutzfeldt
- Harborview Medical Center, Seattle, WA, USA
- University of Washington, Seattle, WA, USA
- Cambia Palliative Care Center of Excellence, Seattle, WA, USA
| | - Judy Illes
- University of British Columbia, Vancouver, BC, Canada
| | | | | | - Joseph J Fins
- Weill Cornell Medical College, New York, NY, USA
- Yale Law School, New Haven, CT, USA
- Rockefeller University, New York, NY, USA
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10
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Simmons JM, Breeden A, Ferrer RA, Gillman AS, Moore H, Green P, Pariyadath V, Quinlan EB, Vicentic A. Affective Science Research: Perspectives and Priorities from the National Institutes of Health. AFFECTIVE SCIENCE 2023; 4:600-607. [PMID: 37744987 PMCID: PMC10513969 DOI: 10.1007/s42761-023-00218-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 08/09/2023] [Indexed: 09/26/2023]
Abstract
Affective science is a broad and burgeoning field, and the National Institutes of Health (NIH) support research on a similarly broad range of topics. Across NIH, funding is available for basic, translational, and intervention research, including research in non-human animals, healthy populations, and those with or at risk for disease. Multiple NIH Institutes and Centers have specific programs devoted to topics within the affective science umbrella. Here, we introduce the funding priorities of these six: the National Cancer Institute (NCI), National Center for Complementary and Integrative Health (NCCIH), National Institute of Mental Health (NIMH), National Institute on Aging (NIA), National Institute on Drug Abuse (NIDA), and National Institute on Minority Health and Health Disparities (NIMHD). We then discuss overlapping themes and offer a perspective on promising research directions.
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Affiliation(s)
| | | | | | - Arielle S. Gillman
- National Institute On Minority Health and Health Disparities, Bethesda, MD USA
| | - Holly Moore
- National Institute On Drug Abuse, Bethesda, MD USA
| | | | | | - Erin B. Quinlan
- National Center for Complementary and Integrative Health, Bethesda, MD USA
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11
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Zhao C, Jarecka D, Covitz S, Chen Y, Eickhoff SB, Fair DA, Franco AR, Halchenko YO, Hendrickson TJ, Hoffstaedter F, Houghton A, Kiar G, Macdonald A, Mehta K, Milham MP, Salo T, Hanke M, Ghosh SS, Cieslak M, Satterthwaite TD. A reproducible and generalizable software workflow for analysis of large-scale neuroimaging data collections using BIDS Apps. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.16.552472. [PMID: 37645999 PMCID: PMC10461987 DOI: 10.1101/2023.08.16.552472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
Neuroimaging research faces a crisis of reproducibility. With massive sample sizes and greater data complexity, this problem becomes more acute. Software that operates on imaging data defined using the Brain Imaging Data Structure (BIDS) - BIDS Apps - have provided a substantial advance. However, even using BIDS Apps, a full audit trail of data processing is a necessary prerequisite for fully reproducible research. Obtaining a faithful record of the audit trail is challenging - especially for large datasets. Recently, the FAIRly big framework was introduced as a way to facilitate reproducible processing of large-scale data by leveraging DataLad - a version control system for data management. However, the current implementation of this framework was more of a proof of concept, and could not be immediately reused by other investigators for different use cases. Here we introduce the BIDS App Bootstrap (BABS), a user-friendly and generalizable Python package for reproducible image processing at scale. BABS facilitates the reproducible application of BIDS Apps to large-scale datasets. Leveraging DataLad and the FAIRly big framework, BABS tracks the full audit trail of data processing in a scalable way by automatically preparing all scripts necessary for data processing and version tracking on high performance computing (HPC) systems. Currently, BABS supports jobs submissions and audits on Sun Grid Engine (SGE) and Slurm HPCs with a parsimonious set of programs. To demonstrate its scalability, we applied BABS to data from the Healthy Brain Network (HBN; n=2,565). Taken together, BABS allows reproducible and scalable image processing and is broadly extensible via an open-source development model.
