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Decker AL, Meisler SL, Hubbard NA, Bauer CCC, Leonard J, Grotzinger H, Giebler MA, Torres YC, Imhof A, Romeo R, Gabrieli JDE. Striatal and Behavioral Responses to Reward Vary by Socioeconomic Status in Adolescents. J Neurosci 2024; 44:e1633232023. [PMID: 38253532 PMCID: PMC10941242 DOI: 10.1523/jneurosci.1633-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Revised: 12/11/2023] [Accepted: 12/13/2023] [Indexed: 01/24/2024] Open
Abstract
Disparities in socioeconomic status (SES) lead to unequal access to financial and social support. These disparities are believed to influence reward sensitivity, which in turn are hypothesized to shape how individuals respond to and pursue rewarding experiences. However, surprisingly little is known about how SES shapes reward sensitivity in adolescence. Here, we investigated how SES influenced adolescent responses to reward, both in behavior and the striatum-a brain region that is highly sensitive to reward. We examined responses to both immediate reward (tracked by phasic dopamine) and average reward rate fluctuations (tracked by tonic dopamine) as these distinct signals independently shape learning and motivation. Adolescents (n = 114; 12-14 years; 58 female) performed a gambling task during functional magnetic resonance imaging. We manipulated trial-by-trial reward and loss outcomes, leading to fluctuations between periods of reward scarcity and abundance. We found that a higher reward rate hastened behavioral responses, and increased guess switching, consistent with the idea that reward abundance increases response vigor and exploration. Moreover, immediate reward reinforced previously rewarding decisions (win-stay, lose-switch) and slowed responses (postreward pausing), particularly when rewards were scarce. Notably, lower-SES adolescents slowed down less after rare rewards than higher-SES adolescents. In the brain, striatal activations covaried with the average reward rate across time and showed greater activations during rewarding blocks. However, these striatal effects were diminished in lower-SES adolescents. These findings show that the striatum tracks reward rate fluctuations, which shape decisions and motivation. Moreover, lower SES appears to attenuate reward-driven behavioral and brain responses.
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Affiliation(s)
- Alexandra L Decker
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
| | - Steven L Meisler
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
- Program in Speech and Hearing Bioscience and Technology, Harvard University, Cambridge, Massachusetts 02138
| | - Nicholas A Hubbard
- Department of Psychology, University of Nebraska, Lincoln, Nebraska 68588
| | - Clemens C C Bauer
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
- Department of Psychology, Northeastern University, Boston, Massachusetts 02115
| | - Julia Leonard
- Department of Psychology, Yale University, New Haven, Connecticut 06511
| | - Hannah Grotzinger
- Department of Psychological & Brain Sciences, University of California, Santa Barbara, California 93106
| | | | - Yesi Camacho Torres
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
| | - Andrea Imhof
- Department of Psychology, University of Oregon, Eugene, Oregon 97403
| | - Rachel Romeo
- Departments of Human Development & Quantitative Methodology and Hearing & Speech Sciences, and Program in Neuroscience & Cognitive Science, University of Maryland College Park, Baltimore, Maryland 20742
| | - John D E Gabrieli
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
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2
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Marks RA, Pollack C, Meisler SL, D'Mello AM, Centanni TM, Romeo RR, Wade K, Matejko AA, Ansari D, Gabrieli JDE, Christodoulou JA. Neurocognitive mechanisms of co-occurring math difficulties in dyslexia: Differences in executive function and visuospatial processing. Dev Sci 2024; 27:e13443. [PMID: 37675857 PMCID: PMC10918042 DOI: 10.1111/desc.13443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 08/02/2023] [Accepted: 08/14/2023] [Indexed: 09/08/2023]
Abstract
Children with dyslexia frequently also struggle with math. However, studies of reading disability (RD) rarely assess math skill, and the neurocognitive mechanisms underlying co-occurring reading and math disability (RD+MD) are not clear. The current study aimed to identify behavioral and neurocognitive factors associated with co-occurring MD among 86 children with RD. Within this sample, 43% had co-occurring RD+MD and 22% demonstrated a possible vulnerability in math, while 35% had no math difficulties (RD-Only). We investigated whether RD-Only and RD+MD students differed behaviorally in their phonological awareness, reading skills, or executive functions, as well as in the brain mechanisms underlying word reading and visuospatial working memory using functional magnetic resonance imaging (fMRI). The RD+MD group did not differ from RD-Only on behavioral or brain measures of phonological awareness related to speech or print. However, the RD+MD group demonstrated significantly worse working memory and processing speed performance than the RD-Only group. The RD+MD group also exhibited reduced brain activations for visuospatial working memory relative to RD-Only. Exploratory brain-behavior correlations along a broad spectrum of math ability revealed that stronger math skills were associated with greater activation in bilateral visual cortex. These converging neuro-behavioral findings suggest that poor executive functions in general, including differences in visuospatial working memory, are specifically associated with co-occurring MD in the context of RD. RESEARCH HIGHLIGHTS: Children with reading disabilities (RD) frequently have a co-occurring math disability (MD), but the mechanisms behind this high comorbidity are not well understood. We examined differences in phonological awareness, reading skills, and executive function between children with RD only versus co-occurring RD+MD using behavioral and fMRI measures. Children with RD only versus RD+MD did not differ in their phonological processing, either behaviorally or in the brain. RD+MD was associated with additional behavioral difficulties in working memory, and reduced visual cortex activation during a visuospatial working memory task.
