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Provins C, MacNicol E, Seeley SH, Hagmann P, Esteban O. Quality control in functional MRI studies with MRIQC and fMRIPrep. FRONTIERS IN NEUROIMAGING 2023; 1:1073734. [PMID: 37555175 PMCID: PMC10406249 DOI: 10.3389/fnimg.2022.1073734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 12/19/2022] [Indexed: 08/10/2023]
Abstract
The implementation of adequate quality assessment (QA) and quality control (QC) protocols within the magnetic resonance imaging (MRI) research workflow is resource- and time-consuming and even more so is their execution. As a result, QA/QC practices highly vary across laboratories and "MRI schools", ranging from highly specialized knowledge spots to environments where QA/QC is considered overly onerous and costly despite evidence showing that below-standard data increase the false positive and false negative rates of the final results. Here, we demonstrate a protocol based on the visual assessment of images one-by-one with reports generated by MRIQC and fMRIPrep, for the QC of data in functional (blood-oxygen dependent-level; BOLD) MRI analyses. We particularize the proposed, open-ended scope of application to whole-brain voxel-wise analyses of BOLD to correspondingly enumerate and define the exclusion criteria applied at the QC checkpoints. We apply our protocol on a composite dataset (n = 181 subjects) drawn from open fMRI studies, resulting in the exclusion of 97% of the data (176 subjects). This high exclusion rate was expected because subjects were selected to showcase artifacts. We describe the artifacts and defects more commonly found in the dataset that justified exclusion. We moreover release all the materials we generated in this assessment and document all the QC decisions with the expectation of contributing to the standardization of these procedures and engaging in the discussion of QA/QC by the community.
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Affiliation(s)
- Céline Provins
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Eilidh MacNicol
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Saren H. Seeley
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Patric Hagmann
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Oscar Esteban
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
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Personalized Dietary Advice to Increase Protein Intake in Older Adults Does Not Affect the Gut Microbiota, Appetite or Central Processing of Food Stimuli in Community-Dwelling Older Adults: A Six-Month Randomized Controlled Trial. Nutrients 2023; 15:nu15020332. [PMID: 36678203 PMCID: PMC9862486 DOI: 10.3390/nu15020332] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 12/30/2022] [Accepted: 01/03/2023] [Indexed: 01/11/2023] Open
Abstract
Expert groups argue to raise the recommended daily allowance for protein in older adults from 0.8 to 1.2 g/kg/day to prevent undernutrition. However, protein is thought to increase satiety, possibly through effects on gut microbiota and central appetite regulation. If true, raising daily protein intake may work counterproductively. In a randomized controlled trial, we evaluated the effects of dietary advice aimed at increasing protein intake to 1.2 g/kg adjusted body weight/day (g/kg aBW/day) on appetite and gut microbiota in 90 community-dwelling older adults with habitual protein intake <1.0 g/kg aBW/day (Nintervention = 47, Ncontrol = 43). Food intake was determined by 24-h dietary recalls and gut microbiota by 16S rRNA sequencing. Functional magnetic resonance imaging (fMRI) scans were performed in a subgroup of 48 participants to evaluate central nervous system responses to food-related stimuli. Both groups had mean baseline protein intake of 0.8 ± 0.2 g/kg aBW/day. At 6 months’ follow-up this increased to 1.2 ± 0.2 g/kg aBW/day for the intervention group and 0.9 ± 0.2 g/kg aBW/day for the control group. Microbiota composition was not affected, nor were appetite or brain activity in response to food-related stimuli. Increasing protein intake in older adults to 1.2 g/kg aBW/day does not negatively impact the gut microbiota or suppress appetite.
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Kaiser A, Broeder C, Cohen JR, Douw L, Reneman L, Schrantee A. Effects of a single-dose methylphenidate challenge on resting-state functional connectivity in stimulant-treatment naive children and adults with ADHD. Hum Brain Mapp 2022; 43:4664-4675. [PMID: 35781371 PMCID: PMC9491277 DOI: 10.1002/hbm.25981] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 05/17/2022] [Accepted: 05/27/2022] [Indexed: 11/09/2022] Open
Abstract
Prior studies suggest that methylphenidate, the primary pharmacological treatment for attention-deficit/hyperactivity disorder (ADHD), alters functional brain connectivity. As the neurotransmitter systems targeted by methylphenidate undergo significant alterations throughout development, the effects of methylphenidate on functional connectivity may also be modulated by age. Therefore, we assessed the effects of a single methylphenidate challenge on brain network connectivity in stimulant-treatment naïve children and adults with ADHD. We obtained resting-state functional MRI from 50 boys (10-12 years of age) and 49 men (23-40 years of age) with ADHD (DSM IV, all subtypes), before and after an oral challenge with 0.5 mg/kg methylphenidate; and from 11 boys and 12 men as typically developing controls. Connectivity strength (CS), eigenvector centrality (EC), and betweenness centrality (BC) were calculated for the striatum, thalamus, dorsal anterior cingulate cortex (dACC), and prefrontal cortex (PFC). In line with our hypotheses, we found that methylphenidate decreased measures of connectivity and centrality in the striatum and thalamus in children with ADHD, but increased the same metrics in adults with ADHD. Surprisingly, we found no major effects of methylphenidate in the dACC and PFC in either children or adults. Interestingly, pre-methylphenidate, participants with ADHD showed aberrant connectivity and centrality compared to controls predominantly in frontal regions. Our findings demonstrate that methylphenidate's effects on connectivity of subcortical regions are age-dependent in stimulant-treatment naïve participants with ADHD, likely due to ongoing maturation of dopamine and noradrenaline systems. These findings highlight the importance for future studies to take a developmental perspective when studying the effects of methylphenidate treatment.
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Affiliation(s)
- Antonia Kaiser
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMCUniversity of AmsterdamAmsterdamThe Netherlands
| | - Caroline Broeder
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMCUniversity of AmsterdamAmsterdamThe Netherlands
| | - Jessica R. Cohen
- Department of Psychology and NeuroscienceUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
| | - Linda Douw
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMCVrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Liesbeth Reneman
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMCUniversity of AmsterdamAmsterdamThe Netherlands
| | - Anouk Schrantee
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMCUniversity of AmsterdamAmsterdamThe Netherlands
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Niso G, Botvinik-Nezer R, Appelhoff S, De La Vega A, Esteban O, Etzel JA, Finc K, Ganz M, Gau R, Halchenko YO, Herholz P, Karakuzu A, Keator DB, Markiewicz CJ, Maumet C, Pernet CR, Pestilli F, Queder N, Schmitt T, Sójka W, Wagner AS, Whitaker KJ, Rieger JW. Open and reproducible neuroimaging: From study inception to publication. Neuroimage 2022; 263:119623. [PMID: 36100172 PMCID: PMC10008521 DOI: 10.1016/j.neuroimage.2022.119623] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 08/17/2022] [Accepted: 09/09/2022] [Indexed: 10/31/2022] Open
Abstract
Empirical observations of how labs conduct research indicate that the adoption rate of open practices for transparent, reproducible, and collaborative science remains in its infancy. This is at odds with the overwhelming evidence for the necessity of these practices and their benefits for individual researchers, scientific progress, and society in general. To date, information required for implementing open science practices throughout the different steps of a research project is scattered among many different sources. Even experienced researchers in the topic find it hard to navigate the ecosystem of tools and to make sustainable choices. Here, we provide an integrated overview of community-developed resources that can support collaborative, open, reproducible, replicable, robust and generalizable neuroimaging throughout the entire research cycle from inception to publication and across different neuroimaging modalities. We review tools and practices supporting study inception and planning, data acquisition, research data management, data processing and analysis, and research dissemination. An online version of this resource can be found at https://oreoni.github.io. We believe it will prove helpful for researchers and institutions to make a successful and sustainable move towards open and reproducible science and to eventually take an active role in its future development.