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Affiliation(s)
- Chenying Zhao
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children’s Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dorota Jarecka
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Sydney Covitz
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children’s Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yibei Chen
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Simon B. Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Damien A. Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Institute of Child Development, College of Education and Human Development, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota Medical School, University of Minnesota, Minneapolis, MN, USA
| | - Alexandre R. Franco
- Child Mind Institute, New York, NY, USA
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
- Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, USA
| | | | - Timothy J. Hendrickson
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Audrey Houghton
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | | | - Austin Macdonald
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Kahini Mehta
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children’s Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael P. Milham
- Child Mind Institute, New York, NY, USA
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Taylor Salo
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children’s Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael Hanke
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Satrajit S. Ghosh
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Otolaryngology, Harvard Medical School, Boston, MA, USA
| | - Matthew Cieslak
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children’s Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Theodore D. Satterthwaite
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children’s Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA, USA
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12
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Lammer L, Beyer F, Luppa M, Sanders C, Baber R, Engel C, Wirkner K, Loffler M, Riedel-Heller SG, Villringer A, Witte AV. Impact of social isolation on grey matter structure and cognitive functions: A population-based longitudinal neuroimaging study. eLife 2023; 12:e83660. [PMID: 37337666 DOI: 10.7554/elife.83660] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 04/14/2023] [Indexed: 06/21/2023] Open
Abstract
Background Social isolation has been suggested to increase the risk to develop cognitive decline. However, our knowledge on causality and neurobiological underpinnings is still limited. Methods In this preregistered analysis, we tested the impact of social isolation on central features of brain and cognitive ageing using a longitudinal population-based magnetic resonance imaging (MRI) study. We assayed 1992 cognitively healthy participants (50-82years old, 921women) at baseline and 1409 participants after~6y follow-up. Results We found baseline social isolation and change in social isolation to be associated with smaller volumes of the hippocampus and clusters of reduced cortical thickness. Furthermore, poorer cognitive functions (memory, processing speed, executive functions) were linked to greater social isolation, too. Conclusions Combining advanced neuroimaging outcomes with prevalent lifestyle characteristics from a well-characterized population of middle- to older aged adults, we provide evidence that social isolation contributes to human brain atrophy and cognitive decline. Within-subject effects of social isolation were similar to between-subject effects, indicating an opportunity to reduce dementia risk by promoting social networks. Funding European Union, European Regional Development Fund, Free State of Saxony, LIFE-Leipzig Research Center for Civilization Diseases, University of Leipzig, German Research Foundation.
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Affiliation(s)
- Laurenz Lammer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Frauke Beyer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Clinic for Cognitive Neurology, University of Leipzig Medical Center, Leipzig, Germany
- CRC Obesity Mechanisms, Subproject A1, University of Leipzig, Leipzig, Germany
| | - Melanie Luppa
- Institute of Social Medicine, Occupational Health and Public Health, University of Leipzig, Faculty of Medicine, Leipzig, Germany
| | - Christian Sanders
- Department of Psychiatry and Psychotherapy, University of Leipzig Medical Centre, Leipzig, Germany
- Leipzig Research Center for Civilization Diseases (LIFE), University of Leipzig, Leipzig, Germany
| | - Ronny Baber
- Leipzig Research Center for Civilization Diseases (LIFE), University of Leipzig, Leipzig, Germany
| | - Christoph Engel
- Leipzig Research Center for Civilization Diseases (LIFE), University of Leipzig, Leipzig, Germany
| | - Kerstin Wirkner
- Leipzig Research Center for Civilization Diseases (LIFE), University of Leipzig, Leipzig, Germany
- Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Leipzig, Germany
| | - Markus Loffler
- Leipzig Research Center for Civilization Diseases (LIFE), University of Leipzig, Leipzig, Germany
- Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Leipzig, Germany
| | - Steffi G Riedel-Heller
- Institute of Social Medicine, Occupational Health and Public Health, University of Leipzig, Faculty of Medicine, Leipzig, Germany
| | - Arno Villringer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Clinic for Cognitive Neurology, University of Leipzig Medical Center, Leipzig, Germany
- Berlin School of Mind and Brain, Humboldt University of Berlin, Berlin, Germany
| | - A Veronica Witte
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Clinic for Cognitive Neurology, University of Leipzig Medical Center, Leipzig, Germany
