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Caeyenberghs K, Imms P, Irimia A, Monti MM, Esopenko C, de Souza NL, Dominguez D JF, Newsome MR, Dobryakova E, Cwiek A, Mullin HAC, Kim NJ, Mayer AR, Adamson MM, Bickart K, Breedlove KM, Dennis EL, Disner SG, Haswell C, Hodges CB, Hoskinson KR, Johnson PK, Königs M, Li LM, Liebel SW, Livny A, Morey RA, Muir AM, Olsen A, Razi A, Su M, Tate DF, Velez C, Wilde EA, Zielinski BA, Thompson PM, Hillary FG. ENIGMA's simple seven: Recommendations to enhance the reproducibility of resting-state fMRI in traumatic brain injury. Neuroimage Clin 2024; 42:103585. [PMID: 38531165 PMCID: PMC10982609 DOI: 10.1016/j.nicl.2024.103585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 02/22/2024] [Accepted: 02/25/2024] [Indexed: 03/28/2024]
Abstract
Resting state functional magnetic resonance imaging (rsfMRI) provides researchers and clinicians with a powerful tool to examine functional connectivity across large-scale brain networks, with ever-increasing applications to the study of neurological disorders, such as traumatic brain injury (TBI). While rsfMRI holds unparalleled promise in systems neurosciences, its acquisition and analytical methodology across research groups is variable, resulting in a literature that is challenging to integrate and interpret. The focus of this narrative review is to address the primary methodological issues including investigator decision points in the application of rsfMRI to study the consequences of TBI. As part of the ENIGMA Brain Injury working group, we have collaborated to identify a minimum set of recommendations that are designed to produce results that are reliable, harmonizable, and reproducible for the TBI imaging research community. Part one of this review provides the results of a literature search of current rsfMRI studies of TBI, highlighting key design considerations and data processing pipelines. Part two outlines seven data acquisition, processing, and analysis recommendations with the goal of maximizing study reliability and between-site comparability, while preserving investigator autonomy. Part three summarizes new directions and opportunities for future rsfMRI studies in TBI patients. The goal is to galvanize the TBI community to gain consensus for a set of rigorous and reproducible methods, and to increase analytical transparency and data sharing to address the reproducibility crisis in the field.
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Affiliation(s)
- Karen Caeyenberghs
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia.
| | - Phoebe Imms
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA.
| | - Andrei Irimia
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA; Alfred E. Mann Department of Biomedical Engineering, Andrew & Erna Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA; Department of Quantitative & Computational Biology, Dana and David Dornsife College of Arts & Sciences, University of Southern California, Los Angeles, CA, USA.
| | - Martin M Monti
- Department of Psychology, UCLA, USA; Brain Injury Research Center (BIRC), Department of Neurosurgery, UCLA, USA.
| | - Carrie Esopenko
- Department of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, NY, USA.
| | - Nicola L de Souza
- Department of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, NY, USA.
| | - Juan F Dominguez D
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia.
| | - Mary R Newsome
- Michael E. DeBakey VA Medical Center, Houston, TX, USA; H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA; TBI and Concussion Center, Department of Neurology, University of Utah, Salt Lake City, UT, USA.
| | - Ekaterina Dobryakova
- Center for Traumatic Brain Injury, Kessler Foundation, East Hanover, NJ, USA; Rutgers New Jersey Medical School, Newark, NJ, USA.
| | - Andrew Cwiek
- Department of Psychology, Penn State University, State College, PA, USA.
| | - Hollie A C Mullin
- Department of Psychology, Penn State University, State College, PA, USA.
| | - Nicholas J Kim
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA; Alfred E. Mann Department of Biomedical Engineering, Andrew & Erna Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA.
| | - Andrew R Mayer
- Mind Research Network, Albuquerque, NM, USA; Departments of Neurology and Psychiatry, University of New Mexico School of Medicine, Albuquerque, NM, USA.
| | - Maheen M Adamson
- Women's Operational Military Exposure Network (WOMEN) & Rehabilitation Department, VA Palo Alto, Palo Alto, CA, USA; Rehabilitation Service, VA Palo Alto, Palo Alto, CA, USA; Neurosurgery, Stanford School of Medicine, Stanford, CA, USA.
| | - Kevin Bickart
- UCLA Steve Tisch BrainSPORT Program, USA; Department of Neurology, David Geffen School of Medicine at UCLA, USA.
| | - Katherine M Breedlove
- Center for Clinical Spectroscopy, Brigham and Women's Hospital, Boston, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA.
| | - Emily L Dennis
- TBI and Concussion Center, Department of Neurology, University of Utah, Salt Lake City, UT, USA; George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT, USA.
| | - Seth G Disner
- Minneapolis VA Health Care System, Minneapolis, MN, USA; Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis, MN, USA.
| | - Courtney Haswell
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA.
| | - Cooper B Hodges
- TBI and Concussion Center, Department of Neurology, University of Utah, Salt Lake City, UT, USA; George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT, USA; Department of Psychology, Brigham Young University, Provo, UT, USA.
| | - Kristen R Hoskinson
- Center for Biobehavioral Health, The Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, OH, USA; Department of Pediatrics, The Ohio State University College of Medicine, OH, USA.
| | - Paula K Johnson
- TBI and Concussion Center, Department of Neurology, University of Utah, Salt Lake City, UT, USA; Neuroscience Center, Brigham Young University, Provo, UT, USA.
| | - Marsh Königs
- Emma Children's Hospital, Amsterdam UMC, University of Amsterdam, Emma Neuroscience Group, The Netherlands; Amsterdam Reproduction and Development, Amsterdam, The Netherlands.
| | - Lucia M Li
- C3NL, Imperial College London, United Kingdom; UK DRI Centre for Health Care and Technology, Imperial College London, United Kingdom.
| | - Spencer W Liebel
- TBI and Concussion Center, Department of Neurology, University of Utah, Salt Lake City, UT, USA; George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT, USA.
| | - Abigail Livny
- Division of Diagnostic Imaging, Sheba Medical Center, Tel-Hashomer, Israel; Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel; Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel.
| | - Rajendra A Morey
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA; Duke-UNC Brain Imaging and Analysis Center, Duke University, Durham, NC, USA; VA Mid-Atlantic Mental Illness Research Education and Clinical Center, Durham, NC, USA.
| | - Alexandra M Muir
- Department of Psychology, Brigham Young University, Provo, UT, USA.
| | - Alexander Olsen
- Department of Psychology, Norwegian University of Science and Technology, Trondheim, Norway; Clinic of Rehabilitation, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway; NorHEAD - Norwegian Centre for Headache Research, Norwegian University of Science and Technology, Trondheim, Norway.
| | - Adeel Razi
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, VIC 3800, Australia; Wellcome Centre for Human Neuroimaging, University College London, WC1N 3AR London, United Kingdom; CIFAR Azrieli Global Scholars Program, CIFAR, Toronto, ON, Canada.
| | - Matthew Su
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA.
| | - David F Tate
- TBI and Concussion Center, Department of Neurology, University of Utah, Salt Lake City, UT, USA; George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT, USA.
| | - Carmen Velez
- TBI and Concussion Center, Department of Neurology, University of Utah, Salt Lake City, UT, USA; George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT, USA.
| | - Elisabeth A Wilde
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA; TBI and Concussion Center, Department of Neurology, University of Utah, Salt Lake City, UT, USA; George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT, USA.
| | - Brandon A Zielinski
- Departments of Pediatrics, Neurology, and Neuroscience, University of Florida, Gainesville, FL, USA; Departments of Pediatrics, Neurology, and Radiology, University of Utah, Salt Lake City, UT, USA.
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, USA.
| | - Frank G Hillary
- Department of Psychology, Penn State University, State College, PA, USA; Department of Neurology, Hershey Medical Center, PA, USA.
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Kaptan M, Horn U, Vannesjo SJ, Mildner T, Weiskopf N, Finsterbusch J, Brooks JCW, Eippert F. Reliability of resting-state functional connectivity in the human spinal cord: assessing the impact of distinct noise sources. Neuroimage 2023; 275:120152. [PMID: 37142169 DOI: 10.1016/j.neuroimage.2023.120152] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Revised: 04/20/2023] [Accepted: 05/01/2023] [Indexed: 05/06/2023] Open
Abstract
The investigation of spontaneous fluctuations of the blood-oxygen-level-dependent (BOLD) signal has recently been extended from the brain to the spinal cord, where it has stimulated interest from a clinical perspective. A number of resting-state functional magnetic resonance imaging (fMRI) studies have demonstrated robust functional connectivity between the time series of BOLD fluctuations in bilateral dorsal horns and between those in bilateral ventral horns, in line with the functional neuroanatomy of the spinal cord. A necessary step prior to extension to clinical studies is assessing the reliability of such resting-state signals, which we aimed to do here in a group of 45 healthy young adults at the clinically prevalent field strength of 3T. When investigating connectivity in the entire cervical spinal cord, we observed fair to good reliability for dorsal-dorsal and ventral-ventral connectivity, whereas reliability was poor for within- and between-hemicord dorsal-ventral connectivity. Considering how prone spinal cord fMRI is to noise, we extensively investigated the impact of distinct noise sources and made two crucial observations: removal of physiological noise led to a reduction in functional connectivity strength and reliability - due to the removal of stable and participant-specific noise patterns - whereas removal of thermal noise considerably increased the detectability of functional connectivity without a clear influence on reliability. Finally, we also assessed connectivity within spinal cord segments and observed that while the pattern of connectivity was similar to that of whole cervical cord, reliability at the level of single segments was consistently poor. Taken together, our results demonstrate the presence of reliable resting-state functional connectivity in the human spinal cord even after thoroughly accounting for physiological and thermal noise, but at the same time urge caution if focal changes in connectivity (e.g. due to segmental lesions) are to be studied, especially in a longitudinal manner.
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Affiliation(s)
- Merve Kaptan
- 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
| | - S Johanna Vannesjo
- Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway
| | - Toralf Mildner
- 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
| | - Jürgen Finsterbusch
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jonathan C W Brooks
- School of Psychology, University of East Anglia Wellcome Wolfson Brain Imaging Centre (UWWBIC), Norwich, United Kingdom
| | - Falk Eippert
- Max Planck Research Group Pain Perception, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
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Dowdle LT, Vizioli L, Moeller S, Akçakaya M, Olman C, Ghose G, Yacoub E, Uğurbil K. Evaluating increases in sensitivity from NORDIC for diverse fMRI acquisition strategies. Neuroimage 2023; 270:119949. [PMID: 36804422 DOI: 10.1016/j.neuroimage.2023.119949] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 01/27/2023] [Accepted: 02/15/2023] [Indexed: 02/19/2023] Open
Abstract
As the neuroimaging field moves towards detecting smaller effects at higher spatial resolutions, and faster sampling rates, there is increased attention given to the deleterious contribution of unstructured, thermal noise. Here, we critically evaluate the performance of a recently developed reconstruction method, termed NORDIC, for suppressing thermal noise using datasets acquired with various field strengths, voxel sizes, sampling rates, and task designs. Following minimal preprocessing, statistical activation (t-values) of NORDIC processed data was compared to the results obtained with alternative denoising methods. Additionally, we examined the consistency of the estimates of task responses at the single-voxel, single run level, using a finite impulse response (FIR) model. To examine the potential impact on effective image resolution, the overall smoothness of the data processed with different methods was estimated. Finally, to determine if NORDIC alters or removes temporal information important for modeling responses, we employed an exhaustive leave-p-out cross validation approach, using FIR task responses to predict held out timeseries, quantified using R2. After NORDIC, the t-values are increased, an improvement comparable to what could be achieved by 1.5 voxels smoothing, and task events are clearly visible and have less cross-run error. These advantages are achieved with smoothness estimates increasing by less than 4%, while 1.5 voxel smoothing is associated with increases of over 140%. Cross-validated R2s based on the FIR models show that NORDIC is not measurably distorting the temporal structure of the data under this approach and is the best predictor of non-denoised time courses. The results demonstrate that analyzing 1 run of data after NORDIC produces results equivalent to using 2 to 3 original runs and that NORDIC performs equally well across a diverse array of functional imaging protocols. Significance Statement: For functional neuroimaging, the increasing availability of higher field strengths and ever higher spatiotemporal resolutions has led to concomitant increase in concerns about the deleterious effects of thermal noise. Historically this noise source was suppressed using methods that reduce spatial precision such as image blurring or averaging over a large number of trials or sessions, which necessitates large data collection efforts. Here, we critically evaluate the performance of a recently developed reconstruction method, termed NORDIC, which suppresses thermal noise. Across datasets varying in field strength, voxel sizes, sampling rates, and task designs, NORDIC produces substantial gains in data quality. Both conventional t-statistics derived from general linear models and coefficients of determination for predicting unseen data are improved. These gains match or even exceed those associated with 1 voxel Full Width Half Max image smoothing, however, even such small amounts of smoothing are associated with a 52% reduction in estimates of spatial precision, whereas the measurable difference in spatial precision is less than 4% following NORDIC.
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Affiliation(s)
- Logan T Dowdle
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, 2021 6th Street SE, MN 55455, United States; Department of Neuroscience, University of Minnesota, Minneapolis, MN, United States.
| | - Luca Vizioli
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, 2021 6th Street SE, MN 55455, United States
| | - Steen Moeller
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, 2021 6th Street SE, MN 55455, United States
| | - Mehmet Akçakaya
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, 2021 6th Street SE, MN 55455, United States; Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, United States
| | - Cheryl Olman
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, 2021 6th Street SE, MN 55455, United States; Department of Psychology, University of Minnesota, Minneapolis, MN, United States
| | - Geoffrey Ghose
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, 2021 6th Street SE, MN 55455, United States; Department of Neuroscience, University of Minnesota, Minneapolis, MN, United States; Department of Psychology, University of Minnesota, Minneapolis, MN, United States
| | - Essa Yacoub
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, 2021 6th Street SE, MN 55455, United States
| | - Kâmil Uğurbil
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, 2021 6th Street SE, MN 55455, United States
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4
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Fernandes FF, Olesen JL, Jespersen SN, Shemesh N. MP-PCA denoising of fMRI time-series data can lead to artificial activation "spreading". Neuroimage 2023; 273:120118. [PMID: 37062372 DOI: 10.1016/j.neuroimage.2023.120118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Revised: 04/10/2023] [Accepted: 04/13/2023] [Indexed: 04/18/2023] Open
Abstract
MP-PCA denoising has become the method of choice for denoising MRI data since it provides an objective threshold to separate the signal components from unwanted thermal noise components. In rodents, thermal noise in the coils is an important source of noise that can reduce the accuracy of activation mapping in fMRI. Further confounding this problem, vendor data often contains zero-filling and other post-processing steps that may violate MP-PCA assumptions. Here, we develop an approach to denoise vendor data and assess activation "spreading" caused by MP-PCA denoising in rodent task-based fMRI data. Data was obtained from N = 3 mice using conventional multislice and ultrafast acquisitions (1 s and 50 ms temporal resolution, respectively), during visual stimulation. MP-PCA denoising produced SNR gains of 64% and 39% and Fourier Spectral Amplitude (FSA) increases in BOLD maps of 9% and 7% for multislice and ultrafast data, respectively, when using a small [2 2] denoising window. Larger windows provided higher SNR and FSA gains with increased spatial extent of activation that may or may not represent real activation. Simulations showed that MP-PCA denoising can incur activation "spreading" with increased false positive rate and smoother functional maps due to local "bleeding" of principal components, and that the optimal denoising window for improved specificity of functional mapping, based on Dice score calculations, depends on the data's tSNR and functional CNR. This "spreading" effect applies also to another recently proposed low-rank denoising method (NORDIC), although to a lesser degree. Our results bode well for enhancing spatial and/or temporal resolution in future fMRI work, while taking into account the sensitivity/specificity trade-offs of low-rank denoising methods.
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Affiliation(s)
| | - Jonas L Olesen
- Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark
| | - Sune N Jespersen
- Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark
| | - Noam Shemesh
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal.
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5
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Jangraw DC, Keren H, Sun H, Bedder RL, Rutledge RB, Pereira F, Thomas AG, Pine DS, Zheng C, Nielson DM, Stringaris A. A highly replicable decline in mood during rest and simple tasks. Nat Hum Behav 2023; 7:596-610. [PMID: 36849591 PMCID: PMC10192073 DOI: 10.1038/s41562-023-01519-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 01/04/2023] [Indexed: 03/01/2023]
Abstract
Does our mood change as time passes? This question is central to behavioural and affective science, yet it remains largely unexamined. To investigate, we intermixed subjective momentary mood ratings into repetitive psychology paradigms. Here we demonstrate that task and rest periods lowered participants' mood, an effect we call 'Mood Drift Over Time'. This finding was replicated in 19 cohorts totalling 28,482 adult and adolescent participants. The drift was relatively large (-13.8% after 7.3 min of rest, Cohen's d = 0.574) and was consistent across cohorts. Behaviour was also impacted: participants were less likely to gamble in a task that followed a rest period. Importantly, the drift slope was inversely related to reward sensitivity. We show that accounting for time using a linear term significantly improves the fit of a computational model of mood. Our work provides conceptual and methodological reasons for researchers to account for time's effects when studying mood and behaviour.
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Affiliation(s)
- David C Jangraw
- National Institute of Mental Health, Bethesda, MD, USA.
- Department of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT, USA.
| | - Hanna Keren
- Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel
| | - Haorui Sun
- Department of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT, USA
| | - Rachel L Bedder
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Robb B Rutledge
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
- Departments of Psychology and Psychiatry, Yale University, New Haven, CT, USA
| | | | - Adam G Thomas
- National Institute of Mental Health, Bethesda, MD, USA
| | - Daniel S Pine
- National Institute of Mental Health, Bethesda, MD, USA
| | - Charles Zheng
- National Institute of Mental Health, Bethesda, MD, USA
| | | | - Argyris Stringaris
- Department of Psychiatry, National and Kapodistrian University of Athens, Athens, Greece
- Faculty of Brain Sciences, Division of Psychiatry, University College London, London, UK
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Wingrove J, Makaronidis J, Prados F, Kanber B, Yiannakas MC, Magee C, Castellazzi G, Grandjean L, Golay X, Tur C, Ciccarelli O, D'Angelo E, Gandini Wheeler-Kingshott CA, Batterham RL. Aberrant olfactory network functional connectivity in people with olfactory dysfunction following COVID-19 infection: an exploratory, observational study. EClinicalMedicine 2023; 58:101883. [PMID: 36883140 PMCID: PMC9980836 DOI: 10.1016/j.eclinm.2023.101883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 02/02/2023] [Accepted: 02/07/2023] [Indexed: 03/06/2023] Open
Abstract
BACKGROUND Olfactory impairments and anosmia from COVID-19 infection typically resolve within 2-4 weeks, although in some cases, symptoms persist longer. COVID-19-related anosmia is associated with olfactory bulb atrophy, however, the impact on cortical structures is relatively unknown, particularly in those with long-term symptoms. METHODS In this exploratory, observational study, we studied individuals who experienced COVID-19-related anosmia, with or without recovered sense of smell, and compared against individuals with no prior COVID-19 infection (confirmed by antibody testing, all vaccine naïve). MRI Imaging was carried out between the 15th July and 17th November 2020 at the Queen Square House Clinical Scanning Facility, UCL, United Kingdom. Using functional magnetic resonance imaging (fMRI) and structural imaging, we assessed differences in functional connectivity (FC) between olfactory regions, whole brain grey matter (GM) cerebral blood flow (CBF) and GM density. FINDINGS Individuals with anosmia showed increased FC between the left orbitofrontal cortex (OFC), visual association cortex and cerebellum and FC reductions between the right OFC and dorsal anterior cingulate cortex compared to those with no prior COVID-19 infection (p < 0.05, from whole brain statistical parametric map analysis). Individuals with anosmia also showed greater CBF in the left insula, hippocampus and ventral posterior cingulate when compared to those with resolved anosmia (p < 0.05, from whole brain statistical parametric map analysis). INTERPRETATION This work describes, for the first time to our knowledge, functional differences within olfactory areas and regions involved in sensory processing and cognitive functioning. This work identifies key areas for further research and potential target sites for therapeutic strategies. FUNDING This study was funded by the National Institute for Health and Care Research and supported by the Queen Square Scanner business case.