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Affiliation(s)
- Rebecca A Marks
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Communication Sciences and Disorders, MGH Institute of Health Professions, Boston, Massachusetts, USA
| | - Courtney Pollack
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Steven L Meisler
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Program in Speech and Hearing Bioscience and Technology, Harvard University, Cambridge, Massachusetts, USA
| | - Anila M D'Mello
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas, USA
- Peter O'Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center, Dallas, Texas, USA
- Department of Psychology, University of Texas at Dallas, Richardson, Texas, USA
| | - Tracy M Centanni
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Psychology, Texas Christian University, Fort Worth, Texas, USA
| | - Rachel R Romeo
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Human Development and Quantitative Methodology, University of Maryland College Park, College Park, Maryland, USA
| | - Karolina Wade
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Anna A Matejko
- Department of Psychology, University of Western Ontario, London, Ontario, Canada
- Brain and Mind Institute, University of Western Ontario, London, Ontario, Canada
- Department of Psychology, Durham University, Durham, UK
| | - Daniel Ansari
- Department of Psychology, University of Western Ontario, London, Ontario, Canada
- Brain and Mind Institute, University of Western Ontario, London, Ontario, Canada
| | - John D E Gabrieli
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Joanna A Christodoulou
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Communication Sciences and Disorders, MGH Institute of Health Professions, Boston, Massachusetts, USA
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3
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Wang HT, Meisler SL, Sharmarke H, Clarke N, Gensollen N, Markiewicz CJ, Paugam F, Thirion B, Bellec P. Continuous evaluation of denoising strategies in resting-state fMRI connectivity using fMRIPrep and Nilearn. PLoS Comput Biol 2024; 20:e1011942. [PMID: 38498530 PMCID: PMC10977879 DOI: 10.1371/journal.pcbi.1011942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 03/28/2024] [Accepted: 02/23/2024] [Indexed: 03/20/2024] Open
Abstract
Reducing contributions from non-neuronal sources is a crucial step in functional magnetic resonance imaging (fMRI) connectivity analyses. Many viable strategies for denoising fMRI are used in the literature, and practitioners rely on denoising benchmarks for guidance in the selection of an appropriate choice for their study. However, fMRI denoising software is an ever-evolving field, and the benchmarks can quickly become obsolete as the techniques or implementations change. In this work, we present a denoising benchmark featuring a range of denoising strategies, datasets and evaluation metrics for connectivity analyses, based on the popular fMRIprep software. The benchmark prototypes an implementation of a reproducible framework, where the provided Jupyter Book enables readers to reproduce or modify the figures on the Neurolibre reproducible preprint server (https://neurolibre.org/). We demonstrate how such a reproducible benchmark can be used for continuous evaluation of research software, by comparing two versions of the fMRIprep. Most of the benchmark results were consistent with prior literature. Scrubbing, a technique which excludes time points with excessive motion, combined with global signal regression, is generally effective at noise removal. Scrubbing was generally effective, but is incompatible with statistical analyses requiring the continuous sampling of brain signal, for which a simpler strategy, using motion parameters, average activity in select brain compartments, and global signal regression, is preferred. Importantly, we found that certain denoising strategies behave inconsistently across datasets and/or versions of fMRIPrep, or had a different behavior than in previously published benchmarks. This work will hopefully provide useful guidelines for the fMRIprep users community, and highlight the importance of continuous evaluation of research methods.
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Affiliation(s)
- Hao-Ting Wang
- Centre de recherche de l’institut Universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada
| | - Steven L. Meisler
- Program in Speech and Hearing Bioscience and Technology, Harvard University, Massachusetts, United States of America
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Massachusetts, United States of America
| | - Hanad Sharmarke
- Centre de recherche de l’institut Universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada
| | - Natasha Clarke
- Centre de recherche de l’institut Universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada
| | | | | | - François Paugam
- Centre de recherche de l’institut Universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada
- Computer Science and Operations Research Department, Université de Montréal, Montréal, Québec, Canada
- Mila—Institut Québécois d’Intelligence Artificielle, Montréal, Canada
| | | | - Pierre Bellec
- Centre de recherche de l’institut Universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada
- Psychology Department, Université de Montréal, Montréal, Québec, Canada
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4
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Hur KH, Meisler SL, Yassin W, Frederick BB, Kohut SJ. Prefrontal-Limbic Circuitry is Associated with Reward Sensitivity in Nonhuman Primates. Biol Psychiatry 2024:S0006-3223(24)01131-4. [PMID: 38432521 DOI: 10.1016/j.biopsych.2024.02.1011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 02/23/2024] [Accepted: 02/24/2024] [Indexed: 03/05/2024]
Abstract
BACKGROUND Abnormal reward sensitivity is a risk factor for psychiatric disorders, including eating disorders such as overeating and binge-eating disorder, but the brain structural mechanisms underlying it are not completely understood. Here, we sought to investigate the relationship between multi-modal whole-brain structural features and reward sensitivity in nonhuman primates. METHODS Reward sensitivity was evaluated through behavioral economic analysis in which monkeys (adult rhesus macaques, 5 males; 7 females) responded for sweetened-condensed milk (10,30,56%), Gatorade, or water using an operant procedure in which the response requirement increased incrementally across sessions (i.e., fixed ratio 1,3,10,etc.). Subjects were divided into high (N=6) or low (N=6) reward sensitivity groups based on essential value for 30% milk. Multi-modal magnetic resonance imaging was used to measure gray matter volume and white matter microstructure. Brain structural features were compared between groups and their correlations with reward sensitivity for various stimuli was investigated. RESULTS Subjects in the High Sensitivity group had greater dorsolateral prefrontal cortex (dlPFC), centromedial amygdaloid complex (CeMA), and middle cingulate cortex (MCC) volumes compared to subjects in the Low Sensitivity group. Further, High Sensitivity monkeys had lower fractional anisotropy in the left dorsal cingulate bundle connecting CeMA and MCC to the dlPFC, and left superior longitudinal fasciculus 1 connecting the MCC to the dlPFC, compared to monkeys in the Low Sensitivity group. CONCLUSIONS These results suggest that neuroanatomical variation in prefrontal-limbic circuitry is associated with reward sensitivity. These brain structural features may serve as predictive biomarkers for vulnerability to food-based and other reward-related disorders.