- CRC Obesity Mechanisms, Subproject A1, University of Leipzig, Leipzig, Germany
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13
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Zhao B, Li T, Fan Z, Yang Y, Shu J, Yang X, Wang X, Luo T, Tang J, Xiong D, Wu Z, Li B, Chen J, Shan Y, Tomlinson C, Zhu Z, Li Y, Stein JL, Zhu H. Heart-brain connections: Phenotypic and genetic insights from magnetic resonance images. Science 2023; 380:abn6598. [PMID: 37262162 DOI: 10.1126/science.abn6598] [Citation(s) in RCA: 33] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 04/11/2023] [Indexed: 06/03/2023]
Abstract
Cardiovascular health interacts with cognitive and mental health in complex ways, yet little is known about the phenotypic and genetic links of heart-brain systems. We quantified heart-brain connections using multiorgan magnetic resonance imaging (MRI) data from more than 40,000 subjects. Heart MRI traits displayed numerous association patterns with brain gray matter morphometry, white matter microstructure, and functional networks. We identified 80 associated genomic loci (P < 6.09 × 10-10) for heart MRI traits, which shared genetic influences with cardiovascular and brain diseases. Genetic correlations were observed between heart MRI traits and brain-related traits and disorders. Mendelian randomization suggests that heart conditions may causally contribute to brain disorders. Our results advance a multiorgan perspective on human health by revealing heart-brain connections and shared genetic influences.
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Affiliation(s)
- Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Tengfei Li
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Zirui Fan
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Yue Yang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Juan Shu
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Xiaochen Yang
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Xifeng Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Tianyou Luo
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jiarui Tang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Di Xiong
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Zhenyi Wu
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Bingxuan Li
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Jie Chen
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yue Shan
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Chalmer Tomlinson
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Ziliang Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jason L Stein
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Hongtu Zhu
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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14
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Hinds W, Modi S, Ankeeta A, Sperling MR, Pustina D, Tracy JI. Pre-surgical features of intrinsic brain networks predict single and joint epilepsy surgery outcomes. Neuroimage Clin 2023; 38:103387. [PMID: 37023491 PMCID: PMC10122017 DOI: 10.1016/j.nicl.2023.103387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 03/02/2023] [Accepted: 03/24/2023] [Indexed: 03/31/2023]
Abstract
Despite the effectiveness of surgical interventions for the treatment of intractable focal temporal lobe epilepsy (TLE), the substrates that support good outcomes are poorly understood. While algorithms have been developed for the prediction of either seizure or cognitive/psychiatric outcomes alone, no study has reported on the functional and structural architecture that supports joint outcomes. We measured key aspects of pre-surgical whole brain functional/structural network architecture and evaluated their ability to predict post-operative seizure control in combination with cognitive/psychiatric outcomes. Pre-surgically, we identified the intrinsic connectivity networks (ICNs) unique to each person through independent component analysis (ICA), and computed: (1) the spatial-temporal match between each person's ICA components and established, canonical ICNs, (2) the connectivity strength within each identified person-specific ICN, (3) the gray matter (GM) volume underlying the person-specific ICNs, and (4) the amount of variance not explained by the canonical ICNs for each person. Post-surgical seizure control and reliable change indices of change (for language [naming, phonemic fluency], verbal episodic memory, and depression) served as binary outcome responses in random forest (RF) models. The above functional and structural measures served as input predictors. Our empirically derived ICN-based measures customized to the individual showed that good joint seizure and cognitive/psychiatric outcomes depended upon higher levels of brain reserve (GM volume) in specific networks. In contrast, singular outcomes relied on systematic, idiosyncratic variance in the case of seizure control, and the weakened pre-surgical presence of functional ICNs that encompassed the ictal temporal lobe in the case of cognitive/psychiatric outcomes. Our data made clear that the ICNs differed in their propensity to provide reserve for adaptive outcomes, with some providing structural (brain), and others functional (cognitive) reserve. Our customized methodology demonstrated that when substantial unique, patient-specific ICNs are present prior to surgery there is a reliable association with poor post-surgical seizure control. These ICNs are idiosyncratic in that they did not match the canonical, normative ICNs and, therefore, could not be defined functionally, with their location likely varying by patient. This important finding suggested the level of highly individualized ICN's in the epileptic brain may signal the emergence of epileptogenic activity after surgery.