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Affiliation(s)
- Jed Wingrove
- Centre for Obesity Research, Department of Medicine, University College London, London, UK
- National Institute for Health and Care Research, Biomedical Research Centre at UCLH and UCL, London, UK
| | - Janine Makaronidis
- Centre for Obesity Research, Department of Medicine, University College London, London, UK
- National Institute for Health and Care Research, Biomedical Research Centre at UCLH and UCL, London, UK
| | - Ferran Prados
- National Institute for Health and Care Research, Biomedical Research Centre at UCLH and UCL, London, UK
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, UK
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Baris Kanber
- National Institute for Health and Care Research, Biomedical Research Centre at UCLH and UCL, London, UK
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, UK
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Marios C. Yiannakas
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Cormac Magee
- Centre for Obesity Research, Department of Medicine, University College London, London, UK
- National Institute for Health and Care Research, Biomedical Research Centre at UCLH and UCL, London, UK
| | - Gloria Castellazzi
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, UK
- Brain Connectivity Research Centre, IRCCS Mondino Foundation, Pavia, Italy
| | - Louis Grandjean
- Department of Infection, Immunity & Inflammation, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Xavier Golay
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, UK
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Carmen Tur
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Olga Ciccarelli
- National Institute for Health and Care Research, Biomedical Research Centre at UCLH and UCL, London, UK
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Egidio D'Angelo
- Brain Connectivity Research Centre, IRCCS Mondino Foundation, Pavia, Italy
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Claudia A.M. Gandini Wheeler-Kingshott
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, UK
- Brain Connectivity Research Centre, IRCCS Mondino Foundation, Pavia, Italy
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Rachel L. Batterham
- Centre for Obesity Research, Department of Medicine, University College London, London, UK
- National Institute for Health and Care Research, Biomedical Research Centre at UCLH and UCL, London, UK
- Corresponding author. Division of Medicine, University College London, Rayne Building, 5 University Street, London, WC1E 6JF, UK.
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7
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Schmidt SA, Shahsavarani S, Khan RA, Tai Y, Granato EC, Willson CM, Ramos P, Sherman P, Esquivel C, Sutton BP, Husain F. An examination of the reliability of seed-to-seed resting state functional connectivity in tinnitus patients. NEUROIMAGE: REPORTS 2023. [DOI: 10.1016/j.ynirp.2023.100158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
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Diffusion-Weighted Imaging in Mild Traumatic Brain Injury: A Systematic Review of the Literature. Neuropsychol Rev 2023; 33:42-121. [PMID: 33721207 DOI: 10.1007/s11065-021-09485-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Accepted: 02/09/2021] [Indexed: 12/14/2022]
Abstract
There is evidence that diffusion-weighted imaging (DWI) is able to detect tissue alterations following mild traumatic brain injury (mTBI) that may not be observed on conventional neuroimaging; however, findings are often inconsistent between studies. This systematic review assesses patterns of differences in DWI metrics between those with and without a history of mTBI. A PubMed literature search was performed using relevant indexing terms for articles published prior to May 14, 2020. Findings were limited to human studies using DWI in mTBI. Articles were excluded if they were not full-length, did not contain original data, if they were case studies, pertained to military populations, had inadequate injury severity classification, or did not report post-injury interval. Findings were reported independently for four subgroups: acute/subacute pediatric mTBI, acute/subacute adult mTBI, chronic adult mTBI, and sport-related concussion, and all DWI acquisition and analysis methods used were included. Patterns of findings between studies were reported, along with strengths and weaknesses of the current state of the literature. Although heterogeneity of sample characteristics and study methods limited the consistency of findings, alterations in DWI metrics were most commonly reported in the corpus callosum, corona radiata, internal capsule, and long association pathways. Many acute/subacute pediatric studies reported higher FA and lower ADC or MD in various regions. In contrast, acute/subacute adult studies most commonly indicate lower FA within the context of higher MD and RD. In the chronic phase of recovery, FA may remain low, possibly indicating overall demyelination or Wallerian degeneration over time. Longitudinal studies, though limited, generally indicate at least a partial normalization of DWI metrics over time, which is often associated with functional improvement. We conclude that DWI is able to detect structural mTBI-related abnormalities that may persist over time, although future DWI research will benefit from larger samples, improved data analysis methods, standardized reporting, and increasing transparency.
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9
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Cheon EJ, Male AG, Gao B, Adhikari BM, Edmond JT, Hare SM, Belger A, Potkin SG, Bustillo JR, Mathalon DH, Ford JM, Lim KO, Mueller BA, Preda A, O'Leary D, Strauss GP, Ahmed AO, Thompson PM, Jahanshad N, Kochunov P, Calhoun VD, Turner JA, van Erp TGM. Five negative symptom domains are differentially associated with resting state amplitude of low frequency fluctuations in Schizophrenia. Psychiatry Res Neuroimaging 2023; 329:111597. [PMID: 36680843 PMCID: PMC11154089 DOI: 10.1016/j.pscychresns.2023.111597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 12/30/2022] [Accepted: 01/10/2023] [Indexed: 01/18/2023]
Abstract
This study examined associations between resting-state amplitude of low frequency fluctuations (ALFF) and negative symptoms represented by total scores, second-order dimension (motivation and pleasure, expressivity), and first-order domain (anhedonia, avolition, asociality, alogia, blunted affect) factor scores in schizophrenia (n = 57). Total negative symptom scores showed positive associations with ALFF in temporal and frontal brain regions. Negative symptom domain scores showed predominantly stronger associations with regional ALFF compared to total scores, suggesting domain scores may better map to neural signatures than total scores. Improving our understanding of the neuropathology underlying negative symptoms may aid in addressing this unmet therapeutic need in schizophrenia.
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Affiliation(s)
- Eun-Jin Cheon
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, CA, United States; Department of Psychiatry, Yeungnam University College of Medicine, Daegu, South Korea
| | - Alie G Male
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, CA, United States
| | - Bingchen Gao
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, CA, United States
| | - Bhim M Adhikari
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, United States
| | - Jesse T Edmond
- Center for Translational Research in Neuroimaging and Data Science (TReNDS), GSU/GATech/Emory, Atlanta, GA, United States
| | - Stephanie M Hare
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, United States
| | - Aysenil Belger
- Department of Psychiatry, University of North Carolina, Chapel Hill, United States
| | - Steven G Potkin
- Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, CA, United States
| | - Juan R Bustillo
- Departments of Psychiatry & Neuroscience, University of New Mexico, Albuquerque, NM, United States
| | - Daniel H Mathalon
- Department of Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco, CA, United States; Veterans Affairs San Francisco Healthcare System, San Francisco, CA, United States
| | - Judith M Ford
- Department of Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco, CA, United States; Veterans Affairs San Francisco Healthcare System, San Francisco, CA, United States
| | - Kelvin O Lim
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, United States
| | - Bryon A Mueller
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, United States
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, CA, United States
| | - Daniel O'Leary
- Department of Psychiatry, University of Iowa, IA, United States
| | - Gregory P Strauss
- Department of Psychology, University of Georgia, Athens, GA, United States
| | - Anthony O Ahmed
- Department of Psychiatry, Weill Cornell Medicine, White Plains, NY, United States
| | - Paul M Thompson
- Imaging Genetics Center, University of Southern California, Marina del Rey, CA, United States
| | - Neda Jahanshad
- Imaging Genetics Center, University of Southern California, Marina del Rey, CA, United States
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, United States
| | - Vince D Calhoun
- Center for Translational Research in Neuroimaging and Data Science (TReNDS), GSU/GATech/Emory, Atlanta, GA, United States
| | - Jessica A Turner
- Department of Psychiatry, Ohio State University, OH, United States
| | - Theo G M van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, CA, United States; Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine, CA, United States.
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10
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De Rosa AP, Esposito F, Valsasina P, d'Ambrosio A, Bisecco A, Rocca MA, Tommasin S, Marzi C, De Stefano N, Battaglini M, Pantano P, Cirillo M, Tedeschi G, Filippi M, Gallo A. Resting-state functional MRI in multicenter studies on multiple sclerosis: a report on raw data quality and functional connectivity features from the Italian Neuroimaging Network Initiative. J Neurol 2023; 270:1047-1066. [PMID: 36350401 PMCID: PMC9886598 DOI: 10.1007/s00415-022-11479-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 11/03/2022] [Accepted: 11/04/2022] [Indexed: 11/11/2022]
Abstract
The Italian Neuroimaging Network Initiative (INNI) is an expanding repository of brain MRI data from multiple sclerosis (MS) patients recruited at four Italian MRI research sites. We describe the raw data quality of resting-state functional MRI (RS-fMRI) time-series in INNI and the inter-site variability in functional connectivity (FC) features after unified automated data preprocessing. MRI datasets from 489 MS patients and 246 healthy control (HC) subjects were retrieved from the INNI database. Raw data quality metrics included temporal signal-to-noise ratio (tSNR), spatial smoothness (FWHM), framewise displacement (FD), and differential variation in signals (DVARS). Automated preprocessing integrated white-matter lesion segmentation (SAMSEG) into a standard fMRI pipeline (fMRIPrep). FC features were calculated on pre-processed data and harmonized between sites (Combat) prior to assessing general MS-related alterations. Across centers (both groups), median tSNR and FWHM ranged from 47 to 84 and from 2.0 to 2.5, and median FD and DVARS ranged from 0.08 to 0.24 and from 1.06 to 1.22. After preprocessing, only global FC-related features were significantly correlated with FD or DVARS. Across large-scale networks, age/sex/FD-adjusted and harmonized FC features exhibited both inter-site and site-specific inter-group effects. Significant general reductions were obtained for somatomotor and limbic networks in MS patients (vs. HC). The implemented procedures provide technical information on raw data quality and outcome of fully automated preprocessing that might serve as reference in future RS-fMRI studies within INNI. The unified pipeline introduced little bias across sites and appears suitable for multisite FC analyses on harmonized network estimates.
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Affiliation(s)
- Alessandro Pasquale De Rosa
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy
| | - Fabrizio Esposito
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy.
| | - Paola Valsasina
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - Alessandro d'Ambrosio
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy
| | - Alvino Bisecco
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy
| | - Maria A Rocca
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
- Vita-Salute San Raffaele University, Via Olgettina 58, 20132, Milan, Italy
| | - Silvia Tommasin
- Department of Human Neurosciences, Sapienza University of Rome, Viale Dell'Università, 30, 00185, Rome, Italy
| | - Chiara Marzi
- Institute of Applied Physics "Nello Cararra" (IFAC), National Research Council (CNR), Via Madonna del Piano, 10, Sesto Fiorentino, 50019, Florence, Italy
| | - Nicola De Stefano
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Marco Battaglini
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Patrizia Pantano
- Department of Human Neurosciences, Sapienza University of Rome, Viale Dell'Università, 30, 00185, Rome, Italy
| | - Mario Cirillo
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy
| | - Gioacchino Tedeschi
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
- Vita-Salute San Raffaele University, Via Olgettina 58, 20132, Milan, Italy
| | - Antonio Gallo
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy
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11
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Cerebral blood flow and cardiovascular risk effects on resting brain regional homogeneity. Neuroimage 2022; 262:119555. [PMID: 35963506 PMCID: PMC10044499 DOI: 10.1016/j.neuroimage.2022.119555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 08/01/2022] [Accepted: 08/09/2022] [Indexed: 11/22/2022] Open
Abstract
Regional homogeneity (ReHo) is a measure of local functional brain connectivity that has been reported to be altered in a wide range of neuropsychiatric disorders. Computed from brain resting-state functional MRI time series, ReHo is also sensitive to fluctuations in cerebral blood flow (CBF) that in turn may be influenced by cerebrovascular health. We accessed cerebrovascular health with Framingham cardiovascular risk score (FCVRS). We hypothesize that ReHo signal may be influenced by regional CBF; and that these associations can be summarized as FCVRS→CBF→ReHo. We used three independent samples to test this hypothesis. A test-retest sample of N = 30 healthy volunteers was used for test-retest evaluation of CBF effects on ReHo. Amish Connectome Project (ACP) sample (N = 204, healthy individuals) was used to evaluate association between FCVRS and ReHo and testing if the association diminishes given CBF. The UKBB sample (N = 6,285, healthy participants) was used to replicate the effects of FCVRS on ReHo. We observed strong CBF→ReHo links (p<2.5 × 10-3) using a three-point longitudinal sample. In ACP sample, marginal and partial correlations analyses demonstrated that both CBF and FCVRS were significantly correlated with the whole-brain average (p<10-6) and regional ReHo values, with the strongest correlations observed in frontal, parietal, and temporal areas. Yet, the association between ReHo and FCVRS became insignificant once the effect of CBF was accounted for. In contrast, CBF→ReHo remained significantly linked after adjusting for FCVRS and demographic covariates (p<10-6). Analysis in N = 6,285 replicated the FCVRS→ReHo effect (p = 2.7 × 10-27). In summary, ReHo alterations in health and neuropsychiatric illnesses may be partially driven by region-specific variability in CBF, which is, in turn, influenced by cardiovascular factors.
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12
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Zhu W, Ma X, Zhu XH, Ugurbil K, Chen W, Wu X. Denoise Functional Magnetic Resonance Imaging With Random Matrix Theory Based Principal Component Analysis. IEEE Trans Biomed Eng 2022; 69:3377-3388. [PMID: 35439125 PMCID: PMC9579216 DOI: 10.1109/tbme.2022.3168592] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
High-resolution functional MRI (fMRI) is largely hindered by random thermal noise. Random matrix theory (RMT)-based principal component analysis (PCA) is promising to reduce such noise in fMRI data. However, there is no consensus about the optimal strategy and practice in implementation. In this work, we propose a comprehensive RMT-based denoising method that consists of 1) rank and noise estimation based on a set of newly derived multiple criteria, and 2) optimal singular value shrinkage, with each module explained and implemented based on the RMT. By incorporating the variance stabilizing approach, the denoising method can deal with low signal-to-noise ratio (SNR) (such as <5) magnitude fMRI data with favorable performance compared to other state-of-the-art methods. Results from both simulation and in-vivo high-resolution fMRI data show that the proposed denoising method dramatically improves image restoration quality, promoting functional sensitivity at the same level of functional mapping blurring compared to existing denoising methods. Moreover, the denoising method can serve as a drop-in step in data preprocessing pipelines along with other procedures aimed at removal of structured physiological noises. We expect that the proposed denoising method will play an important role in leveraging high-quality, high-resolution task fMRI, which is desirable in many neuroscience and clinical applications.
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13
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Flannery JS, Riedel MC, Hill-Bowen LD, Poudel R, Bottenhorn KL, Salo T, Laird AR, Gonzalez R, Sutherland MT. Altered large-scale brain network interactions associated with HIV infection and error processing. Netw Neurosci 2022; 6:791-815. [PMID: 36605414 PMCID: PMC9810366 DOI: 10.1162/netn_a_00241] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 02/14/2022] [Indexed: 01/07/2023] Open
Abstract
Altered activity within and between large-scale brain networks has been implicated across various neuropsychiatric conditions. However, patterns of network dysregulation associated with human immunodeficiency virus (HIV), and further impacted by cannabis (CB) use, remain to be delineated. We examined the impact of HIV and CB on resting-state functional connectivity (rsFC) between brain networks and associations with error awareness and error-related network responsivity. Participants (N = 106), stratified into four groups (HIV+/CB+, HIV+/CB-, HIV-/CB+, HIV-/CB-), underwent fMRI scanning while completing a resting-state scan and a modified Go/NoGo paradigm assessing brain responsivity to errors and explicit error awareness. We examined separate and interactive effects of HIV and CB on resource allocation indexes (RAIs), a measure quantifying rsFC strength between the default mode network (DMN), central executive network (CEN), and salience network (SN). We observed reduced RAIs among HIV+ (vs. HIV-) participants, which was driven by increased SN-DMN rsFC. No group differences were detected for SN-CEN rsFC. Increased SN-DMN rsFC correlated with diminished error awareness, but not with error-related network responsivity. These outcomes highlight altered network interactions among participants with HIV and suggest such rsFC dysregulation may persist during task performance, reflecting an inability to disengage irrelevant mental operations, ultimately hindering error processing.
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Affiliation(s)
- Jessica S. Flannery
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Michael C. Riedel
- Department of Physics, Florida International University, Miami, FL, USA
| | | | - Ranjita Poudel
- Department of Psychology, Florida International University, Miami, FL, USA
| | - Katherine L. Bottenhorn
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA
| | - Taylor Salo
- Department of Psychology, Florida International University, Miami, FL, USA
| | - Angela R. Laird
- Department of Physics, Florida International University, Miami, FL, USA
| | - Raul Gonzalez
- Department of Psychology, Florida International University, Miami, FL, USA
| | - Matthew T. Sutherland
- Department of Psychology, Florida International University, Miami, FL, USA,* Corresponding Author:
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14
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Nandakumar N, Hsu D, Ahmed R, Venkataraman A. DeepEZ: A Graph Convolutional Network for Automated Epileptogenic Zone Localization from Resting-State fMRI Connectivity. IEEE Trans Biomed Eng 2022; 70:216-227. [PMID: 35776823 PMCID: PMC9841829 DOI: 10.1109/tbme.2022.3187942] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
OBJECTIVE Epileptogenic zone (EZ) localization is a crucial step during diagnostic work up and therapeutic planning in medication refractory epilepsy. In this paper, we present the first deep learning approach to localize the EZ based on resting-state fMRI (rs-fMRI) data. METHODS Our network, called DeepEZ, uses a cascade of graph convolutions that emphasize signal propagation along expected anatomical pathways. We also integrate domain-specific information, such as an asymmetry term on the predicted EZ and a learned subject-specific bias to mitigate environmental confounds. RESULTS We validate DeepEZ on rs-fMRI collected from 14 patients with focal epilepsy at the University of Wisconsin Madison. Using cross validation, we demonstrate that DeepEZ achieves consistently high EZ localization performance (Accuracy: 0.88 ± 0.03; AUC: 0.73 ± 0.03) that far outstripped any of the baseline methods. This performance is notable given the variability in EZ locations and scanner type across the cohort. CONCLUSION Our results highlight the promise of using DeepEZ as an accurate and noninvasive therapeutic planning tool for medication refractory epilepsy. SIGNIFICANCE While prior work in EZ localization focused on identifying localized aberrant signatures, there is growing evidence that epileptic seizures affect inter-regional connectivity in the brain. DeepEZ allows clinicians to harness this information from noninvasive imaging that can easily be integrated into the existing clinical workflow.
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Affiliation(s)
- Naresh Nandakumar
- Department of Electrical and Computer Engineering, Johns Hopkins University, USA
| | - David Hsu
- Department of Neurology, University of Wisconsin, USA
| | - Raheel Ahmed
- Department of Neurosurgery, University of Wisconsin, USA
| | - Archana Venkataraman
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218 USA
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15
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Waller L, Erk S, Pozzi E, Toenders YJ, Haswell CC, Büttner M, Thompson PM, Schmaal L, Morey RA, Walter H, Veer IM. ENIGMA HALFpipe: Interactive, reproducible, and efficient analysis for resting-state and task-based fMRI data. Hum Brain Mapp 2022; 43:2727-2742. [PMID: 35305030 PMCID: PMC9120555 DOI: 10.1002/hbm.25829] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 01/26/2022] [Accepted: 02/12/2022] [Indexed: 12/27/2022] Open
Abstract
The reproducibility crisis in neuroimaging has led to an increased demand for standardized data processing workflows. Within the ENIGMA consortium, we developed HALFpipe (Harmonized Analysis of Functional MRI pipeline), an open-source, containerized, user-friendly tool that facilitates reproducible analysis of task-based and resting-state fMRI data through uniform application of preprocessing, quality assessment, single-subject feature extraction, and group-level statistics. It provides state-of-the-art preprocessing using fMRIPrep without the requirement for input data in Brain Imaging Data Structure (BIDS) format. HALFpipe extends the functionality of fMRIPrep with additional preprocessing steps, which include spatial smoothing, grand mean scaling, temporal filtering, and confound regression. HALFpipe generates an interactive quality assessment (QA) webpage to rate the quality of key preprocessing outputs and raw data in general. HALFpipe features myriad post-processing functions at the individual subject level, including calculation of task-based activation, seed-based connectivity, network-template (or dual) regression, atlas-based functional connectivity matrices, regional homogeneity (ReHo), and fractional amplitude of low-frequency fluctuations (fALFF), offering support to evaluate a combinatorial number of features or preprocessing settings in one run. Finally, flexible factorial models can be defined for mixed-effects regression analysis at the group level, including multiple comparison correction. Here, we introduce the theoretical framework in which HALFpipe was developed, and present an overview of the main functions of the pipeline. HALFpipe offers the scientific community a major advance toward addressing the reproducibility crisis in neuroimaging, providing a workflow that encompasses preprocessing, post-processing, and QA of fMRI data, while broadening core principles of data analysis for producing reproducible results. Instructions and code can be found at https://github.com/HALFpipe/HALFpipe.
