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Chen C, Dupré la Tour T, Gallant JL, Klein D, Deniz F. The cortical representation of language timescales is shared between reading and listening. Commun Biol 2024; 7:284. [PMID: 38454134 DOI: 10.1038/s42003-024-05909-z] [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: 01/25/2023] [Accepted: 02/12/2024] [Indexed: 03/09/2024] Open
Abstract
Language comprehension involves integrating low-level sensory inputs into a hierarchy of increasingly high-level features. Prior work studied brain representations of different levels of the language hierarchy, but has not determined whether these brain representations are shared between written and spoken language. To address this issue, we analyze fMRI BOLD data that were recorded while participants read and listened to the same narratives in each modality. Levels of the language hierarchy are operationalized as timescales, where each timescale refers to a set of spectral components of a language stimulus. Voxelwise encoding models are used to determine where different timescales are represented across the cerebral cortex, for each modality separately. These models reveal that between the two modalities timescale representations are organized similarly across the cortical surface. Our results suggest that, after low-level sensory processing, language integration proceeds similarly regardless of stimulus modality.
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Affiliation(s)
- Catherine Chen
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA.
| | - Tom Dupré la Tour
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
| | - Jack L Gallant
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
| | - Daniel Klein
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
| | - Fatma Deniz
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA.
- Institute of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany.
- Bernstein Center for Computational Neuroscience, Berlin, Germany.
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2
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Dabbagh A, Horn U, Kaptan M, Mildner T, Müller R, Lepsien J, Weiskopf N, Brooks JCW, Finsterbusch J, Eippert F. Reliability of task-based fMRI in the dorsal horn of the human spinal cord. bioRxiv 2023:2023.12.22.572825. [PMID: 38187724 PMCID: PMC10769329 DOI: 10.1101/2023.12.22.572825] [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: 01/09/2024]
Abstract
The application of functional magnetic resonance imaging (fMRI) to the human spinal cord is still a relatively small field of research and faces many challenges. Here we aimed to probe the limitations of task-based spinal fMRI at 3T by investigating the reliability of spinal cord blood oxygen level dependent (BOLD) responses to repeated nociceptive stimulation across two consecutive days in 40 healthy volunteers. We assessed the test-retest reliability of subjective ratings, autonomic responses, and spinal cord BOLD responses to short heat pain stimuli (1s duration) using the intraclass correlation coefficient (ICC). At the group level, we observed robust autonomic responses as well as spatially specific spinal cord BOLD responses at the expected location, but no spatial overlap in BOLD response patterns across days. While autonomic indicators of pain processing showed good-to-excellent reliability, both β-estimates and z-scores of task-related BOLD responses showed poor reliability across days in the target region (gray matter of the ipsilateral dorsal horn). When taking into account the sensitivity of gradient-echo echo planar imaging (GE-EPI) to draining vein signals by including the venous plexus in the analysis, we observed BOLD responses with good reliability across days. Taken together, these results demonstrate that heat pain stimuli as short as one second are able to evoke a robust and spatially specific BOLD response, which is however strongly variable within participants across time, resulting in low reliability in the dorsal horn gray matter. Further improvements in data acquisition and analysis techniques are thus necessary before event-related spinal cord fMRI as used here can be reliably employed in longitudinal designs or clinical settings.
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Affiliation(s)
- Alice Dabbagh
- Max Planck Research Group Pain Perception, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Ulrike Horn
- Max Planck Research Group Pain Perception, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Merve Kaptan
- Max Planck Research Group Pain Perception, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, CA, USA
| | - Toralf Mildner
- Methods & Development Group Nuclear Magnetic Resonance, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Roland Müller
- Methods & Development Group Nuclear Magnetic Resonance, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Jöran Lepsien
- Methods & Development Group Nuclear Magnetic Resonance, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, University of Leipzig, Leipzig, Germany
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, UK
| | - Jonathan C W Brooks
- School of Psychology, University of East Anglia Wellcome Wolfson Brain Imaging Centre (UWWBIC), Norwich, United Kingdom
| | - Jürgen Finsterbusch
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Falk Eippert
- Max Planck Research Group Pain Perception, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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3
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Chen C, Dupré la Tour T, Gallant JL, Klein D, Deniz F. The Cortical Representation of Language Timescales is Shared between Reading and Listening. bioRxiv 2023:2023.01.06.522601. [PMID: 37577530 PMCID: PMC10418083 DOI: 10.1101/2023.01.06.522601] [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] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Language comprehension involves integrating low-level sensory inputs into a hierarchy of increasingly high-level features. Prior work studied brain representations of different levels of the language hierarchy, but has not determined whether these brain representations are shared between written and spoken language. To address this issue, we analyzed fMRI BOLD data recorded while participants read and listened to the same narratives in each modality. Levels of the language hierarchy were operationalized as timescales, where each timescale refers to a set of spectral components of a language stimulus. Voxelwise encoding models were used to determine where different timescales are represented across the cerebral cortex, for each modality separately. These models reveal that between the two modalities timescale representations are organized similarly across the cortical surface. Our results suggest that, after low-level sensory processing, language integration proceeds similarly regardless of stimulus modality.
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Affiliation(s)
- Catherine Chen
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, USA
| | - Tom Dupré la Tour
- Helen Wills Neuroscience Institute, University of California, Berkeley, USA
| | - Jack L. Gallant
- Helen Wills Neuroscience Institute, University of California, Berkeley, USA
| | - Dan Klein
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, USA
| | - Fatma Deniz
- Helen Wills Neuroscience Institute, University of California, Berkeley, USA
- Institute of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
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4
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Van AN, Montez DF, Laumann TO, Suljic V, Madison T, Baden NJ, Ramirez-Perez N, Scheidter KM, Monk JS, Whiting FI, Adeyemo B, Chauvin RJ, Krimmel SR, Metoki A, Rajesh A, Roland JL, Salo T, Wang A, Weldon KB, Sotiras A, Shimony JS, Kay BP, Nelson SM, Tervo-Clemmens B, Marek SA, Vizioli L, Yacoub E, Satterthwaite TD, Gordon EM, Fair DA, Tisdall MD, Dosenbach NU. Framewise multi-echo distortion correction for superior functional MRI. bioRxiv 2023:2023.11.28.568744. [PMID: 38077010 PMCID: PMC10705259 DOI: 10.1101/2023.11.28.568744] [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] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Functional MRI (fMRI) data are severely distorted by magnetic field (B0) inhomogeneities which currently must be corrected using separately acquired field map data. However, changes in the head position of a scanning participant across fMRI frames can cause changes in the B0 field, preventing accurate correction of geometric distortions. Additionally, field maps can be corrupted by movement during their acquisition, preventing distortion correction altogether. In this study, we use phase information from multi-echo (ME) fMRI data to dynamically sample distortion due to fluctuating B0 field inhomogeneity across frames by acquiring multiple echoes during a single EPI readout. Our distortion correction approach, MEDIC (Multi-Echo DIstortion Correction), accurately estimates B0 related distortions for each frame of multi-echo fMRI data. Here, we demonstrate that MEDIC's framewise distortion correction produces improved alignment to anatomy and decreases the impact of head motion on resting-state functional connectivity (RSFC) maps, in higher motion data, when compared to the prior gold standard approach (i.e., TOPUP). Enhanced framewise distortion correction with MEDIC, without the requirement for field map collection, furthers the advantage of multi-echo over single-echo fMRI.
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Affiliation(s)
- Andrew N. Van
- Department of Biomedical Engineering, Washington University in St. Louis, MO 63130
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - David F. Montez
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110
| | - Timothy O. Laumann
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110
| | - Vahdeta Suljic
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Thomas Madison
- Institute of Child Development, University of Minnesota Medical School, Minneapolis, MN 55455
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN 55455
| | - Noah J. Baden
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | | | - Kristen M. Scheidter
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Julia S. Monk
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Forrest I. Whiting
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Babatunde Adeyemo
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Roselyne J. Chauvin
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Samuel R. Krimmel
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Athanasia Metoki
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Aishwarya Rajesh
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
| | - Jarod L. Roland
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO 63110
| | - Taylor Salo
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104
| | - Anxu Wang
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
- Division of Computation and Data Science, Washington University School of Medicine, St. Louis, MO 63110
| | - Kimberly B. Weldon
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN 55455
| | - Aristeidis Sotiras
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
- Institute for Informatics, Data Science & Biostatistics, Washington University School of Medicine, St. Louis, MO 63130
| | - Joshua S. Shimony
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
| | - Benjamin P. Kay
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Steven M. Nelson
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN 55455
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN 55455
| | - Brenden Tervo-Clemmens
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN 55455
- Department of Psychiatry & Behavioral Sciences, University of Minnesota Medical School, Minneapolis, MN 55455
| | - Scott A. Marek
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
| | - Luca Vizioli
- Center for Magnetic Resonance Research, University of Minnesota Medical School, Minneapolis, MN 55455
| | - Essa Yacoub
- Center for Magnetic Resonance Research, University of Minnesota Medical School, Minneapolis, MN 55455
| | - Theodore D. Satterthwaite
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104
| | - Evan M. Gordon
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
| | - Damien A. Fair
- Institute of Child Development, University of Minnesota Medical School, Minneapolis, MN 55455
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN 55455
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN 55455
| | - M. Dylan Tisdall
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Nico U.F. Dosenbach
- Department of Biomedical Engineering, Washington University in St. Louis, MO 63130
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110
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Reddy NA, Zvolanek KM, Moia S, Caballero-Gaudes C, Bright MG. Denoising task-correlated head motion from motor-task fMRI data with multi-echo ICA. bioRxiv 2023:2023.07.19.549746. [PMID: 37503125 PMCID: PMC10370165 DOI: 10.1101/2023.07.19.549746] [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: 07/29/2023]
Abstract
Motor-task functional magnetic resonance imaging (fMRI) is crucial in the study of several clinical conditions, including stroke and Parkinson's disease. However, motor-task fMRI is complicated by task-correlated head motion, which can be magnified in clinical populations and confounds motor activation results. One method that may mitigate this issue is multi-echo independent component analysis (ME-ICA), which has been shown to separate the effects of head motion from the desired BOLD signal but has not been tested in motor-task datasets with high amounts of motion. In this study, we collected an fMRI dataset from a healthy population who performed a hand grasp task with and without task-correlated amplified head motion to simulate a motor-impaired population. We analyzed these data using three models: single-echo (SE), multi-echo optimally combined (ME-OC), and ME-ICA. We compared the models' performance in mitigating the effects of head motion on the subject level and group level. On the subject level, ME-ICA better dissociated the effects of head motion from the BOLD signal and reduced noise. Both ME models led to increased t-statistics in brain motor regions. In scans with high levels of motion, ME-ICA additionally mitigated artifacts and increased stability of beta coefficient estimates, compared to SE. On the group level, all three models produced activation clusters in expected motor areas in scans with both low and high motion, indicating that group-level averaging may also sufficiently resolve motion artifacts that vary by subject. These findings demonstrate that ME-ICA is a useful tool for subject-level analysis of motor-task data with high levels of task-correlated head motion. The improvements afforded by ME-ICA are critical to improve reliability of subject-level activation maps for clinical populations in which group-level analysis may not be feasible or appropriate, for example in a chronic stroke cohort with varying stroke location and degree of tissue damage.
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Affiliation(s)
- Neha A. Reddy
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
- Department of Biomedical Engineering, McCormick School of Engineering and Applied Sciences, Northwestern University, Evanston, IL, United States
| | - Kristina M. Zvolanek
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
- Department of Biomedical Engineering, McCormick School of Engineering and Applied Sciences, Northwestern University, Evanston, IL, United States
| | - Stefano Moia
- Basque Center on Cognition, Brain and Language, Donostia, Gipuzkoa, Spain
- Neuro-X Institute, École polytechnique fédérale de Lausanne, Geneva, Switzerland
- Department of Radiology and Medical Informatics (DRIM), Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | | | - Molly G. Bright
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
- Department of Biomedical Engineering, McCormick School of Engineering and Applied Sciences, Northwestern University, Evanston, IL, United States
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Schmidt T, Vannesjo SJ, Sommer S, Nagy Z. fMRI with whole-brain coverage, 75-ms temporal resolution and high SNR by combining HiHi reshuffling and multiband imaging. Magn Reson Imaging 2023; 103:48-53. [PMID: 37385353 DOI: 10.1016/j.mri.2023.06.015] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 06/26/2023] [Accepted: 06/26/2023] [Indexed: 07/01/2023]
Abstract
Increasing the temporal resolution of the blood‑oxygen level-dependent (BOLD) response is usually accompanied by a decrease in repetition time and therefore also a reduction of the magnetic resonance (MR) signal due to incomplete T1 relaxation and thus a loss of signal-to-noise ratio (SNR). A previous data reordering method can achieve higher temporal sampling rate without the loss of SNR but at the cost of increased scan time. In this proof-of-principle work, we show that combining HiHi reshuffling with multiband acceleration allows us to measure the in vivo BOLD response with a 75-ms sampling rate that is decoupled from the acquisition repetition time (here 1.5 s and hence higher SNR) while covering the entire forebrain with 60 2-mm slices in a ~ 35-min scan. We provide single-voxel time-courses of the BOLD responses in the primary visual and primary motor cortices in three fMRI experiments on a 7 T scanner - 1 male (scanned twice on different days for test-retest reproducibility) and 1 female participant.
