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Bhatt RR, Gadewar SP, Shetty A, Ba Gari I, Haddad E, Javid S, Ramesh A, Nourollahimoghadam E, Zhu AH, de Leeuw C, Thompson PM, Medland SE, Jahanshad N. The Genetic Architecture of the Human Corpus Callosum and its Subregions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.22.603147. [PMID: 39091796 PMCID: PMC11291056 DOI: 10.1101/2024.07.22.603147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
The corpus callosum (CC) is the largest set of white matter fibers connecting the two hemispheres of the brain. In humans, it is essential for coordinating sensorimotor responses, performing associative/executive functions, and representing information in multiple dimensions. Understanding which genetic variants underpin corpus callosum morphometry, and their shared influence on cortical structure and susceptibility to neuropsychiatric disorders, can provide molecular insights into the CC's role in mediating cortical development and its contribution to neuropsychiatric disease. To characterize the morphometry of the midsagittal corpus callosum, we developed a publicly available artificial intelligence based tool to extract, parcellate, and calculate its total and regional area and thickness. Using the UK Biobank (UKB) and the Adolescent Brain Cognitive Development study (ABCD), we extracted measures of midsagittal corpus callosum morphometry and performed a genome-wide association study (GWAS) meta-analysis of European participants (combined N = 46,685). We then examined evidence for generalization to the non-European participants of the UKB and ABCD cohorts (combined N = 7,040). Post-GWAS analyses implicate prenatal intracellular organization and cell growth patterns, and high heritability in regions of open chromatin, suggesting transcriptional activity regulation in early development. Results suggest programmed cell death mediated by the immune system drives the thinning of the posterior body and isthmus. Global and local genetic overlap, along with causal genetic liability, between the corpus callosum, cerebral cortex, and neuropsychiatric disorders such as attention-deficit/hyperactivity and bipolar disorders were identified. These results provide insight into variability of corpus callosum development, its genetic influence on the cerebral cortex, and biological mechanisms related to neuropsychiatric dysfunction.
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Stewart BW, Keaser ML, Lee H, Margerison SM, Cormie MA, Moayedi M, Lindquist MA, Chen S, Mathur BN, Seminowicz DA. Pathological claustrum activity drives aberrant cognitive network processing in human chronic pain. Curr Biol 2024; 34:1953-1966.e6. [PMID: 38614082 DOI: 10.1016/j.cub.2024.03.021] [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: 01/17/2024] [Revised: 02/08/2024] [Accepted: 03/13/2024] [Indexed: 04/15/2024]
Abstract
Aberrant cognitive network activity and cognitive deficits are established features of chronic pain. However, the nature of cognitive network alterations associated with chronic pain and their underlying mechanisms require elucidation. Here, we report that the claustrum, a subcortical nucleus implicated in cognitive network modulation, is activated by acute painful stimulation and pain-predictive cues in healthy participants. Moreover, we discover pathological activity of the claustrum and a region near the posterior inferior frontal sulcus of the right dorsolateral prefrontal cortex (piDLPFC) in migraine patients during acute pain and cognitive task performance. Dynamic causal modeling suggests a directional influence of the claustrum on activity in this piDLPFC region, and diffusion weighted imaging verifies their structural connectivity. These findings advance understanding of claustrum function during acute pain and provide evidence of a possible circuit mechanism driving cognitive impairments in chronic pain.
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Affiliation(s)
- Brent W Stewart
- Department of Neural and Pain Sciences, University of Maryland School of Dentistry, W Baltimore Street, Baltimore, MD 21201, USA
| | - Michael L Keaser
- Department of Neural and Pain Sciences, University of Maryland School of Dentistry, W Baltimore Street, Baltimore, MD 21201, USA
| | - Hwiyoung Lee
- Department of Epidemiology & Public Health, Maryland Psychiatric Research Center, Wade Avenue, Catonsville, MD 21228, USA
| | - Sarah M Margerison
- Department of Neural and Pain Sciences, University of Maryland School of Dentistry, W Baltimore Street, Baltimore, MD 21201, USA; Physical Therapy and Rehabilitation Science, University of Maryland School of Medicine, Penn Street, Baltimore, MD 21201, USA
| | - Matthew A Cormie
- Centre for Multimodal Sensorimotor and Pain Research, Faculty of Dentistry, University of Toronto, Edward Street, Toronto, ON M5G 1E2, Canada
| | - Massieh Moayedi
- Centre for Multimodal Sensorimotor and Pain Research, Faculty of Dentistry, University of Toronto, Edward Street, Toronto, ON M5G 1E2, Canada; Department of Dentistry, Mount Sinai Hospital, University Avenue, Toronto, ON M5G 1X5, Canada; Division of Clinical & Computational Neuroscience, Krembil Brain Institute, University Health Network, Nassau Street, Toronto, ON M5T 1M8, Canada
| | - Martin A Lindquist
- Department of Biostatistics, Johns Hopkins University, N Wolfe Street, Baltimore, MD 21205, USA
| | - Shuo Chen
- Department of Epidemiology & Public Health, Maryland Psychiatric Research Center, Wade Avenue, Catonsville, MD 21228, USA
| | - Brian N Mathur
- Department of Pharmacology, University of Maryland School of Medicine, W Baltimore Street, Baltimore, MD 21201, USA; Department of Psychiatry, University of Maryland School of Medicine, W Baltimore Street, Baltimore, MD 21201, USA.
| | - David A Seminowicz
- Department of Neural and Pain Sciences, University of Maryland School of Dentistry, W Baltimore Street, Baltimore, MD 21201, USA; Department of Medical Biophysics, Schulich School of Medicine & Dentistry, University of Western Ontario, Richmond Street, London, ON N6A 5C1, Canada.
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3
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Hermosillo RJM, Moore LA, Feczko E, Miranda-Domínguez Ó, Pines A, Dworetsky A, Conan G, Mooney MA, Randolph A, Graham A, Adeyemo B, Earl E, Perrone A, Carrasco CM, Uriarte-Lopez J, Snider K, Doyle O, Cordova M, Koirala S, Grimsrud GJ, Byington N, Nelson SM, Gratton C, Petersen S, Feldstein Ewing SW, Nagel BJ, Dosenbach NUF, Satterthwaite TD, Fair DA. A precision functional atlas of personalized network topography and probabilities. Nat Neurosci 2024; 27:1000-1013. [PMID: 38532024 PMCID: PMC11089006 DOI: 10.1038/s41593-024-01596-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 02/08/2024] [Indexed: 03/28/2024]
Abstract
Although the general location of functional neural networks is similar across individuals, there is vast person-to-person topographic variability. To capture this, we implemented precision brain mapping functional magnetic resonance imaging methods to establish an open-source, method-flexible set of precision functional network atlases-the Masonic Institute for the Developing Brain (MIDB) Precision Brain Atlas. This atlas is an evolving resource comprising 53,273 individual-specific network maps, from more than 9,900 individuals, across ages and cohorts, including the Adolescent Brain Cognitive Development study, the Developmental Human Connectome Project and others. We also generated probabilistic network maps across multiple ages and integration zones (using a new overlapping mapping technique, Overlapping MultiNetwork Imaging). Using regions of high network invariance improved the reproducibility of executive function statistical maps in brain-wide associations compared to group average-based parcellations. Finally, we provide a potential use case for probabilistic maps for targeted neuromodulation. The atlas is expandable to alternative datasets with an online interface encouraging the scientific community to explore and contribute to understanding the human brain function more precisely.
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Affiliation(s)
- Robert J M Hermosillo
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA.
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA.