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Affiliation(s)
- Walter Hinds
- Thomas Jefferson University, Department of Neurology, and Vicky and Jack Farber Institute for Neuroscience, USA
| | - Shilpi Modi
- Thomas Jefferson University, Department of Neurology, and Vicky and Jack Farber Institute for Neuroscience, USA
| | - Ankeeta Ankeeta
- Thomas Jefferson University, Department of Neurology, and Vicky and Jack Farber Institute for Neuroscience, USA
| | - Michael R Sperling
- Thomas Jefferson University, Department of Neurology, and Vicky and Jack Farber Institute for Neuroscience, USA
| | | | - Joseph I Tracy
- Thomas Jefferson University, Department of Neurology, and Vicky and Jack Farber Institute for Neuroscience, USA.
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15
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Helwegen K, Libedinsky I, van den Heuvel MP. Statistical power in network neuroscience. Trends Cogn Sci 2023; 27:282-301. [PMID: 36725422 DOI: 10.1016/j.tics.2022.12.011] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 12/14/2022] [Accepted: 12/15/2022] [Indexed: 01/31/2023]
Abstract
Network neuroscience has emerged as a leading method to study brain connectivity. The success of these investigations is dependent not only on approaches to accurately map connectivity but also on the ability to detect real effects in the data - that is, statistical power. We review the state of statistical power in the field and discuss sample size, effect size, measurement error, and network topology as key factors that influence the power of brain connectivity investigations. We use the term 'differential power' to describe how power can vary between nodes, edges, and graph metrics, leaving traces in both positive and negative connectome findings. We conclude with strategies for working with, rather than around, power in connectivity studies.
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Affiliation(s)
- Koen Helwegen
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Ilan Libedinsky
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Martijn P van den Heuvel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Department of Child and Adolescent Psychiatry and Psychology, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
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16
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Ly M, Yu GZ, Chwa WJ, Raji CA. Paving the Way for Alzheimer's Disease Prevention: A Systematic Review of Global Open-Access Neuroimaging Datasets in Healthy Individuals. J Alzheimers Dis 2023; 96:1441-1451. [PMID: 37955090 PMCID: PMC10845900 DOI: 10.3233/jad-230738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2023]
Abstract
BACKGROUND Given the advent of large-scale neuroimaging data-driven endeavors for Alzheimer's disease, there is a burgeoning need for well-characterized neuroimaging databases of healthy individuals. With the rise of initiatives around the globe for the rapid and unrestricted sharing of data resources, there is now an abundance of open-source neuroimaging datasets available to the research community. However, there is not yet a systematic review that fully details the demographic information and modalities actually available in all open access neuroimaging databases around the globe. OBJECTIVE This systematic review aims to provide compile a list of MR structural imaging databases encompassing healthy individuals across the lifespan. METHODS In this systematic review, we searched EMBASE and PubMed until May 2022 for open-access neuroimaging databases containing healthy control participants of any age, race, with normal development and cognition having at least one structural T1-weighted neuroimaging scan. RESULTS A total of 403 databases were included, for up to total of 48,268 participants with all available demographic information and imaging modalities detailed in Supplementary Table 1. There were significant trends noted when compiling normative databases for this systematic review, notably that 11.7% of databases included reported ethnicity in their participants, with underrepresentation of many socioeconomic groups globally. CONCLUSIONS As efforts to improve primary prevention of AD may require a broader perspective including increased relevance of earlier stages in life, and strategies in addressing modifiable risk factors may be individualized to specific demographics, improving data characterization to be richer and more rigorous will greatly enhance these efforts.