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Affiliation(s)
- Guiomar Niso
- Psychological & Brain Sciences, Indiana University, Bloomington, IN, USA; Universidad Politecnica de Madrid, Madrid and CIBER-BBN, Spain; Instituto Cajal, CSIC, Madrid, Spain.
| | - Rotem Botvinik-Nezer
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA.
| | - Stefan Appelhoff
- Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany
| | | | - Oscar Esteban
- Dept. of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; Department of Psychology, Stanford University, Stanford, CA, USA
| | - Joset A Etzel
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Karolina Finc
- Centre for Modern Interdisciplinary Technologies, Nicolaus Copernicus University, Toruń, Poland
| | - Melanie Ganz
- Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark; Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Rémi Gau
- Institute of Psychology, Université catholique de Louvain, Louvain la Neuve, Belgium
| | - Yaroslav O Halchenko
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Peer Herholz
- Montreal Neurological Institute-Hospital, McGill University, Montréal, Quebec, Canada
| | - Agah Karakuzu
- Biomedical Engineering Institute, Polytechnique Montréal, Montréal, Quebec, Canada; Montréal Heart Institute, Montréal, Quebec, Canada
| | - David B Keator
- Department of Psychiatry and Human Behavior, University of California, Irvine, CA, USA
| | | | - Camille Maumet
- Inria, Univ Rennes, CNRS, Inserm - IRISA UMR 6074, Empenn ERL U 1228, Rennes, France
| | - Cyril R Pernet
- Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark
| | - Franco Pestilli
- Psychological & Brain Sciences, Indiana University, Bloomington, IN, USA; Department of Psychology, The University of Texas at Austin, Austin, TX, USA
| | - Nazek Queder
- Montreal Neurological Institute-Hospital, McGill University, Montréal, Quebec, Canada; Department of Neurobiology and Behavior, University of California, Irvine, CA, USA
| | - Tina Schmitt
- Neuroimaging Unit, Carl-von-Ossietzky Universität, Oldenburg, Germany
| | - Weronika Sójka
- Faculty of Philosophy and Social Sciences, Nicolaus Copernicus University, Toruń, Poland
| | - Adina S Wagner
- Institute for Neuroscience and Medicine, Research Centre Juelich, Germany
| | | | - Jochem W Rieger
- Neuroimaging Unit, Carl-von-Ossietzky Universität, Oldenburg, Germany; Department of Psychology, Carl-von-Ossietzky Universität, Oldenburg, Germany.
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55
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de la Vega A, Rocca R, Blair RW, Markiewicz CJ, Mentch J, Kent JD, Herholz P, Ghosh SS, Poldrack RA, Yarkoni T. Neuroscout, a unified platform for generalizable and reproducible fMRI research. eLife 2022; 11:e79277. [PMID: 36040302 PMCID: PMC9489206 DOI: 10.7554/elife.79277] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 08/27/2022] [Indexed: 11/28/2022] Open
Abstract
Functional magnetic resonance imaging (fMRI) has revolutionized cognitive neuroscience, but methodological barriers limit the generalizability of findings from the lab to the real world. Here, we present Neuroscout, an end-to-end platform for analysis of naturalistic fMRI data designed to facilitate the adoption of robust and generalizable research practices. Neuroscout leverages state-of-the-art machine learning models to automatically annotate stimuli from dozens of fMRI studies using naturalistic stimuli-such as movies and narratives-allowing researchers to easily test neuroscientific hypotheses across multiple ecologically-valid datasets. In addition, Neuroscout builds on a robust ecosystem of open tools and standards to provide an easy-to-use analysis builder and a fully automated execution engine that reduce the burden of reproducible research. Through a series of meta-analytic case studies, we validate the automatic feature extraction approach and demonstrate its potential to support more robust fMRI research. Owing to its ease of use and a high degree of automation, Neuroscout makes it possible to overcome modeling challenges commonly arising in naturalistic analysis and to easily scale analyses within and across datasets, democratizing generalizable fMRI research.
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Affiliation(s)
| | - Roberta Rocca
- Department of Psychology, The University of Texas at AustinAustinUnited States
- Interacting Minds Centre, Aarhus UniversityAarhusDenmark
| | - Ross W Blair
- Department of Psychology, Stanford UniversityStanfordUnited States
| | | | - Jeff Mentch
- Program in Speech and Hearing Bioscience and Technology, Harvard UniversityCambridgeUnited States
- McGovern Institute for Brain Research, Massachusetts Institute of TechnologyCambridgeUnited States
| | - James D Kent
- Department of Psychology, The University of Texas at AustinAustinUnited States
| | - Peer Herholz
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill UniversityMontrealCanada
| | - Satrajit S Ghosh
- McGovern Institute for Brain Research, Massachusetts Institute of TechnologyCambridgeUnited States
- Department of Otolaryngology, Harvard Medical SchoolBostonUnited States
| | | | - Tal Yarkoni
- Department of Psychology, The University of Texas at AustinAustinUnited States
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Chen Y, Hopp FR, Malik M, Wang PT, Woodman K, Youk S, Weber R. Reproducing FSL's fMRI data analysis via Nipype: Relevance, challenges, and solutions. FRONTIERS IN NEUROIMAGING 2022; 1:953215. [PMID: 37555184 PMCID: PMC10406235 DOI: 10.3389/fnimg.2022.953215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 06/28/2022] [Indexed: 08/10/2023]
Abstract
The "replication crisis" in neuroscientific research has led to calls for improving reproducibility. In traditional neuroscience analyses, irreproducibility may occur as a result of issues across various stages of the methodological process. For example, different operating systems, different software packages, and even different versions of the same package can lead to variable results. Nipype, an open-source Python project, integrates different neuroimaging software packages uniformly to improve the reproducibility of neuroimaging analyses. Nipype has the advantage over traditional software packages (e.g., FSL, ANFI, SPM, etc.) by (1) providing comprehensive software development frameworks and usage information, (2) improving computational efficiency, (3) facilitating reproducibility through sufficient details, and (4) easing the steep learning curve. Despite the rich tutorials it has provided, the Nipype community lacks a standard three-level GLM tutorial for FSL. Using the classical Flanker task dataset, we first precisely reproduce a three-level GLM analysis with FSL via Nipype. Next, we point out some undocumented discrepancies between Nipype and FSL functions that led to substantial differences in results. Finally, we provide revised Nipype code in re-executable notebooks that assure result invariability between FSL and Nipype. Our analyses, notebooks, and operating software specifications (e.g., docker build files) are available on the Open Science Framework platform.