| | - Lucille A Moore
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Eric Feczko
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Óscar Miranda-Domínguez
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Adam Pines
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Ally Dworetsky
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Psychology, Northwestern University, Evanston, IL, USA
- Department of Psychology, Florida State University, Tallahassee, FL, USA
| | - Gregory Conan
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Michael A Mooney
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR, USA
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
- Center for Mental Health Innovation, Oregon Health and Science University, Portland, OR, USA
| | - Anita Randolph
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Alice Graham
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Babatunde Adeyemo
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Eric Earl
- Data Science and Sharing Team, National Institute of Mental Health, Bethesda, MD, USA
| | - Anders Perrone
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Cristian Morales Carrasco
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | | | - Kathy Snider
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Olivia Doyle
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Michaela Cordova
- Joint Doctoral Program in Clinical Psychology, San Diego State University, San Diego, CA, USA
- Joint Doctoral Program in Clinical Psychology, University of California San Diego, San Diego, CA, USA
| | - Sanju Koirala
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
| | - Gracie J Grimsrud
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Nora Byington
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Steven M Nelson
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Caterina Gratton
- Department of Psychology, Northwestern University, Evanston, IL, USA
- Department of Psychology, Florida State University, Tallahassee, FL, USA
- Department of Psychological and Brain Sciences, Washington University School of Medicine, St. Louis, MO, USA
| | - Steven Petersen
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Psychological and Brain Sciences, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, USA
- Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, MO, USA
| | | | - Bonnie J Nagel
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Nico U F Dosenbach
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
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4
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Kumar VA, Lee J, Liu HL, Allen JW, Filippi CG, Holodny AI, Hsu K, Jain R, McAndrews MP, Peck KK, Shah G, Shimony JS, Singh S, Zeineh M, Tanabe J, Vachha B, Vossough A, Welker K, Whitlow C, Wintermark M, Zaharchuk G, Sair HI. Recommended Resting-State fMRI Acquisition and Preprocessing Steps for Preoperative Mapping of Language and Motor and Visual Areas in Adult and Pediatric Patients with Brain Tumors and Epilepsy. AJNR Am J Neuroradiol 2024; 45:139-148. [PMID: 38164572 DOI: 10.3174/ajnr.a8067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Accepted: 10/12/2023] [Indexed: 01/03/2024]
Abstract
Resting-state (rs) fMRI has been shown to be useful for preoperative mapping of functional areas in patients with brain tumors and epilepsy. However, its lack of standardization limits its widespread use and hinders multicenter collaboration. The American Society of Functional Neuroradiology, American Society of Pediatric Neuroradiology, and the American Society of Neuroradiology Functional and Diffusion MR Imaging Study Group recommend specific rs-fMRI acquisition approaches and preprocessing steps that will further support rs-fMRI for future clinical use. A task force with expertise in fMRI from multiple institutions provided recommendations on the rs-fMRI steps needed for mapping of language, motor, and visual areas in adult and pediatric patients with brain tumor and epilepsy. These were based on an extensive literature review and expert consensus.Following rs-fMRI acquisition parameters are recommended: minimum 6-minute acquisition time; scan with eyes open with fixation; obtain rs-fMRI before both task-based fMRI and contrast administration; temporal resolution of ≤2 seconds; scanner field strength of 3T or higher. The following rs-fMRI preprocessing steps and parameters are recommended: motion correction (seed-based correlation analysis [SBC], independent component analysis [ICA]); despiking (SBC); volume censoring (SBC, ICA); nuisance regression of CSF and white matter signals (SBC); head motion regression (SBC, ICA); bandpass filtering (SBC, ICA); and spatial smoothing with a kernel size that is twice the effective voxel size (SBC, ICA).The consensus recommendations put forth for rs-fMRI acquisition and preprocessing steps will aid in standardization of practice and guide rs-fMRI program development across institutions. Standardized rs-fMRI protocols and processing pipelines are essential for multicenter trials and to implement rs-fMRI as part of standard clinical practice.
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Affiliation(s)
- V A Kumar
- From the The University of Texas MD Anderson Cancer Center (V.A.K., J.L., H.-L.L., M.W.), Houston, Texas
| | - J Lee
- From the The University of Texas MD Anderson Cancer Center (V.A.K., J.L., H.-L.L., M.W.), Houston, Texas
| | - H-L Liu
- From the The University of Texas MD Anderson Cancer Center (V.A.K., J.L., H.-L.L., M.W.), Houston, Texas
| | - J W Allen
- Emory University (J.W.A.), Atlanta, Georgia
| | - C G Filippi
- Tufts University (C.G.F.), Boston, Massachusetts
| | - A I Holodny
- Memorial Sloan Kettering Cancer Center (A.I.H., K.K.P.), New York, New York
| | - K Hsu
- New York University (K.H., R.J.), New York, New York
| | - R Jain
- New York University (K.H., R.J.), New York, New York
| | - M P McAndrews
- University of Toronto (M.P.M.), Toronto, Ontario, Canada
| | - K K Peck
- Memorial Sloan Kettering Cancer Center (A.I.H., K.K.P.), New York, New York
| | - G Shah
- University of Michigan (G.S.), Ann Arbor, Michigan
| | - J S Shimony
- Washington University School of Medicine (J.S.S.), St. Louis, Missouri
| | - S Singh
- University of Texas Southwestern Medical Center (S.S.), Dallas, Texas
| | - M Zeineh
- Stanford University (M.Z., G.Z.), Palo Alto, California
| | - J Tanabe
- University of Colorado (J.T.), Aurora, Colorado
| | - B Vachha
- University of Massachusetts (B.V.), Worcester, Massachusetts
| | - A Vossough
- Children's Hospital of Philadelphia, University of Pennsylvania (A.V.), Philadelphia, Pennsylvania
| | - K Welker
- Mayo Clinic (K.W.), Rochester, Minnesota
| | - C Whitlow
- Wake Forest University (C.W.), Winston-Salem, North Carolina
| | - M Wintermark
- From the The University of Texas MD Anderson Cancer Center (V.A.K., J.L., H.-L.L., M.W.), Houston, Texas
| | - G Zaharchuk
- Stanford University (M.Z., G.Z.), Palo Alto, California
| | - H I Sair
- Johns Hopkins University (H.I.S.), Baltimore, Maryland
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5
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Stewart BW, Keaser ML, Lee H, Margerison SM, Cormie MA, Moayedi M, Lindquist MA, Chen S, Mathur BN, Seminowicz DA. Pathological claustrum activity drives aberrant cognitive network processing in human chronic pain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.01.564054. [PMID: 37961503 PMCID: PMC10635040 DOI: 10.1101/2023.11.01.564054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Aberrant cognitive network activity and cognitive deficits are established features of chronic pain. However, the nature of cognitive network alterations associated with chronic pain and their underlying mechanisms require elucidation. Here, we report that the claustrum, a subcortical nucleus implicated in cognitive network modulation, is activated by acute painful stimulation and pain-predictive cues in healthy participants. Moreover, we discover pathological activity of the claustrum and a lateral aspect of the right dorsolateral prefrontal cortex (latDLPFC) in migraine patients. Dynamic causal modeling suggests a directional influence of the claustrum on activity in this latDLPFC region, and diffusion weighted imaging (DWI) verifies their structural connectivity. These findings advance understanding of claustrum function during acute pain and provide evidence of a possible circuit mechanism driving cognitive impairments in chronic pain.
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Affiliation(s)
- Brent W. Stewart
- Department of Neural and Pain Sciences, University of Maryland School of Dentistry, Baltimore, MD, USA
| | - Michael L. Keaser
- Department of Neural and Pain Sciences, University of Maryland School of Dentistry, Baltimore, MD, USA
| | - Hwiyoung Lee
- Department of Epidemiology & Public Health, Maryland Psychiatric Research Center, Catonsville, MD, USA
| | - Sarah M. Margerison
- Department of Neural and Pain Sciences, University of Maryland School of Dentistry, Baltimore, MD, USA
- Physical Therapy and Rehabilitation Science, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Matthew A. Cormie
- Centre for Multimodal Sensorimotor and Pain Research, Faculty of Dentistry, University of Toronto, ON, Canada
| | - Massieh Moayedi
- Centre for Multimodal Sensorimotor and Pain Research, Faculty of Dentistry, University of Toronto, ON, Canada
- Department of Dentistry, Mount Sinai Hospital, Toronto, ON, Canada
- Division of Clinical & Computational Neuroscience, Krembil Brain Institute, University Health Network
| | | | - Shuo Chen
- Department of Epidemiology & Public Health, Maryland Psychiatric Research Center, Catonsville, MD, USA
| | - Brian N. Mathur
- Department of Pharmacology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - David A. Seminowicz
- Department of Neural and Pain Sciences, University of Maryland School of Dentistry, Baltimore, MD, USA
- Department of Medical Biophysics, Schulich School of Medicine & Dentistry, University of Western Ontario, London, Ontario, Canada
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6
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Berger D, Matharoo GS, Levman J. Random matrix theory tools for the predictive analysis of functional magnetic resonance imaging examinations. J Med Imaging (Bellingham) 2023; 10:036003. [PMID: 37323123 PMCID: PMC10266090 DOI: 10.1117/1.jmi.10.3.036003] [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: 11/15/2022] [Revised: 05/16/2023] [Accepted: 05/22/2023] [Indexed: 06/17/2023] Open
Abstract
Purpose Random matrix theory (RMT) is an increasingly useful tool for understanding large, complex systems. Prior studies have examined functional magnetic resonance imaging (fMRI) scans using tools from RMT, with some success. However, RMT computations are highly sensitive to a number of analytic choices, and the robustness of findings involving RMT remains in question. We systematically investigate the usefulness of RMT on a wide variety of fMRI datasets using a rigorous predictive framework. Approach We develop open-source software to efficiently compute RMT features from fMRI images and examine the cross-validated predictive potential of eigenvalue and RMT-based features ("eigenfeatures") with classic machine-learning classifiers. We systematically vary pre-processing extent, normalization procedures, RMT unfolding procedures, and feature selection and compare the impact of these analytic choices on the distributions of cross-validated prediction performance for each combination of dataset binary classification task, classifier, and feature. To deal with class imbalance, we use the area under the receiver operating characteristic curve (AUROC) as the main performance metric. Results Across all classification tasks and analytic choices, we find RMT- and eigenvalue-based "eigenfeatures" to have predictive utility more often than not (82.4% of median AUROCs > 0.5 ; median AUROC range across classification tasks 0.47 to 0.64). Simple baseline reductions on source timeseries, by contrast, were less useful (58.8% of median AUROCs > 0.5 , median AUROC range across classification tasks 0.42 to 0.62). Additionally, eigenfeature AUROC distributions were overall more right-tailed than baseline features, suggesting greater predictive potential. However, performance distributions were wide and often significantly affected by analytic choices. Conclusions Eigenfeatures clearly have potential for understanding fMRI functional connectivity in a wide variety of scenarios. The utility of these features is strongly dependent on analytic decisions, suggesting caution when interpreting past and future studies applying RMT to fMRI. However, our study demonstrates that the inclusion of RMT statistics in fMRI investigations could improve prediction performances across a wide variety of phenomena.