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Affiliation(s)
- Tim Schmidt
- Laboratory for Social and Neural Systems Research, University of Zurich, Zurich, Switzerland; Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland.
| | - S Johanna Vannesjo
- Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway
| | - Stefan Sommer
- Siemens Healthineers International AG, Zurich, Switzerland; Swiss Center for Musculoskeletal Imaging (SCMI), Balgrist Campus, Zurich, Switzerland; Advanced Clinical Imaging Technology (ACIT), Siemens Healthineers International AG, Lausanne, Switzerland
| | - Zoltán Nagy
- Laboratory for Social and Neural Systems Research, University of Zurich, Zurich, Switzerland
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Harel Y, Cyr A, Boyle J, Pinsard B, Bernard J, Fourcade MF, Aggarwal H, Ponce AF, Thirion B, Jerbi K, Bellec P. Open design of a reproducible videogame controller for MRI and MEG. PLoS One 2023; 18:e0290158. [PMID: 37910557 PMCID: PMC10619825 DOI: 10.1371/journal.pone.0290158] [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: 05/12/2023] [Accepted: 08/03/2023] [Indexed: 11/03/2023] Open
Abstract
Videogames are emerging as a promising experimental paradigm in neuroimaging. Acquiring gameplay in a scanner remains challenging due to the lack of a scanner-compatible videogame controller that provides a similar experience to standard, commercial devices. In this paper, we introduce a videogame controller designed for use in the functional magnetic resonance imaging as well as magnetoencephalography. The controller is made exclusively of 3D-printed and commercially available parts. We evaluated the quality of our controller by comparing it to a non-MRI compatible controller that was kept outside the scanner. The comparison of response latencies showed reliable button press accuracies of adequate precision. Comparison of the subjects' motion during fMRI recordings of various tasks showed that the use of our controller did not increase the amount of motion produced compared to a regular MR compatible button press box. Motion levels during an ecological videogame task were of moderate amplitude. In addition, we found that the controller only had marginal effect on temporal SNR in fMRI, as well as on covariance between sensors in MEG, as expected due to the use of non-magnetic building materials. Finally, the reproducibility of the controller was demonstrated by having team members who were not involved in the design build a reproduction using only the documentation. This new videogame controller opens new avenues for ecological tasks in fMRI, including challenging videogames and more generally tasks with complex responses. The detailed controller documentation and build instructions are released under an Open Source Hardware license to increase accessibility, and reproducibility and enable the neuroimaging research community to improve or modify the controller for future experiments.
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Affiliation(s)
- Yann Harel
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, Montréal, Canada
- Psychology department, University of Montréal, Montréal, Canada
| | - André Cyr
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, Montréal, Canada
- Unité de Neuroimagerie Fonctionnelle, Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, Montréal, Canada
| | - Julie Boyle
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, Montréal, Canada
- Psychology department, University of Montréal, Montréal, Canada
- Unité de Neuroimagerie Fonctionnelle, Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, Montréal, Canada
| | - Basile Pinsard
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, Montréal, Canada
- Unité de Neuroimagerie Fonctionnelle, Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, Montréal, Canada
| | | | | | | | | | | | - Karim Jerbi
- Psychology department, University of Montréal, Montréal, Canada
- Unité de Neuroimagerie Fonctionnelle, Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, Montréal, Canada
- MILA, Montréal, Canada
- MEG Imaging Center, University of Montréal, Montréal, Canada
| | - Pierre Bellec
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, Montréal, Canada
- Psychology department, University of Montréal, Montréal, Canada
- Unité de Neuroimagerie Fonctionnelle, Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, Montréal, Canada
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Rogers CS, Jones MS, McConkey S, McLaughlin DJ, Peelle JE. Real-time feedback reduces participant motion during task-based fMRI. bioRxiv 2023:2023.01.12.523791. [PMID: 36711722 PMCID: PMC9882243 DOI: 10.1101/2023.01.12.523791] [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] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
The potential negative impact of head movement during fMRI has long been appreciated. Although a variety of prospective and retrospective approaches have been developed to help mitigate these effects, reducing head movement in the first place remains the most appealing strategy for optimizing data quality. Real-time interventions, in which participants are provided feedback regarding their scan-to-scan motion, have recently shown promise in reducing motion during resting state fMRI. However, whether feedback might similarly reduce motion during task-based fMRI is an open question. In particular, it is unclear whether participants can effectively monitor motion feedback while attending to task-related demands. Here we assessed whether a combination of real-time and between-run feedback could reduce head motion during task-based fMRI. During an auditory word repetition task, 78 adult participants (aged 19-81) were pseudorandomly assigned to receive feedback or not. Feedback was provided FIRMM software that used real-time calculation of realignment parameters to estimate participant motion. We quantified movement using framewise displacement (FD). We found that motion feedback resulted in a statistically significant reduction in participant head motion, with a small-to-moderate effect size (reducing average FD from 0.347 to 0.282). Reductions were most apparent in high-motion events. We conclude that under some circumstances real-time feedback may reduce head motion during task-based fMRI, although its effectiveness may depend on the specific participant population and task demands of a given study.
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Affiliation(s)
| | - Michael S Jones
- Department of Otolaryngology, Washington University in St. Louis
| | - Sarah McConkey
- Department of Otolaryngology, Washington University in St. Louis
| | | | - Jonathan E Peelle
- Center for Cognitive and Brain Health, Northeastern University
- Department of Communication Sciences and Disorders, Northeastern University
- Department of Psychology, Northeastern University
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Sanders AFP, Harms MP, Kandala S, Marek S, Somerville LH, Bookheimer SY, Dapretto M, Thomas KM, Van Essen DC, Yacoub E, Barch DM. Age-related differences in resting-state functional connectivity from childhood to adolescence. Cereb Cortex 2023; 33:6928-6942. [PMID: 36724055 PMCID: PMC10233258 DOI: 10.1093/cercor/bhad011] [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: 08/30/2022] [Revised: 01/06/2023] [Accepted: 01/07/2023] [Indexed: 02/02/2023] Open
Abstract
The human brain is active at rest, and spontaneous fluctuations in functional MRI BOLD signals reveal an intrinsic functional architecture. During childhood and adolescence, functional networks undergo varying patterns of maturation, and measures of functional connectivity within and between networks differ as a function of age. However, many aspects of these developmental patterns (e.g. trajectory shape and directionality) remain unresolved. In the present study, we characterised age-related differences in within- and between-network resting-state functional connectivity (rsFC) and integration (i.e. participation coefficient, PC) in a large cross-sectional sample of children and adolescents (n = 628) aged 8-21 years from the Lifespan Human Connectome Project in Development. We found evidence for both linear and non-linear differences in cortical, subcortical, and cerebellar rsFC, as well as integration, that varied by age. Additionally, we found that sex moderated the relationship between age and putamen integration where males displayed significant age-related increases in putamen PC compared with females. Taken together, these results provide evidence for complex, non-linear differences in some brain systems during development.
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Affiliation(s)
- Ashley F P Sanders
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO 63110, USA
| | - Michael P Harms
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO 63110, USA
| | - Sridhar Kandala
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO 63110, USA
| | - Scott Marek
- Department of Radiology, Washington University School of Medicine, St Louis, MO 63119, USA
| | - Leah H Somerville
- Department of Psychology and Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Susan Y Bookheimer
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles School of Medicine, Los Angeles, CA 90095, USA
| | - Mirella Dapretto
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles School of Medicine, Los Angeles, CA 90095, USA
| | - Kathleen M Thomas
- Institute of Child Development, University of Minnesota, Minneapolis, MN 55455, USA
| | - David C Van Essen
- Department of Neuroscience, Washington University School of Medicine, St Louis, MO 63110, USA
| | - Essa Yacoub
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN 55455, USA
| | - Deanna M Barch
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO 63110, USA
- Department of Psychological and Brain Sciences, Washington University, St Louis, MO 63130, USA
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Tambini A, Miller J, Ehlert L, Kiyonaga A, D’Esposito M. Structured memory representations develop at multiple time scales in hippocampal-cortical networks. bioRxiv 2023:2023.04.06.535935. [PMID: 37066263 PMCID: PMC10104124 DOI: 10.1101/2023.04.06.535935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
Influential views of systems memory consolidation posit that the hippocampus rapidly forms representations of specific events, while neocortical networks extract regularities across events, forming the basis of schemas and semantic knowledge. Neocortical extraction of schematic memory representations is thought to occur on a protracted timescale of months, especially for information that is unrelated to prior knowledge. However, this theorized evolution of memory representations across extended timescales, and differences in the temporal dynamics of consolidation across brain regions, lack reliable empirical support. To examine the temporal dynamics of memory representations, we repeatedly exposed human participants to structured information via sequences of fractals, while undergoing longitudinal fMRI for three months. Sequence-specific activation patterns emerged in the hippocampus during the first 1-2 weeks of learning, followed one week later by high-level visual cortex, and subsequently the medial prefrontal and parietal cortices. Schematic, sequence-general representations emerged in the prefrontal cortex after 3 weeks of learning, followed by the medial temporal lobe and anterior temporal cortex. Moreover, hippocampal and most neocortical representations showed sustained rather than time-limited dynamics, suggesting that representations tend to persist across learning. These results show that specific hippocampal representations emerge early, followed by both specific and schematic representations at a gradient of timescales across hippocampal-cortical networks as learning unfolds. Thus, memory representations do not exist only in specific brain regions at a given point in time, but are simultaneously present at multiple levels of abstraction across hippocampal-cortical networks.
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Affiliation(s)
- Arielle Tambini
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY
| | - Jacob Miller
- Wu Tsai Institute, Department of Psychiatry, Yale University, New Haven, CT
| | - Luke Ehlert
- Department of Neurobiology and Behavior, University of California. Irvine, CA
| | - Anastasia Kiyonaga
- Department of Cognitive Science, University of California, San Diego, CA
| | - Mark D’Esposito
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA
- Department of Psychology, University of California, Berkeley, CA
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11
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Kim J, De Asis‐Cruz J, Kapse K, Limperopoulos C. Systematic evaluation of head motion on resting-state functional connectivity MRI in the neonate. Hum Brain Mapp 2023; 44:1934-1948. [PMID: 36576333 PMCID: PMC9980896 DOI: 10.1002/hbm.26183] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 11/18/2022] [Accepted: 12/01/2022] [Indexed: 12/29/2022] Open
Abstract
Reliability and robustness of resting state functional connectivity MRI (rs-fcMRI) relies, in part, on minimizing the influence of head motion on measured brain signals. The confounding effects of head motion on functional connectivity have been extensively studied in adults, but its impact on newborn brain connectivity remains unexplored. Here, using a large newborn data set consisting of 159 rs-fcMRI scans acquired in the Developing Brain Institute at Children's National Hospital and 416 scans from The Developing Human Connectome Project (dHCP), we systematically investigated associations between head motion and rs-fcMRI. Head motion during the scan significantly affected connectivity at sensory-related networks and default mode networks, and at the whole brain scale; the direction of motion effects varied across the whole brain. Comparing high- versus low-head motion groups suggested that head motion can impact connectivity estimates across the whole brain. Censoring of high-motion volumes using frame-wise displacement significantly reduced the confounding effects of head motion on neonatal rs-fcMRI. Lastly, in the dHCP data set, we demonstrated similar persistent associations between head motion and network connectivity despite implementing a standard denoising strategy. Collectively, our results highlight the importance of using rigorous head motion correction in preprocessing neonatal rs-fcMRI to yield reliable estimates of brain activity.
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Affiliation(s)
- Jung‐Hoon Kim
- Developing Brain Institute, Children's NationalWashingtonDistrict of ColumbiaUSA
| | | | - Kushal Kapse
- Developing Brain Institute, Children's NationalWashingtonDistrict of ColumbiaUSA
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12
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Zhang Z, Good M, Kulikov V, van Horen F, Bartholomew M, Kayser AS, Hsu M. From scanner to court: A neuroscientifically informed "reasonable person" test of trademark infringement. Sci Adv 2023; 9:eabo1095. [PMID: 36753556 PMCID: PMC9908014 DOI: 10.1126/sciadv.abo1095] [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] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 01/06/2023] [Indexed: 06/18/2023]
Abstract
Many legal decisions center on the thoughts or perceptions of some idealized group of individuals, referred to variously as the "average person," "the typical consumer," or the "reasonable person." Substantial concerns exist, however, regarding the subjectivity and vulnerability to biases inherent in conventional means of assessing such responses, particularly the use of self-report evidence. Here, we addressed these concerns by complementing self-report evidence with neural data to inform the mental representations in question. Using an example from intellectual property law, we demonstrate that it is possible to construct a parsimonious neural index of visual similarity that can inform the reasonable person test of trademark infringement. Moreover, when aggregated across multiple participants, this index was able to detect experimenter-induced biases in self-report surveys in a sensitive and replicable fashion. Together, these findings potentially broaden the possibilities for neuroscientific data to inform legal decision-making across a range of settings.