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Affiliation(s)
- Lea Waller
- Charité Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt‐Universität zu BerlinDepartment of Psychiatry and Neurosciences CCMBerlinGermany
| | - Susanne Erk
- Charité Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt‐Universität zu BerlinDepartment of Psychiatry and Neurosciences CCMBerlinGermany
| | - Elena Pozzi
- Centre for Youth Mental HealthUniversity of MelbourneMelbourneAustralia
- OrygenParkvilleAustralia
| | - Yara J. Toenders
- Centre for Youth Mental HealthUniversity of MelbourneMelbourneAustralia
- OrygenParkvilleAustralia
| | | | - Marc Büttner
- Charité Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt‐Universität zu BerlinDepartment of Psychiatry and Neurosciences CCMBerlinGermany
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Lianne Schmaal
- Centre for Youth Mental HealthUniversity of MelbourneMelbourneAustralia
- OrygenParkvilleAustralia
| | - Rajendra A. Morey
- Duke University School of MedicineDurhamNorth CarolinaUSA
- Mid‐Atlantic Mental Illness Research Education and Clinical CenterUS Department of Veterans AffairsDurhamNorth CarolinaUSA
| | - Henrik Walter
- Charité Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt‐Universität zu BerlinDepartment of Psychiatry and Neurosciences CCMBerlinGermany
| | - Ilya M. Veer
- Charité Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt‐Universität zu BerlinDepartment of Psychiatry and Neurosciences CCMBerlinGermany
- Department of Developmental PsychologyUniversity of AmsterdamAmsterdamThe Netherlands
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16
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Interactive Effects of HIV Infection and Cannabis Use on Insula Subregion Functional Connectivity. J Neuroimmune Pharmacol 2022; 17:289-304. [PMID: 34427866 DOI: 10.1007/s11481-021-10005-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 07/31/2021] [Indexed: 12/29/2022]
Abstract
Chronic inflammation in the central nervous system is one mechanism through which human immunodeficiency virus (HIV) may lead to progressive cognitive decline. Given cannabis's (CB's) anti-inflammatory properties, use prevalence among people living with HIV (PLWH), and evidence implicating the insula in both, we examined independent and interactive effects of HIV and CB on insular circuitry, cognition, and immune function. We assessed resting-state functional connectivity (rsFC) of three insula subregions among 106 participants across four groups (co-occurring: HIV+/CB+; HIV-only: HIV+/CB-; CB-only: HIV-/CB+; controls: HIV-/CB-). Participants completed a neurocognitive battery assessing functioning across multiple domains and self-reported somatic complaints. Blood samples quantified immune function (T-cell counts) and inflammation (tumor necrosis factor alpha [TNF-α]). We observed interactive HIV × CB effects on rsFC strength between two anterior insula (aI) subregions and sensorimotor cortices such that, CB appeared to normalize altered rsFC among non-using PLWH. Specifically, compared to controls, HIV-only and CB-only groups displayed decreased dorsal anterior insula (DI) - postcentral gyrus rsFC and increased ventral anterior insula (VI) - supplementary motor area rsFC, whereas the co-occurring group displayed DI and VI rsFC more akin to that of controls. Altered DI - postcentral rsFC correlated with decreased processing speed and somatic complaints, but did not significantly correlate with inflammation (TNF-α). These outcomes implicate insula - sensorimotor neurocircuitries in HIV and CB and are consistent with prior work suggesting that CB use may normalize insula functioning among PLWH.
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17
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Dennis EL, Baron D, Bartnik‐Olson B, Caeyenberghs K, Esopenko C, Hillary FG, Kenney K, Koerte IK, Lin AP, Mayer AR, Mondello S, Olsen A, Thompson PM, Tate DF, Wilde EA. ENIGMA brain injury: Framework, challenges, and opportunities. Hum Brain Mapp 2022; 43:149-166. [PMID: 32476212 PMCID: PMC8675432 DOI: 10.1002/hbm.25046] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Revised: 04/23/2020] [Accepted: 05/03/2020] [Indexed: 12/19/2022] Open
Abstract
Traumatic brain injury (TBI) is a major cause of disability worldwide, but the heterogeneous nature of TBI with respect to injury severity and health comorbidities make patient outcome difficult to predict. Injury severity accounts for only some of this variance, and a wide range of preinjury, injury-related, and postinjury factors may influence outcome, such as sex, socioeconomic status, injury mechanism, and social support. Neuroimaging research in this area has generally been limited by insufficient sample sizes. Additionally, development of reliable biomarkers of mild TBI or repeated subconcussive impacts has been slow, likely due, in part, to subtle effects of injury and the aforementioned variability. The ENIGMA Consortium has established a framework for global collaboration that has resulted in the largest-ever neuroimaging studies of multiple psychiatric and neurological disorders. Here we describe the organization, recent progress, and future goals of the Brain Injury working group.
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Affiliation(s)
- Emily L. Dennis
- Department of NeurologyUniversity of Utah School of MedicineSalt Lake CityUtahUSA
- George E. Wahlen Veterans Affairs Medical CenterSalt Lake CityUtahUSA
- Imaging Genetics CenterStevens Neuroimaging & Informatics Institute, Keck School of Medicine of USCMarina del ReyCaliforniaUSA
| | - David Baron
- Western University of Health SciencesPomonaCaliforniaUSA
| | - Brenda Bartnik‐Olson
- Department of RadiologyLoma Linda University Medical CenterLoma LindaCaliforniaUSA
| | - Karen Caeyenberghs
- Cognitive Neuroscience Unit, School of PsychologyDeakin UniversityBurwoodVictoriaAustralia
| | - Carrie Esopenko
- Department of Rehabilitation and Movement SciencesRutgers Biomedical Health SciencesNewarkNew JerseyUSA
| | - Frank G. Hillary
- Department of PsychologyPennsylvania State UniversityUniversity ParkPennsylvaniaUSA
- Social Life and Engineering Sciences Imaging CenterUniversity ParkPennsylvaniaUSA
| | - Kimbra Kenney
- Department of NeurologyUniformed Services University of the Health SciencesBethesdaMarylandUSA
- National Intrepid Center of ExcellenceWalter Reed National Military Medical CenterBethesdaMarylandUSA
| | - Inga K. Koerte
- Psychiatry Neuroimaging LaboratoryBrigham and Women's HospitalBostonMassachusettsUSA
- Department of Child and Adolescent Psychiatry, Psychosomatics and PsychotherapyLudwig‐Maximilians‐UniversitätMunichGermany
| | - Alexander P. Lin
- Center for Clinical SpectroscopyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Andrew R. Mayer
- Mind Research NetworkAlbuquerqueNew MexicoUSA
- Department of Neurology and PsychiatryUniversity of New Mexico School of MedicineAlbuquerqueNew MexicoUSA
| | - Stefania Mondello
- Department of Biomedical and Dental Sciences and Morphofunctional ImagingUniversity of MessinaMessinaItaly
| | - Alexander Olsen
- Department of PsychologyNorwegian University of Science and TechnologyTrondheimNorway
- Department of Physical Medicine and RehabilitationSt. Olavs Hospital, Trondheim University HospitalTrondheimNorway
| | - Paul M. Thompson
- Imaging Genetics CenterStevens Neuroimaging & Informatics Institute, Keck School of Medicine of USCMarina del ReyCaliforniaUSA
- Department of Neurology, Pediatrics, Psychiatry, Radiology, Engineering, and OphthalmologyUniversity of Southern California (USC)Los AngelesCaliforniaUSA
| | - David F. Tate
- Department of NeurologyUniversity of Utah School of MedicineSalt Lake CityUtahUSA
- George E. Wahlen Veterans Affairs Medical CenterSalt Lake CityUtahUSA
| | - Elisabeth A. Wilde
- Department of NeurologyUniversity of Utah School of MedicineSalt Lake CityUtahUSA
- George E. Wahlen Veterans Affairs Medical CenterSalt Lake CityUtahUSA
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18
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van den Heuvel OA, Boedhoe PS, Bertolin S, Bruin WB, Francks C, Ivanov I, Jahanshad N, Kong X, Kwon JS, O'Neill J, Paus T, Patel Y, Piras F, Schmaal L, Soriano‐Mas C, Spalletta G, van Wingen GA, Yun J, Vriend C, Simpson HB, van Rooij D, Hoexter MQ, Hoogman M, Buitelaar JK, Arnold P, Beucke JC, Benedetti F, Bollettini I, Bose A, Brennan BP, De Nadai AS, Fitzgerald K, Gruner P, Grünblatt E, Hirano Y, Huyser C, James A, Koch K, Kvale G, Lazaro L, Lochner C, Marsh R, Mataix‐Cols D, Morgado P, Nakamae T, Nakao T, Narayanaswamy JC, Nurmi E, Pittenger C, Reddy YJ, Sato JR, Soreni N, Stewart SE, Taylor SF, Tolin D, Thomopoulos SI, Veltman DJ, Venkatasubramanian G, Walitza S, Wang Z, Thompson PM, Stein DJ. An overview of the first 5 years of the ENIGMA obsessive-compulsive disorder working group: The power of worldwide collaboration. Hum Brain Mapp 2022; 43:23-36. [PMID: 32154629 PMCID: PMC8675414 DOI: 10.1002/hbm.24972] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2019] [Revised: 02/12/2020] [Accepted: 02/16/2020] [Indexed: 01/12/2023] Open
Abstract
Neuroimaging has played an important part in advancing our understanding of the neurobiology of obsessive-compulsive disorder (OCD). At the same time, neuroimaging studies of OCD have had notable limitations, including reliance on relatively small samples. International collaborative efforts to increase statistical power by combining samples from across sites have been bolstered by the ENIGMA consortium; this provides specific technical expertise for conducting multi-site analyses, as well as access to a collaborative community of neuroimaging scientists. In this article, we outline the background to, development of, and initial findings from ENIGMA's OCD working group, which currently consists of 47 samples from 34 institutes in 15 countries on 5 continents, with a total sample of 2,323 OCD patients and 2,325 healthy controls. Initial work has focused on studies of cortical thickness and subcortical volumes, structural connectivity, and brain lateralization in children, adolescents and adults with OCD, also including the study on the commonalities and distinctions across different neurodevelopment disorders. Additional work is ongoing, employing machine learning techniques. Findings to date have contributed to the development of neurobiological models of OCD, have provided an important model of global scientific collaboration, and have had a number of clinical implications. Importantly, our work has shed new light on questions about whether structural and functional alterations found in OCD reflect neurodevelopmental changes, effects of the disease process, or medication impacts. We conclude with a summary of ongoing work by ENIGMA-OCD, and a consideration of future directions for neuroimaging research on OCD within and beyond ENIGMA.
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Affiliation(s)
- Odile A. van den Heuvel
- Department of Psychiatry, Department of Anatomy & Neurosciences, Amsterdam NeuroscienceAmsterdam UMC, Vrije Universiteit AmsterdamAmsterdamThe Netherlands
- Bergen Center for Brain PlasticityHaukeland University HospitalBergenNorway
| | - Premika S.W. Boedhoe
- Department of Psychiatry, Department of Anatomy & Neurosciences, Amsterdam NeuroscienceAmsterdam UMC, Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Sara Bertolin
- Department of PsychiatryBellvitge University Hospital, Bellvitge Biomedical Research Institute‐IDIBELLBarcelonaSpain
| | - Willem B. Bruin
- Department of Psychiatry, Amsterdam NeuroscienceAmsterdam UMC, University of AmsterdamAmsterdamThe Netherlands
| | - Clyde Francks
- Department of Language & GeneticsMax Planck Institute for PsycholinguisticsNijmegenThe Netherlands
- Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenThe Netherlands
| | - Iliyan Ivanov
- Icahn School of Medicine at Mount SinaiNew YorkNew York
| | - Neda Jahanshad
- Keck USC School of MedicineImaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & InformaticsMarina del ReyCalifornia
| | - Xiang‐Zhen Kong
- Department of Language & GeneticsMax Planck Institute for PsycholinguisticsNijmegenThe Netherlands
| | - Jun Soo Kwon
- Department of PsychiatrySeoul National University College of MedicineSeoulSouth Korea
- Department of Brain & Cognitive SciencesSeoul National University College of Natural SciencesSeoulSouth Korea
| | - Joseph O'Neill
- Division of Child & Adolescent PsychiatryUCLA Jane & Terry Semel Institute For NeuroscienceLos AngelesCalifornia
| | - Tomas Paus
- Holland Bloorview Kids Rehabilitation HospitalBloorview Research InstituteTorontoOntarioCanada
| | - Yash Patel
- Holland Bloorview Kids Rehabilitation HospitalBloorview Research InstituteTorontoOntarioCanada
| | - Fabrizio Piras
- Laboratory of NeuropsychiatryIRCCS Santa Lucia FoundationRomeItaly
| | - Lianne Schmaal
- Orygen, The National Centre of Excellence in Youth Mental HealthParkvilleAustralia
- Centre for Youth Mental Health, The University of MelbourneMelbourneAustralia
| | - Carles Soriano‐Mas
- Department of PsychiatryBellvitge University Hospital, Bellvitge Biomedical Research Institute‐IDIBELLBarcelonaSpain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM)BarcelonaSpain
- Department of Psychobiology and Methodology in Health SciencesUniversitat Autònoma de BarcelonaBarcelonaSpain
| | - Gianfranco Spalletta
- Laboratory of NeuropsychiatryIRCCS Santa Lucia FoundationRomeItaly
- Division of Neuropsychiatry, Menninger Department of Psychiatry and Behavioral SciencesBaylor College of MedicineHoustonTexsas
| | - Guido A. van Wingen
- Department of Psychiatry, Amsterdam NeuroscienceAmsterdam UMC, University of AmsterdamAmsterdamThe Netherlands
| | - Je‐Yeon Yun
- Seoul National University HospitalSeoulRepublic of Korea
- Yeongeon Student Support Center, Seoul National University College of MedicineSeoulRepublic of Korea
| | - Chris Vriend
- Department of Psychiatry, Department of Anatomy & Neurosciences, Amsterdam NeuroscienceAmsterdam UMC, Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - H. Blair Simpson
- Center for OC and Related Disorders at the New York State Psychiatric Institute and Columbia University Irving Medical CenterNew YorkNew York
| | - Daan van Rooij
- Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenThe Netherlands
| | - Marcelo Q. Hoexter
- Departamento e Instituto de Psiquiatria do Hospital das Clinicas, IPQ HCFMUSP, Faculdade de MedicinaUniversidade de São PauloSão PauloBrazil
| | - Martine Hoogman
- Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenThe Netherlands
- Department of Human GeneticsRadboud University Medical CenterNijmegenThe Netherlands
| | - Jan K. Buitelaar
- Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenThe Netherlands
| | - Paul Arnold
- Mathison Centre for Mental Health Research & Education and Department of PsychiatryCumming School of Medicine, University of CalgaryCalgaryAlbertaCanada
| | - Jan C. Beucke
- Humboldt‐Universität zu BerlinDepartment of PsychologyBerlinGermany
- Karolinska InstitutetDepartment of Clinical NeuroscienceStockholmSweden
| | - Francesco Benedetti
- Department of Psychiatry and Clinical PsychobiologyScientific Institute OspedaleMilanItaly
| | - Irene Bollettini
- Department of Psychiatry and Clinical PsychobiologyScientific Institute OspedaleMilanItaly
| | - Anushree Bose
- Obsessive‐Compulsive Disorder (OCD) Clinic Department of PsychiatryNational Institute of Mental Health and NeurosciencesBangaloreIndia
| | | | | | - Kate Fitzgerald
- Department of PsychiatryUniversity of Michigan Medical SchoolAnn ArborMichigan
| | | | - Edna Grünblatt
- Department of Child and Adolescent Psychiatry and PsychotherapyUniversity Hospital of Psychiatry, University of ZurichZurichSwitzerland
- Neuroscience Center ZurichUniversity of Zurich and ETH ZurichZurichSwitzerland
- Zurich Center for Integrative Human PhysiologyUniversity of ZurichZurichSwitzerland
| | - Yoshiyuki Hirano
- Research Center for Child Mental DevelopmentChiba UniversityChibaJapan
| | - Chaim Huyser
- De Bascule, academic center child and adolescent psychiatryAmsterdamThe Netherlands
| | - Anthony James
- Department of PsychiatryUniversity of OxfordOxfordUK
| | - Kathrin Koch
- Department of Neuroradiology, School of MedicineKlinikum Rechts der Isar, Technical University of MunichMunichGermany
| | - Gerd Kvale
- Bergen Center for Brain PlasticityHaukeland University HospitalBergenNorway
| | - Luisa Lazaro
- Department of Child and Adolescent Psychiatry and Psychology, IDIBAPS, CIBERSAM, Department of MedicineFaculty of BarcelonaBarcelonaSpain
| | - Christine Lochner
- SAMRC Unit on Risk & Resilience in Mental Disorders, Department of PsychiatryStellenbosch UniversityMatielandSouth Africa
| | - Rachel Marsh
- Center for OC and Related Disorders at the New York State Psychiatric Institute and Columbia University Irving Medical CenterNew YorkNew York
| | - David Mataix‐Cols
- Department of Psychiatry and Clinical PsychobiologyScientific Institute OspedaleMilanItaly
| | - Pedro Morgado
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of MinhoBragaPortugal
- ICVS/3B's, PT Government Associate LaboratoryBraga/GuimarãesPortugal
- Clinical Academic Center–BragaBragaPortugal
| | - Takashi Nakamae
- Department of PsychiatryGraduate School of Medical Science, Kyoto Prefectural University of MedicineKyotoJapan
| | - Tomohiro Nakao
- Department of Neuropsychiatry, Graduate School of Medical SciencesKyushu UniversityKyushuJapan
| | - Janardhanan C. Narayanaswamy
- Obsessive‐Compulsive Disorder (OCD) Clinic Department of PsychiatryNational Institute of Mental Health and NeurosciencesBangaloreIndia
| | - Erika Nurmi
- Department of Psychiatry and Biobehavioral SciencesUniversity of CaliforniaLos AngelesCalifornia
| | | | | | - João R. Sato
- Center of Mathematics, Computing and CognitionUniversidade Federal do ABCSanto AndréBrazil
| | - Noam Soreni
- Pediatric OCD Consultation Service, Anxiety Treatment and Research CenterMcMaster UniversityHamiltonOntarioCanada
| | - S. Evelyn Stewart
- Department of PsychiatryUniversity of British ColumbiaVancouverBritish ColumbiaCanada
- BC Mental Health and Addictions Research InstituteVancouverBritish ColumbiaCanada
- BC Children's HospitalVancouverBritish ColumbiaCanada
| | - Stephan F. Taylor
- Department of PsychiatryUniversity of Michigan Medical SchoolAnn ArborMichigan
| | - David Tolin
- Anxiety Disorders Center, The Institute of LivingHartfordConnecticut
| | - Sophia I. Thomopoulos
- Keck USC School of MedicineImaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & InformaticsMarina del ReyCalifornia
| | - Dick J. Veltman
- Department of Psychiatry, Department of Anatomy & Neurosciences, Amsterdam NeuroscienceAmsterdam UMC, Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Ganesan Venkatasubramanian
- Obsessive‐Compulsive Disorder (OCD) Clinic Department of PsychiatryNational Institute of Mental Health and NeurosciencesBangaloreIndia
| | - Susanne Walitza
- Department of PsychiatryUniversity of Michigan Medical SchoolAnn ArborMichigan
| | - Zhen Wang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of MedicineShanghaiChina
- Institute of Psychological and Behavioral Science, Shanghai Jiao Tong UniversityShanghaiChina
| | - Paul M. Thompson
- Keck USC School of MedicineImaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & InformaticsMarina del ReyCalifornia
| | - Dan J. Stein
- SAMRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry & Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
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19
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Bas‐Hoogendam JM, Groenewold NA, Aghajani M, Freitag GF, Harrewijn A, Hilbert K, Jahanshad N, Thomopoulos SI, Thompson PM, Veltman DJ, Winkler AM, Lueken U, Pine DS, Wee NJA, Stein DJ, Agosta F, Åhs F, An I, Alberton BAV, Andreescu C, Asami T, Assaf M, Avery SN, Nicholas L, Balderston, Barber JP, Battaglia M, Bayram A, Beesdo‐Baum K, Benedetti F, Berta R, Björkstrand J, Blackford JU, Blair JR, Karina S, Blair, Boehme S, Brambilla P, Burkhouse K, Cano M, Canu E, Cardinale EM, Cardoner N, Clauss JA, Cividini C, Critchley HD, Udo, Dannlowski, Deckert J, Demiralp T, Diefenbach GJ, Domschke K, Doruyter A, Dresler T, Erhardt A, Fallgatter AJ, Fañanás L, Brandee, Feola, Filippi CA, Filippi M, Fonzo GA, Forbes EE, Fox NA, Fredrikson M, Furmark T, Ge T, Gerber AJ, Gosnell SN, Grabe HJ, Grotegerd D, Gur RE, Gur RC, Harmer CJ, Harper J, Heeren A, Hettema J, Hofmann D, Hofmann SG, Jackowski AP, Andreas, Jansen, Kaczkurkin AN, Kingsley E, Kircher T, Kosti c M, Kreifelts B, Krug A, Larsen B, Lee S, Leehr EJ, Leibenluft E, Lochner C, Maggioni E, Makovac E, Mancini M, Manfro GG, Månsson KNT, Meeten F, Michałowski J, Milrod BL, Mühlberger A, Lilianne R, Mujica‐Parodi, Munjiza A, Mwangi B, Myers M, Igor Nenadi C, Neufang S, Nielsen JA, Oh H, Ottaviani C, Pan PM, Pantazatos SP, Martin P, Paulus, Perez‐Edgar K, Peñate W, Perino MT, Peterburs J, Pfleiderer B, Phan KL, Poletti S, Porta‐Casteràs D, Price RB, Pujol J, Andrea, Reinecke, Rivero F, Roelofs K, Rosso I, Saemann P, Salas R, Salum GA, Satterthwaite TD, Schneier F, Schruers KRJ, Schulz SM, Schwarzmeier H, Seeger FR, Smoller JW, Soares JC, Stark R, Stein MB, Straube B, Straube T, Strawn JR, Suarez‐Jimenez B, Boris, Suchan, Sylvester CM, Talati A, Tamburo E, Tükel R, Heuvel OA, Van der Auwera S, Nieuwenhuizen H, Tol M, van Velzen LS, Bort CV, Vermeiren RRJM, Visser RM, Volman I, Wannemüller A, Wendt J, Werwath KE, Westenberg PM, Wiemer J, Katharina, Wittfeld, Wu M, Yang Y, Zilverstand A, Zugman A, Zwiebel HL. ENIGMA-anxiety working group: Rationale for and organization of large-scale neuroimaging studies of anxiety disorders. Hum Brain Mapp 2022; 43:83-112. [PMID: 32618421 PMCID: PMC8805695 DOI: 10.1002/hbm.25100] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 05/09/2020] [Accepted: 06/08/2020] [Indexed: 12/11/2022] Open
Abstract
Anxiety disorders are highly prevalent and disabling but seem particularly tractable to investigation with translational neuroscience methodologies. Neuroimaging has informed our understanding of the neurobiology of anxiety disorders, but research has been limited by small sample sizes and low statistical power, as well as heterogenous imaging methodology. The ENIGMA-Anxiety Working Group has brought together researchers from around the world, in a harmonized and coordinated effort to address these challenges and generate more robust and reproducible findings. This paper elaborates on the concepts and methods informing the work of the working group to date, and describes the initial approach of the four subgroups studying generalized anxiety disorder, panic disorder, social anxiety disorder, and specific phobia. At present, the ENIGMA-Anxiety database contains information about more than 100 unique samples, from 16 countries and 59 institutes. Future directions include examining additional imaging modalities, integrating imaging and genetic data, and collaborating with other ENIGMA working groups. The ENIGMA consortium creates synergy at the intersection of global mental health and clinical neuroscience, and the ENIGMA-Anxiety Working Group extends the promise of this approach to neuroimaging research on anxiety disorders.