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Affiliation(s)
- Derek Berger
- St. Francis Xavier University, Department of Computer Science, Antigonish, Nova Scotia, Canada
| | - Gurpreet S. Matharoo
- St. Francis Xavier University, ACENET, Antigonish, Nova Scotia, Canada
- St. Francis Xavier University, Department of Physics, Antigonish, Nova Scotia, Canada
| | - Jacob Levman
- St. Francis Xavier University, Department of Computer Science, Antigonish, Nova Scotia, Canada
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, United States
- Nova Scotia Health Authority, Research Affiliate, Antigonish, Nova Scotia, Canada
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7
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Khalil AA, Tanritanir AC, Grittner U, Kirilina E, Villringer A, Fiebach JB, Mekle R. Reproducibility of cerebral perfusion measurements using BOLD delay. Hum Brain Mapp 2023; 44:2778-2789. [PMID: 36840928 PMCID: PMC10089099 DOI: 10.1002/hbm.26244] [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: 12/03/2022] [Revised: 02/06/2023] [Accepted: 02/11/2023] [Indexed: 02/26/2023] Open
Abstract
BOLD delay is an emerging, noninvasive method for assessing cerebral perfusion that does not require the use of intravenous contrast agents and is thus particularly suited for longitudinal monitoring. In this study, we assess the reproducibility of BOLD delay using data from 136 subjects with normal cerebral perfusion scanned on two separate occasions with scanners, sequence parameters, and intervals between scans varying between subjects. The effects of various factors on the reproducibility of BOLD delay, defined here as the differences in BOLD delay values between the scanning sessions, were investigated using a linear mixed model. Reproducibility was additionally assessed using the intraclass correlation coefficient of BOLD delay between sessions. Reproducibility was highest in the posterior cerebral artery territory. The mean BOLD delay test-retest difference after accounting for the aforementioned factors was 1.2 s (95% CI = 1.0 to 1.4 s). Overall, BOLD delay shows good reproducibility, but care should be taken when interpreting longitudinal BOLD delay changes that are either very small or are located in certain brain regions.
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Affiliation(s)
- Ahmed A Khalil
- Center for Stroke Research Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany.,Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany.,Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Berlin Institute of Health (BIH), Berlin, Germany
| | - Ayse C Tanritanir
- Center for Stroke Research Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Ulrike Grittner
- Berlin Institute of Health (BIH), Berlin, Germany.,Charité - Universitätsmedizin Berlin, Institute of Biometry and Clinical Epidemiology, Berlin, Germany
| | - Evgeniya Kirilina
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Center for Cognitive Neuroscience Berlin, Free University, Berlin, Germany
| | - Arno Villringer
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany.,Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Jochen B Fiebach
- Center for Stroke Research Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Ralf Mekle
- Center for Stroke Research Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany
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8
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Cohesive parcellation of the human brain using resting-state fMRI. J Neurosci Methods 2022; 377:109629. [PMID: 35618164 DOI: 10.1016/j.jneumeth.2022.109629] [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: 08/17/2021] [Revised: 04/14/2022] [Accepted: 05/19/2022] [Indexed: 11/23/2022]
Abstract
BACKGROUND The data burden for resting-state fMRI analysis rises with increasing resolutions available at ultrahigh fields. Therefore, a fundamental preprocessing step in brain network analysis is to reduce the data, usually by performing some kind of data parcellation. Most functional parcellations based on rsfMRI connectivity are synthesized from the dense connectome. In contrast, most network analyses begin by reducing each parcel to a single exemplar time series. This disconnect between parcel formation and usage assumes that parcel exemplars adequately represent their member voxels, which is not always the case for commonly used parcellations.
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9
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Sbaihat H, Rajkumar R, Ramkiran S, Assi AAN, Felder J, Shah NJ, Veselinović T, Neuner I. Test-retest stability of spontaneous brain activity and functional connectivity in the core resting-state networks assessed with ultrahigh field 7-Tesla resting-state functional magnetic resonance imaging. Hum Brain Mapp 2022; 43:2026-2040. [PMID: 35044722 PMCID: PMC8933332 DOI: 10.1002/hbm.25771] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 11/26/2021] [Accepted: 12/14/2021] [Indexed: 12/12/2022] Open
Abstract
The growing demand for precise and reliable biomarkers in psychiatry is fueling research interest in the hope that identifying quantifiable indicators will improve diagnoses and treatment planning across a range of mental health conditions. The individual properties of brain networks at rest have been highlighted as a possible source for such biomarkers, with the added advantage that they are relatively straightforward to obtain. However, an important prerequisite for their consideration is their reproducibility. While the reliability of resting‐state (RS) measurements has often been studied at standard field strengths, they have rarely been investigated using ultrahigh‐field (UHF) magnetic resonance imaging (MRI) systems. We investigated the intersession stability of four functional MRI RS parameters—amplitude of low‐frequency fluctuations (ALFF) and fractional ALFF (fALFF; representing the spontaneous brain activity), regional homogeneity (ReHo; measure of local connectivity), and degree centrality (DC; measure of long‐range connectivity)—in three RS networks, previously shown to play an important role in several psychiatric diseases—the default mode network (DMN), the central executive network (CEN), and the salience network (SN). Our investigation at individual subject space revealed a strong stability for ALFF, ReHo, and DC in all three networks, and a moderate level of stability in fALFF. Furthermore, the internetwork connectivity between each network pair was strongly stable between CEN/SN and moderately stable between DMN/SN and DMN/SN. The high degree of reliability and reproducibility in capturing the properties of the three major RS networks by means of UHF‐MRI points to its applicability as a potentially useful tool in the search for disease‐relevant biomarkers.
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Affiliation(s)
- Hasan Sbaihat
- Institute of Neuroscience and Medicine, INM-4, Jülich, Germany.,Department of Medical Imaging, Arab-American University Palestine (AAUP), Jenin, Palestine.,Department of Psychiatry, Psychotherapy, and Psychosomatics, RWTH Aachen University, Aachen, Germany
| | - Ravichandran Rajkumar
- Institute of Neuroscience and Medicine, INM-4, Jülich, Germany.,Department of Psychiatry, Psychotherapy, and Psychosomatics, RWTH Aachen University, Aachen, Germany.,JARA-BRAIN-Translational Medicine, Aachen, Germany
| | - Shukti Ramkiran
- Institute of Neuroscience and Medicine, INM-4, Jülich, Germany.,Department of Psychiatry, Psychotherapy, and Psychosomatics, RWTH Aachen University, Aachen, Germany.,JARA-BRAIN-Translational Medicine, Aachen, Germany
| | - Abed Al-Nasser Assi
- Department of Medical Imaging, Arab-American University Palestine (AAUP), Jenin, Palestine
| | - Jörg Felder
- Institute of Neuroscience and Medicine, INM-4, Jülich, Germany.,Department of Medical Imaging, Arab-American University Palestine (AAUP), Jenin, Palestine
| | - Nadim Jon Shah
- Institute of Neuroscience and Medicine, INM-4, Jülich, Germany.,JARA-BRAIN-Translational Medicine, Aachen, Germany.,Department of Neurology, RWTH Aachen University, Aachen, Germany.,Institute of Neuroscience and Medicine, INM-11, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Tanja Veselinović
- Department of Psychiatry, Psychotherapy, and Psychosomatics, RWTH Aachen University, Aachen, Germany
| | - Irene Neuner
- Institute of Neuroscience and Medicine, INM-4, Jülich, Germany.,Department of Psychiatry, Psychotherapy, and Psychosomatics, RWTH Aachen University, Aachen, Germany.,JARA-BRAIN-Translational Medicine, Aachen, Germany
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10
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Scan Once, Analyse Many: Using Large Open-Access Neuroimaging Datasets to Understand the Brain. Neuroinformatics 2022; 20:109-137. [PMID: 33974213 PMCID: PMC8111663 DOI: 10.1007/s12021-021-09519-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/07/2021] [Indexed: 02/06/2023]
Abstract
We are now in a time of readily available brain imaging data. Not only are researchers now sharing data more than ever before, but additionally large-scale data collecting initiatives are underway with the vision that many future researchers will use the data for secondary analyses. Here I provide an overview of available datasets and some example use cases. Example use cases include examining individual differences, more robust findings, reproducibility-both in public input data and availability as a replication sample, and methods development. I further discuss a variety of considerations associated with using existing data and the opportunities associated with large datasets. Suggestions for further readings on general neuroimaging and topic-specific discussions are also provided.