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Affiliation(s)
- Kwang-Hyun Hur
- Behavioral Neuroimaging Laboratory, McLean Hospital; Department of Psychiatry, Harvard Medical School
| | - Steven L Meisler
- Program in Speech and Hearing Bioscience and Technology, Harvard Medical School
| | - Walid Yassin
- Behavioral Neuroimaging Laboratory, McLean Hospital; Department of Psychiatry, Harvard Medical School
| | - Blaise B Frederick
- Department of Psychiatry, Harvard Medical School; McLean Imaging Center, McLean Hospital
| | - Stephen J Kohut
- Behavioral Neuroimaging Laboratory, McLean Hospital; Department of Psychiatry, Harvard Medical School; McLean Imaging Center, McLean Hospital.
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5
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Poldrack RA, Markiewicz CJ, Appelhoff S, Ashar YK, Auer T, Baillet S, Bansal S, Beltrachini L, Benar CG, Bertazzoli G, Bhogawar S, Blair RW, Bortoletto M, Boudreau M, Brooks TL, Calhoun VD, Castelli FM, Clement P, Cohen AL, Cohen-Adad J, D'Ambrosio S, de Hollander G, de la Iglesia-Vayá M, de la Vega A, Delorme A, Devinsky O, Draschkow D, Duff EP, DuPre E, Earl E, Esteban O, Feingold FW, Flandin G, Galassi A, Gallitto G, Ganz M, Gau R, Gholam J, Ghosh SS, Giacomel A, Gillman AG, Gleeson P, Gramfort A, Guay S, Guidali G, Halchenko YO, Handwerker DA, Hardcastle N, Herholz P, Hermes D, Honey CJ, Innis RB, Ioanas HI, Jahn A, Karakuzu A, Keator DB, Kiar G, Kincses B, Laird AR, Lau JC, Lazari A, Legarreta JH, Li A, Li X, Love BC, Lu H, Marcantoni E, Maumet C, Mazzamuto G, Meisler SL, Mikkelsen M, Mutsaerts H, Nichols TE, Nikolaidis A, Nilsonne G, Niso G, Norgaard M, Okell TW, Oostenveld R, Ort E, Park PJ, Pawlik M, Pernet CR, Pestilli F, Petr J, Phillips C, Poline JB, Pollonini L, Raamana PR, Ritter P, Rizzo G, Robbins KA, Rockhill AP, Rogers C, Rokem A, Rorden C, Routier A, Saborit-Torres JM, Salo T, Schirner M, Smith RE, Spisak T, Sprenger J, Swann NC, Szinte M, Takerkart S, Thirion B, Thomas AG, Torabian S, Varoquaux G, Voytek B, Welzel J, Wilson M, Yarkoni T, Gorgolewski KJ. The Past, Present, and Future of the Brain Imaging Data Structure (BIDS). ArXiv 2024:arXiv:2309.05768v2. [PMID: 37744469 PMCID: PMC10516110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS.
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Affiliation(s)
| | | | | | - Yoni K Ashar
- University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Tibor Auer
- School of Psychology, University of Surrey, Guildford, UK
- Artificial Intelligence and Informatics group, Rosalind Franklin Institute, Harwell Campus, Didcot, UK
| | - Sylvain Baillet
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Shashank Bansal
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Leandro Beltrachini
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Wales, UK
| | - Christian G Benar
- Aix Marseille Université, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Giacomo Bertazzoli
- Neurophysiology Lab, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
- Center for Mind/Brain Sciences - CIMeC, University of Trento, Rovereto, TN, Italy
- Brigham and Women's Hospital, Boston, MA, USA
- Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | | | - Ross W Blair
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Marta Bortoletto
- Neurophysiology Lab, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | | | - Teon L Brooks
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, USA
| | - Filippo Maria Castelli
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino, Italy
- Bioretics srl, Cesena, Italy
| | - Patricia Clement
- Department of Medical Imaging, Ghent University Hospital, Ghent, Belgium
- Department of Diagnostic Sciences, Ghent University, Ghent, Belgium
| | - Alexander L Cohen
- Department of Neurology, Boston Children's Hospital, Boston, MA, USA
| | | | - Sasha D'Ambrosio
- Dipartimento di Scienze della Salute dell'Università degli Studi di Milano, Milan, Italy
- Department of Clinical and Experimental Epilepsy, University College London, UK
| | - Gilles de Hollander
- Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, Zurich, Switzerland
| | | | | | - Arnaud Delorme
- SCCN, University of California, San Diego, La Jolla CA USA
| | - Orrin Devinsky
- Department of Neurology, NYU Langone Medical Center, New York, NY, USA
| | - Dejan Draschkow
- Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Eugene Paul Duff
- UK Dementia Research Institute, Department of Brain Sciences, Imperial College London, London, UK
| | - Elizabeth DuPre
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Eric Earl
- Intramural Research Program, National Institute of Mental Health, Bethesda, MD, USA
| | - Oscar Esteban
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | | | - Guillaume Flandin
- Wellcome Centre for Human Neuroimaging, University College London, London, England, UK
| | - Anthony Galassi
- Intramural Research Program, National Institute of Mental Health, Bethesda, MD, USA
| | - Giuseppe Gallitto
- Center for Translational Neuro- and Behavioral Sciences, University Medicine Essen, Essen, Germany
- Department of Neurology, University Medicine Essen, Essen, Germany
| | - Melanie Ganz
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
- Neurobiology Research Unit, Copenhagen University Hospital, Copenhagen, Denmark
| | - Rémi Gau
- Origamin Lab, The Neuro, McGill University, Montreal, Quebec, Canada
| | - James Gholam
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Wales, UK
| | | | - Alessio Giacomel
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, England, UK
| | - Ashley G Gillman
- The Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Townsville, Queensland, Australia