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Affiliation(s)
- Janna Marie Bas‐Hoogendam
- Department of Developmental and Educational PsychologyLeiden University, Institute of Psychology Leiden The Netherlands
- Department of PsychiatryLeiden University Medical Center Leiden The Netherlands
- Leiden Institute for Brain and Cognition Leiden The Netherlands
| | - Nynke A. Groenewold
- Department of Psychiatry & Mental HealthUniversity of Cape Town Cape Town South Africa
| | - Moji Aghajani
- Department of PsychiatryAmsterdam UMC / VUMC Amsterdam The Netherlands
- Department of Research & InnovationGGZ inGeest Amsterdam The Netherlands
| | - Gabrielle F. Freitag
- National Institute of Mental Health, Emotion and Development Branch Bethesda Maryland USA
| | - Anita Harrewijn
- National Institute of Mental Health, Emotion and Development Branch Bethesda Maryland USA
| | - Kevin Hilbert
- Department of PsychologyHumboldt‐Universität zu Berlin Berlin Germany
| | - Neda Jahanshad
- University of Southern California Keck School of MedicineImaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute Los Angeles California USA
| | - Sophia I. Thomopoulos
- University of Southern California Keck School of MedicineImaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute Los Angeles California USA
| | - Paul M. Thompson
- University of Southern California Keck School of MedicineImaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute Los Angeles California USA
| | - Dick J. Veltman
- Department of PsychiatryAmsterdam UMC / VUMC Amsterdam The Netherlands
| | - Anderson M. Winkler
- National Institute of Mental Health, Emotion and Development Branch Bethesda Maryland USA
| | - Ulrike Lueken
- Department of PsychologyHumboldt‐Universität zu Berlin Berlin Germany
| | - Daniel S. Pine
- National Institute of Mental Health, Emotion and Development Branch Bethesda Maryland USA
| | - Nic J. A. Wee
- Department of PsychiatryLeiden University Medical Center Leiden The Netherlands
- Leiden Institute for Brain and Cognition Leiden The Netherlands
| | - Dan J. Stein
- Department of Psychiatry & Mental HealthUniversity of Cape Town Cape Town South Africa
- University of Cape TownSouth African MRC Unit on Risk & Resilience in Mental Disorders Cape Town South Africa
- University of Cape TownNeuroscience Institute Cape Town South Africa
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20
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Liew S, Zavaliangos‐Petropulu A, Jahanshad N, Lang CE, Hayward KS, Lohse KR, Juliano JM, Assogna F, Baugh LA, Bhattacharya AK, Bigjahan B, Borich MR, Boyd LA, Brodtmann A, Buetefisch CM, Byblow WD, Cassidy JM, Conforto AB, Craddock RC, Dimyan MA, Dula AN, Ermer E, Etherton MR, Fercho KA, Gregory CM, Hadidchi S, Holguin JA, Hwang DH, Jung S, Kautz SA, Khlif MS, Khoshab N, Kim B, Kim H, Kuceyeski A, Lotze M, MacIntosh BJ, Margetis JL, Mohamed FB, Piras F, Ramos‐Murguialday A, Richard G, Roberts P, Robertson AD, Rondina JM, Rost NS, Sanossian N, Schweighofer N, Seo NJ, Shiroishi MS, Soekadar SR, Spalletta G, Stinear CM, Suri A, Tang WKW, Thielman GT, Vecchio D, Villringer A, Ward NS, Werden E, Westlye LT, Winstein C, Wittenberg GF, Wong KA, Yu C, Cramer SC, Thompson PM. The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain-behavior relationships after stroke. Hum Brain Mapp 2022; 43:129-148. [PMID: 32310331 PMCID: PMC8675421 DOI: 10.1002/hbm.25015] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 04/03/2020] [Accepted: 04/08/2020] [Indexed: 01/28/2023] Open
Abstract
The goal of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well-powered meta- and mega-analytic approaches. ENIGMA Stroke Recovery has data from over 2,100 stroke patients collected across 39 research studies and 10 countries around the world, comprising the largest multisite retrospective stroke data collaboration to date. This article outlines the efforts taken by the ENIGMA Stroke Recovery working group to develop neuroinformatics protocols and methods to manage multisite stroke brain magnetic resonance imaging, behavioral and demographics data. Specifically, the processes for scalable data intake and preprocessing, multisite data harmonization, and large-scale stroke lesion analysis are described, and challenges unique to this type of big data collaboration in stroke research are discussed. Finally, future directions and limitations, as well as recommendations for improved data harmonization through prospective data collection and data management, are provided.
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Affiliation(s)
- Sook‐Lei Liew
- Chan Division of Occupational Science and Occupational TherapyUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
- Department of NeurologyUSC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern CaliforniaLos AngelesCaliforniaUSA
- Department of Biomedical Engineering, University of Southern CaliforniaLos AngelesCaliforniaUSA
- Neuroscience Graduate ProgramUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Artemis Zavaliangos‐Petropulu
- Department of NeurologyUSC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern CaliforniaLos AngelesCaliforniaUSA
- Neuroscience Graduate ProgramUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
- Imaging Genetics CenterUSC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Neda Jahanshad
- Department of NeurologyUSC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern CaliforniaLos AngelesCaliforniaUSA
- Imaging Genetics CenterUSC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Catherine E. Lang
- Program in Physical TherapyWashington University School of MedicineSt. LouisMissouriUSA
| | - Kathryn S. Hayward
- Department of Physiotherapyand Florey Institute of Neuroscience and Mental Health, University of MelbourneParkvilleVictoriaAustralia
- NHMRC Centre of Research Excellence in Stroke Rehabilitation and Brain Recovery, University of MelbourneParkvilleVictoriaAustralia
| | - Keith R. Lohse
- Department of Health, Kinesiology, and RecreationUniversity of UtahSalt Lake CityUtahUSA
- Department of Physical Therapy and Athletic TrainingUniversity of UtahSalt Lake CityUtahUSA
| | - Julia M. Juliano
- Neuroscience Graduate ProgramUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Francesca Assogna
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral NeurologyIRCCS Santa Lucia FoundationRomeItaly
| | - Lee A. Baugh
- Division of Basic Biomedical Sciences, Sanford School of Medicine, University of South DakotaVermillionSouth DakotaUSA
- Sioux Falls VA Health Care SystemSioux FallsSouth DakotaUSA
| | - Anup K. Bhattacharya
- Mallinckrodt Institute of Radiology, Washington University School of MedicineSt. LouisMissouriUSA
| | - Bavrina Bigjahan
- Department of NeurologyUSC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern CaliforniaLos AngelesCaliforniaUSA
- Department of Radiology, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Michael R. Borich
- Department of Rehabilitation MedicineEmory UniversityAtlantaGeorgiaUSA
| | - Lara A. Boyd
- Department of Physical Therapy, Faculty of MedicineUniversity of British ColumbiaVancouverBritish ColumbiaCanada
- Djavad Mowafaghian Centre for Brain HealthVancouverBritish ColumbiaCanada
| | - Amy Brodtmann
- Florey Institute for Neuroscience and Mental Health, University of MelbourneParkvilleVictoriaAustralia
| | - Cathrin M. Buetefisch
- Department of Rehabilitation MedicineEmory UniversityAtlantaGeorgiaUSA
- Department of NeurologyEmory UniversityAtlantaGeorgiaUSA
| | - Winston D. Byblow
- Department of Exercise Sciences, Centre for Brain ResearchUniversity of AucklandAucklandNew Zealand
| | - Jessica M. Cassidy
- Division of Physical Therapy, Department Allied Health SciencesUniversity of North Carolina, Chapel HillChapel HillNorth CarolinaUSA
| | - Adriana B. Conforto
- Neurology Clinical Division, Hospital das Clínicas/São Paulo UniversitySão PauloBrazil
- Hospital Israelita Albert EinsteinSão PauloBrazil
| | - R. Cameron Craddock
- Department of Diagnostic MedicineThe University of Texas at Austin Dell Medical SchoolAustinTexasUSA
| | - Michael A. Dimyan
- Department of Neurology and Neurorehabilitation, School of MedicineUniversity of Maryland, BaltimoreBaltimoreMarylandUSA
- VA Maryland Health Care SystemBaltimoreMarylandUSA
| | - Adrienne N. Dula
- Department of Diagnostic MedicineThe University of Texas at Austin Dell Medical SchoolAustinTexasUSA
- Department of NeurologyDell Medical School at University of Texas at AustinAustinTexasUSA
| | - Elsa Ermer
- Department of Neurology and Neurorehabilitation, School of MedicineUniversity of Maryland, BaltimoreBaltimoreMarylandUSA
| | - Mark R. Etherton
- Department of NeurologyMassachusetts General HospitalBostonMassachusettsUSA
- J. Philip Kistler Stroke Research CenterHarvard Medical SchoolBostonMassachusettsUSA
| | - Kelene A. Fercho
- Division of Basic Biomedical Sciences, Sanford School of Medicine, University of South DakotaVermillionSouth DakotaUSA
- Federal Aviation Administration, Civil Aerospace Medical InstituteOklahoma CityOklahomaUSA
| | - Chris M. Gregory
- Department of Health Sciences and ResearchMedical University of South CarolinaCharlestonSouth CarolinaUSA
| | - Shahram Hadidchi
- Department of RadiologyWayne State University/Detroit Medical CenterDetroitMichiganUSA
- Department of Internal MedicineWayne State University/Detroit Medical CenterDetroitMichiganUSA
| | - Jess A. Holguin
- Chan Division of Occupational Science and Occupational TherapyUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Darryl H. Hwang
- Department of Radiology, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Simon Jung
- Department of Neurology, University of BernBernSwitzerland
| | - Steven A. Kautz
- Department of Health Sciences and ResearchMedical University of South CarolinaCharlestonSouth CarolinaUSA
- Ralph H Johnson VA Medical CenterCharlestonSouth CarolinaUSA
| | - Mohamed Salah Khlif
- Florey Institute for Neuroscience and Mental Health, University of MelbourneParkvilleVictoriaAustralia
| | - Nima Khoshab
- Department of Anatomy and NeurobiologyUniversity of CaliforniaIrvineCaliforniaUSA
| | - Bokkyu Kim
- Department of Physical Therapy EducationState University of New York Upstate Medical UniversitySyracuseNew YorkUSA
- Division of Biokinesiology and Physical TherapyUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Hosung Kim
- Department of NeurologyUSC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Amy Kuceyeski
- Department of RadiologyWeill Cornell MedicineNew YorkNew YorkUSA
- Brain and Mind Research Institute, Weill Cornell MedicineNew YorkNew YorkUSA
| | - Martin Lotze
- Functional Imaging Unit, Center for Diagnostic RadiologySchool of Medicine, University of GreifswaldGreifswaldGermany
| | - Bradley J. MacIntosh
- Department of Medical BiophysicsUniversity of TorontoTorontoOntarioCanada
- Physical Sciences Platform, Brain Sciences ProgramSunnybrook Research InstituteTorontoOntarioCanada
| | - John L. Margetis
- Chan Division of Occupational Science and Occupational TherapyUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Feroze B. Mohamed
- Department of RadiologyThomas Jefferson UniversityPhiladelphiaPennsylvaniaUSA
| | - Fabrizio Piras
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral NeurologyIRCCS Santa Lucia FoundationRomeItaly
| | - Ander Ramos‐Murguialday
- TECNALIA, Basque Research and Technology Alliance (BRTA), Neurotechnology LaboratoryDerioSpain
- Institute of Medical Psychology and Behavioural Neurobiology, University of TubingenTübingenGermany
| | - Geneviève Richard
- Department of PsychologyUniversity of OsloOsloNorway
- NORMENT, Division of Mental Health and AddictionOslo University HospitalOsloNorway
- Institute of Clinical Medicine, University of OsloOsloNorway
| | - Pamela Roberts
- Department of Physical Medicine and RehabilitationCedars‐SinaiLos AngelesCaliforniaUSA
| | - Andrew D. Robertson
- Department of KinesiologyUniversity of WaterlooWaterlooOntarioCanada
- Schlegel‐UW Research Institute for Aging, University of WaterlooWaterlooOntarioCanada
| | - Jane M. Rondina
- Department of Clinical and Movement NeurosciencesUCL Queen Square Institute of Neurology, University College LondonLondonUK
| | - Natalia S. Rost
- Stroke Division, Department of NeurologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Nerses Sanossian
- Division of Neurocritical Care and Stroke, Department of Neurology, Keck School of Medicine, University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Nicolas Schweighofer
- Division of Biokinesiology and Physical Therapy, University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Na Jin Seo
- Department of Health Sciences and ResearchMedical University of South CarolinaCharlestonSouth CarolinaUSA
- Ralph H Johnson VA Medical CenterCharlestonSouth CarolinaUSA
- Division of Occupational Therapy, Department of Health Professions, Medical University of South CarolinaCharlestonSouth CarolinaUSA
| | - Mark S. Shiroishi
- Division of Neuroradiology, Department of RadiologyKeck School of Medicine, University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Surjo R. Soekadar
- Department of Psychiatry and Psychotherapy, Clinical Neurotechnology LaboratoryCharité ‐ University Medicine BerlinBerlinGermany
- Applied Neurotechnology Laboratory, Department of Psychiatry and PsychotherapyUniversity of TübingenTübingenGermany
| | - Gianfranco Spalletta
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral NeurologyIRCCS Santa Lucia FoundationRomeItaly
- Division of Neuropsychiatry, Menninger Department of Psychiatry and Behavioral SciencesBaylor College of MedicineHoustonTexasUSA
| | | | - Anisha Suri
- Department of Electrical and Computer EngineeringUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Wai Kwong W. Tang
- Department of PsychiatryThe Chinese University of Hong KongHong KongPeople's Republic of China
| | - Gregory T. Thielman
- Physical Therapy and Neuroscience, University of the SciencesPhiladelphiaPennsylvaniaUSA
- Samson CollegeQuezon CityPhilippines
| | - Daniela Vecchio
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral NeurologyIRCCS Santa Lucia FoundationRomeItaly
| | - Arno Villringer
- Department of NeurologyMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Department of Cognitive NeurologyUniversity Hospital LeipzigLeipzigGermany
- Center for Stroke Research, Charité‐Universitätsmedizin BerlinBerlinGermany
| | - Nick S. Ward
- UCL Queen Square Institute of Neurology, University College LondonLondonUK
| | - Emilio Werden
- Florey Institute for Neuroscience and Mental Health, University of MelbourneParkvilleVictoriaAustralia
| | - Lars T. Westlye
- Department of PsychologyUniversity of OsloOsloNorway
- NORMENT, Division of Mental Health and AddictionOslo University HospitalOsloNorway
| | - Carolee Winstein
- Division of Biokinesiology and Physical Therapy, University of Southern CaliforniaLos AngelesCaliforniaUSA
- Department of NeurologyUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - George F. Wittenberg
- Department of NeurologyUniversity of PittsburghPittsburghPennsylvaniaUSA
- Department of Veterans AffairsUniversity Drive CampusPittsburghPennsylvaniaUSA
| | - Kristin A. Wong
- Department of Physical Medicine and RehabilitationDell Medical School, University of Texas AustinAustinTexasUSA
| | - Chunshui Yu
- Department of RadiologyTianjin Medical University General HospitalTianjinChina
- Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Steven C. Cramer
- Department of NeurologyUCLA and California Rehabilitation InstituteLos AngelesCaliforniaUSA
| | - Paul M. Thompson
- Department of NeurologyUSC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern CaliforniaLos AngelesCaliforniaUSA
- Imaging Genetics CenterUSC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern CaliforniaLos AngelesCaliforniaUSA
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21
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Zugman A, Harrewijn A, Cardinale EM, Zwiebel H, Freitag GF, Werwath KE, Bas‐Hoogendam JM, Groenewold NA, Aghajani M, Hilbert K, Cardoner N, Porta‐Casteràs D, Gosnell S, Salas R, Blair KS, Blair JR, Hammoud MZ, Milad M, Burkhouse K, Phan KL, Schroeder HK, Strawn JR, Beesdo‐Baum K, Thomopoulos SI, Grabe HJ, Van der Auwera S, Wittfeld K, Nielsen JA, Buckner R, Smoller JW, Mwangi B, Soares JC, Wu M, Zunta‐Soares GB, Jackowski AP, Pan PM, Salum GA, Assaf M, Diefenbach GJ, Brambilla P, Maggioni E, Hofmann D, Straube T, Andreescu C, Berta R, Tamburo E, Price R, Manfro GG, Critchley HD, Makovac E, Mancini M, Meeten F, Ottaviani C, Agosta F, Canu E, Cividini C, Filippi M, Kostić M, Munjiza A, Filippi CA, Leibenluft E, Alberton BAV, Balderston NL, Ernst M, Grillon C, Mujica‐Parodi LR, van Nieuwenhuizen H, Fonzo GA, Paulus MP, Stein MB, Gur RE, Gur RC, Kaczkurkin AN, Larsen B, Satterthwaite TD, Harper J, Myers M, Perino MT, Yu Q, Sylvester CM, Veltman DJ, Lueken U, Van der Wee NJA, Stein DJ, Jahanshad N, Thompson PM, Pine DS, Winkler AM. Mega-analysis methods in ENIGMA: The experience of the generalized anxiety disorder working group. Hum Brain Mapp 2022; 43:255-277. [PMID: 32596977 PMCID: PMC8675407 DOI: 10.1002/hbm.25096] [Citation(s) in RCA: 44] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 05/26/2020] [Accepted: 05/31/2020] [Indexed: 12/15/2022] Open
Abstract
The ENIGMA group on Generalized Anxiety Disorder (ENIGMA-Anxiety/GAD) is part of a broader effort to investigate anxiety disorders using imaging and genetic data across multiple sites worldwide. The group is actively conducting a mega-analysis of a large number of brain structural scans. In this process, the group was confronted with many methodological challenges related to study planning and implementation, between-country transfer of subject-level data, quality control of a considerable amount of imaging data, and choices related to statistical methods and efficient use of resources. This report summarizes the background information and rationale for the various methodological decisions, as well as the approach taken to implement them. The goal is to document the approach and help guide other research groups working with large brain imaging data sets as they develop their own analytic pipelines for mega-analyses.