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Affiliation(s)
- Zhihao Zhang
- Darden School of Business, University of Virginia, Charlottesville, VA, USA
- Department of Psychology, University of Virginia, Charlottesville, VA, USA
- Haas School of Business, University of California, Berkeley, Berkeley, CA, USA
| | - Maxwell Good
- Haas School of Business, University of California, Berkeley, Berkeley, CA, USA
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
- Department of Veterans Affairs Northern California Health Care System, CA, USA
| | - Vera Kulikov
- Haas School of Business, University of California, Berkeley, Berkeley, CA, USA
| | - Femke van Horen
- School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Mark Bartholomew
- School of Law, State University of New York at Buffalo, Buffalo, NY, USA
| | - Andrew S. Kayser
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
- Department of Veterans Affairs Northern California Health Care System, CA, USA
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Ming Hsu
- Haas School of Business, University of California, Berkeley, Berkeley, CA, USA
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
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13
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Di X, Biswal BB. A functional MRI pre-processing and quality control protocol based on statistical parametric mapping (SPM) and MATLAB. Front Neuroimaging 2023; 1:1070151. [PMID: 37555150 PMCID: PMC10406300 DOI: 10.3389/fnimg.2022.1070151] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 12/19/2022] [Indexed: 08/10/2023]
Abstract
Functional MRI (fMRI) has become a popular technique to study brain functions and their alterations in psychiatric and neurological conditions. The sample sizes for fMRI studies have been increasing steadily, and growing studies are sourced from open-access brain imaging repositories. Quality control becomes critical to ensure successful data processing and valid statistical results. Here, we outline a simple protocol for fMRI data pre-processing and quality control based on statistical parametric mapping (SPM) and MATLAB. The focus of this protocol is not only to identify and remove data with artifacts and anomalies, but also to ensure the processing has been performed properly. We apply this protocol to the data from fMRI Open quality control (QC) Project, and illustrate how each quality control step can help to identify potential issues. We also show that simple steps such as skull stripping can improve coregistration between the functional and anatomical images.
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Affiliation(s)
- Xin Di
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States
| | - Bharat B. Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States
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14
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Gulban OF, Bollmann S, Huber LR, Wagstyl K, Goebel R, Poser BA, Kay K, Ivanov D. Mesoscopic in vivo human T(2)(*) dataset acquired using quantitative MRI at 7 Tesla. Neuroimage 2022; 264:119733. [PMID: 36375782 DOI: 10.1016/j.neuroimage.2022.119733] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 10/15/2022] [Accepted: 11/03/2022] [Indexed: 11/13/2022] Open
Abstract
Mesoscopic (0.1-0.5 mm) interrogation of the living human brain is critical for advancing neuroscience and bridging the resolution gap with animal models. Despite the variety of MRI contrasts measured in recent years at the mesoscopic scale, in vivo quantitative imaging of T2* has not been performed. Here we provide a dataset containing empirical T2* measurements acquired at 0.35 × 0.35 × 0.35 mm3 voxel resolution using 7 Tesla MRI. To demonstrate unique features and high quality of this dataset, we generate flat map visualizations that reveal fine-scale cortical substructures such as layers and vessels, and we report quantitative depth-dependent T2* (as well as R2*) values in primary visual cortex and auditory cortex that are highly consistent across subjects. This dataset is freely available at https://doi.org/10.17605/OSF.IO/N5BJ7, and may prove useful for anatomical investigations of the human brain, as well as for improving our understanding of the basis of the T2*-weighted (f)MRI signal.
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15
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Miller JA, Tambini A, Kiyonaga A, D'Esposito M. Long-term learning transforms prefrontal cortex representations during working memory. Neuron 2022; 110:3805-3819.e6. [PMID: 36240768 PMCID: PMC9768795 DOI: 10.1016/j.neuron.2022.09.019] [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: 02/23/2022] [Revised: 06/28/2022] [Accepted: 09/14/2022] [Indexed: 11/06/2022]
Abstract
The role of the lateral prefrontal cortex (lPFC) in working memory (WM) is debated. Non-human primate (NHP) electrophysiology shows that the lPFC stores WM representations, but human neuroimaging suggests that the lPFC controls WM content in sensory cortices. These accounts are confounded by differences in task training and stimulus exposure. We tested whether long-term training alters lPFC function by densely sampling WM activity using functional MRI. Over 3 months, participants trained on both a WM and serial reaction time (SRT) task, wherein fractal stimuli were embedded within sequences. WM performance improved for trained (but not novel) fractals and, neurally, delay activity increased in distributed lPFC voxels across learning. Item-level WM representations became detectable within lPFC patterns, and lPFC activity reflected sequence relationships from the SRT task. These findings demonstrate that human lPFC develops stimulus-selective responses with learning, and WM representations are shaped by long-term experience, which could reconcile competing accounts of WM functioning.
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Affiliation(s)
- Jacob A Miller
- Wu Tsai Institute, Department of Psychiatry, Yale University, New Haven, CT, USA.
| | - Arielle Tambini
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Anastasia Kiyonaga
- Department of Cognitive Science, University of California, San Diego, CA, USA
| | - Mark D'Esposito
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA; Department of Psychology, University of California, Berkeley, CA, USA
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16
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Nagy Z, Hutton C, David G, Hinterholzer N, Deichmann R, Weiskopf N, Vannesjo SJ. HiHi fMRI: a data-reordering method for measuring the hemodynamic response of the brain with high temporal resolution and high SNR. Cereb Cortex 2022; 33:4606-4611. [PMID: 36169574 PMCID: PMC10110425 DOI: 10.1093/cercor/bhac364] [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] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 08/19/2022] [Accepted: 08/22/2022] [Indexed: 11/14/2022] Open
Abstract
There is emerging evidence that sampling the blood-oxygen-level-dependent (BOLD) response with high temporal resolution opens up new avenues to study the in vivo functioning of the human brain with functional magnetic resonance imaging. Because the speed of sampling and the signal level are intrinsically connected in magnetic resonance imaging via the T1 relaxation time, optimization efforts usually must make a trade-off to increase the temporal sampling rate at the cost of the signal level. We present a method, which combines a sparse event-related stimulus paradigm with subsequent data reshuffling to achieve high temporal resolution while maintaining high signal levels (HiHi). The proof-of-principle is presented by separately measuring the single-voxel time course of the BOLD response in both the primary visual and primary motor cortices with 100-ms temporal resolution.
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Affiliation(s)
- Zoltan Nagy
- Laboratory for Social and Neural Systems Research (SNS Lab), University Hospital Zurich, Rämistrasse 100, University of Zurich, Zurich CH-8091, Switzerland.,Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, 12 Queen Square, University College London, London WC1N 3BG, UK
| | - Chloe Hutton
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, 12 Queen Square, University College London, London WC1N 3BG, UK
| | - Gergely David
- Spinal Cord Injury Center, Balgrist University Hospital, Forchstrasse 340, University of Zurich, Zurich CH-8008, Switzerland
| | - Natalie Hinterholzer
- SCMI, Swiss Center for Musculoskeletal Imaging, Balgrist Campus AG, Lengghalde 5, Zurich CH-8008, Switzerland
| | - Ralf Deichmann
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, 12 Queen Square, University College London, London WC1N 3BG, UK.,Brain Imaging Centre, Goethe University Frankfurt, University Hospital Campus, Haus 95H, Schleusenweg 2-16, Frankfurt am Main D-60528, Germany
| | - Nikolaus Weiskopf
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, 12 Queen Square, University College London, London WC1N 3BG, UK.,Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstrasse 1a, Leipzig 04103, Germany
| | - S Johanna Vannesjo
- Department of Physics, Norwegian University of Science and Technology, Høgskoleringen 5, Trondheim 7491, Norway
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17
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Davis BR, Garza A, Church JA. Key considerations for child and adolescent MRI data collection. Front Neuroimaging 2022; 1. [PMID: 36312216 PMCID: PMC9615104 DOI: 10.3389/fnimg.2022.981947] [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] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Cognitive neuroimaging researchers’ ability to infer accurate statistical conclusions from neuroimaging depends greatly on the quality of the data analyzed. This need for quality control is never more evident than when conducting neuroimaging studies with children and adolescents. Developmental neuroimaging requires patience, flexibility, adaptability, extra time, and effort. It also provides us a unique, non-invasive way to understand the development of cognitive processes, individual differences, and the changing relations between brain and behavior over the lifespan. In this discussion, we focus on collecting magnetic resonance imaging (MRI) data, as it is one of the more complex protocols used with children and youth. Through our extensive experience collecting MRI datasets with children and families, as well as a review of current best practices, we will cover three main topics to help neuroimaging researchers collect high-quality datasets. First, we review key recruitment and retention techniques, and note the importance for consistency and inclusion across groups. Second, we discuss ways to reduce scan anxiety for families and ways to increase scan success by describing the pre-screening process, use of a scanner simulator, and the need to focus on participant and family comfort. Finally, we outline several important design considerations in developmental neuroimaging such as asking a developmentally appropriate question, minimizing data loss, and the applicability of public datasets. Altogether, we hope this article serves as a useful tool for those wishing to enter or learn more about developmental cognitive neuroscience.
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18
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Go CC, Taskin HO, Ahmadi SA, Frazzetta G, Cutler L, Malhotra S, Morgan JI, Flanagin VL, Aguirre GK. Persistent horizontal and vertical, MR-induced nystagmus in resting state Human Connectome Project data. Neuroimage 2022; 255:119170. [PMID: 35367649 DOI: 10.1016/j.neuroimage.2022.119170] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [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: 02/23/2022] [Revised: 03/28/2022] [Accepted: 03/30/2022] [Indexed: 10/18/2022] Open
Abstract
OBJECTIVE Strong magnetic fields from magnetic resonance (MR) scanners induce a Lorentz force that contributes to vertigo and persistent nystagmus. Prior studies have reported a predominantly horizontal direction for healthy subjects in a 7 Tesla (T) MR scanner, with slow phase velocity (SPV) dependent on head orientation. Less is known about vestibular signal behavior for subjects in a weaker, 3T magnetic field, the standard strength used in the Human Connectome Project (HCP). The purpose of this study is to characterize the form and magnitude of nystagmus induced at 3T. METHODS Forty-two subjects were studied after being introduced head-first, supine into a Siemens Prisma 3T scanner. Eye movements were recorded in four separate acquisitions over 20 minutes. A biometric eye model was fitted to the recordings to derive rotational eye position and then SPV. An anatomical template of the semi-circular canals was fitted to the T2 anatomical image from each subject, and used to derive the angle of the B0 magnetic field with respect to the vestibular apparatus. RESULTS Recordings from 37 subjects yielded valid measures of eye movements. The population-mean SPV ± SD for the horizontal component was -1.38 ± 1.27 deg/sec, and vertical component was -0.93 ± 1.44 deg/sec, corresponding to drift movement in the rightward and downward direction. Although there was substantial inter-subject variability, persistent nystagmus was present in half of subjects with no significant adaptation over the 20 minute scanning period. The amplitude of vertical drift was correlated with the roll angle of the vestibular system, with a non-zero vertical SPV present at a 0 degree roll. INTERPRETATION Non-habituating vestibular signals of varying amplitude are present in resting state data collected at 3T.
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Affiliation(s)
- Cammille C Go
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Huseyin O Taskin
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Seyed-Ahmad Ahmadi
- NVIDIA GmbH, Einsteinstraße 172, 81677 Munich, Germany; German Center for Vertigo and Balance Disorders, LMU Klinikum, 81377, Munich, Germany
| | - Giulia Frazzetta
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Laura Cutler
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Saguna Malhotra
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jessica Iw Morgan
- Department of Ophthalmology, Scheie Eye Institute, Penn Presbyterian Medical Center, 51 N 39th St, Philadelphia, PA 19104, USA
| | - Virginia L Flanagin
- German Center for Vertigo and Balance Disorders, LMU Klinikum, 81377, Munich, Germany
| | - Geoffrey K Aguirre
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
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Williams JC, Tubiolo PN, Luceno JR, Van Snellenberg JX. Advancing motion denoising of multiband resting-state functional connectivity fMRI data. Neuroimage 2022; 249:118907. [PMID: 35033673 PMCID: PMC9057309 DOI: 10.1016/j.neuroimage.2022.118907] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [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: 09/09/2021] [Revised: 01/11/2022] [Accepted: 01/12/2022] [Indexed: 11/26/2022] Open
Abstract
Simultaneous multi-slice (multiband) accelerated functional magnetic resonance imaging (fMRI) provides dramatically improved temporal and spatial resolution for resting-state functional connectivity (RSFC) studies of the human brain in health and disease. However, multiband acceleration also poses unique challenges for denoising of subject motion induced data artifacts, the presence of which is a major confound in RSFC research that substantively diminishes reliability and reproducibility. We comprehensively evaluated existing and novel approaches to volume censoring-based motion denoising in the Human Connectome Project (HCP) dataset. We show that assumptions underlying common metrics for evaluating motion denoising pipelines, especially those based on quality control-functional connectivity (QC-FC) correlations and differences between high- and low-motion participants, are problematic, and appear to be inappropriate in their current widespread use as indicators of comparative pipeline performance and as targets for investigators to use when tuning pipelines for their own datasets. We further develop two new quantitative metrics that are instead agnostic to QC-FC correlations and other measures that rely upon the null assumption that no true relationships exist between trait measures of subject motion and functional connectivity, and demonstrate their use as benchmarks for comparing volume censoring methods. Finally, we develop and validate quantitative methods for determining dataset-specific optimal volume censoring parameters prior to the final analysis of a dataset, and provide straightforward recommendations and code for all investigators to apply this optimized approach to their own RSFC datasets.