| | - Padraig Gleeson
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, England, UK
| | | | - Samuel Guay
- Université de Montréal, Montréal, QC, Canada
| | - Giacomo Guidali
- Department of Psychology & NeuroMI - Milan Centre for Neuroscience, University of Milano-Bicocca, Milan, Italy
| | - Yaroslav O Halchenko
- Center for Open Neuroscience, Department of Psychological and Brain Sciences, Dartmouth College, NH, USA
| | - Daniel A Handwerker
- Intramural Research Program, National Institute of Mental Health, Bethesda, MD, USA
| | - Nell Hardcastle
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Peer Herholz
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada
| | - Dora Hermes
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
| | - Christopher J Honey
- Department of Psychological & Brain Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Robert B Innis
- Intramural Research Program, National Institute of Mental Health, Bethesda, MD, USA
| | - Horea-Ioan Ioanas
- Center for Open Neuroscience, Department of Psychological and Brain Sciences, Dartmouth College, NH, USA
| | - Andrew Jahn
- Functional MRI Laboratory, University of Michigan, Ann Arbor, MI, USA
| | - Agah Karakuzu
- NeuroPoly Lab, Polytechnique Montréal, Montréal, Quebec, Canada
| | - David B Keator
- Change Your Brain Change Your Life Foundation, Costa Mesa, CA, USA
- Amen Clinics, Costa Mesa, CA, USA
- Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, CA, USA
| | - Gregory Kiar
- Center for Data Analytics, Innovation, and Rigor, Child Mind Institute, New York, NY USA
| | - Balint Kincses
- Center for Translational Neuro- and Behavioral Sciences, University Medicine Essen, Essen, Germany
- Department of Neurology, University Medicine Essen, Essen, Germany
| | - Angela R Laird
- Department of Physics, Florida International University, Miami, FL, USA
| | - Jonathan C Lau
- Department of Clinical Neurological Sciences, Western University, London, Ontario, Canada
| | - Alberto Lazari
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Jon Haitz Legarreta
- Department of Radiology, Brigham and Women's Hospital, Mass General Brigham/Harvard Medical School, Boston, MA, USA
| | - Adam Li
- Columbia University, New York, NY, USA
| | - Xiangrui Li
- Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, Columbus, OH, USA
| | | | - Hanzhang Lu
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Eleonora Marcantoni
- School for Psychology and Neuroscience and Centre for Cognitive Neuroimaging, University of Glasgow, Glasgow
| | - Camille Maumet
- Inria, Univ Rennes, CNRS, Inserm, IRISA UMR 6074, Empenn ERL U 1228, Rennes, France
| | - Giacomo Mazzamuto
- National Research Council - National Institute of Optics (CNR-INO), Florence, Italy
| | - Steven L Meisler
- Program in Speech and Hearing Bioscience and Technology, Harvard University, Cambridge, MA, USA
| | - Mark Mikkelsen
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Henk Mutsaerts
- Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
| | - Thomas E Nichols
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Aki Nikolaidis
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA
| | - Gustav Nilsonne
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Swedish National Data Service, Gothenburg University, Gothenburg, Sweden
| | | | - Martin Norgaard
- Intramural Research Program, National Institute of Mental Health, Bethesda, MD, USA
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Thomas W Okell
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Robert Oostenveld
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
- NatMEG, Karolinska Institutet, Stockholm, Sweden
| | - Eduard Ort
- Heinrich Heine University, Department of Biological Psychology of Decision Making, Düsseldorf, Germany
| | | | - Mateusz Pawlik
- Paris-Lodron-University of Salzburg, Department of Psychology, Centre for Cognitive Neuroscience, Salzburg, Austria
| | - Cyril R Pernet
- Neurobiology Research Unit, Copenhagen University Hospital, Copenhagen, Denmark
| | | | - Jan Petr
- Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiopharmaceutical Cancer Research, Dresden, Germany
| | | | - Jean-Baptiste Poline
- Neuro Data Science ORIGAMI Laboratory, McConnell Brain Imaging Centre, Faculty of Medicine, McGill University, Montréal, Canada
| | - Luca Pollonini
- Department of Engineering Technology, University of Houston, Houston, TX
- Basque Center on Cognition, Brain and Language, Donostia-San Sebastián, Spain
| | | | - Petra Ritter
- Berlin Institute of Health at Charité, Universitätsmedizin Berlin, Charitéplatz 1, Berlin 10117, Germany
- Department of Neurology with Experimental Neurology, Charité, Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Charitéplatz 1, Berlin 10117, Germany
- Bernstein Focus State Dependencies of Learning and Bernstein Center for Computational Neuroscience, Berlin, Germany
- Einstein Center for Neuroscience Berlin, Charitéplatz 1, Berlin 10117, Germany
- Einstein Center Digital Future, Wilhelmstraße 67, Berlin 10117, Germany
| | - Gaia Rizzo
- Invicro, London, UK
- Division of Brain Sciences, Imperial College London, London, UK
| | - Kay A Robbins
- Department of Computer Science, University of Texas at San Antonio, San Antonio, TX, USA
| | - Alexander P Rockhill
- Department of Neurosurgery, Oregon Health & Science University, Portland, OR, USA
| | - Christine Rogers
- McGill Centre for Integrative Neuroscience (MCIN), Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Ariel Rokem
- University of Washington, Department of Psychology and eScience Institute, Seattle, WA, USA
| | - Chris Rorden
- University of South Carolina, Department of Psychology, Columbia, SC, USA
| | | | | | - Taylor Salo