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Affiliation(s)
- Yibei Chen
- Media Neuroscience Lab, Department of Communication, College of Letters and Science, University of California, Santa Barbara, Santa Barbara, CA, United States
| | - Frederic R. Hopp
- Amsterdam School of Communication Research, University of Amsterdam, Amsterdam, Netherlands
| | - Musa Malik
- Media Neuroscience Lab, Department of Communication, College of Letters and Science, University of California, Santa Barbara, Santa Barbara, CA, United States
| | - Paula T. Wang
- Media Neuroscience Lab, Department of Communication, College of Letters and Science, University of California, Santa Barbara, Santa Barbara, CA, United States
| | - Kylie Woodman
- Media Neuroscience Lab, Department of Communication, College of Letters and Science, University of California, Santa Barbara, Santa Barbara, CA, United States
| | - Sungbin Youk
- Media Neuroscience Lab, Department of Communication, College of Letters and Science, University of California, Santa Barbara, Santa Barbara, CA, United States
| | - René Weber
- Media Neuroscience Lab, Department of Communication, College of Letters and Science, University of California, Santa Barbara, Santa Barbara, CA, United States
- Department of Communication and Media, Ewha Womans University, Seoul, South Korea
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Waller L, Erk S, Pozzi E, Toenders YJ, Haswell CC, Büttner M, Thompson PM, Schmaal L, Morey RA, Walter H, Veer IM. ENIGMA HALFpipe: Interactive, reproducible, and efficient analysis for resting-state and task-based fMRI data. Hum Brain Mapp 2022; 43:2727-2742. [PMID: 35305030 PMCID: PMC9120555 DOI: 10.1002/hbm.25829] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 01/26/2022] [Accepted: 02/12/2022] [Indexed: 12/27/2022] Open
Abstract
The reproducibility crisis in neuroimaging has led to an increased demand for standardized data processing workflows. Within the ENIGMA consortium, we developed HALFpipe (Harmonized Analysis of Functional MRI pipeline), an open-source, containerized, user-friendly tool that facilitates reproducible analysis of task-based and resting-state fMRI data through uniform application of preprocessing, quality assessment, single-subject feature extraction, and group-level statistics. It provides state-of-the-art preprocessing using fMRIPrep without the requirement for input data in Brain Imaging Data Structure (BIDS) format. HALFpipe extends the functionality of fMRIPrep with additional preprocessing steps, which include spatial smoothing, grand mean scaling, temporal filtering, and confound regression. HALFpipe generates an interactive quality assessment (QA) webpage to rate the quality of key preprocessing outputs and raw data in general. HALFpipe features myriad post-processing functions at the individual subject level, including calculation of task-based activation, seed-based connectivity, network-template (or dual) regression, atlas-based functional connectivity matrices, regional homogeneity (ReHo), and fractional amplitude of low-frequency fluctuations (fALFF), offering support to evaluate a combinatorial number of features or preprocessing settings in one run. Finally, flexible factorial models can be defined for mixed-effects regression analysis at the group level, including multiple comparison correction. Here, we introduce the theoretical framework in which HALFpipe was developed, and present an overview of the main functions of the pipeline. HALFpipe offers the scientific community a major advance toward addressing the reproducibility crisis in neuroimaging, providing a workflow that encompasses preprocessing, post-processing, and QA of fMRI data, while broadening core principles of data analysis for producing reproducible results. Instructions and code can be found at https://github.com/HALFpipe/HALFpipe.
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Affiliation(s)
- Lea Waller
- Charité Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt‐Universität zu BerlinDepartment of Psychiatry and Neurosciences CCMBerlinGermany
| | - Susanne Erk
- Charité Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt‐Universität zu BerlinDepartment of Psychiatry and Neurosciences CCMBerlinGermany
| | - Elena Pozzi
- Centre for Youth Mental HealthUniversity of MelbourneMelbourneAustralia
- OrygenParkvilleAustralia
| | - Yara J. Toenders
- Centre for Youth Mental HealthUniversity of MelbourneMelbourneAustralia
- OrygenParkvilleAustralia
| | | | - Marc Büttner
- Charité Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt‐Universität zu BerlinDepartment of Psychiatry and Neurosciences CCMBerlinGermany
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Lianne Schmaal
- Centre for Youth Mental HealthUniversity of MelbourneMelbourneAustralia
- OrygenParkvilleAustralia
| | - Rajendra A. Morey
- Duke University School of MedicineDurhamNorth CarolinaUSA
- Mid‐Atlantic Mental Illness Research Education and Clinical CenterUS Department of Veterans AffairsDurhamNorth CarolinaUSA
| | - Henrik Walter
- Charité Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt‐Universität zu BerlinDepartment of Psychiatry and Neurosciences CCMBerlinGermany
| | - Ilya M. Veer
- Charité Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt‐Universität zu BerlinDepartment of Psychiatry and Neurosciences CCMBerlinGermany
- Department of Developmental PsychologyUniversity of AmsterdamAmsterdamThe Netherlands
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Farb NAS, Desormeau P, Anderson AK, Segal ZV. Static and treatment-responsive brain biomarkers of depression relapse vulnerability following prophylactic psychotherapy: Evidence from a randomized control trial. Neuroimage Clin 2022; 34:102969. [PMID: 35367955 PMCID: PMC8978278 DOI: 10.1016/j.nicl.2022.102969] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 01/18/2022] [Accepted: 02/17/2022] [Indexed: 12/18/2022]
Abstract
A prospective study of neural biomarkers of relapse in remitted depressed patients. Assessed neural response to dysphoric mood-induction before and after psychotherapy. Relapse over a 2-year follow-up linked to dysphoria-evoked sensory inhibition. Relapse risk was lower when dorsolateral prefrontal reactivity decreased over time. Depression prophylaxis may involve reducing dysphoria-evoked sensory inhibition.
Background Neural reactivity to dysphoric mood induction indexes the tendency for distress to promote cognitive reactivity and sensory avoidance. Linking these responses to illness prognosis following recovery from Major Depressive Disorder informs our understanding of depression vulnerability and provides engagement targets for prophylactic interventions. Methods A prospective fMRI neuroimaging design investigated the relationship between dysphoric reactivity and relapse following prophylactic intervention. Remitted depressed outpatients (N = 85) were randomized to 8 weeks of Cognitive Therapy with a Well-Being focus or Mindfulness Based Cognitive Therapy. Participants were assessed before and after therapy and followed for 2 years to assess relapse status. Neural reactivity common to both assessment points identified static biomarkers of relapse, whereas reactivity change identified dynamic biomarkers. Results Dysphoric mood induction evoked prefrontal activation and sensory deactivation. Controlling for past episodes, concurrent symptoms and medication status, somatosensory deactivation was associated with depression recurrence in a static pattern that was unaffected by prophylactic treatment, HR 0.04, 95% CI [0.01, 0.14], p < .001. Treatment-related prophylaxis was linked to reduced activation of the left lateral prefrontal cortex (LPFC), HR 3.73, 95% CI [1.33, 10.46], p = .013. Contralaterally, the right LPFC showed dysphoria-evoked inhibitory connectivity with the right somatosensory biomarker Conclusions These findings support a two-factor model of depression relapse vulnerability, in which: enduring patterns of dysphoria-evoked sensory deactivation contribute to episode return, but vulnerability may be mitigated by targeting prefrontal regions responsive to clinical intervention. Emotion regulation during illness remission may be enhanced by reducing prefrontal cognitive processes in favor of sensory representation and integration.
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Affiliation(s)
- Norman A S Farb
- Department of Psychology, University of Toronto Mississauga, 3359 Mississauga Road, Mississauga, Ontario L5L 1C6, Canada; Graduate Department of Psychological Clinical Science, University of Toronto Scarborough, 1265 Military Trail, Toronto, ON M1C 1A4, Canada.