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Affiliation(s)
- André Zugman
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | - Anita Harrewijn
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | - Elise M. Cardinale
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | - Hannah Zwiebel
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | - Gabrielle F. Freitag
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | - Katy E. Werwath
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | - Janna M. Bas‐Hoogendam
- Leiden University Medical Center, Department of PsychiatryLeidenThe Netherlands
- Leiden Institute for Brain and Cognition (LIBC)LeidenThe Netherlands
- Leiden University, Institute of Psychology, Developmental and Educational PsychologyLeidenThe Netherlands
| | - Nynke A. Groenewold
- Department of Psychiatry & Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
| | - Moji Aghajani
- Department. of PsychiatryAmsterdam UMC/VUMCAmsterdamThe Netherlands
- GGZ InGeestDepartment of Research & InnovationAmsterdamThe Netherlands
| | - Kevin Hilbert
- Department of PsychologyHumboldt‐Universität zu BerlinBerlinGermany
| | - Narcis Cardoner
- Department of Mental HealthUniversity Hospital Parc Taulí‐I3PTBarcelonaSpain
- Department of Psychiatry and Forensic MedicineUniversitat Autònoma de BarcelonaBarcelonaSpain
- Centro de Investigación Biomédica en Red de Salud MentalCarlos III Health InstituteMadridSpain
| | - Daniel Porta‐Casteràs
- Department of Mental HealthUniversity Hospital Parc Taulí‐I3PTBarcelonaSpain
- Department of Psychiatry and Forensic MedicineUniversitat Autònoma de BarcelonaBarcelonaSpain
- Centro de Investigación Biomédica en Red de Salud MentalCarlos III Health InstituteMadridSpain
| | - Savannah Gosnell
- Menninger Department of Psychiatry and Behavioral SciencesBaylor College of MedicineHoustonTexasUSA
| | - Ramiro Salas
- Menninger Department of Psychiatry and Behavioral SciencesBaylor College of MedicineHoustonTexasUSA
| | - Karina S. Blair
- Center for Neurobehavioral ResearchBoys Town National Research HospitalBoys TownNebraskaUSA
| | - James R. Blair
- Center for Neurobehavioral ResearchBoys Town National Research HospitalBoys TownNebraskaUSA
| | - Mira Z. Hammoud
- Department of PsychiatryNew York UniversityNew YorkNew YorkUSA
| | - Mohammed Milad
- Department of PsychiatryNew York UniversityNew YorkNew YorkUSA
| | - Katie Burkhouse
- Department of PsychiatryUniversity of Illinois at ChicagoChicagoIllinoisUSA
| | - K. Luan Phan
- Department of Psychiatry and Behavioral HealthThe Ohio State UniversityColumbusOhioUSA
| | - Heidi K. Schroeder
- Department of Psychiatry & Behavioral NeuroscienceUniversity of CincinnatiCincinnatiOhioUSA
| | - Jeffrey R. Strawn
- Department of Psychiatry & Behavioral NeuroscienceUniversity of CincinnatiCincinnatiOhioUSA
| | - Katja Beesdo‐Baum
- Behavioral EpidemiologyInstitute of Clinical Psychology and Psychotherapy, Technische Universität DresdenDresdenGermany
| | - Sophia I. Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Hans J. Grabe
- Department of Psychiatry and PsychotherapyUniversity Medicine GreifswaldGreifswaldGermany
- German Center for Neurodegenerative Diseases (DZNE)Site Rostock/GreifswaldGreifswaldGermany
| | - Sandra Van der Auwera
- Department of Psychiatry and PsychotherapyUniversity Medicine GreifswaldGreifswaldGermany
- German Center for Neurodegenerative Diseases (DZNE)Site Rostock/GreifswaldGreifswaldGermany
| | - Katharina Wittfeld
- Department of Psychiatry and PsychotherapyUniversity Medicine GreifswaldGreifswaldGermany
- German Center for Neurodegenerative Diseases (DZNE)Site Rostock/GreifswaldGreifswaldGermany
| | - Jared A. Nielsen
- Department of PsychologyHarvard UniversityCambridgeMassachusettsUSA
- Center for Brain ScienceHarvard UniversityCambridgeMassachusettsUSA
| | - Randy Buckner
- Department of PsychologyHarvard UniversityCambridgeMassachusettsUSA
- Center for Brain ScienceHarvard UniversityCambridgeMassachusettsUSA
- Department of PsychiatryMassachusetts General HospitalBostonMassachusettsUSA
| | - Jordan W. Smoller
- Department of PsychiatryMassachusetts General HospitalBostonMassachusettsUSA
| | - Benson Mwangi
- Center Of Excellence On Mood Disorders, Department of Psychiatry and Behavioral SciencesThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Jair C. Soares
- Center Of Excellence On Mood Disorders, Department of Psychiatry and Behavioral SciencesThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Mon‐Ju Wu
- Center Of Excellence On Mood Disorders, Department of Psychiatry and Behavioral SciencesThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Giovana B. Zunta‐Soares
- Center Of Excellence On Mood Disorders, Department of Psychiatry and Behavioral SciencesThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Andrea P. Jackowski
- LiNC, Department of PsychiatryFederal University of São PauloSão PauloSão PauloBrazil
| | - Pedro M. Pan
- LiNC, Department of PsychiatryFederal University of São PauloSão PauloSão PauloBrazil
| | - Giovanni A. Salum
- Section on Negative Affect and Social Processes, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do SulPorto AlegreRio Grande do SulBrazil
| | - Michal Assaf
- Olin Neuropsychiatry Research CenterInstitute of Living, Hartford HospitalHartfordConnecticutUSA
- Department of PsychiatryYale School of MedicineNew HavenConnecticutUSA
| | - Gretchen J. Diefenbach
- Anxiety Disorders CenterInstitute of Living, Hartford HospitalHartfordConnecticutUSA
- Yale School of MedicineNew HavenConnecticutUSA
| | - Paolo Brambilla
- Department of Neurosciences and Mental HealthFondazione IRCCS Ca' Granda Ospedale Maggiore PoliclinicoMilanItaly
| | - Eleonora Maggioni
- Department of Neurosciences and Mental HealthFondazione IRCCS Ca' Granda Ospedale Maggiore PoliclinicoMilanItaly
| | - David Hofmann
- Institute of Medical Psychology and Systems Neuroscience, University of MuensterMuensterGermany
| | - Thomas Straube
- Institute of Medical Psychology and Systems Neuroscience, University of MuensterMuensterGermany
| | - Carmen Andreescu
- Department of PsychiatryUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Rachel Berta
- Department of PsychiatryUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Erica Tamburo
- Department of PsychiatryUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Rebecca Price
- Department of Psychiatry & PsychologyUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Gisele G. Manfro
- Anxiety Disorder ProgramHospital de Clínicas de Porto AlegrePorto AlegreRio Grande do SulBrazil
- Department of PsychiatryFederal University of Rio Grande do SulPorto AlegreRio Grande do SulBrazil
| | - Hugo D. Critchley
- Department of NeuroscienceBrighton and Sussex Medical School, University of SussexBrightonUK
| | - Elena Makovac
- Centre for Neuroimaging ScienceKings College LondonLondonUK
| | - Matteo Mancini
- Department of NeuroscienceBrighton and Sussex Medical School, University of SussexBrightonUK
| | | | | | - Federica Agosta
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of NeuroscienceIRCCS San Raffaele Scientific InstituteMilanItaly
- Vita‐Salute San Raffaele UniversityMilanItaly
| | - Elisa Canu
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of NeuroscienceIRCCS San Raffaele Scientific InstituteMilanItaly
| | - Camilla Cividini
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of NeuroscienceIRCCS San Raffaele Scientific InstituteMilanItaly
| | - Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of NeuroscienceIRCCS San Raffaele Scientific InstituteMilanItaly
- Vita‐Salute San Raffaele UniversityMilanItaly
- Neurology and Neurophysiology UnitIRCCS San Raffaele Scientific InstituteMilanItaly
| | - Milutin Kostić
- Institute of Mental Health, University of BelgradeBelgradeSerbia
- Department of Psychiatry, School of MedicineUniversity of BelgradeBelgradeSerbia
| | - Ana Munjiza
- Institute of Mental Health, University of BelgradeBelgradeSerbia
| | - Courtney A. Filippi
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | - Ellen Leibenluft
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | - Bianca A. V. Alberton
- Graduate Program in Electrical and Computer Engineering, Universidade Tecnológica Federal do ParanáCuritibaPuerto RicoBrazil
| | - Nicholas L. Balderston
- Center for Neuromodulation in Depression and StressUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Monique Ernst
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | - Christian Grillon
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | | | | | - Gregory A. Fonzo
- Department of PsychiatryThe University of Texas at Austin Dell Medical SchoolAustinTexasUSA
| | | | - Murray B. Stein
- Department of Psychiatry & Family Medicine and Public HealthUniversity of CaliforniaLa JollaCaliforniaUSA
| | - Raquel E. Gur
- Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Ruben C. Gur
- Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | | | - Bart Larsen
- Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | | | - Jennifer Harper
- Department of PsychiatryWashington UniversitySt. LouisMissouriUSA
| | - Michael Myers
- Department of PsychiatryWashington UniversitySt. LouisMissouriUSA
| | | | - Qiongru Yu
- Department of PsychiatryWashington UniversitySt. LouisMissouriUSA
| | | | - Dick J. Veltman
- Department. of PsychiatryAmsterdam UMC/VUMCAmsterdamThe Netherlands
| | - Ulrike Lueken
- Department of PsychologyHumboldt‐Universität zu BerlinBerlinGermany
| | - Nic J. A. Van der Wee
- Leiden University Medical Center, Department of PsychiatryLeidenThe Netherlands
- Leiden Institute for Brain and Cognition (LIBC)LeidenThe Netherlands
| | - Dan J. Stein
- Department of Psychiatry & Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
- SAMRC Unite on Risk & Resilience in Mental Disorders, Department of Psychiatry & Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Daniel S. Pine
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | - Anderson M. Winkler
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
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22
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Lee SH, Broadwater MA, Ban W, Wang TWW, Kim HJ, Dumas JS, Vetreno RP, Herman MA, Morrow AL, Besheer J, Kash TL, Boettiger CA, Robinson DL, Crews FT, Shih YYI. An isotropic EPI database and analytical pipelines for rat brain resting-state fMRI. Neuroimage 2021; 243:118541. [PMID: 34478824 PMCID: PMC8561231 DOI: 10.1016/j.neuroimage.2021.118541] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 08/08/2021] [Accepted: 08/30/2021] [Indexed: 12/24/2022] Open
Abstract
Resting-state functional magnetic resonance imaging (fMRI) has drastically expanded the scope of brain research by advancing our knowledge about the topologies, dynamics, and interspecies translatability of functional brain networks. Several databases have been developed and shared in accordance with recent key initiatives in the rodent fMRI community to enhance the transparency, reproducibility, and interpretability of data acquired at various sites. Despite these pioneering efforts, one notable challenge preventing efficient standardization in the field is the customary choice of anisotropic echo planar imaging (EPI) schemes with limited spatial coverage. Imaging with anisotropic resolution and/or reduced brain coverage has significant shortcomings including reduced registration accuracy and increased deviation in brain feature detection. Here we proposed a high-spatial-resolution (0.4 mm), isotropic, whole-brain EPI protocol for the rat brain using a horizontal slicing scheme that can maintain a functionally relevant repetition time (TR), avoid high gradient duty cycles, and offer unequivocal whole-brain coverage. Using this protocol, we acquired resting-state EPI fMRI data from 87 healthy rats under the widely used dexmedetomidine sedation supplemented with low-dose isoflurane on a 9.4 T MRI system. We developed an EPI template that closely approximates the Paxinos and Watson's rat brain coordinate system and demonstrated its ability to improve the accuracy of group-level approaches and streamline fMRI data pre-processing. Using this database, we employed a multi-scale dictionary-learning approach to identify reliable spatiotemporal features representing rat brain intrinsic activity. Subsequently, we performed k-means clustering on those features to obtain spatially discrete, functional regions of interest (ROIs). Using Euclidean-based hierarchical clustering and modularity-based partitioning, we identified the topological organizations of the rat brain. Additionally, the identified group-level FC network appeared robust across strains and sexes. The "triple-network" commonly adapted in human fMRI were resembled in the rat brain. Through this work, we disseminate raw and pre-processed isotropic EPI data, a rat brain EPI template, as well as identified functional ROIs and networks in standardized rat brain coordinates. We also make our analytical pipelines and scripts publicly available, with the hope of facilitating rat brain resting-state fMRI study standardization.
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Affiliation(s)
- Sung-Ho Lee
- Center for Animal MRI, University of North Carolina, Chapel Hill, NC, USA,Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA,Department of Neurology, University of North Carolina, Chapel Hill, NC, USA,Bowles Center for Alcohol Studies University of North Carolina, Chapel Hill, NC, USA,Corresponding authors at: Center for Animal MRI, 125 Mason Farm Road, CB# 7513, University of North Carolina, Chapel Hill, NC 27599, USA. (S.-H. Lee), (Y.-Y.I. Shih)
| | - Margaret A. Broadwater
- Center for Animal MRI, University of North Carolina, Chapel Hill, NC, USA,Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA,Department of Neurology, University of North Carolina, Chapel Hill, NC, USA,Bowles Center for Alcohol Studies University of North Carolina, Chapel Hill, NC, USA
| | - Woomi Ban
- Center for Animal MRI, University of North Carolina, Chapel Hill, NC, USA,Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Tzu-Wen Winnie Wang
- Center for Animal MRI, University of North Carolina, Chapel Hill, NC, USA,Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Hyeon-Joong Kim
- Center for Animal MRI, University of North Carolina, Chapel Hill, NC, USA,Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA,Department of Neurology, University of North Carolina, Chapel Hill, NC, USA
| | - Jaiden Seongmi Dumas
- Center for Animal MRI, University of North Carolina, Chapel Hill, NC, USA,Department of Neurology, University of North Carolina, Chapel Hill, NC, USA,Department of Quantitative Biology, University of North Carolina, Chapel Hill, NC, USA
| | - Ryan P. Vetreno
- Bowles Center for Alcohol Studies University of North Carolina, Chapel Hill, NC, USA,Department of Pharmacology, University of North Carolina, Chapel Hill, NC, USA
| | - Melissa A. Herman
- Bowles Center for Alcohol Studies University of North Carolina, Chapel Hill, NC, USA,Department of Pharmacology, University of North Carolina, Chapel Hill, NC, USA
| | - A. Leslie Morrow
- Bowles Center for Alcohol Studies University of North Carolina, Chapel Hill, NC, USA,Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA,Department of Pharmacology, University of North Carolina, Chapel Hill, NC, USA
| | - Joyce Besheer
- Bowles Center for Alcohol Studies University of North Carolina, Chapel Hill, NC, USA,Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - Thomas L. Kash
- Bowles Center for Alcohol Studies University of North Carolina, Chapel Hill, NC, USA,Department of Pharmacology, University of North Carolina, Chapel Hill, NC, USA
| | - Charlotte A. Boettiger
- Bowles Center for Alcohol Studies University of North Carolina, Chapel Hill, NC, USA,Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA,Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, NC, USA
| | - Donita L. Robinson
- Bowles Center for Alcohol Studies University of North Carolina, Chapel Hill, NC, USA,Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - Fulton T. Crews
- Bowles Center for Alcohol Studies University of North Carolina, Chapel Hill, NC, USA,Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA,Department of Pharmacology, University of North Carolina, Chapel Hill, NC, USA
| | - Yen-Yu Ian Shih
- Center for Animal MRI, University of North Carolina, Chapel Hill, NC, USA,Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA,Department of Neurology, University of North Carolina, Chapel Hill, NC, USA,Bowles Center for Alcohol Studies University of North Carolina, Chapel Hill, NC, USA,Corresponding authors at: Center for Animal MRI, 125 Mason Farm Road, CB# 7513, University of North Carolina, Chapel Hill, NC 27599, USA. (S.-H. Lee), (Y.-Y.I. Shih)
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23
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Vizioli L, Moeller S, Dowdle L, Akçakaya M, De Martino F, Yacoub E, Uğurbil K. Lowering the thermal noise barrier in functional brain mapping with magnetic resonance imaging. Nat Commun 2021; 12:5181. [PMID: 34462435 PMCID: PMC8405721 DOI: 10.1038/s41467-021-25431-8] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Accepted: 08/05/2021] [Indexed: 01/05/2023] Open
Abstract
Functional magnetic resonance imaging (fMRI) has become an indispensable tool for investigating the human brain. However, the inherently poor signal-to-noise-ratio (SNR) of the fMRI measurement represents a major barrier to expanding its spatiotemporal scale as well as its utility and ultimate impact. Here we introduce a denoising technique that selectively suppresses the thermal noise contribution to the fMRI experiment. Using 7-Tesla, high-resolution human brain data, we demonstrate improvements in key metrics of functional mapping (temporal-SNR, the detection and reproducibility of stimulus-induced signal changes, and accuracy of functional maps) while leaving the amplitude of the stimulus-induced signal changes, spatial precision, and functional point-spread-function unaltered. We demonstrate that the method enables the acquisition of ultrahigh resolution (0.5 mm isotropic) functional maps but is also equally beneficial for a large variety of fMRI applications, including supra-millimeter resolution 3- and 7-Tesla data obtained over different cortical regions with different stimulation/task paradigms and acquisition strategies. The signal-to-noise ratio is a key consideration when selecting a magnetic resonance imaging protocol. Thermal noise is major issue, especially in high resolution functional images. Here the authors introduce a method to suppress thermal noise in functional images without losses in spatial precision, increasing the signal-to-noise ratio.
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Affiliation(s)
- Luca Vizioli
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA. .,Department of Neurosurgery, University of Minnesota, Minneapolis, MN, USA.
| | - Steen Moeller
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA
| | - Logan Dowdle
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA.,Department of Neurosurgery, University of Minnesota, Minneapolis, MN, USA
| | - Mehmet Akçakaya
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA.,Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, USA
| | - Federico De Martino
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA.,Faculty of Psychology and Neuroscience, Department of Cognitive Neurosciences, Maastricht University, Maastricht, the Netherlands
| | - Essa Yacoub
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA
| | - Kamil Uğurbil
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA.
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24
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Zou R, Liu G. Motor Dysfunction of Autonomic Nervous System Based on Resting State Functional Magnetic Resonance Imaging. JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS 2021. [DOI: 10.1166/jmihi.2021.3532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Epilepsy is caused by highly synchronized abnormal discharges of neurons in the brain. Resting State FMRI is widely used as a non-invasive checking method, clinical and basic research in the field of epilepsy has a huge influence, to study the plant nerve system movement function of
brain activity and the neural network function of the complex contact provides broad prospects, greatly promote the development of clinical neurology and imaging. Therefore, this paper studies the functional connection and cognitive function of verbal working memory (VWM) in patients with
epilepsy by applying resting state FMRI. The experimental results showed that VWM and cognitive functions of epileptic patients showed a trend of decline or even disappearance, to realize the detection of autonomic nervous system motor dysfunction of patients.
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Affiliation(s)
- Ruyun Zou
- Department of Sports, Chongqing College of Finance and Economics, Chongqing 402160, China
| | - Guiyou Liu
- Department of Sports, Chongqing College of Finance and Economics, Chongqing 402160, China
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25
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Statistical and Machine Learning Link Selection Methods for Brain Functional Networks: Review and Comparison. Brain Sci 2021; 11:brainsci11060735. [PMID: 34073098 PMCID: PMC8227272 DOI: 10.3390/brainsci11060735] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 05/24/2021] [Accepted: 05/28/2021] [Indexed: 11/28/2022] Open
Abstract
Network-based representations have introduced a revolution in neuroscience, expanding the understanding of the brain from the activity of individual regions to the interactions between them. This augmented network view comes at the cost of high dimensionality, which hinders both our capacity of deciphering the main mechanisms behind pathologies, and the significance of any statistical and/or machine learning task used in processing this data. A link selection method, allowing to remove irrelevant connections in a given scenario, is an obvious solution that provides improved utilization of these network representations. In this contribution we review a large set of statistical and machine learning link selection methods and evaluate them on real brain functional networks. Results indicate that most methods perform in a qualitatively similar way, with NBS (Network Based Statistics) winning in terms of quantity of retained information, AnovaNet in terms of stability and ExT (Extra Trees) in terms of lower computational cost. While machine learning methods are conceptually more complex than statistical ones, they do not yield a clear advantage. At the same time, the high heterogeneity in the set of links retained by each method suggests that they are offering complementary views to the data. The implications of these results in neuroscience tasks are finally discussed.
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Wilde EA, Dennis EL, Tate DF. The ENIGMA Brain Injury working group: approach, challenges, and potential benefits. Brain Imaging Behav 2021; 15:465-474. [PMID: 33506440 PMCID: PMC8035294 DOI: 10.1007/s11682-021-00450-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Revised: 12/29/2020] [Accepted: 01/03/2021] [Indexed: 12/26/2022]
Abstract
The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium brings together researchers from around the world to try to identify the genetic underpinnings of brain structure and function, along with robust, generalizable effects of neurological and psychiatric disorders. The recently-formed ENIGMA Brain Injury working group includes 10 subgroups, based largely on injury mechanism and patient population. This introduction to the special issue summarizes the history, organization, and objectives of ENIGMA Brain Injury, and includes a discussion of strategies, challenges, opportunities and goals common across 6 of the subgroups under the umbrella of ENIGMA Brain Injury. The following articles in this special issue, including 6 articles from different subgroups, will detail the challenges and opportunities specific to each subgroup.
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Affiliation(s)
- Elisabeth A Wilde
- TBICC, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- George E. Wahlen VA Medical Center, Salt Lake City, UT, USA
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA
| | - Emily L Dennis
- TBICC, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA.