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael Schirner
- Berlin Institute of Health at Charité, Universitätsmedizin Berlin, Charitéplatz 1, Berlin 10117, Germany
- Department of Neurology with Experimental Neurology, Charité, Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Charitéplatz 1, Berlin 10117, Germany
- Bernstein Focus State Dependencies of Learning and Bernstein Center for Computational Neuroscience, Berlin, Germany
- Einstein Center for Neuroscience Berlin, Charitéplatz 1, Berlin 10117, Germany
- Einstein Center Digital Future, Wilhelmstraße 67, Berlin 10117, Germany
| | - Robert E Smith
- The Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia
- The Florey Department of Neuroscience and Mental Heath, The University of Melbourne, Parkville, Victoria, Australia
| | - Tamas Spisak
- Center for Translational Neuro- and Behavioral Sciences, University Medicine Essen, Essen, Germany
- Institute for Diagnostic and Interventional Radiology and Neuroradiology, University Medicine Essen, Essen, Germany
| | - Julia Sprenger
- Institut de Neurosciences de la Timone (INT), UMR7289, CNRS, Aix-Marseille Université, France
| | - Nicole C Swann
- University of Oregon, Department of Human Physiology, Eugene, OR, USA
| | - Martin Szinte
- Institut de Neurosciences de la Timone (INT), UMR7289, CNRS, Aix-Marseille Université, France
| | - Sylvain Takerkart
- Institut de Neurosciences de la Timone (INT), UMR7289, CNRS, Aix-Marseille Université, France
| | | | - Adam G Thomas
- Intramural Research Program, National Institute of Mental Health, Bethesda, MD, USA
| | | | | | - Bradley Voytek
- Department of Cognitive Science, Halıcıoğlu Data Science Institute, and Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA, USA
| | | | - Martin Wilson
- University of Birmingham, Centre for Human Brain Health and School of Psychology, Birmingham, UK
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6
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Yu T, Cai LY, Torrisi S, Vu AT, Morgan VL, Goodale SE, Ramadass K, Meisler SL, Lv J, Warren AEL, Englot DJ, Cutting L, Chang C, Gore JC, Landman BA, Schilling KG. Distortion correction of functional MRI without reverse phase encoding scans or field maps. Magn Reson Imaging 2023; 103:18-27. [PMID: 37400042 PMCID: PMC10528451 DOI: 10.1016/j.mri.2023.06.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 06/27/2023] [Accepted: 06/28/2023] [Indexed: 07/05/2023]
Abstract
Functional magnetic resonance images (fMRI) acquired using echo planar sequences typically suffer from spatial distortions due to susceptibility induced off-resonance fields, which may cause geometric mismatch with structural images and affect subsequent quantification and localization of brain function. State-of-the art distortion correction methods (for example, using FSL's topup or AFNI's 3dQwarp algorithms) require the collection of additional scans - either field maps or images with reverse phase encoding directions (i.e., blip-up/blip-down acquisitions) - to estimate and correct distortions. However, not all imaging protocols acquire these additional data and thus cannot take advantage of these post-acquisition corrections. In this study, we aim to enable state-of-the art processing of historical or limited datasets that do not include specific sequences for distortion correction by using only the acquired functional data and a single commonly acquired structural image. To achieve this, we synthesize an undistorted image with contrast similar to the fMRI data and use the non-distorted synthetic image as an anatomical target for distortion correction. We evaluate the efficacy of this approach, named SynBOLD-DisCo (Synthetic BOLD contrast for Distortion Correction), and show that this distortion correction process yields fMRI data that are geometrically similar to non-distorted structural images, with distortion correction virtually equivalent to acquisitions that do contain both blip-up/blip-down images. Our method is available as a Singularity container, source code, and an executable trained model to facilitate evaluation and integration into existing fMRI preprocessing pipelines.
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Affiliation(s)
- Tian Yu
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Leon Y Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Salvatore Torrisi
- San Francisco VA Health Care System, San Francisco, CA, USA; Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
| | - An Thanh Vu
- San Francisco VA Health Care System, San Francisco, CA, USA; Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
| | - Victoria L Morgan
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Sarah E Goodale
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Karthik Ramadass
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Steven L Meisler
- Program in Speech and Hearing Bioscience and Technology, Harvard University, Cambridge, MA, USA
| | - Jinglei Lv
- School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, NSW, Australia; Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia
| | - Aaron E L Warren
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Dario J Englot
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA; Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Laurie Cutting
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Special Education, Vanderbilt University, Nashville, TN, USA; Department of Psychology, Vanderbilt University, Nashville, TN, USA
| | - Catie Chang
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - John C Gore
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Bennett A Landman
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Kurt G Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA.