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11
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Nemani A, Lowe MJ. Seed-based test-retest reliability of resting state functional magnetic resonance imaging at 3T and 7T. Med Phys 2021; 48:5756-5764. [PMID: 34486120 DOI: 10.1002/mp.15210] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Revised: 05/08/2021] [Accepted: 08/20/2021] [Indexed: 11/08/2022] Open
Abstract
PURPOSE Ultrahigh field (UHF) resting state functional magnetic resonance imaging (rsfMRI) has become increasingly available for clinical and basic research, bringing improvements in resolution and contrast over standard high field imaging. Despite these improvements, UHF connectivity studies present several challenges, including increased sensitivity to physiological confounds and a vastly increased data burden. We present a direct quantitative assessment of test-retest reliability of functional connectivity in several standard functional networks between subjects scanned at 3T and 7T. METHODS Five healthy subjects were scanned over four sessions each in a scan-rescan design at both 3T and 7T field strengths. Resting state fMRI data were segmented into four major intrinsic connectivity networks, and seed-based peak correlations within and between these networks examined. The reliability of these correlations was assessed using intra-class correlation coefficients (ICC). RESULTS Across all data, over 4000 peak correlations were extracted for assessment. The reliability over all intrinsic networks was greater at 7T than 3T (median ICC 0.40 vs. 0.33, p ≤ 0.0014), with each network individually showing improvement. Inter-network reliability was stronger than intra-network reliability, but intra-network reliability showed the greatest improvement between field strengths. CONCLUSION We demonstrate significantly increased reliability of resting state connectivity at UHF strengths over conventional field strengths using a novel hybrid seed-based analysis. This result adds to the growing body of work supporting the migration of functional imaging studies to UHFs.
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Affiliation(s)
- Ajay Nemani
- Imaging Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Mark J Lowe
- Imaging Institute, Cleveland Clinic, Cleveland, Ohio, USA
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12
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Kucyi A, Esterman M, Capella J, Green A, Uchida M, Biederman J, Gabrieli JDE, Valera EM, Whitfield-Gabrieli S. Prediction of stimulus-independent and task-unrelated thought from functional brain networks. Nat Commun 2021; 12:1793. [PMID: 33741956 PMCID: PMC7979817 DOI: 10.1038/s41467-021-22027-0] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 02/23/2021] [Indexed: 12/20/2022] Open
Abstract
Neural substrates of "mind wandering" have been widely reported, yet experiments have varied in their contexts and their definitions of this psychological phenomenon, limiting generalizability. We aimed to develop and test the generalizability, specificity, and clinical relevance of a functional brain network-based marker for a well-defined feature of mind wandering-stimulus-independent, task-unrelated thought (SITUT). Combining functional MRI (fMRI) with online experience sampling in healthy adults, we defined a connectome-wide model of inter-regional coupling-dominated by default-frontoparietal control subnetwork interactions-that predicted trial-by-trial SITUT fluctuations within novel individuals. Model predictions generalized in an independent sample of adults with attention-deficit/hyperactivity disorder (ADHD). In three additional resting-state fMRI studies (total n = 1115), including healthy individuals and individuals with ADHD, we demonstrated further prediction of SITUT (at modest effect sizes) defined using multiple trait-level and in-scanner measures. Our findings suggest that SITUT is represented within a common pattern of brain network interactions across time scales and contexts.
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Affiliation(s)
- Aaron Kucyi
- Department of Psychology, Northeastern University, Boston, MA, USA.
| | - Michael Esterman
- National Center for PTSD & Neuroimaging Research for Veterans Center (NeRVe), Veterans Administration Boston Healthcare System, Boston, MA, USA
- Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA
| | - James Capella
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Allison Green
- Clinical and Research Program in Pediatric Psychopharmacology and Adult ADHD, Massachusetts General Hospital, Boston, MA, USA
| | - Mai Uchida
- Clinical and Research Program in Pediatric Psychopharmacology and Adult ADHD, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Joseph Biederman
- Clinical and Research Program in Pediatric Psychopharmacology and Adult ADHD, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - John D E Gabrieli
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- Athinoula A. Martinos Imaging Center at the McGovern Institute for Brain Research, Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
| | - Eve M Valera
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Charlestown, MA, USA
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13
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Brennan D, Murrough JW, Morris LS. Intrasubject functional connectivity related to self-generated thoughts. Brain Behav 2021; 11:e01860. [PMID: 33320436 PMCID: PMC7821575 DOI: 10.1002/brb3.1860] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 08/26/2020] [Accepted: 08/27/2020] [Indexed: 12/26/2022] Open
Abstract
INTRODUCTION In psychiatric research, functional connectivity (FC) derived from resting-state functional MRI (rsfMRI) is often used to investigate brain abnormalities in psychiatric disorders. This approach assumes implicitly that FC can recover reliable maps of the functional architecture of the brain and that these profiles of connectivity reflect trait differences underlying pathology. However, evidence of FC related to self-generated thoughts (mind-wandering) stands in contrast with these assumptions, as FC may reflect thought patterns rather than functional architecture. METHODS Multi-factor analysis (MFA) was used to investigate the reported content of self-generated thoughts during high-field (7T) rsfMRI in a repeated sample of 22 healthy individuals. To investigate the relationship between these experiences and FC, individual scores for each of these dimensions were compared with whole-brain connectivity using the network-based statistic (NBS) method. RESULTS This analysis revealed three dimensions of thought content: self-referential thought, negative thoughts about one's surroundings, and thoughts in the form of imagery. A network of connections within the sensorimotor cortices negatively correlated with self-generated thoughts concerning the self was observed (p = .0081, .0486 FDR). CONCLUSION These results suggest a potentially confounding relationship between self-generated thoughts and FC, and contribute to the body of research concerning the functional representation of mind-wandering.
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Affiliation(s)
- Daniel Brennan
- Department of Psychology, Graduate School of Arts & Sciences, NYU, New York, NY, USA
| | - James W Murrough
- Depression and Anxiety Center for Discovery and Treatment, Department of Psychiatry, Icahn School of Medicine of Mount Sinai, New York, NY, USA
| | - Laurel S Morris
- Depression and Anxiety Center for Discovery and Treatment, Department of Psychiatry, Icahn School of Medicine of Mount Sinai, New York, NY, USA
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14
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Suzuki S, Lawlor VM, Cooper JA, Arulpragasam AR, Treadway MT. Distinct regions of the striatum underlying effort, movement initiation and effort discounting. Nat Hum Behav 2020; 5:378-388. [PMID: 33230282 PMCID: PMC8555699 DOI: 10.1038/s41562-020-00972-y] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Accepted: 09/18/2020] [Indexed: 11/10/2022]
Abstract
The ventral striatum is believed to encode the subjective value of cost-benefit options; however, this effect has notably been absent during choices that involve physical effort. Previous work in freely moving animals has revealed opposing striatal signals, with greater response to increasing effort demands and reduced responses to rewards requiring effort. Yet, the relationship between these conflicting signals remains unknown. Using functional magnetic resonance imaging with a naturalistic maze-navigation paradigm, we identified functionally segregated regions within the ventral striatum that separately encoded effort activation, movement initiation and effort discounting of rewards. In addition, activity in regions associated with effort activation and discounting oppositely predicted striatal encoding of effort during effort-based decision-making. Our results suggest that the dorsomedial region hitherto associated with action may instead represent the cost of effort and raise fundamental questions regarding the interpretation of striatal 'reward' signals in the context of effort demands. This has implications for uncovering the neural architecture underlying motivated behaviour.
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Affiliation(s)
- Shosuke Suzuki
- Translational Research in Affective Disorders Laboratory, Department of Psychology, Emory University, Atlanta, GA, USA
| | - Victoria M Lawlor
- Translational Research in Affective Disorders Laboratory, Department of Psychology, Emory University, Atlanta, GA, USA
| | - Jessica A Cooper
- Translational Research in Affective Disorders Laboratory, Department of Psychology, Emory University, Atlanta, GA, USA
| | - Amanda R Arulpragasam
- Translational Research in Affective Disorders Laboratory, Department of Psychology, Emory University, Atlanta, GA, USA
| | - Michael T Treadway
- Translational Research in Affective Disorders Laboratory, Department of Psychology, Emory University, Atlanta, GA, USA. .,Department of Psychiatry and Behavioral Science, Emory University, Atlanta, GA, USA.