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Affiliation(s)
- John C Williams
- Department of Psychiatry and Behavioral Health, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, 11794 USA; Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, 11794 USA
| | - Philip N Tubiolo
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, 11794 USA
| | - Jacob R Luceno
- Department of Psychiatry and Behavioral Health, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, 11794 USA
| | - Jared X Van Snellenberg
- Department of Psychiatry and Behavioral Health, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, 11794 USA; Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, 11794 USA; Department of Psychology, Stony Brook University, Stony Brook, NY, 11794 USA; Division of Translational Imaging, New York State Psychiatric Institute, New York, NY, 10032 USA; Department of Psychiatry, Columbia University Medical Center, New York, NY, 10032 USA.
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20
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Echevarria-Cooper SL, Zhou G, Zelano C, Pestilli F, Parrish TB, Kahnt T. Mapping the Microstructure and Striae of the Human Olfactory Tract with Diffusion MRI. J Neurosci 2022; 42:58-68. [PMID: 34759031 PMCID: PMC8741165 DOI: 10.1523/jneurosci.1552-21.2021] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [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: 07/29/2021] [Revised: 09/30/2021] [Accepted: 10/31/2021] [Indexed: 11/21/2022] Open
Abstract
The human sense of smell plays an important role in appetite and food intake, detecting environmental threats, social interactions, and memory processing. However, little is known about the neural circuity supporting its function. The olfactory tracts project from the olfactory bulb along the base of the frontal cortex, branching into several striae to meet diverse cortical regions. Historically, using diffusion magnetic resonance imaging (dMRI) to reconstruct the human olfactory tracts has been prevented by susceptibility and motion artifacts. Here, we used a dMRI method with readout segmentation of long variable echo-trains (RESOLVE) to minimize image distortions and characterize the human olfactory tracts in vivo We collected high-resolution dMRI data from 25 healthy human participants (12 male and 13 female) and performed probabilistic tractography using constrained spherical deconvolution (CSD). At the individual subject level, we identified the lateral, medial, and intermediate striae with their respective cortical connections to the piriform cortex and amygdala (AMY), olfactory tubercle (OT), and anterior olfactory nucleus (AON). We combined individual results across subjects to create a normalized, probabilistic atlas of the olfactory tracts. We then investigated the relationship between olfactory perceptual scores and measures of white matter integrity, including mean diffusivity (MD). Importantly, we found that olfactory tract MD negatively correlated with odor discrimination performance. In summary, our results provide a detailed characterization of the connectivity of the human olfactory tracts and demonstrate an association between their structural integrity and olfactory perceptual function.SIGNIFICANCE STATEMENT This study provides the first detailed in vivo description of the cortical connectivity of the three olfactory tract striae in the human brain, using diffusion magnetic resonance imaging (dMRI). Additionally, we show that tract microstructure correlates with performance on an odor discrimination task, suggesting a link between the structural integrity of the olfactory tracts and odor perception. Lastly, we generated a normalized probabilistic atlas of the olfactory tracts that may be used in future research to study its integrity in health and disease.
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Affiliation(s)
- Shiloh L Echevarria-Cooper
- Department of Neurology, Northwestern University, Feinberg School of Medicine, Chicago, Illinois 60611
- The Graduate School, Northwestern University Interdepartmental Neuroscience (NUIN), Evanston, Illinois 60208
| | - Guangyu Zhou
- Department of Neurology, Northwestern University, Feinberg School of Medicine, Chicago, Illinois 60611
| | - Christina Zelano
- Department of Neurology, Northwestern University, Feinberg School of Medicine, Chicago, Illinois 60611
| | - Franco Pestilli
- Department of Psychology, The University of Texas at Austin, Austin, Texas 78712
- Center for Perceptual Systems, The University of Texas at Austin, Austin, Texas 78712
| | - Todd B Parrish
- Department of Radiology, Northwestern University, Chicago, Illinois 60611
| | - Thorsten Kahnt
- Department of Neurology, Northwestern University, Feinberg School of Medicine, Chicago, Illinois 60611
- Department of Psychology, Northwestern University, Weinberg College of Arts and Sciences, Evanston, Illinois 60208
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21
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Allen EJ, St-Yves G, Wu Y, Breedlove JL, Prince JS, Dowdle LT, Nau M, Caron B, Pestilli F, Charest I, Hutchinson JB, Naselaris T, Kay K. A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence. Nat Neurosci 2022; 25:116-126. [PMID: 34916659 DOI: 10.1038/s41593-021-00962-x] [Citation(s) in RCA: 69] [Impact Index Per Article: 34.5] [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: 03/01/2021] [Accepted: 10/12/2021] [Indexed: 11/09/2022]
Abstract
Extensive sampling of neural activity during rich cognitive phenomena is critical for robust understanding of brain function. Here we present the Natural Scenes Dataset (NSD), in which high-resolution functional magnetic resonance imaging responses to tens of thousands of richly annotated natural scenes were measured while participants performed a continuous recognition task. To optimize data quality, we developed and applied novel estimation and denoising techniques. Simple visual inspections of the NSD data reveal clear representational transformations along the ventral visual pathway. Further exemplifying the inferential power of the dataset, we used NSD to build and train deep neural network models that predict brain activity more accurately than state-of-the-art models from computer vision. NSD also includes substantial resting-state and diffusion data, enabling network neuroscience perspectives to constrain and enhance models of perception and memory. Given its unprecedented scale, quality and breadth, NSD opens new avenues of inquiry in cognitive neuroscience and artificial intelligence.
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Affiliation(s)
- Emily J Allen
- Center for Magnetic Resonance Research (CMRR), Department of Radiology, University of Minnesota, Minneapolis, MN, USA
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Ghislain St-Yves
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA
| | - Yihan Wu
- Graduate Program in Cognitive Science, University of Minnesota, Minneapolis, MN, USA
| | - Jesse L Breedlove
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Jacob S Prince
- Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Psychology, Harvard University, Cambridge, MA, USA
| | - Logan T Dowdle
- Department of Neuroscience, Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA
- Department of Neurosurgery, Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA
| | - Matthias Nau
- National Institute of Mental Health (NIMH), Bethesda MD, USA
| | - Brad Caron
- Program in Neuroscience, Indiana University, Bloomington IN, USA
- Program in Vision Science, Indiana University, Bloomington IN, USA
| | - Franco Pestilli
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
- Center for Perceptual Systems, University of Texas at Austin, Austin, TX, USA
- Institute for Neuroscience, University of Texas at Austin, Austin, TX, USA
| | - Ian Charest
- Center for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK
- cerebrUM, Département de Psychologie, Université de Montréal, Montréal QC, Canada
| | | | - Thomas Naselaris
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA
| | - Kendrick Kay
- Center for Magnetic Resonance Research (CMRR), Department of Radiology, University of Minnesota, Minneapolis, MN, USA.
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22
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Abstract
Research into the human connectome (i.e., all connections in the human brain) with the use of resting state functional MRI has rapidly increased in popularity in recent years, especially with the growing availability of large-scale neuroimaging datasets. The goal of this review article is to describe innovations in functional connectome representations that have come about in the past 8 years, since the 2013 NeuroImage special issue on 'Mapping the Connectome'. In the period, research has shifted from group-level brain parcellations towards the characterization of the individualized connectome and of relationships between individual connectomic differences and behavioral/clinical variation. Achieving subject-specific accuracy in parcel boundaries while retaining cross-subject correspondence is challenging, and a variety of different approaches are being developed to meet this challenge, including improved alignment, improved noise reduction, and robust group-to-subject mapping approaches. Beyond the interest in the individualized connectome, new representations of the data are being studied to complement the traditional parcellated connectome representation (i.e., pairwise connections between distinct brain regions), such as methods that capture overlapping and smoothly varying patterns of connectivity ('gradients'). These different connectome representations offer complimentary insights into the inherent functional organization of the brain, but challenges for functional connectome research remain. Interpretability will be improved by future research towards gaining insights into the neural mechanisms underlying connectome observations obtained from functional MRI. Validation studies comparing different connectome representations are also needed to build consensus and confidence to proceed with clinical trials that may produce meaningful clinical translation of connectome insights.
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Affiliation(s)
- Janine D Bijsterbosch
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO, 63110, USA.
| | - Sofie L Valk
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; INM-7, Forschungszentrum Jülich, Jülich, Germany
| | - Danhong Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Matthew F Glasser
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO, 63110, USA; Department of Neuroscience, Washington University School of Medicine, Saint Louis, Missouri, 63110, USA
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23
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Raimondo L, Oliveira ĹAF, Heij J, Priovoulos N, Kundu P, Leoni RF, van der Zwaag W. Advances in resting state fMRI acquisitions for functional connectomics. Neuroimage 2021; 243:118503. [PMID: 34479041 DOI: 10.1016/j.neuroimage.2021.118503] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [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: 02/26/2021] [Revised: 08/16/2021] [Accepted: 08/22/2021] [Indexed: 01/21/2023] Open
Abstract
Resting state functional magnetic resonance imaging (rs-fMRI) is based on spontaneous fluctuations in the blood oxygen level dependent (BOLD) signal, which occur simultaneously in different brain regions, without the subject performing an explicit task. The low-frequency oscillations of the rs-fMRI signal demonstrate an intrinsic spatiotemporal organization in the brain (brain networks) that may relate to the underlying neural activity. In this review article, we briefly describe the current acquisition techniques for rs-fMRI data, from the most common approaches for resting state acquisition strategies, to more recent investigations with dedicated hardware and ultra-high fields. Specific sequences that allow very fast acquisitions, or multiple echoes, are discussed next. We then consider how acquisition methods weighted towards specific parts of the BOLD signal, like the Cerebral Blood Flow (CBF) or Volume (CBV), can provide more spatially specific network information. These approaches are being developed alongside the commonly used BOLD-weighted acquisitions. Finally, specific applications of rs-fMRI to challenging regions such as the laminae in the neocortex, and the networks within the large areas of subcortical white matter regions are discussed. We finish the review with recommendations for acquisition strategies for a range of typical applications of resting state fMRI.
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Affiliation(s)
- Luisa Raimondo
- Spinoza Centre for Neuroimaging, Amsterdam, the Netherlands; Experimental and Applied Psychology, VU University, Amsterdam, the Netherlands
| | - Ĺcaro A F Oliveira
- Spinoza Centre for Neuroimaging, Amsterdam, the Netherlands; Experimental and Applied Psychology, VU University, Amsterdam, the Netherlands
| | - Jurjen Heij
- Spinoza Centre for Neuroimaging, Amsterdam, the Netherlands; Experimental and Applied Psychology, VU University, Amsterdam, the Netherlands
| | | | - Prantik Kundu
- Hyperfine Research Inc, Guilford, CT, United States; Icahn School of Medicine at Mt. Sinai, New York, United States
| | - Renata Ferranti Leoni
- InBrain, Department of Physics, FFCLRP, University of São Paulo, Ribeirão Preto, Brazil
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24
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Jo S, Kim HC, Lustig N, Chen G, Lee JH. Mixed-effects multilevel analysis followed by canonical correlation analysis is an effective fMRI tool for the investigation of idiosyncrasies. Hum Brain Mapp 2021; 42:5374-5396. [PMID: 34415651 PMCID: PMC8519860 DOI: 10.1002/hbm.25627] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
We report that regions-of-interest (ROIs) associated with idiosyncratic individual behavior can be identified from functional magnetic resonance imaging (fMRI) data using statistical approaches that explicitly model individual variability in neuronal activations, such as mixed-effects multilevel analysis (MEMA). We also show that the relationship between neuronal activation in fMRI and behavioral data can be modeled using canonical correlation analysis (CCA). A real-world dataset for the neuronal response to nicotine use was acquired using a custom-made MRI-compatible apparatus for the smoking of electronic cigarettes (e-cigarettes). Nineteen participants smoked e-cigarettes in an MRI scanner using the apparatus with two experimental conditions: e-cigarettes with nicotine (ECIG) and sham e-cigarettes without nicotine (SCIG) and subjective ratings were collected. The right insula was identified in the ECIG condition from the χ2 -test of the MEMA but not from the t-test, and the corresponding activations were significantly associated with the similarity scores (r = -.52, p = .041, confidence interval [CI] = [-0.78, -0.17]) and the urge-to-smoke scores (r = .73, p <.001, CI = [0.52, 0.88]). From the contrast between the two conditions (i.e., ECIG > SCIG), the right orbitofrontal cortex was identified from the χ2 -tests, and the corresponding neuronal activations showed a statistically meaningful association with similarity (r = -.58, p = .01, CI = [-0.84, -0.17]) and the urge to smoke (r = .34, p = .15, CI = [0.09, 0.56]). The validity of our analysis pipeline (i.e., MEMA followed by CCA) was further evaluated using the fMRI and behavioral data acquired from the working memory and gambling tasks available from the Human Connectome Project.