- George E. Wahlen VA Medical Center, Salt Lake City, UT, USA.
- Psychiatry Neuroimaging Laboratory, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, Los Angeles, CA, USA.
| | - David F Tate
- TBICC, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- George E. Wahlen VA Medical Center, Salt Lake City, UT, USA
- Missouri Institute of Mental Health, University of Missouri, St. Louis, MO, USA
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27
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Dennis EL, Caeyenberghs K, Asarnow RF, Babikian T, Bartnik-Olson B, Bigler ED, Figaji A, Giza CC, Goodrich-Hunsaker NJ, Hodges CB, Hoskinson KR, Königs M, Levin HS, Lindsey HM, Livny A, Max JE, Merkley TL, Newsome MR, Olsen A, Ryan NP, Spruiell MS, Suskauer SJ, Thomopoulos SI, Ware AL, Watson CG, Wheeler AL, Yeates KO, Zielinski BA, Thompson PM, Tate DF, Wilde EA. Challenges and opportunities for neuroimaging in young patients with traumatic brain injury: a coordinated effort towards advancing discovery from the ENIGMA pediatric moderate/severe TBI group. Brain Imaging Behav 2021; 15:555-575. [PMID: 32734437 PMCID: PMC7855317 DOI: 10.1007/s11682-020-00363-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Traumatic brain injury (TBI) is a major cause of death and disability in children in both developed and developing nations. Children and adolescents suffer from TBI at a higher rate than the general population, and specific developmental issues require a unique context since findings from adult research do not necessarily directly translate to children. Findings in pediatric cohorts tend to lag behind those in adult samples. This may be due, in part, both to the smaller number of investigators engaged in research with this population and may also be related to changes in safety laws and clinical practice that have altered length of hospital stays, treatment, and access to this population. The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Pediatric Moderate/Severe TBI (msTBI) group aims to advance research in this area through global collaborative meta-analysis of neuroimaging data. In this paper, we discuss important challenges in pediatric TBI research and opportunities that we believe the ENIGMA Pediatric msTBI group can provide to address them. With the paucity of research studies examining neuroimaging biomarkers in pediatric patients with TBI and the challenges of recruiting large numbers of participants, collaborating to improve statistical power and to address technical challenges like lesions will significantly advance the field. We conclude with recommendations for future research in this field of study.
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Affiliation(s)
- Emily L Dennis
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA.
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, Los Angeles, CA, USA.
- Psychiatry Neuroimaging Laboratory, Brigham & Women's Hospital, Boston, MA, USA.
| | - Karen Caeyenberghs
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia
| | - Robert F Asarnow
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, CA, USA
- Brain Research Institute, UCLA, Los Angeles, CA, USA
- Department of Psychology, UCLA, Los Angeles, CA, USA
| | - Talin Babikian
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, CA, USA
- UCLA Steve Tisch BrainSPORT Program, Los Angeles, CA, USA
| | - Brenda Bartnik-Olson
- Department of Radiology, Loma Linda University Medical Center, Loma Linda, CA, USA
| | - Erin D Bigler
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Psychology, Brigham Young University, Provo, UT, USA
- Neuroscience Center, Brigham Young University, Provo, UT, USA
| | - Anthony Figaji
- Division of Neurosurgery, University of Cape Town, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Christopher C Giza
- UCLA Steve Tisch BrainSPORT Program, Los Angeles, CA, USA
- Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Naomi J Goodrich-Hunsaker
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Psychology, Brigham Young University, Provo, UT, USA
- George E. Wahlen Veterans Affairs Salt Lake City Healthcare System, Salt Lake City, UT, USA
| | - Cooper B Hodges
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Psychology, Brigham Young University, Provo, UT, USA
- George E. Wahlen Veterans Affairs Salt Lake City Healthcare System, Salt Lake City, UT, USA
| | - Kristen R Hoskinson
- Center for Biobehavioral Health, The Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, OH, USA
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH, USA
| | - Marsh Königs
- Emma Children's Hospital, Amsterdam UMC, University of Amsterdam, Emma Neuroscience Group, Amsterdam, The Netherlands
| | - Harvey S Levin
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA
- Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA
| | - Hannah M Lindsey
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Psychology, Brigham Young University, Provo, UT, USA
- George E. Wahlen Veterans Affairs Salt Lake City Healthcare System, Salt Lake City, UT, USA
| | - Abigail Livny
- Department of Diagnostic Imaging, Sheba Medical Center, Ramat Gan, Tel-Hashomer, Israel
- Joseph Sagol Neuroscience Center, Sheba Medical Center, Ramat Gan, Tel-Hashomer, Israel
| | - Jeffrey E Max
- Department of Psychiatry, University of California, La Jolla, San Diego, CA, USA
- Department of Psychiatry, Rady Children's Hospital, San Diego, CA, USA
| | - Tricia L Merkley
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Psychology, Brigham Young University, Provo, UT, USA
- Neuroscience Center, Brigham Young University, Provo, UT, USA
| | - Mary R Newsome
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA
- Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA
| | - Alexander Olsen
- Department of Psychology, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Physical Medicine and Rehabilitation, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Nicholas P Ryan
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia
- Department of Paediatrics, The University of Melbourne, Melbourne, Australia
- Department of Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia
| | - Matthew S Spruiell
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA
| | - Stacy J Suskauer
- Kennedy Krieger Institute, Baltimore, MD, USA
- Departments of Physical Medicine & Rehabilitation and Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, Los Angeles, CA, USA
| | - Ashley L Ware
- Department of Psychology, University of Calgary, Calgary, Alberta, Canada
| | - Christopher G Watson
- Department of Pediatrics, Children's Learning Institute, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Anne L Wheeler
- Hospital for Sick Children, Neuroscience and Mental Health Program, Toronto, Canada
- Physiology Department, University of Toronto, Toronto, Canada
| | - Keith Owen Yeates
- Department of Psychology, University of Calgary, Calgary, Alberta, Canada
- Alberta Children's Hospital Research Institute and Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Departments of Pediatrics and Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada
| | - Brandon A Zielinski
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, Los Angeles, CA, USA
- Departments of Neurology, Pediatrics, Psychiatry, Radiology, Engineering, and Ophthalmology, USC, Los Angeles, CA, USA
| | - David F Tate
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Psychology, Brigham Young University, Provo, UT, USA
- George E. Wahlen Veterans Affairs Salt Lake City Healthcare System, Salt Lake City, UT, USA
- Missouri Institute of Mental Health and University of Missouri, St Louis, MO, USA
| | - Elisabeth A Wilde
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- George E. Wahlen Veterans Affairs Salt Lake City Healthcare System, Salt Lake City, UT, USA
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA
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Field AS, Birn RM. Wheat from the Chaff: Denoising Functional MRI Data. Radiology 2021; 299:49-50. [PMID: 33595393 DOI: 10.1148/radiol.2021210247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Aaron S Field
- From the Departments of Radiology (A.S.F.) and Psychiatry (R.M.B.), University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, M/C 3252, Madison, WI 53792
| | - Rasmus M Birn
- From the Departments of Radiology (A.S.F.) and Psychiatry (R.M.B.), University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, M/C 3252, Madison, WI 53792
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Hare SM, Adhikari BM, Du X, Garcia L, Bruce H, Kochunov P, Simon JZ, Hong LE. Local versus long-range connectivity patterns of auditory disturbance in schizophrenia. Schizophr Res 2021; 228:262-270. [PMID: 33493774 PMCID: PMC7987759 DOI: 10.1016/j.schres.2020.11.052] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 11/25/2020] [Accepted: 11/28/2020] [Indexed: 01/01/2023]
Abstract
Auditory hallucinations are a debilitating symptom of schizophrenia. Effective treatment is limited because the underlying neural mechanisms remain unknown. Our study investigates how local and long-range functional connectivity is associated with auditory perceptual disturbances (APD) in schizophrenia. APD was assessed using the Auditory Perceptual Trait and State Scale. Resting state fMRI data were collected for N=99 patients with schizophrenia. Local functional connectivity was estimated using regional homogeneity (ReHo) analysis; long-range connectivity was estimated using resting state functional connectivity (rsFC) analysis. Mediation analyses tested whether local (ReHo) connectivity significantly mediated associations between long-distance rsFC and APD. Severity of APD was significantly associated with reduced ReHo in left and right putamen, left temporoparietal junction (TPJ), and right hippocampus-pallidum. Higher APD was also associated with reduced rsFC between the right putamen and the contralateral putamen and auditory cortex. Local and long-distance connectivity measures together explained 40.3% of variance in APD (P < 0.001), with the strongest predictor being the left TPJ ReHo (P < 0.001). Additionally, TPJ ReHo significantly mediated the relationship between right putamen - left putamen rsFC and APD (Sobel test, P = 0.001). Our findings suggest that both local and long-range functional connectivity deficits contribute to APD, emphasizing the role of striatum and auditory cortex. Considering the translational impact of these circuit-based findings within the context of prior clinical trials to treat auditory hallucinations, we propose a model in which correction of both local and long-distance functional connectivity deficits may be necessary to treat auditory hallucinations.
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Affiliation(s)
- Stephanie M. Hare
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA,Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Bhim M. Adhikari
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA,Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Xiaoming Du
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA; Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, USA.
| | - Laura Garcia
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA; Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, USA.
| | - Heather Bruce
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA; Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, USA.
| | - Peter Kochunov
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA; Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, USA.
| | - Jonathan Z. Simon
- Department of Electrical and Computer Engineering, College Park, MD, USA
| | - L. Elliot Hong
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA,Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, USA
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Culbreth AJ, Wu Q, Chen S, Adhikari BM, Hong LE, Gold JM, Waltz JA. Temporal-thalamic and cingulo-opercular connectivity in people with schizophrenia. Neuroimage Clin 2020; 29:102531. [PMID: 33340977 PMCID: PMC7750447 DOI: 10.1016/j.nicl.2020.102531] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 12/01/2020] [Accepted: 12/08/2020] [Indexed: 01/22/2023]
Abstract
A growing body of research has suggested that people with schizophrenia (SZ) exhibit altered patterns of functional and anatomical brain connectivity. For example, many previous resting state functional connectivity (rsFC) studies have shown that, compared to healthy controls (HC), people with SZ demonstrate hyperconnectivity between subregions of the thalamus and sensory cortices, as well as hypoconnectivity between subregions of the thalamus and prefrontal cortex. In addition to thalamic findings, hypoconnectivity between cingulo-opercular brain regions thought to be involved in salience detection has also been commonly reported in people with SZ. However, previous studies have largely relied on seed-based analyses. Seed-based approaches require researchers to define a single a priori brain region, which is then used to create a rsFC map across the entire brain. While useful for testing specific hypotheses, these analyses are limited in that only a subset of connections across the brain are explored. In the current manuscript, we leverage novel network statistical techniques in order to detect latent functional connectivity networks with organized topology that successfully differentiate people with SZ from HCs. Importantly, these techniques do not require a priori seed selection and allow for whole brain investigation, representing a comprehensive, data-driven approach to determining differential connectivity between diagnostic groups. Across two samples, (Sample 1: 35 SZ, 44 HC; Sample 2: 65 SZ, 79 HC), we found evidence for differential rsFC within a network including temporal and thalamic regions. Connectivity in this network was greater for people with SZ compared to HCs. In the second sample, we also found evidence for hypoconnectivity within a cingulo-opercular network of brain regions in people with SZ compared to HCs. In summary, our results replicate and extend previous studies suggesting hyperconnectivity between the thalamus and sensory cortices and hypoconnectivity between cingulo-opercular regions in people with SZ using data-driven statistical and graph theoretical techniques.
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Affiliation(s)
- Adam J Culbreth
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland, School of Medicine, United States.
| | - Qiong Wu
- Department of Mathematics, University of Maryland, College Park, United States
| | - Shuo Chen
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland, School of Medicine, United States; Division of Biostatistics and Bioinformatics, University of Maryland, Baltimore, United States
| | - Bhim M Adhikari
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland, School of Medicine, United States
| | - L Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland, School of Medicine, United States
| | - James M Gold
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland, School of Medicine, United States
| | - James A Waltz
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland, School of Medicine, United States
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Ades-Aron B, Lemberskiy G, Veraart J, Golfinos J, Fieremans E, Novikov DS, Shepherd T. Improved Task-based Functional MRI Language Mapping in Patients with Brain Tumors through Marchenko-Pastur Principal Component Analysis Denoising. Radiology 2020; 298:365-373. [PMID: 33289611 DOI: 10.1148/radiol.2020200822] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Background Functional MRI improves preoperative planning in patients with brain tumors, but task-correlated signal intensity changes are only 2%-3% above baseline. This makes accurate functional mapping challenging. Marchenko-Pastur principal component analysis (MP-PCA) provides a novel strategy to separate functional MRI signal from noise without requiring user input or prior data representation. Purpose To determine whether MP-PCA denoising improves activation magnitude for task-based functional MRI language mapping in patients with brain tumors. Materials and Methods In this Health Insurance Portability and Accountability Act-compliant study, MP-PCA performance was first evaluated by using simulated functional MRI data with a known ground truth. Right-handed, left-language-dominant patients with brain tumors who successfully performed verb generation, sentence completion, and finger tapping functional MRI tasks were retrospectively identified between January 2017 and August 2018. On the group level, for each task, histograms of z scores for original and MP-PCA denoised data were extracted from relevant regions and contralateral homologs were seeded by a neuroradiologist blinded to functional MRI findings. Z scores were compared with paired two-sided t tests, and distributions were compared with effect size measurements and the Kolmogorov-Smirnov test. The number of voxels with a z score greater than 3 was used to measure task sensitivity relative to task duration. Results Twenty-three patients (mean age ± standard deviation, 43 years ± 18; 13 women) were evaluated. MP-PCA denoising led to a higher median z score of task-based functional MRI voxel activation in left hemisphere cortical regions for verb generation (from 3.8 ± 1.0 to 4.5 ± 1.4; P < .001), sentence completion (from 3.7 ± 1.0 to 4.3 ± 1.4; P < .001), and finger tapping (from 6.9 ± 2.4 to 7.9 ± 2.9; P < .001). Median z scores did not improve in contralateral homolog regions for verb generation (from -2.7 ± 0.54 to -2.5 ± 0.40; P = .90), sentence completion (from -2.3 ± 0.21 to -2.4 ± 0.37; P = .39), or finger tapping (from -2.3 ± 1.20 to -2.7 ± 1.40; P = .07). Individual functional MRI task durations could be truncated by at least 40% after MP-PCA without degradation of clinically relevant correlations between functional cortex and functional MRI tasks. Conclusion Denoising with Marchenko-Pastur principal component analysis led to higher task correlations in relevant cortical regions during functional MRI language mapping in patients with brain tumors. © RSNA, 2020 Online supplemental material is available for this article.
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Affiliation(s)
- Benjamin Ades-Aron
- From the Center for Biomedical Imaging, Department of Radiology (B.A.A., G.L., J.V., E.F., D.S.N., T.S.) and Department of Neurosurgery (J.G.), New York University School of Medicine, 2nd Floor, 660 First Ave, New York, NY 10016; and Department of Electrical and Computer Engineering, New York University Tandon School of Engineering, New York, NY (B.A.A.)
| | - Gregory Lemberskiy
- From the Center for Biomedical Imaging, Department of Radiology (B.A.A., G.L., J.V., E.F., D.S.N., T.S.) and Department of Neurosurgery (J.G.), New York University School of Medicine, 2nd Floor, 660 First Ave, New York, NY 10016; and Department of Electrical and Computer Engineering, New York University Tandon School of Engineering, New York, NY (B.A.A.)
| | - Jelle Veraart
- From the Center for Biomedical Imaging, Department of Radiology (B.A.A., G.L., J.V., E.F., D.S.N., T.S.) and Department of Neurosurgery (J.G.), New York University School of Medicine, 2nd Floor, 660 First Ave, New York, NY 10016; and Department of Electrical and Computer Engineering, New York University Tandon School of Engineering, New York, NY (B.A.A.)
| | - John Golfinos
- From the Center for Biomedical Imaging, Department of Radiology (B.A.A., G.L., J.V., E.F., D.S.N., T.S.) and Department of Neurosurgery (J.G.), New York University School of Medicine, 2nd Floor, 660 First Ave, New York, NY 10016; and Department of Electrical and Computer Engineering, New York University Tandon School of Engineering, New York, NY (B.A.A.)
| | - Els Fieremans
- From the Center for Biomedical Imaging, Department of Radiology (B.A.A., G.L., J.V., E.F., D.S.N., T.S.) and Department of Neurosurgery (J.G.), New York University School of Medicine, 2nd Floor, 660 First Ave, New York, NY 10016; and Department of Electrical and Computer Engineering, New York University Tandon School of Engineering, New York, NY (B.A.A.)
| | - Dmitry S Novikov
- From the Center for Biomedical Imaging, Department of Radiology (B.A.A., G.L., J.V., E.F., D.S.N., T.S.) and Department of Neurosurgery (J.G.), New York University School of Medicine, 2nd Floor, 660 First Ave, New York, NY 10016; and Department of Electrical and Computer Engineering, New York University Tandon School of Engineering, New York, NY (B.A.A.)
| | - Timothy Shepherd
- From the Center for Biomedical Imaging, Department of Radiology (B.A.A., G.L., J.V., E.F., D.S.N., T.S.) and Department of Neurosurgery (J.G.), New York University School of Medicine, 2nd Floor, 660 First Ave, New York, NY 10016; and Department of Electrical and Computer Engineering, New York University Tandon School of Engineering, New York, NY (B.A.A.)
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Deruelle T, Kober F, Perles-Barbacaru A, Delzescaux T, Noblet V, Barbier EL, Dojat M. A Multicenter Preclinical MRI Study: Definition of Rat Brain Relaxometry Reference Maps. Front Neuroinform 2020; 14:22. [PMID: 32508614 PMCID: PMC7248563 DOI: 10.3389/fninf.2020.00022] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Accepted: 04/21/2020] [Indexed: 11/13/2022] Open
Abstract
Similarly to human population imaging, there are several well-founded motivations for animal population imaging, the most notable being the improvement of the validity of statistical results by pooling a sufficient number of animal data provided by different imaging centers. In this paper, we demonstrate the feasibility of such a multicenter animal study, sharing raw data from forty rats and processing pipelines between four imaging centers. As specific use case, we focused on T1 and T2 mapping of the healthy rat brain at 7T. We quantitatively report about the variability observed across two MR data providers and evaluate the influence of image processing steps on the final maps, using three fitting algorithms from three centers. Finally, to derive relaxation times from different brain areas, two multi-atlas segmentation pipelines from different centers were performed on two different platforms. Differences between the two data providers were 2.21% for T1 and 9.52% for T2. Differences between processing pipelines were 1.04% for T1 and 3.33% for T2. These maps, obtained in healthy conditions, may be used in the future as reference when exploring alterations in animal models of pathology.
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Affiliation(s)
- Tristan Deruelle
- INSERM, U1216, Grenoble Institut des Neurosciences, Université Grenoble Alpes, Grenoble, France
| | - Frank Kober
- CNRS, CRMBM, Aix-Marseille Université, Marseille, France
| | | | | | - Vincent Noblet
- CNRS, ICube - IMAGeS, Strasbourg University, Strasbourg, France
| | - Emmanuel L Barbier
- INSERM, U1216, Grenoble Institut des Neurosciences, Université Grenoble Alpes, Grenoble, France
| | - Michel Dojat
- INSERM, U1216, Grenoble Institut des Neurosciences, Université Grenoble Alpes, Grenoble, France
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Diagnostic and Predictive Neuroimaging Biomarkers for Posttraumatic Stress Disorder. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2020; 5:688-696. [PMID: 32507508 DOI: 10.1016/j.bpsc.2020.03.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Revised: 03/29/2020] [Accepted: 03/30/2020] [Indexed: 12/16/2022]
Abstract
BACKGROUND Comorbidity between posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) has been commonly overlooked by studies examining resting-state functional connectivity patterns in PTSD. The current study used a data-driven approach to identify resting-state functional connectivity biomarkers to 1) differentiate individuals with PTSD (with or without MDD) from trauma-exposed healthy control subjects (TEHCs), 2) compare individuals with PTSD alone with those with comorbid PTSD+MDD, and 3) explore the clinical utility of the identified biomarkers by testing their associations with clinical symptoms and treatment response. METHODS Resting-state magnetic resonance images were obtained from 51 individuals with PTSD alone, 52 individuals with PTSD+MDD, and 76 TEHCs. Of the 103 individuals with PTSD, 55 were enrolled in prolonged exposure treatment. A support vector machine model was used to identify resting-state functional connectivity biomarkers differentiating individuals with PTSD (with or without MDD) from TEHCs and differentiating individuals with PTSD alone from those with PTSD+MDD. The associations between the identified features and symptomatology were tested with Pearson correlations. RESULTS The support vector machine model achieved 70.6% accuracy in discriminating between individuals with PTSD and TEHCs and achieved 76.7% accuracy in discriminating between individuals with PTSD alone and those with PTSD+MDD for out-of-sample prediction. Within-network connectivity in the executive control network, prefrontal network, and salience network discriminated individuals with PTSD from TEHCs. The basal ganglia network played an important role in differentiating individuals with PTSD alone from those with PTSD+MDD. PTSD scores were inversely correlated with within-executive control network connectivity (p < .001), and executive control network connectivity was positively correlated with treatment response (p < .001). CONCLUSIONS Results suggest that unique brain-based abnormalities differentiate individuals with PTSD from TEHCs, differentiate individuals with PTSD from those with PTSD+MDD, and demonstrate clinical utility in predicting levels of symptomatology and treatment response.