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15
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Yuan R, Biswal BB, Zaborszky L. Functional Subdivisions of Magnocellular Cell Groups in Human Basal Forebrain: Test-Retest Resting-State Study at Ultra-high Field, and Meta-analysis. Cereb Cortex 2020; 29:2844-2858. [PMID: 30137295 DOI: 10.1093/cercor/bhy150] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2017] [Revised: 05/11/2018] [Indexed: 12/23/2022] Open
Abstract
The heterogeneous neuronal subgroups of the basal forebrain corticopetal system (BFcs) have been shown to modulate cortical functions through their cholinergic, gamma-aminobutyric acid-ergic, and glutamatergic projections to the entire cortex. Although previous studies suggested that the basalo-cortical projection system influences various cognitive functions, particularly via its cholinergic component, these studies only focused on certain parts of the BFcs or nearby structures, leaving aside a more systematic picture of the functional connectivity of BFcs subcompartments. Moreover, these studies lacked the high-spatial resolution and the probability maps needed to identify specific subcompartments. Recent advances in the ultra-high field 7T functional magnetic resonance imaging (fMRI) provided potentially unprecedented spatial resolution of functional MRI images to study the subdivision of the BFcs. In this study, the BF space containing corticopetal cells was divided into 3 functionally distinct subdivisions based on functional connection to cortical regions derived from fMRI. The overall functional connection of each BFcs subdivision was examined with a test-retest study. Finally, a meta-analysis was used to study the related functional topics of each BF subdivision. Our results demonstrate distinct functional connectivity patterns of these subdivisions along the rostrocaudal axis of the BF. All three compartments have shown consistent segregation and overlap at specific target regions including the hippocampus, insula, thalamus, and the cingulate gyrus, suggesting functional integration and separation in BFcs.
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Affiliation(s)
- Rui Yuan
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA
| | - Bharat B Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA.,The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, PR China
| | - Laszlo Zaborszky
- Center for Molecular and Behavioral Neuroscience, Rutgers, The State University of New Jersey, Newark, NJ, USA
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16
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Yang J, Gohel S, Vachha B. Current methods and new directions in resting state fMRI. Clin Imaging 2020; 65:47-53. [PMID: 32353718 DOI: 10.1016/j.clinimag.2020.04.004] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Revised: 03/24/2020] [Accepted: 04/08/2020] [Indexed: 12/12/2022]
Abstract
Resting state functional connectivity magnetic resonance imaging (rsfcMRI) has become a key component of investigations of neurocognitive and psychiatric behaviors. Over the past two decades, several methods and paradigms have been adopted to utilize and interpret data from resting-state fluctuations in the brain. These findings have increased our understanding of changes in many disease states. As the amount of resting state data available for research increases with big datasets and data-sharing projects, it is important to review the established traditional analysis methods and recognize areas where research methodology can be adapted to better accommodate the scale and complexity of rsfcMRI analysis. In this paper, we review established methods of analysis as well as areas that have been receiving increasing attention such as dynamic rsfcMRI, independent vector analysis, multiband rsfcMRI and network of networks.
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Affiliation(s)
- Jackie Yang
- NYU Grossman School of Medicine, 550 1(st) Avenue, New York, NY 10016, USA
| | - Suril Gohel
- Department of Health Informatics, Rutgers University School of Health Professions, 65 Bergen Street, Newark, NJ 07107, USA
| | - Behroze Vachha
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065, USA.
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17
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Kashyap R, Bhattacharjee S, Yeo BTT, Chen SHA. Maximizing dissimilarity in resting state detects heterogeneous subtypes in healthy population associated with high substance use and problems in antisocial personality. Hum Brain Mapp 2020; 41:1261-1273. [PMID: 31773817 PMCID: PMC7267929 DOI: 10.1002/hbm.24873] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Revised: 11/05/2019] [Accepted: 11/12/2019] [Indexed: 01/08/2023] Open
Abstract
Patterns in resting-state fMRI (rs-fMRI) are widely used to characterize the trait effects of brain function. In this aspect, multiple rs-fMRI scans from single subjects can provide interesting clues about the rs-fMRI patterns, though scan-to-scan variability pose challenges. Therefore, rs-fMRI's are either concatenated or the functional connectivity is averaged. This leads to loss of information. Here, we use an alternative way to extract the rs-fMRI features that are common across all the scans by applying common-and-orthogonal-basis-extraction (COBE) technique. To address this, we employed rs-fMRI of 788 subjects from the human connectome project and estimated the common-COBE-component of each subject from the four rs-fMRI runs. Since the common-COBE-component is specific to a subject, the pattern was used to classify the subjects based on the similarity/dissimilarity of the features. The subset of subjects (n = 107) with maximal-COBE-dissimilarity (MCD) was extracted and the remaining subjects (n = 681) formed the COBE-similarity (CS) group. The distribution of weights of the common-COBE-component for the two groups across rs-fMRI networks and subcortical regions was evaluated. We found the weights in the default mode network to be lower in the MCD compared to the CS. We compared the scores of 69 behavioral measures and found six behaviors related to the use of marijuana, illicit drugs, alcohol, and tobacco; and including a measure of antisocial personality to differentiate the two groups. Gender differences were also significant. Altogether the findings suggested that subtypes exist even in healthy control population, and comparison studies (case vs. control) need to be mindful of it.
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Affiliation(s)
- Rajan Kashyap
- Centre for Research and Development in Learning (CRADLE)Nanyang Technological UniversitySingaporeSingapore
- Department of Electrical and Computer EngineeringCentre for Sleep and Cognition, Clinical Imaging Research Centre, N.1 Institute for Health, National University of SingaporeSingapore
| | - Sagarika Bhattacharjee
- Psychology, School of Social Sciences (SSS)Nanyang Technological UniversitySingaporeSingapore
| | - B. T. Thomas Yeo
- Department of Electrical and Computer EngineeringCentre for Sleep and Cognition, Clinical Imaging Research Centre, N.1 Institute for Health, National University of SingaporeSingapore
- Centre for Cognitive Neuroscience, Neuroscience and Behavioral Disorders ProgramDuke‐NUS Medical SchoolSingaporeSingapore
- Martinos Center for Biomedical ImagingMassachusetts General HospitalCharlestownMassachusetts
- NUS Graduate School for Integrative Sciences and EngineeringNational University of SingaporeSingaporeSingapore
| | - S. H. Annabel Chen
- Centre for Research and Development in Learning (CRADLE)Nanyang Technological UniversitySingaporeSingapore
- Psychology, School of Social Sciences (SSS)Nanyang Technological UniversitySingaporeSingapore
- Lee Kong Chian School of Medicine (LKC Medicine)Nanyang Technological UniversitySingaporeSingapore
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18
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Li X, Pan Y, Fang Z, Lei H, Zhang X, Shi H, Ma N, Raine P, Wetherill R, Kim JJ, Wan Y, Rao H. Test-retest reliability of brain responses to risk-taking during the balloon analogue risk task. Neuroimage 2019; 209:116495. [PMID: 31887425 PMCID: PMC7061333 DOI: 10.1016/j.neuroimage.2019.116495] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Revised: 12/20/2019] [Accepted: 12/23/2019] [Indexed: 12/24/2022] Open
Abstract
The Balloon Analogue Risk Task (BART) provides a reliable and ecologically valid model for the assessment of individual risk-taking propensity and is frequently used in neuroimaging and developmental research. Although the test-retest reliability of risk-taking behavior during the BART is well established, the reliability of brain activation patterns in response to risk-taking during the BART remains elusive. In this study, we used functional magnetic resonance imaging (fMRI) and evaluated the test-retest reliability of brain responses in 34 healthy adults during a modified BART by calculating the intraclass correlation coefficients (ICC) and Dice’s similarity coefficients (DSC). Analyses revealed that risk-induced brain activation patterns showed good test-retest reliability (median ICC = 0.62) and moderate to high spatial consistency, while brain activation patterns associated with win or loss outcomes only had poor to fair reliability (median ICC = 0.33 for win and 0.42 for loss). These findings have important implications for future utility of the BART in fMRI to examine brain responses to risk-taking and decision-making.
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Affiliation(s)
- Xiong Li
- School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China
| | - Yu Pan
- School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China; Key Laboratory of Applied Brain and Cognitive Sciences, School of Business and Management, Shanghai International Studies University, Shanghai, China
| | - Zhuo Fang
- Key Laboratory of Applied Brain and Cognitive Sciences, School of Business and Management, Shanghai International Studies University, Shanghai, China; Center for Functional Neuroimaging, Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Hui Lei
- Center for Functional Neuroimaging, Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Xiaocui Zhang
- Center for Functional Neuroimaging, Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Hui Shi
- Center for Functional Neuroimaging, Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Ning Ma
- Center for Functional Neuroimaging, Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Philip Raine
- Center for Functional Neuroimaging, Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Reagan Wetherill
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Junghoon J Kim
- Department of Molecular, Cellular, and Biomedical Sciences, CUNY School of Medicine, The City College of New York, New York, NY, USA
| | - Yan Wan
- School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China
| | - Hengyi Rao
- Key Laboratory of Applied Brain and Cognitive Sciences, School of Business and Management, Shanghai International Studies University, Shanghai, China; Center for Functional Neuroimaging, Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
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19
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Kayser AS. Functional imaging. HANDBOOK OF CLINICAL NEUROLOGY 2019; 163:61-72. [PMID: 31590748 DOI: 10.1016/b978-0-12-804281-6.00004-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
Abstract
Functional imaging methodology has revolutionized our ability to understand brain-behavior relationships. In contrast with the static images obtained with standard imaging methods, functional images permit us to track brain activity as humans view stimuli, hear sounds, consider choices, and make decisions. The insights now possible because of this technology have not only provided new potential markers for disease but have also permitted questions of neural mechanism to be addressed in living humans. Because of the breadth and depth of research that directly or tangentially touches upon functional imaging, it is impossible to do justice to the various subfields, analysis streams, and methodological complexities in one chapter. Instead, this chapter will provide a brief overview of the underlying conceptual framework, basic analytic techniques, and details of the imaging methodologies available for the acquisition of functional imaging data.