| | - Philip Desormeau
- Graduate Department of Psychological Clinical Science, University of Toronto Scarborough, 1265 Military Trail, Toronto, ON M1C 1A4, Canada
| | - Adam K Anderson
- College of Human Ecology, Cornell University, Ithaca, NY 14853, USA
| | - Zindel V Segal
- Graduate Department of Psychological Clinical Science, University of Toronto Scarborough, 1265 Military Trail, Toronto, ON M1C 1A4, Canada
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59
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Yun JY, Lee YI, Park S, Choi JM, Choi SH, Jang JH. Functional activation of insula and dorsal anterior cingulate for conflict control against larger monetary loss in young adults with subthreshold depression: a preliminary study. Sci Rep 2022; 12:6956. [PMID: 35484391 PMCID: PMC9050651 DOI: 10.1038/s41598-022-10989-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 04/15/2022] [Indexed: 11/08/2022] Open
Abstract
Subthreshold depression (StD) is associated with higher risk of later developing major depressive disorder (MDD). Deficits of goal-directed behaviors regarding the motional, motivational, and conflict control are found in MDD. The current study examined neural underpinning of conflict control against monetary punishment in StD compared to MDD and healthy controls (HC). Seventy-one participants (HC, n = 27; StD, n = 21; MDD, n = 23) in their mid-20's completed self-reports. Preprocessing of functional magnetic resonance imaging acquired for the Simon task against larger or smaller monetary punishment was conducted using ENIGMA HALFpipe version 1.2.1. Neural correlates of conflict control against monetary punishment that could vary with either diagnosis or PHQ-9 total score were examined using a general linear model of FSL. Simon effect was effective for reaction time and accuracy in every subgroup of diagnosis and regardless of the size of monetary punishment. Conflict control against larger monetary loss was associated with higher functional activation of left insula in StD than HC and MDD. StD showed lower functional activation of left dorsal anterior cingulate (dACC) than MDD for conflict control against larger monetary loss. For conflict control against smaller monetary loss, StD demonstrated higher functional activation of left paracentral lobule and right putamen compared to HC. Directed acyclic graphs showed directional associations from suicidal ideation, sadness, and concentration difficulty to functional activation of paracentral lobule, ventromedial prefrontal cortex (vmPFC), and thalamus for conflict control against monetary loss. Differential functional activation of insula and dACC for conflict control against larger monetary loss could be a brain phenotype of StD. Item-level depressive symptoms of suicidal ideation, sadness, and concentration difficulty could be reflected in the conflict control-related functional activation of paracentral lobule (against smaller monetary loss), vmPFC and thalamus (against larger monetary loss), respectively.
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Affiliation(s)
- Je-Yeon Yun
- Seoul National University Hospital, Seoul, Republic of Korea
- Yeongeon Student Support Center, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Yoonji Irene Lee
- Department of Psychiatry, Seoul National University Hospital, Seoul, Republic of Korea
| | - Susan Park
- Department of Psychiatry, Seoul National University Hospital, Seoul, Republic of Korea
| | - Jong Moon Choi
- Department of Psychology, Louisiana State University, Baton Rouge, USA
| | - Soo-Hee Choi
- Department of Psychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Joon Hwan Jang
- Department of Psychiatry, Seoul National University Health Service Center, Gwanak-ro 1, Gwanak-gu, Seoul, 08826, Republic of Korea.
- Department of Human Systems Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.
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60
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Mallett R, Lorenc ES, Lewis-Peacock JA. Working Memory Swap Errors Have Identifiable Neural Representations. J Cogn Neurosci 2022; 34:776-786. [PMID: 35171256 PMCID: PMC11126154 DOI: 10.1162/jocn_a_01831] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Working memory is an essential component of cognition that facilitates goal-directed behavior. Famously, it is severely limited and performance suffers when memory load exceeds an individual's capacity. Modeling of visual working memory responses has identified two likely types of errors: guesses and swaps. Swap errors may arise from a misbinding between the features of different items. Alternatively, these errors could arise from memory noise in the feature dimension used for cueing a to-be-tested memory item, resulting in the wrong item being selected. Finally, it is possible that so-called swap errors actually reflect informed guessing, which could occur at the time of a cue, or alternatively, at the time of the response. Here, we combined behavioral response modeling and fMRI pattern analysis to test the hypothesis that swap errors involve the active maintenance of an incorrect memory item. After the encoding of six spatial locations, a retro-cue indicated which location would be tested after memory retention. On accurate trials, we could reconstruct a memory representation of the cued location in both early visual cortex and intraparietal sulcus. On swap error trials identified with mixture modeling, we were able to reconstruct a representation of the swapped location, but not of the cued location, suggesting the maintenance of the incorrect memory item before response. Moreover, participants subjectively responded with some level of confidence, rather than complete guessing, on a majority of swap error trials. Together, these results suggest that swap errors are not mere response-phase guesses, but instead result from failures of selection in working memory, contextual binding errors, or informed guesses, which produce active maintenance of incorrect memory representations.
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Ekhtiari H, Zare-Bidoky M, Sangchooli A, Janes AC, Kaufman MJ, Oliver JA, Prisciandaro JJ, Wüstenberg T, Anton RF, Bach P, Baldacchino A, Beck A, Bjork JM, Brewer J, Childress AR, Claus ED, Courtney KE, Ebrahimi M, Filbey FM, Ghahremani DG, Azbari PG, Goldstein RZ, Goudriaan AE, Grodin EN, Hamilton JP, Hanlon CA, Hassani-Abharian P, Heinz A, Joseph JE, Kiefer F, Zonoozi AK, Kober H, Kuplicki R, Li Q, London ED, McClernon J, Noori HR, Owens MM, Paulus MP, Perini I, Potenza M, Potvin S, Ray L, Schacht JP, Seo D, Sinha R, Smolka MN, Spanagel R, Steele VR, Stein EA, Steins-Loeber S, Tapert SF, Verdejo-Garcia A, Vollstädt-Klein S, Wetherill RR, Wilson SJ, Witkiewitz K, Yuan K, Zhang X, Zilverstand A. A methodological checklist for fMRI drug cue reactivity studies: development and expert consensus. Nat Protoc 2022; 17:567-595. [PMID: 35121856 PMCID: PMC9063851 DOI: 10.1038/s41596-021-00649-4] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 10/21/2021] [Indexed: 12/23/2022]
Abstract
Cue reactivity is one of the most frequently used paradigms in functional magnetic resonance imaging (fMRI) studies of substance use disorders (SUDs). Although there have been promising results elucidating the neurocognitive mechanisms of SUDs and SUD treatments, the interpretability and reproducibility of these studies is limited by incomplete reporting of participants' characteristics, task design, craving assessment, scanning preparation and analysis decisions in fMRI drug cue reactivity (FDCR) experiments. This hampers clinical translation, not least because systematic review and meta-analysis of published work are difficult. This consensus paper and Delphi study aims to outline the important methodological aspects of FDCR research, present structured recommendations for more comprehensive methods reporting and review the FDCR literature to assess the reporting of items that are deemed important. Forty-five FDCR scientists from around the world participated in this study. First, an initial checklist of items deemed important in FDCR studies was developed by several members of the Enhanced NeuroImaging Genetics through Meta-Analyses (ENIGMA) Addiction working group on the basis of a systematic review. Using a modified Delphi consensus method, all experts were asked to comment on, revise or add items to the initial checklist, and then to rate the importance of each item in subsequent rounds. The reporting status of the items in the final checklist was investigated in 108 recently published FDCR studies identified through a systematic review. By the final round, 38 items reached the consensus threshold and were classified under seven major categories: 'Participants' Characteristics', 'General fMRI Information', 'General Task Information', 'Cue Information', 'Craving Assessment Inside Scanner', 'Craving Assessment Outside Scanner' and 'Pre- and Post-Scanning Considerations'. The review of the 108 FDCR papers revealed significant gaps in the reporting of the items considered important by the experts. For instance, whereas items in the 'General fMRI Information' category were reported in 90.5% of the reviewed papers, items in the 'Pre- and Post-Scanning Considerations' category were reported by only 44.7% of reviewed FDCR studies. Considering the notable and sometimes unexpected gaps in the reporting of items deemed to be important by experts in any FDCR study, the protocols could benefit from the adoption of reporting standards. This checklist, a living document to be updated as the field and its methods advance, can help improve experimental design, reporting and the widespread understanding of the FDCR protocols. This checklist can also provide a sample for developing consensus statements for protocols in other areas of task-based fMRI.
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Affiliation(s)
- Hamed Ekhtiari
- Laureate Institute for Brain Research, Tulsa, OK, USA.
- Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA.
| | - Mehran Zare-Bidoky
- Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran
- Shahid-Sadoughi University of Medical Sciences, Yazd, Iran
| | - Arshiya Sangchooli
- Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran
| | - Amy C Janes
- Department of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, MA, USA
| | - Marc J Kaufman
- Department of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, MA, USA
| | - Jason A Oliver
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- TSET Health Promotion Research Center, Stephenson Cancer Center, Oklahoma City, OK, USA
- Department of Psychiatry & Behavioral Sciences, Oklahoma State University Center for Health Sciences, Tulsa, OK, USA
| | - James J Prisciandaro
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA
| | - Torsten Wüstenberg
- Department of Psychiatry and Neurosciences, Charité Campus Mitte, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Raymond F Anton
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA
| | - Patrick Bach
- Department of Addictive Behaviour and Addiction Medicine, Central Institute of Mental Health (CIMH), Heidelberg University, Mannheim, Germany
| | - Alex Baldacchino
- Division of Population Studies and Behavioural Sciences, St Andrews University Medical School, University of St Andrews, Scotland, UK
| | - Anne Beck
- Department of Psychiatry and Neurosciences, Charité Campus Mitte, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Faculty of Health, Health and Medical University, Campus Potsdam, Potsdam, Germany
| | - James M Bjork
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Judson Brewer
- Department of Behavioral and Social Sciences, Brown University School of Public Health, Providence, RI, USA
| | - Anna Rose Childress
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Eric D Claus
- Department of Biobehavioral Health, The Pennsylvania State University, University Park, PA, USA
| | - Kelly E Courtney
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Mohsen Ebrahimi
- Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran
| | - Francesca M Filbey
- Center for BrainHealth, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, USA
| | - Dara G Ghahremani
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
| | - Peyman Ghobadi Azbari
- Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran
- Department of Biomedical Engineering, Shahed University, Tehran, Iran
| | - Rita Z Goldstein
- Departments of Psychiatry & Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Anna E Goudriaan
- Department of Psychiatry, Amsterdam University Medical Center, University of Amsterdam and Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Erica N Grodin
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
| | - J Paul Hamilton
- Center for Social and Affective Neuroscience, Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization, Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
| | - Colleen A Hanlon
- Department of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | | | - Andreas Heinz
- Department of Psychiatry and Neurosciences, Charité Campus Mitte, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Jane E Joseph
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA
| | - Falk Kiefer
- Department of Addictive Behaviour and Addiction Medicine, Central Institute of Mental Health (CIMH), Heidelberg University, Mannheim, Germany
| | - Arash Khojasteh Zonoozi
- Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran
- Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hedy Kober
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | | | - Qiang Li
- Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, China
| | - Edythe D London
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
| | - Joseph McClernon
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Hamid R Noori
- International Center for Primate Brain Research, Center for Excellence in Brain Science and Intelligence Technology (CEBSIT)/Institute of Neuroscience (ION), Chinese Academy of Sciences, Shanghai, China
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Max M Owens
- Department of Psychiatry, University of Vermont, Burlington, VT, USA
| | | | - Irene Perini
- Center for Social and Affective Neuroscience, Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization, Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
| | - Marc Potenza
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
- Connecticut Council on Problem Gambling, Wethersfield, CT, USA
- Department of Neuroscience, Child Study Center and Wu Tsai Institute, Yale School of Medicine, New Haven, CT, USA
| | - Stéphane Potvin
- Centre de recherche de l'Institut Universitaire en Santé Mentale de Montréal, University of Montreal, Montreal, Canada
| | - Lara Ray
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Dongju Seo
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Rajita Sinha
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Michael N Smolka
- Department of Psychiatry, Technische Universität Dresden, Dresden, Germany
| | - Rainer Spanagel
- Institute of Psychopharmacology, Central Institute of Mental Health, Mannheim, Germany
| | - Vaughn R Steele
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Elliot A Stein
- Intramural Research Program, National Institute on Drug Abuse, Baltimore, MD, USA
| | - Sabine Steins-Loeber
- Department of Clinical Psychology and Psychotherapy, Otto-Friedrich-University of Bamberg, Bamberg, Germany
| | - Susan F Tapert
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | | | - Sabine Vollstädt-Klein
- Department of Addictive Behaviour and Addiction Medicine, Central Institute of Mental Health (CIMH), Heidelberg University, Mannheim, Germany
| | - Reagan R Wetherill
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Stephen J Wilson
- Department of Psychology, The Pennsylvania State University, University Park, PA, USA
| | - Katie Witkiewitz
- Department of Psychology, University of New Mexico, Albuquerque, NM, USA
| | - Kai Yuan
- School of Life Science and Technology, Xidian University, Xi'an, China
| | - Xiaochu Zhang
- Department of Psychology, School of Humanities and Social Science, University of Science and Technology of China, Anhui, China
- Department of Radiology, First Affiliated Hospital of USTC, Hefei National Laboratory for Physical Science at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science and Technology of China, Anhui, China
| | - Anna Zilverstand
- Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
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Singh MF, Wang A, Cole M, Ching S, Braver TS. Enhancing task fMRI preprocessing via individualized model-based filtering of intrinsic activity dynamics. Neuroimage 2022; 247:118836. [PMID: 34942364 PMCID: PMC10069385 DOI: 10.1016/j.neuroimage.2021.118836] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 12/15/2021] [Accepted: 12/18/2021] [Indexed: 11/27/2022] Open
Abstract
Brain responses recorded during fMRI are thought to reflect both rapid, stimulus-evoked activity and the propagation of spontaneous activity through brain networks. In the current work, we describe a method to improve the estimation of task-evoked brain activity by first "filtering-out the intrinsic propagation of pre-event activity from the BOLD signal. We do so using Mesoscale Individualized NeuroDynamic (MINDy; Singh et al. 2020b) models built from individualized resting-state data to subtract the propagation of spontaneous activity from the task-fMRI signal (MINDy-based Filtering). After filtering, time-series are analyzed using conventional techniques. Results demonstrate that this simple operation significantly improves the statistical power and temporal precision of estimated group-level effects. Moreover, use of MINDy-based filtering increased the similarity of neural activation profiles and prediction accuracy of individual differences in behavior across tasks measuring the same construct (cognitive control). Thus, by subtracting the propagation of previous activity, we obtain better estimates of task-related neural effects.
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Affiliation(s)
- Matthew F Singh
- Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA; Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA; Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, USA.
| | - Anxu Wang
- Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA; Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Michael Cole
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, USA
| | - ShiNung Ching
- Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA; Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Todd S Braver
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA; Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
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63
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Feng C, Thompson WK, Paulus MP. Effect sizes of associations between neuroimaging measures and affective symptoms: A meta-analysis. Depress Anxiety 2022; 39:19-25. [PMID: 34516701 DOI: 10.1002/da.23215] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Revised: 07/14/2021] [Accepted: 08/20/2021] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND The utility of brain-based biomarkers for psychiatric disorders hinges among other factors on their ability to explain a significant portion of the phenotypic variance. In particular, many small scale studies have been unable to arbitrate whether structural or functional magnetic resonance imaging has potential to be a biological marker for these disorders. METHODS This study conducted a meta-analysis to examine the relationship between study power and published effect sizes for the relationship between affective symptoms and structural or functional magnetic resonance imaging measures. The current analyses are based on 821 brain-affective symptom association effect sizes derived from 120 publications, which employed a univariate region-of-interest approach. RESULTS For self-assessed affective symptoms published brain imaging measures accounted for on average 8% (confidence interval: 1.6%-23%) of between-subject variation. This average effect size was based mostly on studies with small sample sizes, which have likely led to inflation of these effect size estimates. CONCLUSIONS These findings support the conclusion that brain imaging measures currently account for a smaller proportion of the interindividual variance in affective symptoms than has been previously reported. The current findings support the need for both large-sample clinical studies and new statistical and theoretical models to more robustly capture systematic variance of brain-affective symptom relationships.