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Abstract
OBJECTIVE Schizophrenia is associated with excess medical mortality: patients have an average life expectancy one to two decades shorter than the general population. This study investigates the relationship between aberrant hippocampal resting-state functional connectivity in schizophrenia and cumulative subclinical effects of chronic stress on metabolic, cardiovascular, and immune function using the allostatic load index. METHODS Cumulative stress was estimated using allostatic load total score (range, 0-13) in 46 patients with schizophrenia and 31 controls matched for age and sex (patients: age = 36.1 [13.7] years, sex = 32/14 male/female; controls: age = 35.5 [14.1], sex = 21/10 male/female). Hippocampal functional connectivity was assessed using resting-state functional magnetic resonance imaging; hippocampal structural connectivity was assessed using fornix fractional anisotropy. Linear regression analysis was used a) to test the hypothesis that aberrant hippocampal resting-state functional connectivity in schizophrenia (identified in analysis of schizophrenia - control differences) is associated with elevated allostatic load scores in patients and b) to determine the association between fornix fractional anisotropy with allostatic load. RESULTS In patients, higher allostatic load was significantly associated with reduced resting functional connectivity between the left hippocampus and right cingulate cortex and left precentral gyrus, but higher connectivity between the right hippocampus and left cerebellum lobe VI (corrected p values <. 05). In controls, reductions in both hippocampal structural connectivity and hippocampal-cingulate functional connectivity were associated with higher allostatic load scores. CONCLUSIONS These findings support basic neuroscience evidence that cumulative stress and hippocampal function are closely connected and suggest that abnormal hippocampal functional communication in schizophrenia may be related to elevated multisystem subclinical medical issues in patients as indexed by allostatic load.
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Lerma-Usabiaga G, Mukherjee P, Ren Z, Perry ML, Wandell BA. Replication and generalization in applied neuroimaging. Neuroimage 2019; 202:116048. [PMID: 31356879 PMCID: PMC6819246 DOI: 10.1016/j.neuroimage.2019.116048] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Revised: 04/29/2019] [Accepted: 07/22/2019] [Indexed: 10/26/2022] Open
Abstract
There is much interest in translating neuroimaging findings into meaningful clinical diagnostics. The goal of scientific discoveries differs from clinical diagnostics. Scientific discoveries must replicate under a specific set of conditions; to translate to the clinic we must show that findings using purpose-built scientific instruments will be observable in clinical populations and instruments. Here we describe and evaluate data and computational methods designed to translate a scientific observation to a clinical setting. Using diffusion weighted imaging (DWI), Wahl et al. (2010) observed that across subjects the mean fractional anisotropy (FA) of homologous pairs of tracts is highly correlated. We hypothesize that this is a fundamental biological trait that should be present in most healthy participants, and deviations from this assessment may be a useful diagnostic metric. Using this metric as an illustration of our methods, we analyzed six pairs of homologous white matter tracts in nine different DWI datasets with 44 subjects each. Considering the original FA measurement as a baseline, we show that the new metric is between 2 and 4 times more precise when used in a clinical context. Our framework to translate research findings into clinical practice can be applied, in principle, to other neuroimaging results.
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Affiliation(s)
- Garikoitz Lerma-Usabiaga
- Department of Psychology, Stanford University, 450 Serra Mall, Jordan Hall Building, 94305, Stanford, CA, USA; BCBL. Basque Center on Cognition, Brain and Language, Mikeletegi Pasealekua 69, Donostia - San Sebastián, 20009, Gipuzkoa, Spain.
| | - Pratik Mukherjee
- Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA; Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
| | - Zhimei Ren
- Department of Statistics, Stanford University, 390 Serra Mall, Sequoia Hall Building, 94305, Stanford, CA, USA
| | - Michael L Perry
- Department of Psychology, Stanford University, 450 Serra Mall, Jordan Hall Building, 94305, Stanford, CA, USA
| | - Brian A Wandell
- Department of Psychology, Stanford University, 450 Serra Mall, Jordan Hall Building, 94305, Stanford, CA, USA
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Adhikari BM, Dukart J, Hipp JF, Forsyth A, McMillan R, Muthukumaraswamy SD, Ryan MC, Hong LE, Eickhoff SB, Jahandshad N, Thompson PM, Rowland LM, Kochunov P. Effects of ketamine and midazolam on resting state connectivity and comparison with ENIGMA connectivity deficit patterns in schizophrenia. Hum Brain Mapp 2019; 41:767-778. [PMID: 31633254 PMCID: PMC7267897 DOI: 10.1002/hbm.24838] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Revised: 09/27/2019] [Accepted: 10/09/2019] [Indexed: 12/16/2022] Open
Abstract
Subanesthetic administration of ketamine is a pharmacological model to elicit positive and negative symptoms of psychosis in healthy volunteers. We used resting‐state pharmacological functional MRI (rsPhfMRI) to identify cerebral networks affected by ketamine and compared them to the functional connectivity (FC) in schizophrenia. Ketamine can produce sedation and we contrasted its effects with the effects of the anxiolytic drug midazolam. Thirty healthy male volunteers (age = 19–37 years) underwent a randomized, three‐way, cross‐over study consisting of three imaging sessions, with 48 hr between sessions. A session consisted of a control period followed by infusion of placebo or ketamine or midazolam. The ENIGMA rsfMRI pipeline was used to derive two long‐distance (seed‐based and dual‐regression) and one local (regional homogeneity, ReHo) FC measures. Ketamine induced significant reductions in the connectivity of the salience network (Cohen's d: 1.13 ± 0.28, p = 4.0 × 10−3), auditory network (d: 0.67 ± 0.26, p = .04) and default mode network (DMN, d: 0.63 ± 0.26, p = .05). Midazolam significantly reduced connectivity in the DMN (d: 0.77 ± 0.27, p = .03). The effect sizes for ketamine for resting networks showed a positive correlation (r = .59, p = .07) with the effect sizes for schizophrenia‐related deficits derived from ENIGMA's study of 261 patients and 327 controls. Effect sizes for midazolam were not correlated with the schizophrenia pattern (r = −.17, p = .65). The subtraction of ketamine and midazolam patterns showed a significant positive correlation with the pattern of schizophrenia deficits (r = .68, p = .03). RsPhfMRI reliably detected the shared and divergent pharmacological actions of ketamine and midazolam on cerebral networks. The pattern of disconnectivity produced by ketamine was positively correlated with the pattern of connectivity deficits observed in schizophrenia, suggesting a brain functional basis for previously poorly understood effects of the drug.
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Affiliation(s)
- Bhim M Adhikari
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Juergen Dukart
- F. Hoffmann-La Roche, Pharma Research Early Development, Roche Innovation Centre Basel, Basel, Switzerland.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Joerg F Hipp
- F. Hoffmann-La Roche, Pharma Research Early Development, Roche Innovation Centre Basel, Basel, Switzerland
| | - Anna Forsyth
- School of Pharmacy, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
| | - Rebecca McMillan
- School of Pharmacy, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
| | - Suresh D Muthukumaraswamy
- School of Pharmacy, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
| | - Meghann C Ryan
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - L Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Neda Jahandshad
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California
| | - Laura M Rowland
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
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Osadchiy V, Mayer EA, Bhatt R, Labus JS, Gao L, Kilpatrick LA, Liu C, Tillisch K, Naliboff B, Chang L, Gupta A. History of early life adversity is associated with increased food addiction and sex-specific alterations in reward network connectivity in obesity. Obes Sci Pract 2019; 5:416-436. [PMID: 31687167 PMCID: PMC6819979 DOI: 10.1002/osp4.362] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2019] [Revised: 07/17/2019] [Accepted: 07/18/2019] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Neuroimaging studies have identified obesity-related differences in the brain's resting state activity. An imbalance between homeostatic and reward aspects of ingestive behaviour may contribute to obesity and food addiction. The interactions between early life adversity (ELA), the reward network and food addiction were investigated to identify obesity and sex-related differences, which may drive obesity and food addiction. METHODS Functional resting state magnetic resonance imaging was acquired in 186 participants (high body mass index [BMI]: ≥25: 53 women and 54 men; normal BMI: 18.50-24.99: 49 women and 30 men). Participants completed questionnaires to assess ELA (Early Traumatic Inventory) and food addiction (Yale Food Addiction Scale). A tripartite network analysis based on graph theory was used to investigate the interaction between ELA, brain connectivity and food addiction. Interactions were determined by computing Spearman rank correlations, thresholded at q < 0.05 corrected for multiple comparisons. RESULTS Participants with high BMI demonstrate an association between ELA and food addiction, with reward regions playing a role in this interaction. Among women with high BMI, increased ELA was associated with increased centrality of reward and emotion regulation regions. Men with high BMI showed associations between ELA and food addiction with somatosensory regions playing a role in this interaction. CONCLUSIONS The findings suggest that ELA may alter brain networks, leading to increased vulnerability for food addiction and obesity later in life. These alterations are sex specific and involve brain regions influenced by dopaminergic or serotonergic signalling.
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Affiliation(s)
- V. Osadchiy
- G. Oppenheimer Family Center for Neurobiology of Stress and Resilience, Ingestive Behavior and Obesity ProgramUniversity of California, Los Angeles (UCLA)Los AngelesCAUSA
- David Geffen School of MedicineUniversity of California, Los Angeles (UCLA)Los AngelesCAUSA
| | - E. A. Mayer
- G. Oppenheimer Family Center for Neurobiology of Stress and Resilience, Ingestive Behavior and Obesity ProgramUniversity of California, Los Angeles (UCLA)Los AngelesCAUSA
- David Geffen School of MedicineUniversity of California, Los Angeles (UCLA)Los AngelesCAUSA
- Vatche and Tamar Manoukin Division of Digestive DiseasesUniversity of California, Los Angeles (UCLA)Los AngelesCAUSA
- Ahmanson‐Lovelace Brain Mapping CenterUniversity of California, Los Angeles (UCLA)Los AngelesCAUSA
| | - R. Bhatt
- G. Oppenheimer Family Center for Neurobiology of Stress and Resilience, Ingestive Behavior and Obesity ProgramUniversity of California, Los Angeles (UCLA)Los AngelesCAUSA
- Pediatric Pain and Palliative Care ProgramUniversity of California, Los Angeles (UCLA)Los AngelesCAUSA
| | - J. S. Labus
- G. Oppenheimer Family Center for Neurobiology of Stress and Resilience, Ingestive Behavior and Obesity ProgramUniversity of California, Los Angeles (UCLA)Los AngelesCAUSA
- David Geffen School of MedicineUniversity of California, Los Angeles (UCLA)Los AngelesCAUSA
- Vatche and Tamar Manoukin Division of Digestive DiseasesUniversity of California, Los Angeles (UCLA)Los AngelesCAUSA
| | - L. Gao
- G. Oppenheimer Family Center for Neurobiology of Stress and Resilience, Ingestive Behavior and Obesity ProgramUniversity of California, Los Angeles (UCLA)Los AngelesCAUSA
| | - L. A. Kilpatrick
- G. Oppenheimer Family Center for Neurobiology of Stress and Resilience, Ingestive Behavior and Obesity ProgramUniversity of California, Los Angeles (UCLA)Los AngelesCAUSA
- David Geffen School of MedicineUniversity of California, Los Angeles (UCLA)Los AngelesCAUSA
- Vatche and Tamar Manoukin Division of Digestive DiseasesUniversity of California, Los Angeles (UCLA)Los AngelesCAUSA
| | - C. Liu
- G. Oppenheimer Family Center for Neurobiology of Stress and Resilience, Ingestive Behavior and Obesity ProgramUniversity of California, Los Angeles (UCLA)Los AngelesCAUSA
- David Geffen School of MedicineUniversity of California, Los Angeles (UCLA)Los AngelesCAUSA
- Vatche and Tamar Manoukin Division of Digestive DiseasesUniversity of California, Los Angeles (UCLA)Los AngelesCAUSA
| | - K. Tillisch
- G. Oppenheimer Family Center for Neurobiology of Stress and Resilience, Ingestive Behavior and Obesity ProgramUniversity of California, Los Angeles (UCLA)Los AngelesCAUSA
- David Geffen School of MedicineUniversity of California, Los Angeles (UCLA)Los AngelesCAUSA
- Vatche and Tamar Manoukin Division of Digestive DiseasesUniversity of California, Los Angeles (UCLA)Los AngelesCAUSA
- Pediatric Pain and Palliative Care ProgramUniversity of California, Los Angeles (UCLA)Los AngelesCAUSA
| | - B. Naliboff
- G. Oppenheimer Family Center for Neurobiology of Stress and Resilience, Ingestive Behavior and Obesity ProgramUniversity of California, Los Angeles (UCLA)Los AngelesCAUSA
- David Geffen School of MedicineUniversity of California, Los Angeles (UCLA)Los AngelesCAUSA
- Vatche and Tamar Manoukin Division of Digestive DiseasesUniversity of California, Los Angeles (UCLA)Los AngelesCAUSA
| | - L. Chang
- G. Oppenheimer Family Center for Neurobiology of Stress and Resilience, Ingestive Behavior and Obesity ProgramUniversity of California, Los Angeles (UCLA)Los AngelesCAUSA
- David Geffen School of MedicineUniversity of California, Los Angeles (UCLA)Los AngelesCAUSA
- Vatche and Tamar Manoukin Division of Digestive DiseasesUniversity of California, Los Angeles (UCLA)Los AngelesCAUSA
| | - A. Gupta
- G. Oppenheimer Family Center for Neurobiology of Stress and Resilience, Ingestive Behavior and Obesity ProgramUniversity of California, Los Angeles (UCLA)Los AngelesCAUSA
- David Geffen School of MedicineUniversity of California, Los Angeles (UCLA)Los AngelesCAUSA
- Vatche and Tamar Manoukin Division of Digestive DiseasesUniversity of California, Los Angeles (UCLA)Los AngelesCAUSA
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Sala A, Caminiti SP, Iaccarino L, Beretta L, Iannaccone S, Magnani G, Padovani A, Ferini-Strambi L, Perani D. Vulnerability of multiple large-scale brain networks in dementia with Lewy bodies. Hum Brain Mapp 2019; 40:4537-4550. [PMID: 31322307 DOI: 10.1002/hbm.24719] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Revised: 06/01/2019] [Accepted: 06/19/2019] [Indexed: 01/08/2023] Open
Abstract
Aberrations of large-scale brain networks are found in the majority of neurodegenerative disorders. The brain connectivity alterations underlying dementia with Lewy bodies (DLB) remain, however, still elusive, with contrasting results possibly due to the pathological and clinical heterogeneity characterizing this disorder. Here, we provide a molecular assessment of brain network alterations, based on cerebral metabolic measurements as proxies of synaptic activity and density, in a large cohort of DLB patients (N = 72). We applied a seed-based interregional correlation analysis approach (p < .01, false discovery rate corrected) to evaluate large-scale resting-state networks' integrity and their interactions. We found both local and long-distance metabolic connectivity alterations, affecting the posterior cortical networks, that is, primary visual and the posterior default mode network, as well as the limbic and attention networks, suggesting a widespread derangement of the brain connectome. Notably, patients with the lowest visual and attention cognitive scores showed the most severe connectivity derangement in regions of the primary visual network. In addition, network-level alterations were differentially associated with the core clinical manifestations, namely, hallucinations with more severe metabolic dysfunction of the attention and visual networks, and rapid eye movement sleep behavior disorder with alterations of connectivity of attention and subcortical networks. These multiple network-level vulnerabilities may modulate the core clinical and cognitive features of DLB and suggest that DLB should be considered as a complex multinetwork disorder.
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Affiliation(s)
- Arianna Sala
- Vita-Salute San Raffaele University, Milan, Italy.,In Vivo Human Molecular and Structural Neuroimaging Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Silvia Paola Caminiti
- Vita-Salute San Raffaele University, Milan, Italy.,In Vivo Human Molecular and Structural Neuroimaging Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Leonardo Iaccarino
- Vita-Salute San Raffaele University, Milan, Italy.,In Vivo Human Molecular and Structural Neuroimaging Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Luca Beretta
- In Vivo Human Molecular and Structural Neuroimaging Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Sandro Iannaccone
- Clinical Neuroscience Department, San Raffaele Turro Hospital, Milan, Italy
| | - Giuseppe Magnani
- Department of Neurology, IRCCS San Raffaele Hospital, Milan, Italy
| | - Alessandro Padovani
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Luigi Ferini-Strambi
- Vita-Salute San Raffaele University, Milan, Italy.,Department of Clinical Neurosciences, San Raffaele Scientific Institute, Neurology, Sleep Disorders Center, Milan, Italy
| | - Daniela Perani
- Vita-Salute San Raffaele University, Milan, Italy.,In Vivo Human Molecular and Structural Neuroimaging Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Nuclear Medicine Unit, IRCCS San Raffaele Hospital, Milan, Italy
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Adhikari BM, Hong LE, Sampath H, Chiappelli J, Jahanshad N, Thompson PM, Rowland LM, Calhoun VD, Du X, Chen S, Kochunov P. Functional network connectivity impairments and core cognitive deficits in schizophrenia. Hum Brain Mapp 2019; 40:4593-4605. [PMID: 31313441 DOI: 10.1002/hbm.24723] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Revised: 07/03/2019] [Accepted: 07/08/2019] [Indexed: 12/19/2022] Open
Abstract
Cognitive deficits contribute to functional disability in patients with schizophrenia and may be related to altered functional networks that serve cognition. We evaluated the integrity of major functional networks and assessed their role in supporting two cognitive functions affected in schizophrenia: processing speed (PS) and working memory (WM). Resting-state functional magnetic resonance imaging (rsfMRI) data, N = 261 patients and 327 controls, were aggregated from three independent cohorts and evaluated using Enhancing NeuroImaging Genetics through Meta Analysis rsfMRI analysis pipeline. Meta- and mega-analyses were used to evaluate patient-control differences in functional connectivity (FC) measures. Canonical correlation analysis was used to study the association between cognitive deficits and FC measures. Patients showed consistent patterns of cognitive and resting-state FC (rsFC) deficits across three cohorts. Patient-control differences in rsFC calculated using seed-based and dual-regression approaches were consistent (Cohen's d: 0.31 ± 0.09 and 0.29 ± 0.08, p < 10-4 ). RsFC measures explained 12-17% of the individual variations in PS and WM in the full sample and in patients and controls separately, with the strongest correlations found in salience, auditory, somatosensory, and default-mode networks. The pattern of association between rsFC (within-network) and PS (r = .45, p = .07) and WM (r = .36, p = .16), and rsFC (between-network) and PS (r = .52, p = 8.4 × 10-3 ) and WM (r = .47, p = .02), derived from multiple networks was related to effect size of patient-control differences in the functional networks. No association was detected between rsFC and current medication dose or psychosis ratings. Patients demonstrated significant reduction in several FC networks that may partially underlie some of the core neurocognitive deficits in schizophrenia. The strength of connectivity-cognition relationships in different networks was strongly associated with network's vulnerability to schizophrenia.