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Affiliation(s)
- Andrew S Kayser
- Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, United States.
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20
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Krimmel SR, White MG, Panicker MH, Barrett FS, Mathur BN, Seminowicz DA. Resting state functional connectivity and cognitive task-related activation of the human claustrum. Neuroimage 2019; 196:59-67. [PMID: 30954711 DOI: 10.1016/j.neuroimage.2019.03.075] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Revised: 03/07/2019] [Accepted: 03/30/2019] [Indexed: 10/27/2022] Open
Abstract
Structural and functional analyses of the human claustrum, a poorly understood telencephalic gray matter structure, are hampered by its sheet-like anatomical arrangement. Here, we first describe a functional magnetic resonance imaging (fMRI) method to reveal claustrum signal with no linear relationship with adjacent regions in human subjects. We applied this approach to resting state functional connectivity (RSFC) analysis of the claustrum at high resolution (1.5 mm isotropic voxels) using a 7T dataset (n = 20) and a separate 3T dataset for replication (n = 35). We then assessed claustrum activation during performance of a cognitive task, the multi-source interference task, at 3T (n = 33). Extensive functional connectivity was observed between claustrum and cortical regions associated with cognitive control, including anterior cingulate, prefrontal and parietal cortices. Cognitive task performance was associated with widespread activation and deactivation that overlapped with the cortical areas showing functional connectivity to the claustrum. Furthermore, during high cognitive conflict conditions of the task, the claustrum was significantly activated at the onset of the task, but not during the remainder of the difficult condition. Both of these findings suggest that the human claustrum can be functionally isolated with fMRI, and that it may play a role in cognitive control, and specifically task switching, independent of sensorimotor processing.
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Affiliation(s)
- Samuel R Krimmel
- Department of Neural and Pain Sciences, School of Dentistry, and Center to Advance Chronic Pain Research, University of Maryland, Baltimore, MD, 21201, USA
| | - Michael G White
- Department of Biology, Stanford University, Stanford, CA, 94305, USA
| | - Matthew H Panicker
- Department of Pharmacology, School of Medicine, University of Maryland, Baltimore, MD, 21201, USA
| | - Frederick S Barrett
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, 21224, USA
| | - Brian N Mathur
- Department of Pharmacology, School of Medicine, University of Maryland, Baltimore, MD, 21201, USA.
| | - David A Seminowicz
- Department of Neural and Pain Sciences, School of Dentistry, and Center to Advance Chronic Pain Research, University of Maryland, Baltimore, MD, 21201, USA
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21
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Huntenburg JM, Steele CJ, Bazin PL. Nighres: processing tools for high-resolution neuroimaging. Gigascience 2018; 7:5049008. [PMID: 29982501 PMCID: PMC6065481 DOI: 10.1093/gigascience/giy082] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Accepted: 06/29/2018] [Indexed: 01/22/2023] Open
Abstract
With recent improvements in human magnetic resonance imaging (MRI) at ultra-high fields, the amount of data collected per subject in a given MRI experiment has increased considerably. Standard image processing packages are often challenged by the size of these data. Dedicated methods are needed to leverage their extraordinary spatial resolution. Here, we introduce a flexible Python toolbox that implements a set of advanced techniques for high-resolution neuroimaging. With these tools, segmentation and laminar analysis of cortical MRI data can be performed at resolutions up to 500 μm in reasonable times. Comprehensive online documentation makes the toolbox easy to use and install. An extensive developer’s guide encourages contributions from other researchers that will help to accelerate progress in the promising field of high-resolution neuroimaging.
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Affiliation(s)
- Julia M Huntenburg
- Max Planck Research Group for Neuroanatomy & Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstrasse 1a, Leipzig, 04103, Germany.,Neurocomputation and Neuroimaging Unit, Department of Education and Psychology, Free University of Berlin, Habelschwerdter Allee 45, Berlin, 14195, Germany
| | - Christopher J Steele
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstrasse 1a, Leipzig, 04103, Germany.,Cerebral Imaging Center, Douglas Mental Health University Institute, 6875 LaSalle Boulevard, Montreal, Quebec, H4H 1R3, Canada.,Department of Psychology, Concordia University, 7141 Sherbrooke West, Montreal, Quebec, H4B IR6, Canada
| | - Pierre-Louis Bazin
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstrasse 1a, Leipzig, 04103, Germany.,Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstrasse 1a, Leipzig, 04103, Germany.,Psychology Department, University of Amsterdam, Nieuwe Achtergracht 129B, Amsterdam, 1018 WT, Netherlands
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22
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Temporal reliability of ultra-high field resting-state MRI for single-subject sensorimotor and language mapping. Neuroimage 2018; 168:499-508. [DOI: 10.1016/j.neuroimage.2016.11.029] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2016] [Revised: 10/29/2016] [Accepted: 11/12/2016] [Indexed: 11/19/2022] Open
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23
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Budinger TF, Bird MD. MRI and MRS of the human brain at magnetic fields of 14 T to 20 T: Technical feasibility, safety, and neuroscience horizons. Neuroimage 2018; 168:509-531. [DOI: 10.1016/j.neuroimage.2017.01.067] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2016] [Revised: 01/23/2017] [Accepted: 01/27/2017] [Indexed: 11/16/2022] Open
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24
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de Flores R, Mutlu J, Bejanin A, Gonneaud J, Landeau B, Tomadesso C, Mézenge F, de La Sayette V, Eustache F, Chételat G. Intrinsic connectivity of hippocampal subfields in normal elderly and mild cognitive impairment patients. Hum Brain Mapp 2017; 38:4922-4932. [PMID: 28653793 PMCID: PMC6866942 DOI: 10.1002/hbm.23704] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2017] [Revised: 06/14/2017] [Accepted: 06/15/2017] [Indexed: 12/11/2022] Open
Abstract
Hippocampal connectivity has been widely described but connectivity specificities of hippocampal subfields and their changes in early AD are poorly known. The aim of this study was to highlight hippocampal subfield networks in healthy elderly (HE) and their changes in amnestic patients with mild cognitive impairment (aMCI). Thirty-six HE and 27 aMCI patients underwent resting-state functional MRI scans. Specific intrinsic connectivity of bilateral CA1, SUB (subiculum), and CA2/3/4/DG was identified in HE (using seeds derived from manually delineation on high-resolution scans) and compared between HE and aMCI. Compared to the other subfields, CA1 was more strongly connected to the amygdala and occipital regions, CA2/3/4/DG to the left anterior cingulate cortex, temporal, and occipital regions, and SUB to the angular, precuneus, putamen, posterior cingulate, and frontal regions. aMCI patients showed reduced connectivity within the SUB network (with frontal and posterior cingulate regions). Our study highlighted for the first time three specific and distinct hippocampal subfield functional networks in HE, and their alterations in aMCI. These findings are important to understand AD specificities in both cognitive deficits and lesion topography, given the role of functional connectivity in these processes. Hum Brain Mapp 38:4922-4932, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Robin de Flores
- INSERMCaenU1077France
- Université de Caen Normandie, UMR‐S1077CaenFrance
- Ecole Pratique des Hautes Etudes, UMR‐S1077CaenFrance
- CHU de CaenCaenU1077France
| | - Justine Mutlu
- INSERMCaenU1077France
- Université de Caen Normandie, UMR‐S1077CaenFrance
- Ecole Pratique des Hautes Etudes, UMR‐S1077CaenFrance
- CHU de CaenCaenU1077France
| | - Alexandre Bejanin
- INSERMCaenU1077France
- Université de Caen Normandie, UMR‐S1077CaenFrance
- Ecole Pratique des Hautes Etudes, UMR‐S1077CaenFrance
- CHU de CaenCaenU1077France
| | - Julie Gonneaud
- INSERMCaenU1077France
- Université de Caen Normandie, UMR‐S1077CaenFrance
- Ecole Pratique des Hautes Etudes, UMR‐S1077CaenFrance
- CHU de CaenCaenU1077France
| | - Brigitte Landeau
- INSERMCaenU1077France
- Université de Caen Normandie, UMR‐S1077CaenFrance
- Ecole Pratique des Hautes Etudes, UMR‐S1077CaenFrance
- CHU de CaenCaenU1077France
| | - Clémence Tomadesso
- INSERMCaenU1077France
- Université de Caen Normandie, UMR‐S1077CaenFrance
- Ecole Pratique des Hautes Etudes, UMR‐S1077CaenFrance
- CHU de CaenCaenU1077France
| | - Florence Mézenge
- INSERMCaenU1077France
- Université de Caen Normandie, UMR‐S1077CaenFrance
- Ecole Pratique des Hautes Etudes, UMR‐S1077CaenFrance
- CHU de CaenCaenU1077France
| | - Vincent de La Sayette
- INSERMCaenU1077France
- Université de Caen Normandie, UMR‐S1077CaenFrance
- Ecole Pratique des Hautes Etudes, UMR‐S1077CaenFrance
- CHU de Caen, Service de NeurologieCaenFrance
| | - Francis Eustache
- INSERMCaenU1077France
- Université de Caen Normandie, UMR‐S1077CaenFrance
- Ecole Pratique des Hautes Etudes, UMR‐S1077CaenFrance
- CHU de CaenCaenU1077France
| | - Gaël Chételat
- INSERMCaenU1077France
- Université de Caen Normandie, UMR‐S1077CaenFrance
- Ecole Pratique des Hautes Etudes, UMR‐S1077CaenFrance
- CHU de CaenCaenU1077France
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25
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Madan CR, Kensinger EA. Test-retest reliability of brain morphology estimates. Brain Inform 2017; 4:107-121. [PMID: 28054317 PMCID: PMC5413592 DOI: 10.1007/s40708-016-0060-4] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2016] [Accepted: 12/26/2016] [Indexed: 12/17/2022] Open
Abstract
Metrics of brain morphology are increasingly being used to examine inter-individual differences, making it important to evaluate the reliability of these structural measures. Here we used two open-access datasets to assess the intersession reliability of three cortical measures (thickness, gyrification, and fractal dimensionality) and two subcortical measures (volume and fractal dimensionality). Reliability was generally good, particularly with the gyrification and fractal dimensionality measures. One dataset used a sequence previously optimized for brain morphology analyses and had particularly high reliability. Examining the reliability of morphological measures is critical before the measures can be validly used to investigate inter-individual differences.