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Affiliation(s)
- Chunliang Feng
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, China; School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
| | - Wesley K Thompson
- Division of Biostatistics, University of California San Diego, La Jolla, California, USA
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64
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Large, open datasets for human connectomics research: Considerations for reproducible and responsible data use. Neuroimage 2021; 244:118579. [PMID: 34536537 DOI: 10.1016/j.neuroimage.2021.118579] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Revised: 08/27/2021] [Accepted: 09/14/2021] [Indexed: 12/19/2022] Open
Abstract
Large, open datasets have emerged as important resources in the field of human connectomics. In this review, the evolution of data sharing involving magnetic resonance imaging is described. A summary of the challenges and progress in conducting reproducible data analyses is provided, including description of recent progress made in the development of community guidelines and recommendations, software and data management tools, and initiatives to enhance training and education. Finally, this review concludes with a discussion of ethical conduct relevant to analyses of large, open datasets and a researcher's responsibility to prevent further stigmatization of historically marginalized racial and ethnic groups. Moving forward, future work should include an enhanced emphasis on the social determinants of health, which may further contextualize findings among diverse population-based samples. Leveraging the progress to date and guided by interdisciplinary collaborations, the future of connectomics promises to be an impressive era of innovative research, yielding a more inclusive understanding of brain structure and function.
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65
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Chang Y, Saritac M. Decoding Brain Activity Features to Recognize Distorted Objects. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:5776-5779. [PMID: 34892432 DOI: 10.1109/embc46164.2021.9630360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Brain decoding is able to make human interact with an external machine or robot for assisting patient's rehabilitation. Brain generic object recognition ability can be decoded through multiple neuroimaging modalities like functional magnetic resonance imaging (fMRI). On the other hand, external machine may wrongly recognize objects due to distorted noisy or blurring images caused by many factors, and therefore deteriorate performance of brain-machine interaction. In order to create better machine, generalization capability of human brain is transferred to classifier for enhancing classification accuracy of distorted images. Since homology existing between human and machine vision has been demonstrated, through decoding neural activity features of fMRI signals into feature units of convolutional neural network layers, an enhanced object recognition method is proposed to integrate brain activity into classifier for increasing classification accuracy. Experimental results show that the proposed method is able to enhance generalization capability of distorted object recognition.
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66
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Targeting working memory to modify emotional reactivity in adult attention deficit hyperactivity disorder: a functional magnetic resonance imaging study. Brain Imaging Behav 2021; 16:680-691. [PMID: 34524649 PMCID: PMC9010388 DOI: 10.1007/s11682-021-00532-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/31/2021] [Indexed: 11/10/2022]
Abstract
Understanding the neural mechanisms of emotional reactivity in Attention-Deficit/Hyperactivity Disorder (ADHD) may help develop more effective treatments that target emotion dysregulation. In adult ADHD, emotion regulation problems cover a range of dimensions, including emotional reactivity (ER). One important process that could underlie an impaired ER in ADHD might be impaired working memory (WM) processing. We recently demonstrated that taxing WM prior to the exposure of emotionally salient stimuli reduced physiological and subjective reactivity to such cues in heavy drinkers, suggesting lasting effects of WM activation on ER. Here, we investigated neural mechanisms that could underlie the interaction between WM and ER in adult ADHD participants. We included 30 male ADHD participants and 30 matched controls. Participants performed a novel functional magnetic resonance imaging paradigm in which active WM-blocks were alternated with passive blocks of negative and neutral images. We demonstrated group-independent significant main effects of negative emotional images on amygdala activation, and WM-load on paracingulate gyrus and dorsolateral prefrontal cortex activation. Contrary to earlier reports in adolescent ADHD, no impairments were found in neural correlates of WM or ER. Moreover, taxing WM did not alter the neural correlates of ER in either ADHD or control participants. While we did find effects on the amygdala, paCG, and dlPFC activation, we did not find interactions between WM and ER, possibly due to the relatively unimpaired ADHD population and a well-matched control group. Whether targeting WM might be effective in participants with ADHD with severe ER impairments remains to be investigated.
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67
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Kaiser A, Bottelier MA, de Ruiter MB, Solleveld MM, Tamminga HGH, Bouziane C, Geurts HM, Lindauer RJL, Kooij JJS, Lucassen PJ, Schrantee A, Reneman L. Effects of prolonged methylphenidate treatment on amygdala reactivity and connectivity: a randomized controlled trial in stimulant treatment-naive, male participants with ADHD. PSYCHORADIOLOGY 2021; 1:152-163. [PMID: 38665807 PMCID: PMC10917223 DOI: 10.1093/psyrad/kkab013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 08/07/2021] [Accepted: 09/24/2021] [Indexed: 04/28/2024]
Abstract
Background Problems with emotional processing are widely reported in individuals with attention-deficit/hyperactivity disorder (ADHD). Although methylphenidate (MPH) effectively alleviates inattention and hyperactivity symptoms in ADHD, its effects on emotional processing and internalizing symptoms have remained elusive. While we previously found that acute MPH administration modulated neural mechanisms underlying emotional processing in an age-dependent manner, the effects of prolonged administration remained unknown. Objectives Therefore, we investigated: (i) whether prolonged MPH treatment influences neural substrates (amygdala reactivity and connectivity) of emotional processing, and (ii) whether these effects are modulated by age. Methods The "effects of Psychotropic drugs On Developing brain-MPH" ("ePOD-MPH") randomized controlled trial was a 16-week double-blind, placebo-controlled, multi-center trial with MPH in 50 boys (10-12 years of age) and 49 men (23-40 years of age), all stimulant treatment-naive and diagnosed with ADHD. Participants performed an emotional face-matching task during functional magnetic resonance imaging. We assessed their symptoms of ADHD and internalizing symptoms at baseline, during the trial (8 weeks), and 1 week after the trial end (17 weeks). Results and Conclusions We did not find effects of prolonged MPH treatment on emotional processing, as measured by amygdala reactivity and connectivity and internalizing symptoms in this trial with stimulant treatment-naive participants. This differs from our findings on emotional processing following acute MPH administration and the effects of prolonged MPH treatment on the dopamine system, which were both modulated by age. Interestingly, prolonged MPH treatment did improve ADHD symptoms, although depressive and anxiety symptoms showed a medication-independent decrease. Furthermore, our data indicate that baseline internalizing symptoms may be used to predict MPH treatment effects on ADHD symptoms, particularly in (male) adults with ADHD.