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Affiliation(s)
- Maria Ly
- Mallinckrodt Institute of Radiology, Division of Neuroradiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Gary Z. Yu
- Mallinckrodt Institute of Radiology, Division of Neuroradiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Won Jong Chwa
- Mallinckrodt Institute of Radiology, Division of Neuroradiology, Washington University in St. Louis, St. Louis, MO, USA
- Saint Louis University, School of Medicine, St Louis, MO, USA
| | - Cyrus A. Raji
- Mallinckrodt Institute of Radiology, Division of Neuroradiology, Washington University in St. Louis, St. Louis, MO, USA
- Department of Neurology, Washington University in Saint Louis, St Louis, MO, USA
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17
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Abstract
Most societies witness an ever increasing prevalence of both obesity and dementia, a scenario related to often underestimated individual and public health burden. Overnutrition and weight gain have been linked with abnormal functionality of homoeostasis brain networks and changes in higher cognitive functions such as reward evaluation, executive functions and learning and memory. In parallel, evidence has accumulated that modifiable factors such as obesity and diet impact the gut-brain axis and modulate brain health and cognition through various pathways. Using neuroimaging data from epidemiological studies and randomised clinical trials, we aim to shed light on the underlying mechanisms and to determine both determinants and consequences of obesity and diet at the level of human brain structure and function. We analysed multimodal 3T MRI of about 2600 randomly selected adults (47 % female, 18-80 years of age, BMI 18-47 kg/m2) of the LIFE-Adult study, a deeply phenotyped population-based cohort. In addition, brain MRI data of controlled intervention studies on weight loss and healthy diets acquired in lean, overweight and obese participants may help to understand the role of the gut-brain axis in food craving and cognitive ageing. We find that higher BMI and visceral fat accumulation correlate with accelerated brain age, microstructure of the hypothalamus, lower thickness and connectivity in default mode- and reward-related areas, as well as with subtle grey matter atrophy and white matter lesion load in non-demented individuals. Mediation analyses indicated that higher visceral fat affects brain tissue through systemic low-grade inflammation, and that obesity-related regional changes translate into cognitive disadvantages. Considering longitudinal studies, some, but not all data indicate beneficial effects of weight loss and healthy diets such as plant-based nutrients and dietary patterns on brain ageing and cognition. Confounding effects of concurrent changes in other lifestyle factors or false positives might help to explain these findings. Therefore a more holistic intervention approach, along with open science tools such as data and code sharing, in-depth pre-registration and pooling of data could help to overcome these limitations. In addition, as higher BMI relates to increased head micro-movements during MRI, and as head motion in turn systematically induces image artefacts, future studies need to rigorously control for head motion during MRI to enable valid neuroimaging results. In sum, our results support the view that overweight and obesity are intertwined with markers of brain health in the general population, and that weight loss and plant-based diets may help to promote brain plasticity. Meta-analyses and longitudinal cohort studies are underway to further differentiate causation from correlation in obesity- and nutrition-brain research.
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18
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The brain bases of regulation of eating behaviors: the role of reward, executive control, and valuation processes, and new paths to propel the field forward. Curr Opin Behav Sci 2022. [DOI: 10.1016/j.cobeha.2022.101214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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19
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Richie-Halford A, Cieslak M, Ai L, Caffarra S, Covitz S, Franco AR, Karipidis II, Kruper J, Milham M, Avelar-Pereira B, Roy E, Sydnor VJ, Yeatman JD, Satterthwaite TD, Rokem A. An analysis-ready and quality controlled resource for pediatric brain white-matter research. Sci Data 2022; 9:616. [PMID: 36224186 PMCID: PMC9556519 DOI: 10.1038/s41597-022-01695-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 09/12/2022] [Indexed: 11/08/2022] Open
Abstract
We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets.
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Affiliation(s)
- Adam Richie-Halford
- Stanford University, Division of Developmental and Behavioral Pediatrics, Stanford, California, 94305, USA.
- Stanford University, Graduate School of Education, Stanford, California, 94305, USA.
| | - Matthew Cieslak
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA.
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania, 19104, USA.