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Affiliation(s)
- Bhim M Adhikari
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - L Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Hemalatha Sampath
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Joshua Chiappelli
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Neda Jahanshad
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine of USC, Marina del Rey, California
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine of USC, Marina del Rey, California
| | - Laura M Rowland
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Vince D Calhoun
- Department of Electrical and Computer Engineering, The Mind Research Network, Albuquerque, New Mexico.,Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, New Mexico
| | - Xiaoming Du
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Shuo Chen
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
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Hoogman M, Muetzel R, Guimaraes JP, Shumskaya E, Mennes M, Zwiers MP, Jahanshad N, Sudre G, Mostert J, Wolfers T, Earl EA, Vila JCS, Vives-Gilabert Y, Khadka S, Novotny SE, Hartman CA, Heslenfeld DJ, Schweren LJ, Ambrosino S, Oranje B, de Zeeuw P, Chaim-Avancini TM, Rosa PGP, Zanetti MV, Malpas CB, Kohls G, von Polier GG, Seitz J, Biederman J, Doyle AE, Dale AM, van Erp TG, Epstein JN, Jernigan TL, Baur-Streubel R, Ziegler GC, Zierhut KC, Schrantee A, Høvik MF, Lundervold AJ, Kelly C, McCarthy H, Skokauskas N, O'Gorman Tuura RL, Calvo A, Lera-Miguel S, Nicolau R, Chantiluke KC, Christakou A, Vance A, Cercignani M, Gabel MC, Asherson P, Baumeister S, Brandeis D, Hohmann S, Bramati IE, Tovar-Moll F, Fallgatter AJ, Kardatzki B, Schwarz L, Anikin A, Baranov A, Gogberashvili T, Kapilushniy D, Solovieva A, El Marroun H, White T, Karkashadze G, Namazova-Baranova L, Ethofer T, Mattos P, Banaschewski T, Coghill D, Plessen KJ, Kuntsi J, Mehta MA, Paloyelis Y, Harrison NA, Bellgrove MA, Silk TJ, Cubillo AI, Rubia K, Lazaro L, Brem S, Walitza S, Frodl T, Zentis M, Castellanos FX, Yoncheva YN, Haavik J, Reneman L, Conzelmann A, Lesch KP, Pauli P, Reif A, Tamm L, Konrad K, Weiss EO, Busatto GF, Louza MR, Durston S, Hoekstra PJ, Oosterlaan J, Stevens MC, Ramos-Quiroga JA, Vilarroya O, Fair DA, Nigg JT, Thompson PM, Buitelaar JK, Faraone SV, Shaw P, Tiemeier H, Bralten J, Franke B. Brain Imaging of the Cortex in ADHD: A Coordinated Analysis of Large-Scale Clinical and Population-Based Samples. Am J Psychiatry 2019; 176:531-542. [PMID: 31014101 PMCID: PMC6879185 DOI: 10.1176/appi.ajp.2019.18091033] [Citation(s) in RCA: 210] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
OBJECTIVE Neuroimaging studies show structural alterations of various brain regions in children and adults with attention deficit hyperactivity disorder (ADHD), although nonreplications are frequent. The authors sought to identify cortical characteristics related to ADHD using large-scale studies. METHODS Cortical thickness and surface area (based on the Desikan-Killiany atlas) were compared between case subjects with ADHD (N=2,246) and control subjects (N=1,934) for children, adolescents, and adults separately in ENIGMA-ADHD, a consortium of 36 centers. To assess familial effects on cortical measures, case subjects, unaffected siblings, and control subjects in the NeuroIMAGE study (N=506) were compared. Associations of the attention scale from the Child Behavior Checklist with cortical measures were determined in a pediatric population sample (Generation-R, N=2,707). RESULTS In the ENIGMA-ADHD sample, lower surface area values were found in children with ADHD, mainly in frontal, cingulate, and temporal regions; the largest significant effect was for total surface area (Cohen's d=-0.21). Fusiform gyrus and temporal pole cortical thickness was also lower in children with ADHD. Neither surface area nor thickness differences were found in the adolescent or adult groups. Familial effects were seen for surface area in several regions. In an overlapping set of regions, surface area, but not thickness, was associated with attention problems in the Generation-R sample. CONCLUSIONS Subtle differences in cortical surface area are widespread in children but not adolescents and adults with ADHD, confirming involvement of the frontal cortex and highlighting regions deserving further attention. Notably, the alterations behave like endophenotypes in families and are linked to ADHD symptoms in the population, extending evidence that ADHD behaves as a continuous trait in the population. Future longitudinal studies should clarify individual lifespan trajectories that lead to nonsignificant findings in adolescent and adult groups despite the presence of an ADHD diagnosis.
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Affiliation(s)
- Martine Hoogman
- Department of Human Genetics, Radboud university medical center, Nijmegen, Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Ryan Muetzel
- Department of Child and Adolescent Psychiatry, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Joao P. Guimaraes
- Department of Human Genetics, Radboud university medical center, Nijmegen, Netherlands
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition, and Behaviour, Nijmegen, The Netherlands
| | - Elena Shumskaya
- Department of Human Genetics, Radboud university medical center, Nijmegen, Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Maarten Mennes
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Marcel P. Zwiers
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition, and Behaviour, Nijmegen, The Netherlands
| | - Neda Jahanshad
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
| | - Gustavo Sudre
- National Human Genome Research Institute, Bethesda, MD, USA
| | - Jeanette Mostert
- Department of Human Genetics, Radboud university medical center, Nijmegen, Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Thomas Wolfers
- Department of Human Genetics, Radboud university medical center, Nijmegen, Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Eric A. Earl
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland OR, USA
| | | | | | - Sabin Khadka
- Olin Neuropsychiatry Research Center, Hartford Hospital, Hartford, CT, USA
| | | | - Catharina A. Hartman
- University of Groningen, University Medical Center Groningen, Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), Groningen, The Netherlands
| | - Dirk J. Heslenfeld
- Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Lizanne J.S. Schweren
- University of Groningen, University Medical Center Groningen, Department of Child and Adolescent Psychiatry, Groningen, The Netherlands
| | - Sara Ambrosino
- NICHE Lab, Department of Psychiatry, UMC Utrecht Brain Center, Utrecht, The Netherlands
| | - Bob Oranje
- NICHE Lab, Department of Psychiatry, UMC Utrecht Brain Center, Utrecht, The Netherlands
| | - Patrick de Zeeuw
- NICHE Lab, Department of Psychiatry, UMC Utrecht Brain Center, Utrecht, The Netherlands
| | - Tiffany M. Chaim-Avancini
- Laboratory of Psychiatric Neuroimaging (LIM-21), Department and Institute of Psychiatry, Faculty of Medicine, University of São Paulo, Sao Paulo, Brazil
- Center for Interdisciplinary Research on Applied Neurosciences (NAPNA), University of São Paulo, Sao Paulo, Brazil
| | - Pedro G. P. Rosa
- Laboratory of Psychiatric Neuroimaging (LIM-21), Department and Institute of Psychiatry, Faculty of Medicine, University of São Paulo, Sao Paulo, Brazil
- Center for Interdisciplinary Research on Applied Neurosciences (NAPNA), University of São Paulo, Sao Paulo, Brazil
| | - Marcus V. Zanetti
- Laboratory of Psychiatric Neuroimaging (LIM-21), Department and Institute of Psychiatry, Faculty of Medicine, University of São Paulo, Sao Paulo, Brazil
- Center for Interdisciplinary Research on Applied Neurosciences (NAPNA), University of São Paulo, Sao Paulo, Brazil
| | - Charles B. Malpas
- Developmental Imaging Group, Murdoch Children's Research Institute, Melbourne, Australia
- Clinical Outcomes Research Unit (CORe), Department of Medicine, Royal Melbourne Hospital, The University of Melbourne, Melbourne, Australia
- Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, Australia
| | - Gregor Kohls
- Child Neuropsychology Section, University Hospital RWTH Aachen, Aachen, Germany
| | - Georg G. von Polier
- Child and Adolescent Psychiatry, University Hospital RWTH Aachen, Aachen, Germany
| | - Jochen Seitz
- Child and Adolescent Psychiatry, University Hospital RWTH Aachen, Aachen, Germany
| | - Joseph Biederman
- Clinical and Research Programs in Pediatric Psychopharmacology and Adult ADHD, Department of Psychiatry, Massachusetts General Hospital, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, USA
| | - Alysa E. Doyle
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, USA
| | - Anders M. Dale
- Departments of Neurosciences, Radiology, and Psychiatry, UC San Diego, USA
- Center for Multimodal Imaging and Genetics (CMIG), UC San Diego, CA, USA
| | - Theo G.M. van Erp
- Clinical and Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - Jeffrey N. Epstein
- Division of Behavioral Medicine and Clinical Psychology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | | | | | - Georg C. Ziegler
- Division of Molecular Psychiatry, Center of Mental Health, University of Würzburg, Würzburg, Germany
| | | | - Anouk Schrantee
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam; the Netherlands
| | - Marie F. Høvik
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
| | - Astri J. Lundervold
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
- K.G. Jebsen Centre for Neuropsychiatric Disorders, Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Clare Kelly
- School of Psychology and Department of Psychiatry at the School of Medicine, Trinity College Dublin, Ireland
- Trinity College Institute of Neuroscience, Trinity College Dublin, Ireland
- Department of Child and Adolescent Psychiatry, NYU Langone Medical Center, New York, NY, USA
| | - Hazel McCarthy
- Department of Psychiatry, Trinity College Dublin, Ireland
- Centre of Advanced Medical Imaging, St James's Hospital, Dublin, Ireland
| | - Norbert Skokauskas
- Department of Psychiatry, Trinity College Dublin, Ireland
- Institute of Mental Health, Norwegian University of Science and Technology, Norway
| | - Ruth L. O'Gorman Tuura
- Center for MR Research, University Children's Hospital, Zurich, Switzerland
- Zurich Center for Integrative Human Physiology (ZIHP), Zurich, Switserland
| | - Anna Calvo
- Magnetic Resonance Image Core Facility, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Sara Lera-Miguel
- Department of Child and Adolescent Psychiatry and Psychology, Institute of Neurosciencies, Hospital Clínic, Barcelona, Spain
| | - Rosa Nicolau
- Department of Child and Adolescent Psychiatry and Psychology, Institute of Neurosciencies, Hospital Clínic, Barcelona, Spain
| | - Kaylita C. Chantiluke
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Anastasia Christakou
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- School of Psychology and Clinical Language Sciences, Centre for Integrative Neuroscience and Neurodynamics, University of Reading, Reading, UK
| | - Alasdair Vance
- Department of Paediatrics, University of Melbourne, Australia
| | - Mara Cercignani
- Department of Neuroscience, Brighton and Sussex Medical School, Falmer, Brighton, UK
| | - Matt C. Gabel
- Department of Neuroscience, Brighton and Sussex Medical School, Falmer, Brighton, UK
| | - Philip Asherson
- Social, Genetic and Developmental Psychiatry Centre; Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Sarah Baumeister
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Medical Faculty Mannheim / Heidelberg University, Mannheim, Germany
| | - Daniel Brandeis
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Medical Faculty Mannheim / Heidelberg University, Mannheim, Germany
- Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric Hospital, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Sarah Hohmann
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Medical Faculty Mannheim / Heidelberg University, Mannheim, Germany
| | | | - Fernanda Tovar-Moll
- D'Or Institute for Research and Education, Rio de Janeiro, Brazil
- Morphological Sciences Program, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Andreas J. Fallgatter
- Department of Psychiatry and Psychotherapy, University Hospital of Tuebingen, Tuebingen, Germany
- LEAD Graduate School, University of Tuebingen, Germany
| | - Bernd Kardatzki
- Department of Biomedical Magnetic Resonance, University of Tuebingen, Tuebingen, Germany
| | - Lena Schwarz
- Department of Psychiatry and Psychotherapy, University Hospital of Tuebingen, Tuebingen, Germany
| | - Anatoly Anikin
- National Medical Research Center for Children's Health, Department of magnetic resonance imaging and densitometry, Moscow, Russia
| | - Alexandr Baranov
- National Medical Research Center for Children's Health, Moscow, Russia
| | - Tinatin Gogberashvili
- National Medical Research Center for Children's Health, Laboratory of Neurology and Cognitive Health, Moscow, Russia
| | - Dmitry Kapilushniy
- National Medical Research Center for Children's Health, Department of Information Technologies, Moscow, Russia
| | | | - Hanan El Marroun
- Department of Child and Adolescent Psychiatry, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Paediatrics, Erasmus MC - Sophia, Rotterdam, the Netherlands
- Department of Psychology, Education and Child Studies, Erasmus University Rotterdam, Rotterdam, the Netherlands
| | - Tonya White
- Department of Child and Adolescent Psychiatry, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Radiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Georgii Karkashadze
- National Medical Research Center for Children's Health, Laboratory of Neurology and Cognitive Health, Moscow, Russia
| | | | - Thomas Ethofer
- Department of Psychiatry and Psychotherapy, University Hospital of Tuebingen, Tuebingen, Germany
- Department of Biomedical Magnetic Resonance, University of Tuebingen, Tuebingen, Germany
| | - Paulo Mattos
- D'Or Institute for Research and Education, Rio de Janeiro, Brazil
- Federal University of Rio de Janeiro, Brazil
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Medical Faculty Mannheim / Heidelberg University, Mannheim, Germany
| | - David Coghill
- Department of Paediatrics, University of Melbourne, Australia
- Departments of Psychiatry, University of Melbourne, Melbourne, Australia
- Murdoch Children's Research Institute, Melbourne, Australia
- Division of Neuroscience, University of Dundee, Dundee, UK
| | - Kerstin J. Plessen
- Child and Adolescent Mental Health Centre, Capital Region Copenhagen, Denmark
- Division of Child and Adolescent Psychiatry, Department of Psychiatry, University Hospital Lausanne, Switzerland
| | - Jonna Kuntsi
- Social, Genetic and Developmental Psychiatry Centre; Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Mitul A. Mehta
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Yannis Paloyelis
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Neil A. Harrison
- Department of Neuroscience, Brighton and Sussex Medical School, Falmer, Brighton, UK
- Sussex Partnership NHS Foundation Trust, Swandean, East Sussex, UK
| | - Mark A. Bellgrove
- Monash Institute for Cognitive and Clinical Neurosciences (MICCN) and School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Tim J. Silk
- Department of Paediatrics, University of Melbourne, Australia
- Murdoch Children's Research Institute, Melbourne, Australia
- Deakin University, School of Psychology, Geelong, Australia
| | - Ana I. Cubillo
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Katya Rubia
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Luisa Lazaro
- Department of Child and Adolescent Psychiatry and Psychology, Institute of Neurosciencies, Hospital Clínic, Barcelona, Spain
- Department of Medicine, University of Barcelona, Spain
| | - Silvia Brem
- Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric Hospital, University of Zurich, Zurich, Switzerland
- University of Zurich and ETH Zurich, Neuroscience Center Zurich, Zurich, Switzerland
| | - Susanne Walitza
- Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric Hospital, University of Zurich, Zurich, Switzerland
| | - Thomas Frodl
- Department of Psychiatry, Trinity College Dublin, Ireland
- Department of Psychiatry and Psychotherapy, Otto von Guericke University Magdeburg, Germany
- German Center for Neurodegenerative Diseases (DZNE), Germany
| | | | - Francisco X. Castellanos
- Department of Child and Adolescent Psychiatry, NYU Langone Medical Center, New York, NY, USA
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Yuliya N. Yoncheva
- Department of Child and Adolescent Psychiatry, NYU Langone Medical Center, New York, NY, USA
| | - Jan Haavik
- Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
- K.G. Jebsen Centre for Neuropsychiatric Disorders, Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Liesbeth Reneman
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam; the Netherlands
- Brain Imaging Center, Amsterdam University Medical Centers, Amsterdam; the Netherlands
| | - Annette Conzelmann
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Tübingen, Germany
- Department of Biological Psychology, Clinical Psychology and Psychotherapy, Würzburg, Germany
| | - Klaus-Peter Lesch
- Division of Molecular Psychiatry, Center of Mental Health, University of Würzburg, Würzburg, Germany
- Laboratory of Psychiatric Neurobiology, Institute of Molecular Medicine, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
- Department of Neuroscience, School for Mental Health and Neuroscience (MHeNS), Maastricht University, Maastricht, The Netherlands
| | - Paul Pauli
- Department of Biological Psychology, Clinical Psychology and Psychotherapy, Würzburg, Germany
| | - Andreas Reif
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Frankfurt, Germany
| | - Leanne Tamm
- Division of Behavioral Medicine and Clinical Psychology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Kerstin Konrad
- Child Neuropsychology Section, University Hospital RWTH Aachen, Aachen, Germany
- JARA Institute Molecular Neuroscience and Neuroimaging (INM-11), Institute for Neuroscience and Medicine, Research Center Jülich, Germany
| | - Eileen Oberwelland Weiss
- Translational Neuroscience, Child and Adolescent Psychiatry, University Hospital RWTH Aachen, Aachen, Germany
- Cognitive Neuroscience (INM-3), Institute for Neuroscience and Medicine, Research Center Jülich, Germany
| | - Geraldo F. Busatto
- Laboratory of Psychiatric Neuroimaging (LIM-21), Department and Institute of Psychiatry, Faculty of Medicine, University of São Paulo, Sao Paulo, Brazil
- Department of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil
| | - Mario R. Louza
- Department of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil
| | - Sarah Durston
- NICHE Lab, Department of Psychiatry, UMC Utrecht Brain Center, Utrecht, The Netherlands
| | - Pieter J. Hoekstra
- University of Groningen, University Medical Center Groningen, Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), Groningen, The Netherlands
| | - Jaap Oosterlaan
- Clinical Neuropsychology Section, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Emma Children’s Hospital Amsterdam Medical Center, Amsterdam, The Netherlands
- Department of Pediatrics, VU Medical Center, Amsterdam, The Netherlands
| | - Michael C. Stevens
- Olin Neuropsychiatry Research Center, Hartford Hospital, Hartford, CT, USA
- Department of Psychiatry, Yale University School of Medicine, USA
| | - J. Antoni Ramos-Quiroga
- Department of Psychiatry and Forensic Medicine, Universitat Autonoma de Barcelona, Spain
- Department of Psychiatry, Hospital Universitari Vall d’Hebron, Barcelona, Catalonia, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Barcelona, Catalonia, Spain
| | - Oscar Vilarroya
- Department of Psychiatry and Forensic Medicine, Universitat Autonoma de Barcelona, Spain
- Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
| | - Damien A. Fair
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland OR, USA
- Department of Psychiatry, Oregon Health & Science University, Portland OR, USA
| | - Joel T. Nigg
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland OR, USA
- Department of Psychiatry, Oregon Health & Science University, Portland OR, USA
| | - Paul M. Thompson
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
| | - Jan K. Buitelaar
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
- Karakter Child and Adolescent Psychiatry University Center, Nijmegen, The Netherlands
| | | | - Philip Shaw
- National Human Genome Research Institute, Bethesda, MD, USA
- National Institute of Mental Health, Bethesda, MD, USA
| | - Henning Tiemeier
- Department of Child and Adolescent Psychiatry, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Social and Behavioral Science, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Janita Bralten
- Department of Human Genetics, Radboud university medical center, Nijmegen, Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Barbara Franke
- Department of Human Genetics, Radboud university medical center, Nijmegen, Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
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Adhikari BM, Jahanshad N, Shukla D, Glahn DC, Blangero J, Fox PT, Reynolds RC, Cox RW, Fieremans E, Veraart J, Novikov DS, Nichols TE, Hong LE, Thompson PM, Kochunov P. Comparison of heritability estimates on resting state fMRI connectivity phenotypes using the ENIGMA analysis pipeline. Hum Brain Mapp 2018; 39:4893-4902. [PMID: 30052318 DOI: 10.1002/hbm.24331] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2017] [Revised: 06/01/2018] [Accepted: 07/12/2018] [Indexed: 12/20/2022] Open
Abstract
We measured and compared heritability estimates for measures of functional brain connectivity extracted using the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) rsfMRI analysis pipeline in two cohorts: the genetics of brain structure (GOBS) cohort and the HCP (the Human Connectome Project) cohort. These two cohorts were assessed using conventional (GOBS) and advanced (HCP) rsfMRI protocols, offering a test case for harmonization of rsfMRI phenotypes, and to determine measures that show consistent heritability for in-depth genome-wide analysis. The GOBS cohort consisted of 334 Mexican-American individuals (124M/210F, average age = 47.9 ± 13.2 years) from 29 extended pedigrees (average family size = 9 people; range 5-32). The GOBS rsfMRI data was collected using a 7.5-min acquisition sequence (spatial resolution = 1.72 × 1.72 × 3 mm3 ). The HCP cohort consisted of 518 twins and family members (240M/278F; average age = 28.7 ± 3.7 years). rsfMRI data was collected using 28.8-min sequence (spatial resolution = 2 × 2 × 2 mm3 ). We used the single-modality ENIGMA rsfMRI preprocessing pipeline to estimate heritability values for measures from eight major functional networks, using (1) seed-based connectivity and (2) dual regression approaches. We observed significant heritability (h2 = 0.2-0.4, p < .05) for functional connections from seven networks across both cohorts, with a significant positive correlation between heritability estimates across two cohorts. The similarity in heritability estimates for resting state connectivity measurements suggests that the additive genetic contribution to functional connectivity is robustly detectable across populations and imaging acquisition parameters. The overarching genetic influence, and means to consistently detect it, provides an opportunity to define a common genetic search space for future gene discovery studies.
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Affiliation(s)
- Bhim M Adhikari
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, Maryland
| | - Neda Jahanshad
- Imaging Genetics Center, Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, Los Angeles, California
| | - Dinesh Shukla
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, Maryland
| | - David C Glahn
- Department of Psychiatry, School of Medicine, Yale University, New Haven, Connecticut
| | - John Blangero
- Genomics Computing Center, University of Texas at Rio Grande Valley, Edinburg, Texas
| | - Peter T Fox
- University of Texas Health Science Center at San Antonio, San Antonio, Texas
| | | | - Robert W Cox
- National Institute of Mental Health, Bethesda, Maryland
| | - Els Fieremans
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York
| | - Jelle Veraart
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York
| | - Dmitry S Novikov
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York
| | - Thomas E Nichols
- Department of Statistics, University of Warwick, Coventry, United Kingdom
| | - L Elliot Hong
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, Maryland
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, Los Angeles, California
| | - Peter Kochunov
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, Maryland
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