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Affiliation(s)
- Christopher R Madan
- Department of Psychology, Boston College, McGuinn 300, 140 Commonwealth Ave., Chestnut Hill, MA, 02467, USA.
| | - Elizabeth A Kensinger
- Department of Psychology, Boston College, McGuinn 300, 140 Commonwealth Ave., Chestnut Hill, MA, 02467, USA
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26
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Klein A, Ghosh SS, Bao FS, Giard J, Häme Y, Stavsky E, Lee N, Rossa B, Reuter M, Chaibub Neto E, Keshavan A. Mindboggling morphometry of human brains. PLoS Comput Biol 2017; 13:e1005350. [PMID: 28231282 PMCID: PMC5322885 DOI: 10.1371/journal.pcbi.1005350] [Citation(s) in RCA: 306] [Impact Index Per Article: 43.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2016] [Accepted: 01/08/2017] [Indexed: 01/01/2023] Open
Abstract
Mindboggle (http://mindboggle.info) is an open source brain morphometry platform that takes in preprocessed T1-weighted MRI data and outputs volume, surface, and tabular data containing label, feature, and shape information for further analysis. In this article, we document the software and demonstrate its use in studies of shape variation in healthy and diseased humans. The number of different shape measures and the size of the populations make this the largest and most detailed shape analysis of human brains ever conducted. Brain image morphometry shows great potential for providing much-needed biological markers for diagnosing, tracking, and predicting progression of mental health disorders. Very few software algorithms provide more than measures of volume and cortical thickness, while more subtle shape measures may provide more sensitive and specific biomarkers. Mindboggle computes a variety of (primarily surface-based) shapes: area, volume, thickness, curvature, depth, Laplace-Beltrami spectra, Zernike moments, etc. We evaluate Mindboggle's algorithms using the largest set of manually labeled, publicly available brain images in the world and compare them against state-of-the-art algorithms where they exist. All data, code, and results of these evaluations are publicly available.
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Affiliation(s)
- Arno Klein
- Child Mind Institute, New York, New York, United States of America
| | - Satrajit S. Ghosh
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Department of Otolaryngology, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Forrest S. Bao
- Department of Electrical and Computer Engineering, University of Akron, Akron, Ohio, United States of America
| | | | - Yrjö Häme
- Columbia University, New York, New York, United States of America
| | - Eliezer Stavsky
- Columbia University, New York, New York, United States of America
| | - Noah Lee
- Columbia University, New York, New York, United States of America
| | - Brian Rossa
- TankThink Labs, Boston, Massachusetts, United States of America
| | - Martin Reuter
- Harvard Medical School, Cambridge, Massachusetts, United States of America
| | | | - Anisha Keshavan
- University of California San Francisco, San Francisco, California, United States of America
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27
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Huntenburg JM, Bazin PL, Goulas A, Tardif CL, Villringer A, Margulies DS. A Systematic Relationship Between Functional Connectivity and Intracortical Myelin in the Human Cerebral Cortex. Cereb Cortex 2017; 27:981-997. [PMID: 28184415 PMCID: PMC5390400 DOI: 10.1093/cercor/bhx030] [Citation(s) in RCA: 146] [Impact Index Per Article: 20.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2016] [Revised: 01/19/2017] [Accepted: 01/20/2017] [Indexed: 11/14/2022] Open
Abstract
Research in the macaque monkey suggests that cortical areas with similar microstructure are more likely to be connected. Here, we examine this link in the human cerebral cortex using 2 magnetic resonance imaging (MRI) measures: quantitative T1 maps, which are sensitive to intracortical myelin content and provide an in vivo proxy for cortical microstructure, and resting-state functional connectivity. Using ultrahigh-resolution MRI at 7 T and dedicated image processing tools, we demonstrate a systematic relationship between T1-based intracortical myelin content and functional connectivity. This effect is independent of the proximity of areas. We employ nonlinear dimensionality reduction to characterize connectivity components and identify specific aspects of functional connectivity that are linked to myelin content. Our results reveal a consistent spatial pattern throughout different analytic approaches. While functional connectivity and myelin content are closely linked in unimodal areas, the correspondence is lower in transmodal areas, especially in posteromedial cortex and the angular gyrus. Our findings are in agreement with comprehensive reports linking histologically assessed microstructure and connectivity in different mammalian species and extend them to the human cerebral cortex in vivo.
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Affiliation(s)
- Julia M. Huntenburg
- Max Planck Research Group for Neuroanatomy and Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
- Neurocomputation and Neuroimaging Unit, Department of Education and Psychology, Free University of Berlin, 14195 Berlin, Germany
| | - Pierre-Louis Bazin
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
| | - Alexandros Goulas
- Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Christine L. Tardif
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
- Cerebral Imaging Centre, Douglas Mental Health University Institute, McGill University 6875, Montreal, QC, Canada H4H 1R3
| | - Arno Villringer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
| | - Daniel S. Margulies
- Max Planck Research Group for Neuroanatomy and Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
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28
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Chou YH, Sundman M, Whitson HE, Gaur P, Chu ML, Weingarten CP, Madden DJ, Wang L, Kirste I, Joliot M, Diaz MT, Li YJ, Song AW, Chen NK. Maintenance and Representation of Mind Wandering during Resting-State fMRI. Sci Rep 2017; 7:40722. [PMID: 28079189 PMCID: PMC5227708 DOI: 10.1038/srep40722] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2016] [Accepted: 12/09/2016] [Indexed: 11/09/2022] Open
Abstract
Major advances in resting-state functional magnetic resonance imaging (fMRI) techniques in the last two decades have provided a tool to better understand the functional organization of the brain both in health and illness. Despite such developments, characterizing regulation and cerebral representation of mind wandering, which occurs unavoidably during resting-state fMRI scans and may induce variability of the acquired data, remains a work in progress. Here, we demonstrate that a decrease or decoupling in functional connectivity involving the caudate nucleus, insula, medial prefrontal cortex and other domain-specific regions was associated with more sustained mind wandering in particular thought domains during resting-state fMRI. Importantly, our findings suggest that temporal and between-subject variations in functional connectivity of above-mentioned regions might be linked with the continuity of mind wandering. Our study not only provides a preliminary framework for characterizing the maintenance and cerebral representation of different types of mind wandering, but also highlights the importance of taking mind wandering into consideration when studying brain organization with resting-state fMRI in the future.