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Affiliation(s)
- Antonia Kaiser
- Amsterdam UMC, University of Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam, 1105 AZ, the Netherlands
| | - Marco A Bottelier
- Amsterdam UMC, University of Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam, 1105 AZ, the Netherlands
- University Medical Center Groningen, Child Study Center, Accare, Groningen, 9713GZ, the Netherlands
| | - Michiel B de Ruiter
- Amsterdam UMC, University of Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam, 1105 AZ, the Netherlands
- Netherlands Cancer Institute, Division of Psychosocial Research and Epidemiology, Amsterdam, 1066CX, the Netherlands
| | - Michelle M Solleveld
- Amsterdam UMC, University of Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam, 1105 AZ, the Netherlands
| | - Hyke G H Tamminga
- Amsterdam UMC, University of Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam, 1105 AZ, the Netherlands
- University of Amsterdam, Dutch Autism & ADHD Research Center, Department of Psychology, Amsterdam, 1018WT, the Netherlands
| | - Cheima Bouziane
- Amsterdam UMC, University of Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam, 1105 AZ, the Netherlands
| | - Hilde M Geurts
- University of Amsterdam, Dutch Autism & ADHD Research Center, Department of Psychology, Amsterdam, 1018WT, the Netherlands
| | - Ramon J L Lindauer
- Amsterdam UMC, University of Amsterdam, Department of Child and Adolescent Psychiatry, Amsterdam, 1105AZ, the Netherlands
- Academic Centre for Child and Adolescent Psychiatry, Levvel, Amsterdam, 1076EC, the Netherlands
| | - J J Sandra Kooij
- Expertise Center Adult ADHD, PsyQ, The Hague, 2512VA, the Netherlands
- Amsterdam UMC, Vrije Universiteit, Amsterdam Public Health Research Institute, Department of Psychiatry, Amsterdam, 1105AZ, the Netherlands
| | - Paul J Lucassen
- University of Amsterdam, Brain Plasticity Group, Swammerdam Institute for Life Sciences, Amsterdam, 1012WX, The Netherlands
| | - Anouk Schrantee
- Amsterdam UMC, University of Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam, 1105 AZ, the Netherlands
| | - Liesbeth Reneman
- Amsterdam UMC, University of Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam, 1105 AZ, the Netherlands
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Freund MC, Bugg JM, Braver TS. A Representational Similarity Analysis of Cognitive Control during Color-Word Stroop. J Neurosci 2021; 41:7388-7402. [PMID: 34162756 PMCID: PMC8412987 DOI: 10.1523/jneurosci.2956-20.2021] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 05/23/2021] [Accepted: 06/10/2021] [Indexed: 11/21/2022] Open
Abstract
Progress in understanding the neural bases of cognitive control has been supported by the paradigmatic color-word Stroop task, in which a target response (color name) must be selected over a more automatic, yet potentially incongruent, distractor response (word). For this paradigm, models have postulated complementary coding schemes: dorsomedial frontal cortex (DMFC) is proposed to evaluate the demand for control via incongruency-related coding, whereas dorsolateral PFC (DLPFC) is proposed to implement control via goal and target-related coding. Yet, mapping these theorized schemes to measured neural activity within this task has been challenging. Here, we tested for these coding schemes relatively directly, by decomposing an event-related color-word Stroop task via representational similarity analysis. Three neural coding models were fit to the similarity structure of multivoxel patterns of human fMRI activity, acquired from 65 healthy, young-adult males and females. Incongruency coding was predominant in DMFC, whereas both target and incongruency coding were present with indistinguishable strength in DLPFC. In contrast, distractor information was strongly encoded within early visual cortex. Further, these coding schemes were differentially related to behavior: individuals with stronger DLPFC (and lateral posterior parietal cortex) target coding, but weaker DMFC incongruency coding, exhibited less behavioral Stroop interference. These results highlight the utility of the representational similarity analysis framework for investigating neural mechanisms of cognitive control and point to several promising directions to extend the Stroop paradigm.SIGNIFICANCE STATEMENT How the human brain enables cognitive control - the ability to override behavioral habits to pursue internal goals - has been a major focus of neuroscience research. This ability has been frequently investigated by using the Stroop color-word naming task. With the Stroop as a test-bed, many theories have proposed specific neuroanatomical dissociations, in which medial and lateral frontal brain regions underlie cognitive control by encoding distinct types of information. Yet providing a direct confirmation of these claims has been challenging. Here, we demonstrate that representational similarity analysis, which estimates and models the similarity structure of brain activity patterns, can successfully establish the hypothesized functional dissociations within the Stroop task. Representational similarity analysis may provide a useful approach for investigating cognitive control mechanisms.
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Affiliation(s)
- Michael C Freund
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, Missouri 63130
| | - Julie M Bugg
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, Missouri 63130
| | - Todd S Braver
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, Missouri 63130
- Department of Radiology, Washington University in St. Louis School of Medicine, St. Louis, Missouri 63110
- Department of Neuroscience, Washington University in St. Louis School of Medicine, St. Louis, Missouri 63110
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Autio JA, Zhu Q, Li X, Glasser MF, Schwiedrzik CM, Fair DA, Zimmermann J, Yacoub E, Menon RS, Van Essen DC, Hayashi T, Russ B, Vanduffel W. Minimal specifications for non-human primate MRI: Challenges in standardizing and harmonizing data collection. Neuroimage 2021; 236:118082. [PMID: 33882349 PMCID: PMC8594288 DOI: 10.1016/j.neuroimage.2021.118082] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 02/16/2021] [Accepted: 04/07/2021] [Indexed: 02/07/2023] Open
Abstract
Recent methodological advances in MRI have enabled substantial growth in neuroimaging studies of non-human primates (NHPs), while open data-sharing through the PRIME-DE initiative has increased the availability of NHP MRI data and the need for robust multi-subject multi-center analyses. Streamlined acquisition and analysis protocols would accelerate and improve these efforts. However, consensus on minimal standards for data acquisition protocols and analysis pipelines for NHP imaging remains to be established, particularly for multi-center studies. Here, we draw parallels between NHP and human neuroimaging and provide minimal guidelines for harmonizing and standardizing data acquisition. We advocate robust translation of widely used open-access toolkits that are well established for analyzing human data. We also encourage the use of validated, automated pre-processing tools for analyzing NHP data sets. These guidelines aim to refine methodological and analytical strategies for small and large-scale NHP neuroimaging data. This will improve reproducibility of results, and accelerate the convergence between NHP and human neuroimaging strategies which will ultimately benefit fundamental and translational brain science.
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Affiliation(s)
- Joonas A Autio
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan.
| | - Qi Zhu
- Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, KU Leuven Medical School, Leuven 3000, Belgium; Cognitive Neuroimaging Unit, INSERM, CEA, Université Paris-Saclay, NeuroSpin Center, 91191 Gif/Yvette, France
| | - Xiaolian Li
- Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, KU Leuven Medical School, Leuven 3000, Belgium
| | - Matthew F Glasser
- Departments of Radiology, Washington University School of Medicine, St. Louis, MO, USA; Departments of Neuroscience, Washington University School of Medicine, St. Louis, MO, USA
| | - Caspar M Schwiedrzik
- Neural Circuits and Cognition Lab, European Neuroscience Institute Göttingen - A Joint Initiative of the University Medical Center Göttingen and the Max Planck Society, Grisebachstraße 5, 37077 Göttingen, Germany; Perception and Plasticity Group, German Primate Center - Leibniz Institute for Primate Research, Kellnerweg 4, 37077 Göttingen, Germany
| | - Damien A Fair
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Jan Zimmermann
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Essa Yacoub
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Ravi S Menon
- Centre for Functional and Metabolic Mapping, Western University, London, ON, Canada
| | - David C Van Essen
- Departments of Neuroscience, Washington University School of Medicine, St. Louis, MO, USA
| | - Takuya Hayashi
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | - Brian Russ
- Department of Psychiatry, New York University Langone, New York City, New York, USA; Center for the Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, New York, USA; Department of Neuroscience, Icahn School of Medicine, Mount Sinai, New York City, New York, USA
| | - Wim Vanduffel
- Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, KU Leuven Medical School, Leuven 3000, Belgium; Leuven Brain Institute, KU Leuven, Leuven 3000, Belgium; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA; Department of Radiology, Harvard Medical School, Boston, MA 02144, USA
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70
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Hearne LJ, Mill RD, Keane BP, Repovš G, Anticevic A, Cole MW. Activity flow underlying abnormalities in brain activations and cognition in schizophrenia. SCIENCE ADVANCES 2021; 7:7/29/eabf2513. [PMID: 34261649 PMCID: PMC8279516 DOI: 10.1126/sciadv.abf2513] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 05/28/2021] [Indexed: 05/03/2023]
Abstract
Cognitive dysfunction is a core feature of many brain disorders, including schizophrenia (SZ), and has been linked to aberrant brain activations. However, it is unclear how these activation abnormalities emerge. We propose that aberrant flow of brain activity across functional connectivity (FC) pathways leads to altered activations that produce cognitive dysfunction in SZ. We tested this hypothesis using activity flow mapping, an approach that models the movement of task-related activity between brain regions as a function of FC. Using functional magnetic resonance imaging data from SZ individuals and healthy controls during a working memory task, we found that activity flow models accurately predict aberrant cognitive activations across multiple brain networks. Within the same framework, we simulated a connectivity-based clinical intervention, predicting specific treatments that normalized brain activations and behavior in patients. Our results suggest that dysfunctional task-evoked activity flow is a large-scale network mechanism contributing to cognitive dysfunction in SZ.