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7
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Wang HT, Meisler SL, Sharmarke H, Clarke N, Gensollen N, Markiewicz CJ, Paugam F, Thirion B, Bellec P. Continuous Evaluation of Denoising Strategies in Resting-State fMRI Connectivity Using fMRIPrep and Nilearn. bioRxiv 2023:2023.04.18.537240. [PMID: 37131781 PMCID: PMC10153168 DOI: 10.1101/2023.04.18.537240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Reducing contributions from non-neuronal sources is a crucial step in functional magnetic resonance imaging (fMRI) connectivity analyses. Many viable strategies for denoising fMRI are used in the literature, and practitioners rely on denoising benchmarks for guidance in the selection of an appropriate choice for their study. However, fMRI denoising software is an ever-evolving field, and the benchmarks can quickly become obsolete as the techniques or implementations change. In this work, we present a denoising benchmark featuring a range of denoising strategies, datasets and evaluation metrics for connectivity analyses, based on the popular fMRIprep software. The benchmark is implemented in a fully reproducible framework, where the provided research objects enable readers to reproduce or modify core computations, as well as the figures of the article using the Jupyter Book project and the Neurolibre reproducible preprint server (https://neurolibre.org/). We demonstrate how such a reproducible benchmark can be used for continuous evaluation of research software, by comparing two versions of the fMRIprep software package. The majority of benchmark results were consistent with prior literature. Scrubbing, a technique which excludes time points with excessive motion, combined with global signal regression, is generally effective at noise removal. Scrubbing however disrupts the continuous sampling of brain images and is incompatible with some statistical analyses, e.g. auto-regressive modeling. In this case, a simple strategy using motion parameters, average activity in select brain compartments, and global signal regression should be preferred. Importantly, we found that certain denoising strategies behave inconsistently across datasets and/or versions of fMRIPrep, or had a different behavior than in previously published benchmarks. This work will hopefully provide useful guidelines for the fMRIprep users community, and highlight the importance of continuous evaluation of research methods. Our reproducible benchmark infrastructure will facilitate such continuous evaluation in the future, and may also be applied broadly to different tools or even research fields.
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Affiliation(s)
- Hao-Ting Wang
- Centre de recherche de l'institut Universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada
| | - Steven L Meisler
- Program in Speech and Hearing Bioscience and Technology, Harvard University, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, MA, USA
| | - Hanad Sharmarke
- Centre de recherche de l'institut Universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada
| | - Natasha Clarke
- Centre de recherche de l'institut Universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada
| | | | | | - Fraçois Paugam
- Centre de recherche de l'institut Universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada
- Computer Science and Operations Research Department, Université de Montréal, Montréal, Québec, Canada
- Mila - Institut Québécois d'Intelligence Artificielle, Montréal, Canada
| | | | - Pierre Bellec
- Centre de recherche de l'institut Universitaire de gériatrie de Montréal (CRIUGM), Montréal, Québec, Canada
- Psychology Department, Université de Montréal, Montréal, Québec, Canada
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8
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Zhao C, Tapera TM, Bagautdinova J, Bourque J, Covitz S, Gur RE, Gur RC, Larsen B, Mehta K, Meisler SL, Murtha K, Muschelli J, Roalf DR, Sydnor VJ, Valcarcel AM, Shinohara RT, Cieslak M, Satterthwaite TD. ModelArray: An R package for statistical analysis of fixel-wise data. Neuroimage 2023; 271:120037. [PMID: 36931330 PMCID: PMC10119782 DOI: 10.1016/j.neuroimage.2023.120037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 03/08/2023] [Accepted: 03/14/2023] [Indexed: 03/17/2023] Open
Abstract
Diffusion MRI is the dominant non-invasive imaging method used to characterize white matter organization in health and disease. Increasingly, fiber-specific properties within a voxel are analyzed using fixels. While tools for conducting statistical analyses of fixel-wise data exist, currently available tools support only a limited number of statistical models. Here we introduce ModelArray, an R package for mass-univariate statistical analysis of fixel-wise data. At present, ModelArray supports linear models as well as generalized additive models (GAMs), which are particularly useful for studying nonlinear effects in lifespan data. In addition, ModelArray also aims for scalable analysis. With only several lines of code, even large fixel-wise datasets can be analyzed using a standard personal computer. Detailed memory profiling revealed that ModelArray required only limited memory even for large datasets. As an example, we applied ModelArray to fixel-wise data derived from diffusion images acquired as part of the Philadelphia Neurodevelopmental Cohort (n = 938). ModelArray revealed anticipated nonlinear developmental effects in white matter. Moving forward, ModelArray is supported by an open-source software development model that can incorporate additional statistical models and other imaging data types. Taken together, ModelArray provides a flexible and efficient platform for statistical analysis of fixel-wise data.
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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, 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Tinashe M Tapera
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Joëlle Bagautdinova
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Josiane Bourque
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sydney Covitz
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Raquel E Gur
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ruben C Gur
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Bart Larsen
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kahini Mehta
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Steven L Meisler
- Program in Speech and Hearing Bioscience and Technology, Harvard University, Cambridge, MA 02139, USA
| | - Kristin Murtha
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - John Muschelli
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - David R Roalf
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Valerie J Sydnor
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Alessandra M Valcarcel
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Matthew Cieslak
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Theodore D Satterthwaite
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA.