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Affiliation(s)
- Sungman Jo
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Hyun-Chul Kim
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.,Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Niv Lustig
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Gang Chen
- Scientific and Statistical Computing Core, NIMH/NIH/DHHS, Bethesda, Maryland
| | - Jong-Hwan Lee
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
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25
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Zhang J, Kucyi A, Raya J, Nielsen AN, Nomi JS, Damoiseaux JS, Greene DJ, Horovitz SG, Uddin LQ, Whitfield-Gabrieli S. What have we really learned from functional connectivity in clinical populations? Neuroimage 2021; 242:118466. [PMID: 34389443 DOI: 10.1016/j.neuroimage.2021.118466] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [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: 04/15/2021] [Revised: 07/06/2021] [Accepted: 08/09/2021] [Indexed: 02/09/2023] Open
Abstract
Functional connectivity (FC), or the statistical interdependence of blood-oxygen dependent level (BOLD) signals between brain regions using fMRI, has emerged as a widely used tool for probing functional abnormalities in clinical populations due to the promise of the approach across conceptual, technical, and practical levels. With an already vast and steadily accumulating neuroimaging literature on neurodevelopmental, psychiatric, and neurological diseases and disorders in which FC is a primary measure, we aim here to provide a high-level synthesis of major concepts that have arisen from FC findings in a manner that cuts across different clinical conditions and sheds light on overarching principles. We highlight that FC has allowed us to discover the ubiquity of intrinsic functional networks across virtually all brains and clarify typical patterns of neurodevelopment over the lifespan. This understanding of typical FC maturation with age has provided important benchmarks against which to evaluate divergent maturation in early life and degeneration in late life. This in turn has led to the important insight that many clinical conditions are associated with complex, distributed, network-level changes in the brain, as opposed to solely focal abnormalities. We further emphasize the important role that FC studies have played in supporting a dimensional approach to studying transdiagnostic clinical symptoms and in enhancing the multimodal characterization and prediction of the trajectory of symptom progression across conditions. We highlight the unprecedented opportunity offered by FC to probe functional abnormalities in clinical conditions where brain function could not be easily studied otherwise, such as in disorders of consciousness. Lastly, we suggest high priority areas for future research and acknowledge critical barriers associated with the use of FC methods, particularly those related to artifact removal, data denoising and feasibility in clinical contexts.
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Affiliation(s)
- Jiahe Zhang
- Department of Psychology, 125 Nightingale Hall, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA.
| | - Aaron Kucyi
- Department of Psychology, 125 Nightingale Hall, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA
| | - Jovicarole Raya
- Department of Psychology, 125 Nightingale Hall, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA
| | - Ashley N Nielsen
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110, USA
| | - Jason S Nomi
- Department of Psychology, University of Miami, Miami, FL 33124, USA
| | - Jessica S Damoiseaux
- Institute of Gerontology and Department of Psychology, Wayne State University, Detroit, MI 48202, USA
| | - Deanna J Greene
- Department of Cognitive Science, University of California San Diego, La Jolla, CA 92093, USA
| | | | - Lucina Q Uddin
- Department of Psychology, University of Miami, Miami, FL 33124, USA
| | - Susan Whitfield-Gabrieli
- Department of Psychology, 125 Nightingale Hall, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA
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26
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Ashar YK, Clark J, Gunning FM, Goldin P, Gross JJ, Wager TD. Brain markers predicting response to cognitive-behavioral therapy for social anxiety disorder: an independent replication of Whitfield-Gabrieli et al. 2015. Transl Psychiatry 2021; 11:260. [PMID: 33934101 PMCID: PMC8088432 DOI: 10.1038/s41398-021-01366-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 03/18/2021] [Accepted: 04/07/2021] [Indexed: 02/03/2023] Open
Abstract
Predictive brain markers promise a number of important scientific, clinical, and societal applications. Over 600 predictive brain markers have been described in published reports, but very few have been tested in independent replication attempts. Here, we conducted an independent replication of a previously published marker predicting treatment response to cognitive-behavioral therapy for social anxiety disorder from patterns of resting-state fMRI amygdala connectivity1. The replication attempt was conducted in an existing dataset similar to the dataset used in the original report, by a team of independent investigators in consultation with the original authors. The precise model described in the original report positively predicted treatment outcomes in the replication dataset, but with marginal statistical significance, permutation test p = 0.1. The effect size was substantially smaller in the replication dataset, with the model explaining 2% of the variance in treatment outcomes, as compared to 21% in the original report. Several lines of evidence, including the current replication attempt, suggest that features of amygdala function or structure may be able to predict treatment response in anxiety disorders. However, predictive models that explain a substantial amount of variance in independent datasets will be needed for scientific and clinical applications.
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Affiliation(s)
- Yoni K Ashar
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - Joseph Clark
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA
| | - Faith M Gunning
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - Philippe Goldin
- Betty Irene Moore School of Nursing, University of California, Davis, Davis, CA, USA
| | - James J Gross
- Department of Psychology, Stanford University, Palo Alto, CA, USA
| | - Tor D Wager
- Psychological and Brain Sciences Department, Dartmouth College, Hanover, NH, USA.
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27
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Abstract
Rapidly developing approaches to acquiring and analyzing densely-sampled, single-subject fMRI data have opened new avenues for understanding the neurobiological basis of individual differences in behavior and could allow fMRI to become a more clinically useful tool. Here, we review briefly key insights from these precision functional mapping studies and a highlight significant barrier to their clinical translation. Specifically, that reliable delineation of functional brain networks in individual humans can require hours of resting-state fMRI data per-subject. We found recently that multi-echo fMRI improves the test-retest reliability of resting-state functional connectivity measurements, mitigating the need for acquiring large quantities of per -subject data. Because the benefits of multi-echo acquisitions are most pronounced in clinically important but artifact-prone brain regions, such as the subgenual cingulate and structures deep in the subcortex, this approach has the potential to increase the impact of precision functional mapping routines in both healthy and clinical populations.
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Affiliation(s)
- Charles J Lynch
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, 413 East 69 Street, Box 240, New York, NY 10021
| | - Immanuel Elbau
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, 413 East 69 Street, Box 240, New York, NY 10021
| | - Conor Liston
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, 413 East 69 Street, Box 240, New York, NY 10021
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28
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Viessmann O, Polimeni JR. High-resolution fMRI at 7 Tesla: challenges, promises and recent developments for individual-focused fMRI studies. Curr Opin Behav Sci 2021; 40:96-104. [PMID: 33816717 DOI: 10.1016/j.cobeha.2021.01.011] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Limited detection power has been a bottleneck for subject-specific functional MRI (fMRI) studies, however the higher signal-to-noise ratio afforded by ultra-high magnetic fields (≥ 7 Tesla) provides levels of sensitivity and resolution needed to study individual subjects. What may be surprising is that higher imaging resolution may provide both higher specificity and sensitivity due to reductions in partial volume effects and reduced physiological noise. However, challenges remain to ensure high data quality and to reduce variability in ultra-high field fMRI. We discuss session-specific biases including those caused by factors related to instrumentation, anatomy, and physiology-which can translate into variability across sessions-and how to minimize these to help ultra-high field fMRI reach its full potential for individual-focused studies.
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Affiliation(s)
- Olivia Viessmann
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Boston, MA, USA.,Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
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29
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Moia S, Termenon M, Uruñuela E, Chen G, Stickland RC, Bright MG, Caballero-Gaudes C. ICA-based denoising strategies in breath-hold induced cerebrovascular reactivity mapping with multi echo BOLD fMRI. Neuroimage 2021; 233:117914. [PMID: 33684602 PMCID: PMC8351526 DOI: 10.1016/j.neuroimage.2021.117914] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 01/25/2021] [Accepted: 02/22/2021] [Indexed: 12/19/2022] Open
Abstract
Performing a BOLD functional MRI (fMRI) acquisition during breath-hold (BH) tasks is a non-invasive, robust method to estimate cerebrovascular reactivity (CVR). However, movement and breathing-related artefacts caused by the BH can substantially hinder CVR estimates due to their high temporal collinearity with the effect of interest, and attention has to be paid when choosing which analysis model should be applied to the data. In this study, we evaluate the performance of multiple analysis strategies based on lagged general linear models applied on multi-echo BOLD fMRI data, acquired in ten subjects performing a BH task during ten sessions, to obtain subject-specific CVR and haemodynamic lag estimates. The evaluated approaches range from conventional regression models, i.e. including drifts and motion timecourses as nuisance regressors, applied on single-echo or optimally-combined data, to more complex models including regressors obtained from multi-echo independent component analysis with different grades of orthogonalization in order to preserve the effect of interest, i.e. the CVR. We compare these models in terms of their ability to make signal intensity changes independent from motion, as well as the reliability as measured by voxelwise intraclass correlation coefficients of both CVR and lag maps over time. Our results reveal that a conservative independent component analysis model applied on the optimally-combined multi-echo fMRI signal offers the largest reduction of motion-related effects in the signal, while yielding reliable CVR amplitude and lag estimates, although a conventional regression model applied on the optimally-combined data results in similar estimates. This work demonstrates the usefulness of multi-echo based fMRI acquisitions and independent component analysis denoising for precision mapping of CVR in single subjects based on BH paradigms, fostering its potential as a clinically-viable neuroimaging tool for individual patients. It also proves that the way in which data-driven regressors should be incorporated in the analysis model is not straight-forward due to their complex interaction with the BH-induced BOLD response.
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Affiliation(s)
- Stefano Moia
- Basque Center on Cognition, Brain and Language, Donostia, Spain; University of the Basque Country UPV/EHU, Donostia, Spain.
| | - Maite Termenon
- Basque Center on Cognition, Brain and Language, Donostia, Spain
| | - Eneko Uruñuela
- Basque Center on Cognition, Brain and Language, Donostia, Spain; University of the Basque Country UPV/EHU, Donostia, Spain
| | - Gang Chen
- Scientific and Statistical Computing Core, NIMH/NIH/HHS, Bethesda, MD, United States
| | - Rachael C Stickland
- Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Molly G Bright
- Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States; Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, United States
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30
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Hrybouski S, Cribben I, McGonigle J, Olsen F, Carter R, Seres P, Madan CR, Malykhin NV. Investigating the effects of healthy cognitive aging on brain functional connectivity using 4.7 T resting-state functional magnetic resonance imaging. Brain Struct Funct 2021; 226:1067-1098. [PMID: 33604746 DOI: 10.1007/s00429-021-02226-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [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: 07/07/2020] [Accepted: 01/20/2021] [Indexed: 01/05/2023]
Abstract
Functional changes in the aging human brain have been previously reported using functional magnetic resonance imaging (fMRI). Earlier resting-state fMRI studies revealed an age-associated weakening of intra-system functional connectivity (FC) and age-associated strengthening of inter-system FC. However, the majority of such FC studies did not investigate the relationship between age and network amplitude, without which correlation-based measures of FC can be challenging to interpret. Consequently, the main aim of this study was to investigate how three primary measures of resting-state fMRI signal-network amplitude, network topography, and inter-network FC-are affected by healthy cognitive aging. We acquired resting-state fMRI data on a 4.7 T scanner for 105 healthy participants representing the entire adult lifespan (18-85 years of age). To study age differences in network structure, we combined ICA-based network decomposition with sparse graphical models. Older adults displayed lower blood-oxygen-level-dependent (BOLD) signal amplitude in all functional systems, with sensorimotor networks showing the largest age differences. Our age comparisons of network topography and inter-network FC demonstrated a substantial amount of age invariance in the brain's functional architecture. Despite architecture similarities, old adults displayed a loss of communication efficiency in our inter-network FC comparisons, driven primarily by the FC reduction in frontal and parietal association cortices. Together, our results provide a comprehensive overview of age effects on fMRI-based FC.
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Affiliation(s)
- Stanislau Hrybouski
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada
| | - Ivor Cribben
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada.,Department of Accounting and Business Analytics, Alberta School of Business, University of Alberta, Edmonton, AB, Canada
| | - John McGonigle
- Department of Brain Sciences, Imperial College London, London, UK
| | - Fraser Olsen
- Department of Biomedical Engineering, University of Alberta, Edmonton, AB, Canada
| | - Rawle Carter
- Department of Psychiatry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, T6G 2V2, Canada
| | - Peter Seres
- Department of Biomedical Engineering, University of Alberta, Edmonton, AB, Canada
| | | | - Nikolai V Malykhin
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada. .,Department of Biomedical Engineering, University of Alberta, Edmonton, AB, Canada. .,Department of Psychiatry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, T6G 2V2, Canada.
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31
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Klapwijk ET, van den Bos W, Tamnes CK, Raschle NM, Mills KL. Opportunities for increased reproducibility and replicability of developmental neuroimaging. Dev Cogn Neurosci 2021; 47:100902. [PMID: 33383554 PMCID: PMC7779745 DOI: 10.1016/j.dcn.2020.100902] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [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: 01/08/2020] [Revised: 11/19/2020] [Accepted: 12/08/2020] [Indexed: 01/08/2023] Open
Abstract
Many workflows and tools that aim to increase the reproducibility and replicability of research findings have been suggested. In this review, we discuss the opportunities that these efforts offer for the field of developmental cognitive neuroscience, in particular developmental neuroimaging. We focus on issues broadly related to statistical power and to flexibility and transparency in data analyses. Critical considerations relating to statistical power include challenges in recruitment and testing of young populations, how to increase the value of studies with small samples, and the opportunities and challenges related to working with large-scale datasets. Developmental studies involve challenges such as choices about age groupings, lifespan modelling, analyses of longitudinal changes, and data that can be processed and analyzed in a multitude of ways. Flexibility in data acquisition, analyses and description may thereby greatly impact results. We discuss methods for improving transparency in developmental neuroimaging, and how preregistration can improve methodological rigor. While outlining challenges and issues that may arise before, during, and after data collection, solutions and resources are highlighted aiding to overcome some of these. Since the number of useful tools and techniques is ever-growing, we highlight the fact that many practices can be implemented stepwise.