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Affiliation(s)
- Luke J Hearne
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, USA.
| | - Ravi D Mill
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, USA
| | - Brian P Keane
- University Behavioral Health Care, Department of Psychiatry, and Center for Cognitive Science, Rutgers University, Piscataway, NJ, USA
- Departments of Psychiatry and Neuroscience, University of Rochester Medical Center, Rochester, NY, USA
| | - Grega Repovš
- Department of Psychology, University of Ljubljana, Aškerčeva 2, Ljubljana SI-1000, Slovenia
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Michael W Cole
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, USA
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71
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Dynamics of fMRI patterns reflect sub-second activation sequences and reveal replay in human visual cortex. Nat Commun 2021; 12:1795. [PMID: 33741933 PMCID: PMC7979874 DOI: 10.1038/s41467-021-21970-2] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Accepted: 02/16/2021] [Indexed: 01/31/2023] Open
Abstract
Neural computations are often fast and anatomically localized. Yet, investigating such computations in humans is challenging because non-invasive methods have either high temporal or spatial resolution, but not both. Of particular relevance, fast neural replay is known to occur throughout the brain in a coordinated fashion about which little is known. We develop a multivariate analysis method for functional magnetic resonance imaging that makes it possible to study sequentially activated neural patterns separated by less than 100 ms with precise spatial resolution. Human participants viewed five images individually and sequentially with speeds up to 32 ms between items. Probabilistic pattern classifiers were trained on activation patterns in visual and ventrotemporal cortex during individual image trials. Applied to sequence trials, probabilistic classifier time courses allow the detection of neural representations and their order. Order detection remains possible at speeds up to 32 ms between items (plus 100 ms per item). The frequency spectrum of the sequentiality metric distinguishes between sub- versus supra-second sequences. Importantly, applied to resting-state data our method reveals fast replay of task-related stimuli in visual cortex. This indicates that non-hippocampal replay occurs even after tasks without memory requirements and shows that our method can be used to detect such spontaneously occurring replay.
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72
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Snoek L, van der Miesen MM, Beemsterboer T, van der Leij A, Eigenhuis A, Steven Scholte H. The Amsterdam Open MRI Collection, a set of multimodal MRI datasets for individual difference analyses. Sci Data 2021; 8:85. [PMID: 33741990 PMCID: PMC7979787 DOI: 10.1038/s41597-021-00870-6] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 02/18/2021] [Indexed: 02/08/2023] Open
Abstract
We present the Amsterdam Open MRI Collection (AOMIC): three datasets with multimodal (3 T) MRI data including structural (T1-weighted), diffusion-weighted, and (resting-state and task-based) functional BOLD MRI data, as well as detailed demographics and psychometric variables from a large set of healthy participants (N = 928, N = 226, and N = 216). Notably, task-based fMRI was collected during various robust paradigms (targeting naturalistic vision, emotion perception, working memory, face perception, cognitive conflict and control, and response inhibition) for which extensively annotated event-files are available. For each dataset and data modality, we provide the data in both raw and preprocessed form (both compliant with the Brain Imaging Data Structure), which were subjected to extensive (automated and manual) quality control. All data is publicly available from the OpenNeuro data sharing platform.
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Affiliation(s)
- Lukas Snoek
- grid.7177.60000000084992262University of Amsterdam, Department of Psychology, Amsterdam, The Netherlands ,grid.458380.20000 0004 0368 8664Spinoza Centre for Neuroimaging, location Roeterseilandcampus, Amsterdam, The Netherlands
| | - Maite M. van der Miesen
- grid.7177.60000000084992262University of Amsterdam, Department of Psychology, Amsterdam, The Netherlands ,grid.5012.60000 0001 0481 6099Present Address: Maastricht University, School for Mental Health and Neuroscience, Department of Anesthesiology, Maastricht, The Netherlands
| | - Tinka Beemsterboer
- grid.7177.60000000084992262University of Amsterdam, Department of Psychology, Amsterdam, The Netherlands ,grid.458380.20000 0004 0368 8664Spinoza Centre for Neuroimaging, location Roeterseilandcampus, Amsterdam, The Netherlands
| | - Andries van der Leij
- grid.7177.60000000084992262University of Amsterdam, Department of Psychology, Amsterdam, The Netherlands ,Present Address: Brainsfirst BV, Amsterdam, The Netherlands ,Neurensics BV, Amsterdam, The Netherlands
| | - Annemarie Eigenhuis
- grid.7177.60000000084992262University of Amsterdam, Department of Psychology, Amsterdam, The Netherlands
| | - H. Steven Scholte
- grid.7177.60000000084992262University of Amsterdam, Department of Psychology, Amsterdam, The Netherlands ,grid.458380.20000 0004 0368 8664Spinoza Centre for Neuroimaging, location Roeterseilandcampus, Amsterdam, The Netherlands ,Neurensics BV, Amsterdam, The Netherlands
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73
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Schneider JM, Hu A, Legault J, Qi Z. Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques. J Vis Exp 2020. [PMID: 32716372 DOI: 10.3791/61474] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022] Open
Abstract
Statistical learning, a fundamental skill to extract regularities in the environment, is often considered a core supporting mechanism of the first language development. While many studies of statistical learning are conducted within a single domain or modality, recent evidence suggests that this skill may differ based on the context in which the stimuli are presented. In addition, few studies investigate learning as it unfolds in real-time, rather focusing on the outcome of learning. In this protocol, we describe an approach for identifying the cognitive and neural basis of statistical learning, within an individual, across domains (linguistic vs. non-linguistic) and sensory modalities (visual and auditory). The tasks are designed to cast as little cognitive demand as possible on participants, making it ideal for young school-aged children and special populations. The web-based nature of the behavioral tasks offers a unique opportunity for us to reach more representative populations nationwide, to estimate effect sizes with greater precision, and to contribute to open and reproducible research. The neural measures provided by the functional magnetic resonance imaging (fMRI) task can inform researchers about the neural mechanisms engaged during statistical learning, and how these may differ across individuals on the basis of domain or modality. Finally, both tasks allow for the measurement of real-time learning, as changes in reaction time to a target stimulus is tracked across the exposure period. The main limitation of using this protocol relates to the hour-long duration of the experiment. Children might need to complete all four statistical learning tasks in multiple sittings. Therefore, the web-based platform is designed with this limitation in mind so that tasks may be disseminated individually. This methodology will allow users to investigate how the process of statistical learning unfolds across and within domains and modalities in children from different developmental backgrounds.
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Affiliation(s)
- Julie M Schneider
- Department of Linguistics and Cognitive Science, University of Delaware;
| | - Anqi Hu
- Department of Linguistics and Cognitive Science, University of Delaware
| | - Jennifer Legault
- Department of Linguistics and Cognitive Science, University of Delaware
| | - Zhenghan Qi
- Department of Linguistics and Cognitive Science, University of Delaware;
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