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9
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D'Mello AM, Frosch IR, Meisler SL, Grotzinger H, Perrachione TK, Gabrieli JDE. Diminished Repetition Suppression Reveals Selective and Systems-Level Face Processing Differences in ASD. J Neurosci 2023; 43:1952-1962. [PMID: 36759192 PMCID: PMC10027049 DOI: 10.1523/jneurosci.0608-22.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 01/24/2023] [Accepted: 01/28/2023] [Indexed: 02/11/2023] Open
Abstract
Repeated exposure to a stimulus results in reduced neural response, or repetition suppression, in brain regions responsible for processing that stimulus. This rapid accommodation to repetition is thought to underlie learning, stimulus selectivity, and strengthening of perceptual expectations. Importantly, reduced sensitivity to repetition has been identified in several neurodevelopmental, learning, and psychiatric disorders, including autism spectrum disorder (ASD), a neurodevelopmental disorder characterized by challenges in social communication and repetitive behaviors and restricted interests. Reduced ability to exploit or learn from repetition in ASD is hypothesized to contribute to sensory hypersensitivities, and parallels several theoretical frameworks claiming that ASD individuals show difficulty using regularities in the environment to facilitate behavior. Using fMRI in autistic and neurotypical human adults (females and males), we assessed the status of repetition suppression across two modalities (vision, audition) and with four stimulus categories (faces, objects, printed words, and spoken words). ASD individuals showed domain-specific reductions in repetition suppression for face stimuli only, but not for objects, printed words, or spoken words. Reduced repetition suppression for faces was associated with greater challenges in social communication in ASD. We also found altered functional connectivity between atypically adapting cortical regions and higher-order face recognition regions, and microstructural differences in related white matter tracts in ASD. These results suggest that fundamental neural mechanisms and system-wide circuits are selectively altered for face processing in ASD and enhance our understanding of how disruptions in the formation of stable face representations may relate to higher-order social communication processes.SIGNIFICANCE STATEMENT A common finding in neuroscience is that repetition results in plasticity in stimulus-specific processing regions, reflecting selectivity and adaptation (repetition suppression [RS]). RS is reduced in several neurodevelopmental and psychiatric conditions including autism spectrum disorder (ASD). Theoretical frameworks of ASD posit that reduced adaptation may contribute to associated challenges in social communication and sensory processing. However, the scope of RS differences in ASD is unknown. We examined RS for multiple categories across visual and auditory domains (faces, objects, printed words, spoken words) in autistic and neurotypical individuals. We found reduced RS in ASD for face stimuli only and altered functional connectivity and white matter microstructure between cortical face-recognition areas. RS magnitude correlated with social communication challenges among autistic individuals.
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Affiliation(s)
- Anila M D'Mello
- McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139
| | - Isabelle R Frosch
- McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139
| | - Steven L Meisler
- Program in Speech and Hearing Bioscience and Technology, Harvard University, Cambridge, Massachusetts, 02115
| | - Hannah Grotzinger
- McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139
| | - Tyler K Perrachione
- Department of Speech, Language, and Hearing Sciences, Boston University, Boston, Massachusetts 02215
| | - John D E Gabrieli
- McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139
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10
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Meisler SL, Gabrieli JDE. A Large-Scale Investigation of White Matter Microstructural Associations with Reading Ability. Neuroimage 2022; 249:118909. [PMID: 35033675 PMCID: PMC8919267 DOI: 10.1016/j.neuroimage.2022.118909] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 01/05/2022] [Accepted: 01/12/2022] [Indexed: 01/18/2023] Open
Abstract
Reading involves the functioning of a widely distributed brain network,
and white matter tracts are responsible for transmitting information between
constituent network nodes. Several studies have analyzed fiber bundle
microstructural properties to shed insights into the neural basis of reading
abilities and disabilities. Findings have been inconsistent, potentially due to
small sample sizes and varying methodology. To address this, we analyzed a large
data set of 686 children ages 5–18 using state-of-the-art neuroimaging
acquisitions and processing techniques. We searched for associations between
fractional anisotropy (FA) and single-word and single-nonword reading skills in
children with diverse reading abilities across multiple tracts previously
thought to contribute to reading. We also looked for group differences in tract
FA between typically reading children and children with reading disabilities. FA
of the white matter increased with age across all participants. There were no
significant correlations between overall reading abilities and tract FAs across
all children, and no significant group differences in tract FA between children
with and without reading disabilities. There were associations between FA and
nonword reading ability in older children (ages 9 and above). Higher FA in the
right superior longitudinal fasciculus (SLF) and left inferior cerebellar
peduncle (ICP) correlated with better nonword reading skills. These results
suggest that letter-sound correspondence skills, as measured by nonword reading,
are associated with greater white matter coherence among older children in these
two tracts, as indexed by higher FA.
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Affiliation(s)
- Steven L Meisler
- Program in Speech and Hearing Bioscience and Technology, Harvard University, 43 Vassar Street, Bldg. 46, Room 4033 Cambridge, MA, 02139, USA.
| | - John D E Gabrieli
- Department of Brain and Cognitive Sciences and McGovern Institute for Brain Research, Massachusetts Institute of Technology, 43 Vassar Street, Bldg. 46, Room 4033 Cambridge, MA, 02139, USA.