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA.
| | - Lei Ai
- Child Mind Institute, Center for the Developing Brain, New York City, New York, 10022, USA
| | - Sendy Caffarra
- Stanford University, Division of Developmental and Behavioral Pediatrics, Stanford, California, 94305, USA
- Stanford University, Graduate School of Education, Stanford, California, 94305, USA
- University of Modena and Reggio Emilia, Department of Biomedical, Metabolic and Neural Sciences, 41125, Modena, Italy
| | - Sydney Covitz
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Alexandre R Franco
- Child Mind Institute, Center for the Developing Brain, New York City, New York, 10022, USA
- Nathan Kline Institute for Psychiatric Research, Center for Biomedical Imaging and Neuromodulation, Orangeburg, New York, 10962, USA
| | - Iliana I Karipidis
- Stanford University, Graduate School of Education, Stanford, California, 94305, USA
- Stanford University, Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford, California, 94305, USA
- University of Zurich, Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich, Zurich, 8032, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, 8057, Switzerland
| | - John Kruper
- University of Washington, Department of Psychology, Seattle, Washington, 98195, USA
| | - Michael Milham
- Child Mind Institute, Center for the Developing Brain, New York City, New York, 10022, USA
- Nathan Kline Institute for Psychiatric Research, Center for Biomedical Imaging and Neuromodulation, Orangeburg, New York, 10962, USA
| | - Bárbara Avelar-Pereira
- Stanford University, Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford, California, 94305, USA
| | - Ethan Roy
- Stanford University, Graduate School of Education, Stanford, California, 94305, USA
| | - Valerie J Sydnor
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Jason D Yeatman
- Stanford University, Division of Developmental and Behavioral Pediatrics, Stanford, California, 94305, USA
- Stanford University, Graduate School of Education, Stanford, California, 94305, USA
| | - Theodore D Satterthwaite
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Ariel Rokem
- University of Washington, Department of Psychology, Seattle, Washington, 98195, USA
- University of Washington, eScience Institute, Seattle, Washington, 98195, USA
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21
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Veldhuizen MG, Cecchetto C, Fjaeldstad AW, Farruggia MC, Hartig R, Nakamura Y, Pellegrino R, Yeung AWK, Fischmeister FPS. Future Directions for Chemosensory Connectomes: Best Practices and Specific Challenges. Front Syst Neurosci 2022; 16:885304. [PMID: 35707745 PMCID: PMC9190244 DOI: 10.3389/fnsys.2022.885304] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 04/13/2022] [Indexed: 01/14/2023] Open
Abstract
Ecological chemosensory stimuli almost always evoke responses in more than one sensory system. Moreover, any sensory processing takes place along a hierarchy of brain regions. So far, the field of chemosensory neuroimaging is dominated by studies that examine the role of brain regions in isolation. However, to completely understand neural processing of chemosensation, we must also examine interactions between regions. In general, the use of connectivity methods has increased in the neuroimaging field, providing important insights to physical sensory processing, such as vision, audition, and touch. A similar trend has been observed in chemosensory neuroimaging, however, these established techniques have largely not been rigorously applied to imaging studies on the chemical senses, leaving network insights overlooked. In this article, we first highlight some recent work in chemosensory connectomics and we summarize different connectomics techniques. Then, we outline specific challenges for chemosensory connectome neuroimaging studies. Finally, we review best practices from the general connectomics and neuroimaging fields. We recommend future studies to develop or use the following methods we perceive as key to improve chemosensory connectomics: (1) optimized study designs, (2) reporting guidelines, (3) consensus on brain parcellations, (4) consortium research, and (5) data sharing.