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Affiliation(s)
- Eduard T Klapwijk
- Erasmus School of Social and Behavioral Sciences, Erasmus University Rotterdam, the Netherlands; Institute of Psychology, Leiden University, Leiden, the Netherlands; Leiden Institute for Brain and Cognition, Leiden, the Netherlands.
| | - Wouter van den Bos
- Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands; Max Planck Institute for Human Development, Center for Adaptive Rationality, Berlin, Germany
| | - Christian K Tamnes
- PROMENTA Research Center, Department of Psychology, University of Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychiatry, Diakonhjemmet Hospital, Oslo, Norway
| | - Nora M Raschle
- Jacobs Center for Productive Youth Development at the University of Zurich, Zurich, Switzerland
| | - Kathryn L Mills
- PROMENTA Research Center, Department of Psychology, University of Oslo, Norway; Department of Psychology, University of Oregon, Eugene, OR, USA
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32
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Jolly E, Sadhukha S, Chang LJ. Response to Lynch et al: On measuring head motion and effects of head molds during fMRI. Neuroimage 2021; 225:117484. [PMID: 33160085 PMCID: PMC7953432 DOI: 10.1016/j.neuroimage.2020.117484] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [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: 10/19/2020] [Accepted: 10/20/2020] [Indexed: 11/24/2022] Open
Abstract
We recently presented evidence indicating limited efficacy of custom-molded headcases in reducing head motion in two naturalistic experimental contexts - passive movie watching, and speaking in the scanner (Jolly et al., 2020). In a commentary on this work, Lynch et al (2020) present additional data that support the original findings of (Power et al., 2019) and raise several potential issues with our recent work. We appreciate the opportunity to address these criticisms and raise additional points that should be considered when interpreting these conflicting findings. We do not believe that their criticisms diminish the value of our work, but instead, along with this reply, help better elucidate the key factors researchers should consider to make the most informed choice about their own research protocols.
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Affiliation(s)
- E Jolly
- Computational Social Affective Neuroscience Laboratory, Department of Psychological and Brain Science, Dartmouth College, 6207 Moore Hall, Hanover, NH 03755, United States.
| | - S Sadhukha
- Department of Psychology, New York University, United States
| | - L J Chang
- Computational Social Affective Neuroscience Laboratory, Department of Psychological and Brain Science, Dartmouth College, 6207 Moore Hall, Hanover, NH 03755, United States
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33
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Buch AM, Liston C. Dissecting diagnostic heterogeneity in depression by integrating neuroimaging and genetics. Neuropsychopharmacology 2021; 46:156-75. [PMID: 32781460 DOI: 10.1038/s41386-020-00789-3] [Citation(s) in RCA: 88] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 07/07/2020] [Accepted: 07/16/2020] [Indexed: 12/12/2022]
Abstract
Depression is a heterogeneous and etiologically complex psychiatric syndrome, not a unitary disease entity, encompassing a broad spectrum of psychopathology arising from distinct pathophysiological mechanisms. Motivated by a need to advance our understanding of these mechanisms and develop new treatment strategies, there is a renewed interest in investigating the neurobiological basis of heterogeneity in depression and rethinking our approach to diagnosis for research purposes. Large-scale genome-wide association studies have now identified multiple genetic risk variants implicating excitatory neurotransmission and synapse function and underscoring a highly polygenic inheritance pattern that may be another important contributor to heterogeneity in depression. Here, we review various sources of phenotypic heterogeneity and approaches to defining and studying depression subtypes, including symptom-based subtypes and biology-based approaches to decomposing the depression syndrome. We review "dimensional," "categorical," and "hybrid" approaches to parsing phenotypic heterogeneity in depression and defining subtypes using functional neuroimaging. Next, we review recent progress in neuroimaging genetics (correlating neuroimaging patterns of brain function with genetic data) and its potential utility for generating testable hypotheses concerning molecular and circuit-level mechanisms. We discuss how genetic variants and transcriptomic profiles may confer risk for depression by modulating brain structure and function. We conclude by highlighting several promising areas for future research into the neurobiological underpinnings of heterogeneity, including efforts to understand sexually dimorphic mechanisms, the longitudinal dynamics of depressive episodes, and strategies for developing personalized treatments and facilitating clinical decision-making.
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34
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Horien C, Fontenelle S, Joseph K, Powell N, Nutor C, Fortes D, Butler M, Powell K, Macris D, Lee K, Greene AS, McPartland JC, Volkmar FR, Scheinost D, Chawarska K, Constable RT. Low-motion fMRI data can be obtained in pediatric participants undergoing a 60-minute scan protocol. Sci Rep 2020; 10:21855. [PMID: 33318557 PMCID: PMC7736342 DOI: 10.1038/s41598-020-78885-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [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: 09/08/2020] [Accepted: 12/01/2020] [Indexed: 01/21/2023] Open
Abstract
Performing functional magnetic resonance imaging (fMRI) scans of children can be a difficult task, as participants tend to move while being scanned. Head motion represents a significant confound in fMRI connectivity analyses. One approach to limit motion has been to use shorter MRI protocols, though this reduces the reliability of results. Hence, there is a need to implement methods to achieve high-quality, low-motion data while not sacrificing data quantity. Here we show that by using a mock scan protocol prior to a scan, in conjunction with other in-scan steps (weighted blanket and incentive system), it is possible to achieve low-motion fMRI data in pediatric participants (age range: 7-17 years old) undergoing a 60 min MRI session. We also observe that motion is low during the MRI protocol in a separate replication group of participants, including some with autism spectrum disorder. Collectively, the results indicate it is possible to conduct long scan protocols in difficult-to-scan populations and still achieve high-quality data, thus potentially allowing more reliable fMRI findings.
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Affiliation(s)
- Corey Horien
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA.
- MD-PhD Program, Yale School of Medicine, New Haven, CT, USA.
- Magnetic Resonance Research Center, 300 Cedar St, PO Box 208043, New Haven, CT, 06520-8043, USA.
| | | | | | | | | | | | | | | | | | - Kangjoo Lee
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Abigail S Greene
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA
- MD-PhD Program, Yale School of Medicine, New Haven, CT, USA
| | - James C McPartland
- Yale Child Study Center, New Haven, CT, USA
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Fred R Volkmar
- Yale Child Study Center, New Haven, CT, USA
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Dustin Scheinost
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA
- Yale Child Study Center, New Haven, CT, USA
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT, USA
| | - Katarzyna Chawarska
- Yale Child Study Center, New Haven, CT, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT, USA
- Department of Pediatrics, Yale School of Medicine, New Haven, CT, USA
| | - R Todd Constable
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
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35
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Lynch CJ, Power JD, Scult MA, Dubin M, Gunning FM, Liston C. Rapid Precision Functional Mapping of Individuals Using Multi-Echo fMRI. Cell Rep 2020; 33:108540. [PMID: 33357444 PMCID: PMC7792478 DOI: 10.1016/j.celrep.2020.108540] [Citation(s) in RCA: 75] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 10/15/2020] [Accepted: 11/25/2020] [Indexed: 12/20/2022] Open
Abstract
Resting-state functional magnetic resonance imaging (fMRI) is widely used in cognitive and clinical neuroscience, but long-duration scans are currently needed to reliably characterize individual differences in functional connectivity (FC) and brain network topology. In this report, we demonstrate that multi-echo fMRI can improve the reliability of FC-based measurements. In four densely sampled individual humans, just 10 min of multi-echo data yielded better test-retest reliability than 30 min of single-echo data in independent datasets. This effect is pronounced in clinically important brain regions, including the subgenual cingulate, basal ganglia, and cerebellum, and is linked to three biophysical signal mechanisms (thermal noise, regional variability in the rate of T2* decay, and S0-dependent artifacts) with spatially distinct influences. Together, these findings establish the potential utility of multi-echo fMRI for rapid precision mapping using experimentally and clinically tractable scan times and will facilitate longitudinal neuroimaging of clinical populations. Lynch et al. demonstrate that the test-retest reliability of resting-state connectivity measurements can be improved using multi-echo fMRI. This effect is pronounced in clinically important brain regions and could help facilitate precision mapping of functional brain networks in healthy people and patient populations.
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Affiliation(s)
- Charles J Lynch
- Department of Psychiatry, Weill Cornell Medicine, New York, NY 10021, USA.
| | - Jonathan D Power
- Department of Psychiatry, Weill Cornell Medicine, New York, NY 10021, USA
| | - Matthew A Scult
- Department of Psychiatry, Weill Cornell Medicine, New York, NY 10021, USA
| | - Marc Dubin
- Department of Psychiatry, Weill Cornell Medicine, New York, NY 10021, USA
| | - Faith M Gunning
- Department of Psychiatry, Weill Cornell Medicine, New York, NY 10021, USA
| | - Conor Liston
- Department of Psychiatry, Weill Cornell Medicine, New York, NY 10021, USA.
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36
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Ai L, Craddock RC, Tottenham N, Dyke JP, Lim R, Colcombe S, Milham M, Franco AR. Is it time to switch your T1W sequence? Assessing the impact of prospective motion correction on the reliability and quality of structural imaging. Neuroimage 2021; 226:117585. [PMID: 33248256 DOI: 10.1016/j.neuroimage.2020.117585] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Revised: 10/29/2020] [Accepted: 11/18/2020] [Indexed: 12/11/2022] Open
Abstract
New large neuroimaging studies, such as the Adolescent Brain Cognitive Development study (ABCD) and Human Connectome Project (HCP) Development studies are adopting a new T1-weighted imaging sequence with prospective motion correction (PMC) in favor of the more traditional 3-Dimensional Magnetization-Prepared Rapid Gradient-Echo Imaging (MPRAGE) sequence. Here, we used a developmental dataset (ages 5-21, N = 348) from the Healthy Brain Network (HBN) Initiative to directly compare two widely used MRI structural sequences: one based on the Human Connectome Project (MPRAGE) and another based on the ABCD study (MPRAGE+PMC). We aimed to determine if the morphometric measurements obtained from both protocols are equivalent or if one sequence has a clear advantage over the other. The sequences were also compared through quality control measurements. Inter- and intra-sequence reliability were assessed with another set of participants (N = 71) from HBN that performed two MPRAGE and two MPRAGE+PMC sequences within the same imaging session, with one MPRAGE (MPRAGE1) and MPRAGE+PMC (MPRAGE+PMC1) pair at the beginning of the session and another pair (MPRAGE2 and MPRAGE+PMC2) at the end of the session. Intraclass correlation coefficients (ICC) scores for morphometric measurements such as volume and cortical thickness showed that intra-sequence reliability is the highest with the two MPRAGE+PMC sequences and lowest with the two MPRAGE sequences. Regarding inter-sequence reliability, ICC scores were higher for the MPRAGE1 - MPRAGE+PMC1 pair at the beginning of the session than the MPRAGE1 - MPRAGE2 pair, possibly due to the higher motion artifacts in the MPRAGE2 run. Results also indicated that the MPRAGE+PMC sequence is robust, but not impervious, to high head motion. For quality control metrics, the traditional MPRAGE yielded better results than MPRAGE+PMC in 5 of the 8 measurements. In conclusion, morphometric measurements evaluated here showed high inter-sequence reliability between the MPRAGE and MPRAGE+PMC sequences, especially in images with low head motion. We suggest that studies targeting hyperkinetic populations use the MPRAGE+PMC sequence, given its robustness to head motion and higher reliability scores. However, neuroimaging researchers studying non-hyperkinetic participants can choose either MPRAGE or MPRAGE+PMC sequences, but should carefully consider the apparent tradeoff between relatively increased reliability, but reduced quality control metrics when using the MPRAGE+PMC sequence.
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37
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Jolly E, Sadhukha S, Chang LJ. Custom-molded headcases have limited efficacy in reducing head motion during naturalistic fMRI experiments. Neuroimage 2020; 222:117207. [PMID: 32745683 PMCID: PMC7830829 DOI: 10.1016/j.neuroimage.2020.117207] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [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: 03/27/2020] [Revised: 06/23/2020] [Accepted: 07/23/2020] [Indexed: 01/08/2023] Open
Abstract
Effectively minimizing head motion continues to be a challenge for the collection of functional magnetic resonance imaging (fMRI) data. The use of individual-specific custom molded headcases is a promising solution to this issue, but there has been limited work to date. In the present work, we examine the efficacy of headcases in a larger group of participants engaged in naturalistic scanning paradigms including: long movie-watching scans (~20 to 45min) and a recall task that involved talking aloud inside the MRI. Unlike previous work, we find that headcases do not reliably reduce motion during movie viewing compared to alternative methods such as foam pillows or foam pillows plus medical tape. Surprisingly, we also find that motion is worse when participants talk aloud while wearing headcases. These differences appear to be driven by large, brief rotations of the head as well as translations in the z-plane as participants speak. Smaller, constant head movements appear equivalent with or without headcases. The largest reductions in head motion are observable when participants were situated with both foam pillows and medical tape. Altogether, this work suggests that in a healthy adult population, custom-molded headcases may provide limited efficacy in reducing head motion beyond existing tools available to researchers. We hope this work can help improve the quality of custom headcases, motivate the investigation of additional solutions, and provide additional information about head motion in naturalistic contexts.