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11
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Edlow BL, Barra ME, Zhou DW, Foulkes AS, Snider SB, Threlkeld ZD, Chakravarty S, Kirsch JE, Chan ST, Meisler SL, Bleck TP, Fins JJ, Giacino JT, Hochberg LR, Solt K, Brown EN, Bodien YG. Personalized Connectome Mapping to Guide Targeted Therapy and Promote Recovery of Consciousness in the Intensive Care Unit. Neurocrit Care 2020; 33:364-375. [PMID: 32794142 DOI: 10.1007/s12028-020-01062-7] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Accepted: 04/18/2020] [Indexed: 01/05/2023]
Abstract
There are currently no therapies proven to promote early recovery of consciousness in patients with severe brain injuries in the intensive care unit (ICU). For patients whose families face time-sensitive, life-or-death decisions, treatments that promote recovery of consciousness are needed to reduce the likelihood of premature withdrawal of life-sustaining therapy, facilitate autonomous self-expression, and increase access to rehabilitative care. Here, we present the Connectome-based Clinical Trial Platform (CCTP), a new paradigm for developing and testing targeted therapies that promote early recovery of consciousness in the ICU. We report the protocol for STIMPACT (Stimulant Therapy Targeted to Individualized Connectivity Maps to Promote ReACTivation of Consciousness), a CCTP-based trial in which intravenous methylphenidate will be used for targeted stimulation of dopaminergic circuits within the subcortical ascending arousal network (ClinicalTrials.gov NCT03814356). The scientific premise of the CCTP and the STIMPACT trial is that personalized brain network mapping in the ICU can identify patients whose connectomes are amenable to neuromodulation. Phase 1 of the STIMPACT trial is an open-label, safety and dose-finding study in 22 patients with disorders of consciousness caused by acute severe traumatic brain injury. Patients in Phase 1 will receive escalating daily doses (0.5-2.0 mg/kg) of intravenous methylphenidate over a 4-day period and will undergo resting-state functional magnetic resonance imaging and electroencephalography to evaluate the drug's pharmacodynamic properties. The primary outcome measure for Phase 1 relates to safety: the number of drug-related adverse events at each dose. Secondary outcome measures pertain to pharmacokinetics and pharmacodynamics: (1) time to maximal serum concentration; (2) serum half-life; (3) effect of the highest tolerated dose on resting-state functional MRI biomarkers of connectivity; and (4) effect of each dose on EEG biomarkers of cerebral cortical function. Predetermined safety and pharmacodynamic criteria must be fulfilled in Phase 1 to proceed to Phase 2A. Pharmacokinetic data from Phase 1 will also inform the study design of Phase 2A, where we will test the hypothesis that personalized connectome maps predict therapeutic responses to intravenous methylphenidate. Likewise, findings from Phase 2A will inform the design of Phase 2B, where we plan to enroll patients based on their personalized connectome maps. By selecting patients for clinical trials based on a principled, mechanistic assessment of their neuroanatomic potential for a therapeutic response, the CCTP paradigm and the STIMPACT trial have the potential to transform the therapeutic landscape in the ICU and improve outcomes for patients with severe brain injuries.
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Affiliation(s)
- Brian L Edlow
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. .,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.
| | - Megan E Barra
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,Department of Pharmacy, Massachusetts General Hospital, Boston, MA, USA
| | - David W Zhou
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Andrea S Foulkes
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Samuel B Snider
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Zachary D Threlkeld
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,Department of Neurology and Neurological Sciences, Stanford School of Medicine, Stanford, CA, USA
| | - Sourish Chakravarty
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA.,The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - John E Kirsch
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Suk-Tak Chan
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Steven L Meisler
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Thomas P Bleck
- Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Joseph J Fins
- Division of Medical Ethics and Consortium for the Advanced Study of Brain Injury (CASBI), Weill Cornell Medical College, New York, NY, USA.,The Rockefeller University, New York, NY, USA.,Solomon Center for Health Law and Policy, Yale Law School, New Haven, CT, USA
| | - Joseph T Giacino
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Boston, MA, USA
| | - Leigh R Hochberg
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,School of Engineering and Carney Institute for Brain Science, Brown University, Providence, RI, USA.,Veterans Affairs RR&D Center for Neurorestoration and Neurotechnology, VA Medical Center, Providence, RI, USA
| | - Ken Solt
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Emery N Brown
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.,Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA.,The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Yelena G Bodien
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Boston, MA, USA
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Abstract
BACKGROUND Neuroscientists routinely seek to identify and remove noisy or artifactual observations from their data. They do so with the belief that removing such data improves power to detect relations between neural activity and behavior, which are often subtle and can be overwhelmed by noise. Whereas standard methods can exclude certain well-defined noise sources (e.g., 50/60 Hz electrical noise), in many situations there is not a clear difference between noise and signals so it is not obvious how to separate the two. Here we ask whether methods routinely used to "clean" human electrophysiological recordings lead to greater power to detect brain-behavior relations. NEW METHOD This, to the authors' knowledge, is the first large-scale simultaneous evaluation of multiple commonly used methods for removing noise from intracranial EEG recordings. RESULTS We find that several commonly used data cleaning methods (automated methods based on statistical signal properties and manual methods based on expert review) do not increase the power to detect univariate and multivariate electrophysiological biomarkers of successful episodic memory encoding, a well-characterized broadband pattern of neural activity observed across the brain. COMPARISON WITH EXISTING METHODS Researchers may be more likely to increase statistical power to detect physiological phenomena of interest by allocating resources away from cleaning noisy data and toward collecting more within-patient observations. CONCLUSIONS These findings highlight the challenge of partitioning signal and noise in the analysis of brain-behavior relations, and suggest increasing sample size and numbers of observations, rather than data cleaning, as the best approach to improving statistical power.
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Affiliation(s)
- Steven L Meisler
- Dept. of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Michael J Kahana
- Dept. of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Youssef Ezzyat
- Dept. of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA.
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