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Affiliation(s)
- Maria G. Veldhuizen
- Department of Anatomy, Faculty of Medicine, Mersin University, Mersin, Turkey
| | - Cinzia Cecchetto
- Department of General Psychology, University of Padova, Padua, Italy
| | - Alexander W. Fjaeldstad
- Flavour Clinic, Department of Otorhinolaryngology, Regional Hospital West Jutland, Holstebro, Denmark
| | - Michael C. Farruggia
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, United States
| | - Renée Hartig
- Department of Psychiatry and Psychotherapy, University Medical Center, Johannes Gutenberg University of Mainz, Mainz, Germany,Max Planck Institute for Biological Cybernetics, Tübingen, Germany,Functional and Comparative Neuroanatomy Laboratory, Werner Reichardt Centre for Integrative Neuroscience, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Yuko Nakamura
- The Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan
| | | | - Andy W. K. Yeung
- Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Florian Ph. S. Fischmeister
- Institute of Psychology, University of Graz, Graz, Austria,Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria,BioTechMed-Graz, Graz, Austria,*Correspondence: Florian Ph. S. Fischmeister,
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22
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Paret C, Unverhau N, Feingold F, Poldrack RA, Stirner M, Schmahl C, Sicorello M. Survey on Open Science Practices in Functional Neuroimaging. Neuroimage 2022; 257:119306. [PMID: 35595201 DOI: 10.1016/j.neuroimage.2022.119306] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Revised: 05/03/2022] [Accepted: 05/10/2022] [Indexed: 12/12/2022] Open
Abstract
Replicability and reproducibility of scientific findings is paramount for sustainable progress in neuroscience. Preregistration of the hypotheses and methods of an empirical study before analysis, the sharing of primary research data, and compliance with data standards such as the Brain Imaging Data Structure (BIDS), are considered effective practices to secure progress and to substantiate quality of research. We investigated the current level of adoption of open science practices in neuroimaging and the difficulties that prevent researchers from using them. Email invitations to participate in the survey were sent to addresses received through a PubMed search of human functional magnetic resonance imaging studies that were published between 2010 and 2020. 283 persons completed the questionnaire. Although half of the participants were experienced with preregistration, the willingness to preregister studies in the future was modest. The majority of participants had experience with the sharing of primary neuroimaging data. Most of the participants were interested in implementing a standardized data structure such as BIDS in their labs. Based on demographic variables, we compared participants on seven subscales, which had been generated through factor analysis. Exploratory analyses found that experienced researchers at lower career level had higher fear of being transparent and researchers with residence in the EU had a higher need for data governance. Additionally, researchers at medical faculties as compared to other university faculties reported a more unsupportive supervisor with regards to open science practices and a higher need for data governance. The results suggest growing adoption of open science practices but also highlight a number of important impediments.
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Affiliation(s)
- Christian Paret
- Department of Psychosomatic Medicine and Psychotherapy, Central Institute of Mental Health Mannheim, Medical Faculty Mannheim / Heidelberg University, Germany; Sagol Brain Institute, Wohl Institute for Advanced Imaging, Tel-Aviv Sourasky Medical Center and School of Psychological Sciences, Tel-Aviv University, Israel.
| | - Nike Unverhau
- Department of Psychosomatic Medicine and Psychotherapy, Central Institute of Mental Health Mannheim, Medical Faculty Mannheim / Heidelberg University, Germany
| | | | | | - Madita Stirner
- Department of Psychosomatic Medicine and Psychotherapy, Central Institute of Mental Health Mannheim, Medical Faculty Mannheim / Heidelberg University, Germany
| | - Christian Schmahl
- Department of Psychosomatic Medicine and Psychotherapy, Central Institute of Mental Health Mannheim, Medical Faculty Mannheim / Heidelberg University, Germany
| | - Maurizio Sicorello
- Department of Psychosomatic Medicine and Psychotherapy, Central Institute of Mental Health Mannheim, Medical Faculty Mannheim / Heidelberg University, Germany
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23
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Tian D, Zeng Z, Sun X, Tong Q, Li H, He H, Gao JH, He Y, Xia M. A deep learning-based multisite neuroimage harmonization framework established with a traveling-subject dataset. Neuroimage 2022; 257:119297. [PMID: 35568346 DOI: 10.1016/j.neuroimage.2022.119297] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Revised: 03/31/2022] [Accepted: 05/09/2022] [Indexed: 12/12/2022] Open
Abstract
The accumulation of multisite large-sample MRI datasets collected during large brain research projects in the last decade has provided critical resources for understanding the neurobiological mechanisms underlying cognitive functions and brain disorders. However, the significant site effects observed in imaging data and their derived structural and functional features have prevented the derivation of consistent findings across multiple studies. The development of harmonization methods that can effectively eliminate complex site effects while maintaining biological characteristics in neuroimaging data has become a vital and urgent requirement for multisite imaging studies. Here, we propose a deep learning-based framework to harmonize imaging data obtained from pairs of sites, in which site factors and brain features can be disentangled and encoded. We trained the proposed framework with a publicly available traveling subject dataset from the Strategic Research Program for Brain Sciences (SRPBS) and harmonized the gray matter volume maps derived from eight source sites to a target site. The proposed framework significantly eliminated intersite differences in gray matter volumes. The embedded encoders successfully captured both the abstract textures of site factors and the concrete brain features. Moreover, the proposed framework exhibited outstanding performance relative to conventional statistical harmonization methods in terms of site effect removal, data distribution homogenization, and intrasubject similarity improvement. Finally, the proposed harmonization network provided fixable expandability, through which new sites could be linked to the target site via indirect schema without retraining the whole model. Together, the proposed method offers a powerful and interpretable deep learning-based harmonization framework for multisite neuroimaging data that can enhance reliability and reproducibility in multisite studies regarding brain development and brain disorders.