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Affiliation(s)
- E Jolly
- Computational Social Affective Neuroscience Laboratory, Department of Psychological and Brain Science, Dartmouth College, 6207 Moore Hall, Hanover, NH 03755, United States.
| | - S Sadhukha
- Department of Psychology, New York University, United States
| | - L J Chang
- Computational Social Affective Neuroscience Laboratory, Department of Psychological and Brain Science, Dartmouth College, 6207 Moore Hall, Hanover, NH 03755, United States
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38
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Lynch CJ, Voss HU, Silver BM, Power JD. On measuring head motion and effects of head molds during fMRI. Neuroimage 2020; 225:117494. [PMID: 33166644 DOI: 10.1016/j.neuroimage.2020.117494] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 09/30/2020] [Accepted: 10/20/2020] [Indexed: 11/18/2022] Open
Affiliation(s)
- Charles J Lynch
- Department of Psychiatry, Weill Cornell Medicine, 1300 York Avenue, New York, NY 10065 USA.
| | - Henning U Voss
- Department of Radiology, Weill Cornell Medicine, Citigroup Biomedical Imaging Center, 516 East 72nd Street, New York, NY 10021 USA.
| | - Benjamin M Silver
- Department of Psychology, Columbia University, 1180 Amsterdam Avenue, New York, NY 10027 USA.
| | - Jonathan D Power
- Department of Psychiatry, Weill Cornell Medicine, 1300 York Avenue, New York, NY 10065 USA.
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39
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Kopal J, Pidnebesna A, Tomeček D, Tintěra J, Hlinka J. Typicality of functional connectivity robustly captures motion artifacts in rs-fMRI across datasets, atlases, and preprocessing pipelines. Hum Brain Mapp 2020; 41:5325-5340. [PMID: 32881215 PMCID: PMC7670643 DOI: 10.1002/hbm.25195] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 08/07/2020] [Accepted: 08/09/2020] [Indexed: 12/25/2022] Open
Abstract
Functional connectivity analysis of resting-state fMRI data has recently become one of the most common approaches to characterizing individual brain function. It has been widely suggested that the functional connectivity matrix is a useful approximate representation of the brain's connectivity, potentially providing behaviorally or clinically relevant markers. However, functional connectivity estimates are known to be detrimentally affected by various artifacts, including those due to in-scanner head motion. Moreover, as individual functional connections generally covary only very weakly with head motion estimates, motion influence is difficult to quantify robustly, and prone to be neglected in practice. Although the use of individual estimates of head motion, or group-level correlation of motion and functional connectivity has been suggested, a sufficiently sensitive measure of individual functional connectivity quality has not yet been established. We propose a new intuitive summary index, Typicality of Functional Connectivity, to capture deviations from standard brain functional connectivity patterns. In a resting-state fMRI dataset of 245 healthy subjects, this measure was significantly correlated with individual head motion metrics. The results were further robustly reproduced across atlas granularity, preprocessing options, and other datasets, including 1,081 subjects from the Human Connectome Project. In principle, Typicality of Functional Connectivity should be sensitive also to other types of artifacts, processing errors, and possibly also brain pathology, allowing extensive use in data quality screening and quantification in functional connectivity studies as well as methodological investigations.
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Affiliation(s)
- Jakub Kopal
- Institute of Computer Science of the Czech Academy of Sciences, Prague, Czech Republic.,Department of Computing and Control Engineering, University of Chemistry and Technology, Prague, Czech Republic.,Centre de Recherche Cerveau et Cognition, Universite Paul Sabatier, Toulouse, France
| | - Anna Pidnebesna
- Institute of Computer Science of the Czech Academy of Sciences, Prague, Czech Republic.,National Institute of Mental Health, Klecany, Czech Republic.,Faculty of Electrical Engineering, Czech Technical University, Prague, Czech Republic
| | - David Tomeček
- National Institute of Mental Health, Klecany, Czech Republic.,Faculty of Electrical Engineering, Czech Technical University, Prague, Czech Republic
| | - Jaroslav Tintěra
- National Institute of Mental Health, Klecany, Czech Republic.,Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - Jaroslav Hlinka
- Institute of Computer Science of the Czech Academy of Sciences, Prague, Czech Republic.,National Institute of Mental Health, Klecany, Czech Republic
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40
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Kim H, Jin S, Jo S, Lee J. A naturalistic viewing paradigm using 360° panoramic video clips and real-time field-of-view changes with eye-gaze tracking. Neuroimage 2020; 216:116617. [DOI: 10.1016/j.neuroimage.2020.116617] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Accepted: 02/05/2020] [Indexed: 11/18/2022] Open
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41
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Gratton C, Kraus BT, Greene DJ, Gordon EM, Laumann TO, Nelson SM, Dosenbach NUF, Petersen SE. Defining Individual-Specific Functional Neuroanatomy for Precision Psychiatry. Biol Psychiatry 2020; 88:28-39. [PMID: 31916942 PMCID: PMC7203002 DOI: 10.1016/j.biopsych.2019.10.026] [Citation(s) in RCA: 78] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Revised: 10/07/2019] [Accepted: 10/25/2019] [Indexed: 12/28/2022]
Abstract
Studies comparing diverse groups have shown that many psychiatric diseases involve disruptions across distributed large-scale networks of the brain. There is hope that functional magnetic resonance imaging (fMRI) functional connectivity techniques will shed light on these disruptions, providing prognostic and diagnostic biomarkers as well as targets for therapeutic interventions. However, to date, progress on clinical translation of fMRI methods has been limited. Here, we argue that this limited translation is driven by a combination of intersubject heterogeneity and the relatively low reliability of standard fMRI techniques at the individual level. We review a potential solution to these limitations: the use of new "precision" fMRI approaches that shift the focus of analysis from groups to single individuals through the use of extended data acquisition strategies. We begin by discussing the potential advantages of fMRI functional connectivity methods for improving our understanding of functional neuroanatomy and disruptions in psychiatric disorders. We then discuss the budding field of precision fMRI and findings garnered from this work. We demonstrate that precision fMRI can improve the reliability of functional connectivity measures, while showing high stability and sensitivity to individual differences. We close by discussing the application of these approaches to clinical settings.
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Affiliation(s)
- Caterina Gratton
- Department of Psychology, Northwestern University, Evanston, Illinois; Department of Neurology, Northwestern University, Evanston, Illinois.
| | - Brian T Kraus
- Department of Psychology, Northwestern University, Evanston, Illinois
| | - Deanna J Greene
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri; Department of Radiology, Washington University in St. Louis, St. Louis, Missouri
| | - Evan M Gordon
- VISN Center of Excellence for Research on Returning War Veterans, Waco, Texas; Department of Psychology and Neuroscience, Baylor University, Waco, Texas; Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, Texas
| | - Timothy O Laumann
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri
| | - Steven M Nelson
- VISN Center of Excellence for Research on Returning War Veterans, Waco, Texas; Department of Psychology and Neuroscience, Baylor University, Waco, Texas; Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, Texas; Department of Psychiatry and Behavioral Science, Texas A&M Health Science Center, College of Medicine, Bryan, Texas
| | - Nico U F Dosenbach
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri; Department of Neurology, Washington University in St. Louis, St. Louis, Missouri; Department of Pediatrics, Washington University in St. Louis, St. Louis, Missouri; Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri
| | - Steven E Petersen
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri; Department of Radiology, Washington University in St. Louis, St. Louis, Missouri; Department of Neurology, Washington University in St. Louis, St. Louis, Missouri; Department of Neuroscience, Washington University in St. Louis, St. Louis, Missouri; Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, Missouri
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42
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Lynch CJ, Gunning FM, Liston C. Causes and Consequences of Diagnostic Heterogeneity in Depression: Paths to Discovering Novel Biological Depression Subtypes. Biol Psychiatry 2020; 88:83-94. [PMID: 32171465 DOI: 10.1016/j.biopsych.2020.01.012] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [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: 09/26/2019] [Revised: 12/13/2019] [Accepted: 01/18/2020] [Indexed: 12/17/2022]
Abstract
Depression is a highly heterogeneous syndrome that bears only modest correlations with its biological substrates, motivating a renewed interest in rethinking our approach to diagnosing depression for research purposes and new efforts to discover subtypes of depression anchored in biology. Here, we review the major causes of diagnostic heterogeneity in depression, with consideration of both clinical symptoms and behaviors (symptomatology and trajectory of depressive episodes) and biology (genetics and sexually dimorphic factors). Next, we discuss the promise of using data-driven strategies to discover novel subtypes of depression based on functional neuroimaging measures, including dimensional, categorical, and hybrid approaches to parsing diagnostic heterogeneity and understanding its biological basis. The merits of using resting-state functional magnetic resonance imaging functional connectivity techniques for subtyping are considered along with a set of technical challenges and potential solutions. We conclude by identifying promising future directions for defining neurobiologically informed depression subtypes and leveraging them in the future for predicting treatment outcomes and informing clinical decision making.
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Affiliation(s)
- Charles J Lynch
- Brain and Mind Research Institute and Department of Psychiatry, Weill Cornell Medicine, New York, New York
| | - Faith M Gunning
- Brain and Mind Research Institute and Department of Psychiatry, Weill Cornell Medicine, New York, New York
| | - Conor Liston
- Brain and Mind Research Institute and Department of Psychiatry, Weill Cornell Medicine, New York, New York.
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43
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Power JD, Lynch CJ, Adeyemo B, Petersen SE. A Critical, Event-Related Appraisal of Denoising in Resting-State fMRI Studies. Cereb Cortex 2020; 30:5544-5559. [PMID: 32494823 DOI: 10.1093/cercor/bhaa139] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [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: 02/05/2020] [Revised: 05/04/2020] [Accepted: 05/04/2020] [Indexed: 12/12/2022] Open
Abstract
This article advances two parallel lines of argument about resting-state functional magnetic resonance imaging (fMRI) signals, one empirical and one conceptual. The empirical line creates a four-part organization of the text: (1) head motion and respiration commonly cause distinct, major, unwanted influences (artifacts) in fMRI signals; (2) head motion and respiratory changes are, confoundingly, both related to psychological and clinical and biological variables of interest; (3) many fMRI denoising strategies fail to identify and remove one or the other kind of artifact; and (4) unremoved artifact, due to correlations of artifacts with variables of interest, renders studies susceptible to identifying variance of noninterest as variance of interest. Arising from these empirical observations is a conceptual argument: that an event-related approach to task-free scans, targeting common behaviors during scanning, enables fundamental distinctions among the kinds of signals present in the data, information which is vital to understanding the effects of denoising procedures. This event-related perspective permits statements like "Event X is associated with signals A, B, and C, each with particular spatial, temporal, and signal decay properties". Denoising approaches can then be tailored, via performance in known events, to permit or suppress certain kinds of signals based on their desirability.
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Affiliation(s)
- Jonathan D Power
- Sackler Institute for Developmental Psychobiology, Department of Psychiatry, Weill Cornell Medicine, 1300 York Avenue, New York, NY 10065, USA
| | - Charles J Lynch
- Brain and Mind Research Institute, Weill Cornell Medicine, 1300 York Avenue, New York, NY 10065, USA
| | - Babatunde Adeyemo
- Departments of Neurology and Psychology, Washington University School of Medicine, 660 S Euclid Ave, St. Louis, MO 63110, USA
| | - Steven E Petersen
- Departments of Neurology and Psychology, Washington University School of Medicine, 660 S Euclid Ave, St. Louis, MO 63110, USA
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44
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Meissner TW, Walbrin J, Nordt M, Koldewyn K, Weigelt S. Head motion during fMRI tasks is reduced in children and adults if participants take breaks. Dev Cogn Neurosci 2020; 44:100803. [PMID: 32716852 PMCID: PMC7284013 DOI: 10.1016/j.dcn.2020.100803] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Revised: 04/20/2020] [Accepted: 05/27/2020] [Indexed: 12/30/2022] Open
Abstract
In children, fMRI data acquisition split into multiple sessions reduces head motion. In adults, fMRI data acquisition split by inside-scanner breaks reduces head motion. In both children and adults, motion increases over the duration of a study. In both children and adults, motion increases over the duration of a run.
Head motion remains a challenging confound in functional magnetic resonance imaging (fMRI) studies of both children and adults. Most pediatric neuroimaging labs have developed experience-based, child-friendly standards concerning e.g. the maximum length of a session or the time between mock scanner training and actual scanning. However, it is unclear which factors of child-friendly neuroimaging approaches are effective in reducing head motion. Here, we investigate three main factors including (i) time lag of mock scanner training to the actual scan, (ii) prior scan time, and (iii) task engagement in a dataset of 77 children (aged 6–13) and 64 adults (aged 18–35) using a multilevel modeling approach. In children, distributing fMRI data acquisition across multiple same-day sessions reduces head motion. In adults, motion is reduced after inside-scanner breaks. Despite these positive effects of splitting up data acquisition, motion increases over the course of a study as well as over the course of a run in both children and adults. Our results suggest that splitting up fMRI data acquisition is an effective tool to reduce head motion in general. At the same time, different ways of splitting up data acquisition benefit children and adults.
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Affiliation(s)
- Tobias W Meissner
- TU Dortmund University, Faculty of Rehabilitation Sciences, Department of Vision, Visual Impairments & Blindness, Emil-Figge-Str. 50, 44227, Dortmund, Germany; Ruhr University Bochum, Faculty of Psychology, Universitätsstr. 150, 44801, Bochum, Germany.
| | - Jon Walbrin
- Bangor University, School of Psychology, Developmental Social Vision Lab, Penrallt Road, Bangor, LL57 2AS, Wales, United Kingdom; University of Coimbra, Faculty of Psychology and Education Sciences, Proaction Lab, Rua Colégio Novo, 3000-115, Coimbra, Portugal.
| | - Marisa Nordt
- Ruhr University Bochum, Faculty of Psychology, Universitätsstr. 150, 44801, Bochum, Germany; Stanford University, Psychology Department, 450 Serra Mall, Stanford, CA, 94305, USA.
| | - Kami Koldewyn
- Bangor University, School of Psychology, Developmental Social Vision Lab, Penrallt Road, Bangor, LL57 2AS, Wales, United Kingdom.
| | - Sarah Weigelt
- TU Dortmund University, Faculty of Rehabilitation Sciences, Department of Vision, Visual Impairments & Blindness, Emil-Figge-Str. 50, 44227, Dortmund, Germany.
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45
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Beyer F, Prehn K, Wüsten KA, Villringer A, Ordemann J, Flöel A, Witte AV. Weight loss reduces head motion: Revisiting a major confound in neuroimaging. Hum Brain Mapp 2020; 41:2490-2494. [PMID: 32239733 PMCID: PMC7267971 DOI: 10.1002/hbm.24959] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 01/17/2020] [Accepted: 02/11/2020] [Indexed: 01/09/2023] Open
Abstract
Head motion during magnetic resonance imaging (MRI) induces image artifacts that affect virtually every brain measure. In parallel, cross‐sectional observations indicate a correlation of head motion with age, psychiatric disease status and obesity, raising the possibility of a systematic artifact‐induced bias in neuroimaging outcomes in these conditions, due to the differences in head motion. Yet, a causal link between obesity and head motion has not been tested in an experimental design. Here, we show that a change in body mass index (BMI) (i.e., weight loss after bariatric surgery) systematically decreases head motion during MRI. In this setting, reduced imaging artifacts due to lower head motion might result in biased estimates of neural differences induced by changes in BMI. Overall, our finding urges the need to rigorously control for head motion during MRI to enable valid results of neuroimaging outcomes in populations that differ in head motion due to obesity or other conditions.
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Affiliation(s)
- Frauke Beyer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Subproject A1, CRC 1052 "Obesity Mechanisms", University of Leipzig, Leipzig, Germany
| | - Kristin Prehn
- Department of Neurology & NeuroCure Clinical Research Center, Charité University Medicine, Berlin, Germany.,Department of Psychology, Medical School Hamburg, Hamburg, Germany
| | - Katharina A Wüsten
- Department of Neurology, University of Greifswald, Greifswald, Germany.,German Center for Neurodegenerative Diseases, Standort Rostock/Greifswald, Greifswald, Germany
| | - Arno Villringer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Subproject A1, CRC 1052 "Obesity Mechanisms", University of Leipzig, Leipzig, Germany
| | - Jürgen Ordemann
- Center for Bariatric and Metabolic Surgery, Charité University Medicine, Berlin, Germany.,Zentrum für Adipositas und Metabolische Chirurgie, Vivantes Klinikum Spandau, Berlin, Germany
| | - Agnes Flöel
- Department of Neurology & NeuroCure Clinical Research Center, Charité University Medicine, Berlin, Germany.,Department of Neurology, University of Greifswald, Greifswald, Germany.,German Center for Neurodegenerative Diseases, Standort Rostock/Greifswald, Greifswald, Germany.,Center for Stroke Research, Charité University Medicine, Berlin, Germany
| | - A Veronica Witte
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Subproject A1, CRC 1052 "Obesity Mechanisms", University of Leipzig, Leipzig, Germany
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Lord C, Brugha TS, Charman T, Cusack J, Dumas G, Frazier T, Jones EJH, Jones RM, Pickles A, State MW, Taylor JL, Veenstra-VanderWeele J. Autism spectrum disorder. Nat Rev Dis Primers 2020; 6:5. [PMID: 31949163 PMCID: PMC8900942 DOI: 10.1038/s41572-019-0138-4] [Citation(s) in RCA: 559] [Impact Index Per Article: 139.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/26/2019] [Indexed: 12/27/2022]
Abstract
Autism spectrum disorder is a construct used to describe individuals with a specific combination of impairments in social communication and repetitive behaviours, highly restricted interests and/or sensory behaviours beginning early in life. The worldwide prevalence of autism is just under 1%, but estimates are higher in high-income countries. Although gross brain pathology is not characteristic of autism, subtle anatomical and functional differences have been observed in post-mortem, neuroimaging and electrophysiological studies. Initially, it was hoped that accurate measurement of behavioural phenotypes would lead to specific genetic subtypes, but genetic findings have mainly applied to heterogeneous groups that are not specific to autism. Psychosocial interventions in children can improve specific behaviours, such as joint attention, language and social engagement, that may affect further development and could reduce symptom severity. However, further research is necessary to identify the long-term needs of people with autism, and treatments and the mechanisms behind them that could result in improved independence and quality of life over time. Families are often the major source of support for people with autism throughout much of life and need to be considered, along with the perspectives of autistic individuals, in both research and practice.
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Affiliation(s)
- Catherine Lord
- Departments of Psychiatry and School of Education, University of California, Los Angeles, Los Angeles, CA, USA.
| | - Traolach S Brugha
- Department of Health Sciences, University of Leicester, Leicester, UK
| | - Tony Charman
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | | | - Guillaume Dumas
- Institut Pasteur, UMR3571 CNRS, Université de Paris, Paris, France
| | | | - Emily J H Jones
- Centre for Brain & Cognitive Development, University of London, London, UK
| | - Rebecca M Jones
- The Sackler Institute for Developmental Psychobiology, New York, NY, USA
- The Center for Autism and the Developing Brain, White Plains, NY, USA
| | - Andrew Pickles
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Matthew W State
- Department of Psychiatry, Langley Porter Psychiatric Institute and Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Julie Lounds Taylor
- Department of Pediatrics and Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN, USA
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Bolton TAW, Kebets V, Glerean E, Zöller D, Li J, Yeo BTT, Caballero-Gaudes C, Van De Ville D. Agito ergo sum: Correlates of spatio-temporal motion characteristics during fMRI. Neuroimage 2019; 209:116433. [PMID: 31841680 DOI: 10.1016/j.neuroimage.2019.116433] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Revised: 11/11/2019] [Accepted: 12/02/2019] [Indexed: 12/21/2022] Open
Abstract
The impact of in-scanner motion on functional magnetic resonance imaging (fMRI) data has a notorious reputation in the neuroimaging community. State-of-the-art guidelines advise to scrub out excessively corrupted frames as assessed by a composite framewise displacement (FD) score, to regress out models of nuisance variables, and to include average FD as a covariate in group-level analyses. Here, we studied individual motion time courses at time points typically retained in fMRI analyses. We observed that even in this set of putatively clean time points, motion exhibited a very clear spatio-temporal structure, so that we could distinguish subjects into separate groups of movers with varying characteristics. Then, we showed that this spatio-temporal motion cartography tightly relates to a broad array of anthropometric and cognitive factors. Convergent results were obtained from two different analytical perspectives: univariate assessment of behavioural differences across mover subgroups unraveled defining markers, while subsequent multivariate analysis broadened the range of involved factors and clarified that multiple motion/behaviour modes of covariance overlap in the data. Our results demonstrate that even the smaller episodes of motion typically retained in fMRI analyses carry structured, behaviourally relevant information. They call for further examinations of possible biases in current regression-based motion correction strategies.
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Affiliation(s)
- Thomas A W Bolton
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland.
| | - Valeria Kebets
- Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland; Department of Electrical and Computer Engineering, Clinical Imaging Research Centre, Centre for Sleep and Cognition, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore
| | - Enrico Glerean
- Department of Neuroscience and Biomedical Engineering, Aalto University, Helsinki, Finland
| | - Daniela Zöller
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland; Developmental Imaging and Psychopathology Laboratory, Office Médico-Pédagogique, Department of Psychiatry, University of Geneva (UNIGE), Geneva, Switzerland
| | - Jingwei Li
- Department of Electrical and Computer Engineering, Clinical Imaging Research Centre, Centre for Sleep and Cognition, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, Clinical Imaging Research Centre, Centre for Sleep and Cognition, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore
| | | | - Dimitri Van De Ville
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
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48
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Power JD, Lynch CJ, Silver BM, Dubin MJ, Martin A, Jones RM. Distinctions among real and apparent respiratory motions in human fMRI data. Neuroimage 2019; 201:116041. [PMID: 31344484 PMCID: PMC6765416 DOI: 10.1016/j.neuroimage.2019.116041] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [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: 04/06/2019] [Revised: 07/17/2019] [Accepted: 07/20/2019] [Indexed: 11/21/2022] Open
Abstract
Head motion estimates in functional magnetic resonance imaging (fMRI) scans appear qualitatively different with sub-second image sampling rates compared to the multi-second sampling rates common in the past. Whereas formerly the head appeared still for much of a scan with brief excursions from baseline, the head now appears to be in constant motion, and motion estimates often seem to divulge little information about what is happening in a scan. This constant motion has been attributed to respiratory oscillations that do not alias at faster sampling rates, and investigators are divided on the extent to which such motion is "real" motion or only "apparent" pseudomotion. Some investigators have abandoned the use of motion estimates entirely due to these considerations. Here we investigate the properties of motion in several fMRI datasets sampled at rates between 720 and 1160 ms, and describe 5 distinct kinds of respiratory motion: 1) constant real respiratory motion in the form of head nodding most evident in vertical position and pitch, which can be very large; 2) constant pseudomotion at the same respiratory rate as real motion, occurring only in the phase encode direction; 3) punctate real motions occurring at times of very deep breaths; 4) a low-frequency pseudomotion in only the phase encode direction at and after very deep breaths; 5) slow modulation of vertical and anterior-posterior head position by the respiratory envelope. We reformulate motion estimates in light of these considerations and obtain good concordance between motion estimates, physiologic records, image quality measures, and events evident in the fMRI signals. We demonstrate how variables describing respiration or body habitus separately scale with distinct kinds of head motion. We also note heritable aspects of respiration and motion.
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Affiliation(s)
- Jonathan D Power
- Sackler Institute for Developmental Psychobiology, Department of Psychiatry, Weill Cornell Medicine, 1300 York Avenue, Box 140, New York, NY, 10065, USA.
| | - Charles J Lynch
- Brain and Mind Research Institute, Weill Cornell Medicine, 1300 York Avenue, Box 140, New York, NY, 10065, USA.
| | - Benjamin M Silver
- Sackler Institute for Developmental Psychobiology, Department of Psychiatry, Weill Cornell Medicine, 1300 York Avenue, Box 140, New York, NY, 10065, USA.
| | - Marc J Dubin
- Department of Psychiatry, Weill Cornell Medicine, 1300 York Avenue, Box 140, New York, NY, 10065, USA.
| | - Alex Martin
- National Institute for Mental Health, 10 Center Dr., Bethesda, MD, 20814, USA.
| | - Rebecca M Jones
- Sackler Institute for Developmental Psychobiology, Department of Psychiatry, Weill Cornell Medicine, 1300 York Avenue, Box 140, New York, NY, 10065, USA.
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49
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Power JD, Lynch CJ, Dubin MJ, Silver BM, Martin A, Jones RM. Characteristics of respiratory measures in young adults scanned at rest, including systematic changes and "missed" deep breaths. Neuroimage 2020; 204:116234. [PMID: 31589990 DOI: 10.1016/j.neuroimage.2019.116234] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Revised: 09/04/2019] [Accepted: 09/27/2019] [Indexed: 11/20/2022] Open
Abstract
Breathing rate and depth influence the concentration of carbon dioxide in the blood, altering cerebral blood flow and thus functional magnetic resonance imaging (fMRI) signals. Such respiratory fluctuations can have substantial influence in studies of fMRI signal covariance in subjects at rest, the so-called "resting state functional connectivity" technique. If respiration is monitored during fMRI scanning, it is typically done using a belt about the subject's abdomen to record abdominal circumference. Several measures have been derived from these belt records, including the windowed envelope of the waveform (ENV), the windowed variance in the waveform (respiration variation, RV), and a measure of the amplitude of each breath divided by the cycle time of the breath (respiration volume per time, RVT). Any attempt to gauge respiratory contributions to fMRI signals requires a respiratory measure, but little is known about how these measures compare to each other, or how they perform beyond the small studies in which they were initially proposed. Here, we examine the properties of these measures in hundreds of healthy young adults scanned for an hour each at rest, a subset of the Human Connectome Project chosen for having high-quality physiological records. We find: 1) ENV, RV, and RVT are all correlated, and ENV and RV are more highly correlated to each other than to RVT; 2) respiratory events like deep breaths exhibit characteristic heart rate elevations, fMRI signal changes, head motions, and image quality abnormalities time-locked to large deflections in the belt traces; 3) all measures can "miss" deep breaths; 4) RVT "misses" deep breaths more than ENV or RV; 5) all respiratory measures change systematically over the course of a 14.4-min scan. We discuss the implications of these findings for the literature and ways to move forward in modeling respiratory influences on fMRI scans.
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