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Affiliation(s)
- Dezheng Tian
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Zilong Zeng
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Xiaoyi Sun
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; School of Systems Science, Beijing Normal University, Beijing 100875, China
| | - Qiqi Tong
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou 311121, China
| | - Huanjie Li
- School of Biomedical Engineering, Dalian University of Technology, Dalian 116024, China
| | - Hongjian He
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China
| | - Jia-Hong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, China; IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Chinese Institute for Brain Research, Beijing 102206, China
| | - Mingrui Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.
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24
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Federated Analysis of Neuroimaging Data: A Review of the Field. Neuroinformatics 2021; 20:377-390. [PMID: 34807353 PMCID: PMC9124226 DOI: 10.1007/s12021-021-09550-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/21/2021] [Indexed: 10/19/2022]
Abstract
The field of neuroimaging has embraced sharing data to collaboratively advance our understanding of the brain. However, data sharing, especially across sites with large amounts of protected health information (PHI), can be cumbersome and time intensive. Recently, there has been a greater push towards collaborative frameworks that enable large-scale federated analysis of neuroimaging data without the data having to leave its original location. However, there still remains a need for a standardized federated approach that not only allows for data sharing adhering to the FAIR (Findability, Accessibility, Interoperability, Reusability) data principles, but also streamlines analyses and communication while maintaining subject privacy. In this paper, we review a non-exhaustive list of neuroimaging analytic tools and frameworks currently in use. We then provide an update on our federated neuroimaging analysis software system, the Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation (COINSTAC). In the end, we share insights on future research directions for federated analysis of neuroimaging data.
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25
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Gee DG. Early Adversity and Development: Parsing Heterogeneity and Identifying Pathways of Risk and Resilience. Am J Psychiatry 2021; 178:998-1013. [PMID: 34734741 DOI: 10.1176/appi.ajp.2021.21090944] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Adversity early in life is common and is a major risk factor for the onset of psychopathology. Delineating the neurodevelopmental pathways by which early adversity affects mental health is critical for early risk identification and targeted treatment approaches. A rapidly growing cross-species literature has facilitated advances in identifying the mechanisms linking adversity with psychopathology, specific dimensions of adversity and timing-related factors that differentially relate to outcomes, and protective factors that buffer against the effects of adversity. Yet, vast complexity and heterogeneity in early environments and neurodevelopmental trajectories contribute to the challenges of understanding risk and resilience in the context of early adversity. In this overview, the author highlights progress in four major areas-mechanisms, heterogeneity, developmental timing, and protective factors; synthesizes key challenges; and provides recommendations for future research that can facilitate progress in the field. Translation across species and ongoing refinement of conceptual models have strong potential to inform prevention and intervention strategies that can reduce the immense burden of psychopathology associated with early adversity.
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Affiliation(s)
- Dylan G Gee
- Department of Psychology, Yale University, New Haven, Conn
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