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Affiliation(s)
- Ying-Hui Chou
- Department of Psychology, University of Arizona, Tucson, AZ, USA.,Cognitive Science Program, University of Arizona, Tucson, AZ, USA.,Arizona Center on Aging, University of Arizona, Tucson, AZ, USA
| | - Mark Sundman
- Department of Psychology, University of Arizona, Tucson, AZ, USA
| | - Heather E Whitson
- Department of Medicine and Ophthalmology, Duke University Medical Center, Durham, NC, USA.,Geriatrics Research Education and Clinical Center, Durham Veterans Administration Hospital, Durham, NC, USA
| | - Pooja Gaur
- Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| | - Mei-Lan Chu
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA
| | - Carol P Weingarten
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA
| | - David J Madden
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA.,Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA
| | - Lihong Wang
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA.,Department of Psychiatry, University of Connecticut Health Center, Farmington, CT, USA
| | - Imke Kirste
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA
| | - Marc Joliot
- Neuroimaging Group (GIN), UMR5293, CEA CNRS Université de Bordeaux, Bordeaux, CEDEX, France
| | - Michele T Diaz
- Department of Psychology, Penn State University, University Park, PA, USA
| | - Yi-Ju Li
- Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, USA
| | - Allen W Song
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA.,Department of Radiology, Duke University Medical Center, Durham, NC, USA
| | - Nan-Kuei Chen
- Arizona Center on Aging, University of Arizona, Tucson, AZ, USA.,Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA.,Department of Radiology, Duke University Medical Center, Durham, NC, USA.,Department of Biomedical Engineering, University of Arizona, Tucson, AZ, USA.,Department of Medical Imaging, University of Arizona, Tucson, AZ, USA
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29
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30
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Abstract
Much effort has been made to understand the organizational principles of human brain function using functional magnetic resonance imaging (fMRI) methods, among which resting-state fMRI (rfMRI) is an increasingly recognized technique for measuring the intrinsic dynamics of the human brain. Functional connectivity (FC) with rfMRI is the most widely used method to describe remote or long-distance relationships in studies of cerebral cortex parcellation, interindividual variability, and brain disorders. In contrast, local or short-distance functional interactions, especially at a scale of millimeters, have rarely been investigated or systematically reviewed like remote FC, although some local FC algorithms have been developed and applied to the discovery of brain-based changes under neuropsychiatric conditions. To fill this gap between remote and local FC studies, this review will (1) briefly survey the history of studies on organizational principles of human brain function; (2) propose local functional homogeneity as a network centrality to characterize multimodal local features of the brain connectome; (3) render a neurobiological perspective on local functional homogeneity by linking its temporal, spatial, and individual variability to information processing, anatomical morphology, and brain development; and (4) discuss its role in performing connectome-wide association studies and identify relevant challenges, and recommend its use in future brain connectomics studies.
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Affiliation(s)
- Lili Jiang
- Key Laboratory of Behavioral Science, Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Laboratory for Functional Connectome and Development, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Xi-Nian Zuo
- Key Laboratory of Behavioral Science, Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Laboratory for Functional Connectome and Development, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Faculty of Psychology, Southwest University, Beibei, Chongqing, China
- Department of Psychology, School of Education Science, Guangxi Teachers Education University, Nanning, Guangxi, China
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31
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Budinger TF, Bird MD, Frydman L, Long JR, Mareci TH, Rooney WD, Rosen B, Schenck JF, Schepkin VD, Sherry AD, Sodickson DK, Springer CS, Thulborn KR, Uğurbil K, Wald LL. Toward 20 T magnetic resonance for human brain studies: opportunities for discovery and neuroscience rationale. MAGMA (NEW YORK, N.Y.) 2016; 29:617-39. [PMID: 27194154 PMCID: PMC5538368 DOI: 10.1007/s10334-016-0561-4] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2016] [Revised: 04/06/2016] [Accepted: 04/11/2016] [Indexed: 12/16/2022]
Abstract
An initiative to design and build magnetic resonance imaging (MRI) and spectroscopy (MRS) instruments at 14 T and beyond to 20 T has been underway since 2012. This initiative has been supported by 22 interested participants from the USA and Europe, of which 15 are authors of this review. Advances in high temperature superconductor materials, advances in cryocooling engineering, prospects for non-persistent mode stable magnets, and experiences gained from large-bore, high-field magnet engineering for the nuclear fusion endeavors support the feasibility of a human brain MRI and MRS system with 1 ppm homogeneity over at least a 16-cm diameter volume and a bore size of 68 cm. Twelve neuroscience opportunities are presented as well as an analysis of the biophysical and physiological effects to be investigated before exposing human subjects to the high fields of 14 T and beyond.
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Affiliation(s)
- Thomas F Budinger
- Lawrence Berkeley National Laboratory, University of California, Berkeley, CA, USA.
| | - Mark D Bird
- National High Magnetic Field Laboratory, Florida State University, Tallahassee, FL, USA
| | - Lucio Frydman
- National High Magnetic Field Laboratory, Florida State University, Tallahassee, FL, USA
- Weizmann Institute, Rehovot, Israel
| | - Joanna R Long
- McKnight Brain Institute, University of Florida, Gainesville, FL, USA
| | - Thomas H Mareci
- McKnight Brain Institute, University of Florida, Gainesville, FL, USA
| | | | - Bruce Rosen
- Massachusetts General Hospital, Harvard Medical School, Harvard, MA, USA
| | - John F Schenck
- General Electric Corporate Research, Schenectady, NY, USA
| | - Victor D Schepkin
- National High Magnetic Field Laboratory, Florida State University, Tallahassee, FL, USA
| | - A Dean Sherry
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | | | | | | | | | - Lawrence L Wald
- Massachusetts General Hospital, Harvard Medical School, Harvard, MA, USA
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32
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Chang C, Raven EP, Duyn JH. Brain-heart interactions: challenges and opportunities with functional magnetic resonance imaging at ultra-high field. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2016; 374:rsta.2015.0188. [PMID: 27044994 PMCID: PMC4822447 DOI: 10.1098/rsta.2015.0188] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 02/05/2016] [Indexed: 05/24/2023]
Abstract
Magnetic resonance imaging (MRI) at ultra-high field (UHF) strengths (7 T and above) offers unique opportunities for studying the human brain with increased spatial resolution, contrast and sensitivity. However, its reliability can be compromised by factors such as head motion, image distortion and non-neural fluctuations of the functional MRI signal. The objective of this review is to provide a critical discussion of the advantages and trade-offs associated with UHF imaging, focusing on the application to studying brain-heart interactions. We describe how UHF MRI may provide contrast and resolution benefits for measuring neural activity of regions involved in the control and mediation of autonomic processes, and in delineating such regions based on anatomical MRI contrast. Limitations arising from confounding signals are discussed, including challenges with distinguishing non-neural physiological effects from the neural signals of interest that reflect cardiorespiratory function. We also consider how recently developed data analysis techniques may be applied to high-field imaging data to uncover novel information about brain-heart interactions.
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Affiliation(s)
- Catie Chang
- Advanced Magnetic Resonance Imaging Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
| | - Erika P Raven
- Advanced Magnetic Resonance Imaging Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA Center for Functional and Molecular Imaging, Georgetown University Medical Center, Washington, DC 20007, USA
| | - Jeff H Duyn
- Advanced Magnetic Resonance Imaging Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
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33
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Wu GR, Marinazzo D. Sensitivity of the resting-state haemodynamic response function estimation to autonomic nervous system fluctuations. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2016; 374:rsta.2015.0190. [PMID: 27044997 PMCID: PMC4822449 DOI: 10.1098/rsta.2015.0190] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 02/12/2016] [Indexed: 05/03/2023]
Abstract
The haemodynamic response function (HRF) is a key component of the blood oxygen level-dependent (BOLD) signal, providing the mapping between neural activity and the signal measured with functional magnetic resonance imaging (fMRI). Most of the time the HRF is associated with task-based fMRI protocols, in which its onset is explicitly included in the design matrix. On the other hand, the HRF also mediates the relationship between spontaneous neural activity and the BOLD signal in resting-state protocols, in which no explicit stimulus is taken into account. It has been shown that resting-state brain dynamics can be characterized by looking at sparse BOLD 'events', which can be retrieved by point process analysis. These events can be then used to retrieve the HRF at rest. Crucially, cardiac activity can also induce changes in the BOLD signal, thus affecting both the number of these events and the estimation of the haemodynamic response. In this study, we compare the resting-state haemodynamic response retrieved by means of a point process analysis, taking the cardiac fluctuations into account. We find that the resting-state HRF estimation is significantly modulated in the brainstem and surrounding cortical areas. From the analysis of two high-quality datasets with different temporal and spatial resolution, and through the investigation of intersubject correlation, we suggest that spontaneous point process response durations are associated with the mean interbeat interval and low-frequency power of heart rate variability in the brainstem.
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Affiliation(s)
- Guo-Rong Wu
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing 400715, China Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent 9000, Belgium
| | - Daniele Marinazzo
- Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent 9000, Belgium
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34
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Affiliation(s)
- Franco Pestilli
- Department of Psychological and Brain Sciences, Programs in Neuroscience and Cognitive Science, Indiana University , 1101 E 10th Street, Bloomington, Indiana 47405, USA
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35
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An open science resource for establishing reliability and reproducibility in functional connectomics. Sci Data 2014; 1:140049. [PMID: 25977800 PMCID: PMC4421932 DOI: 10.1038/sdata.2014.49] [Citation(s) in RCA: 246] [Impact Index Per Article: 24.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2014] [Accepted: 10/14/2014] [Indexed: 02/05/2023] Open
Abstract
Efforts to identify meaningful functional imaging-based biomarkers are limited by the ability to reliably characterize inter-individual differences in human brain function. Although a growing number of connectomics-based measures are reported to have moderate to high test-retest reliability, the variability in data acquisition, experimental designs, and analytic methods precludes the ability to generalize results. The Consortium for Reliability and Reproducibility (CoRR) is working to address this challenge and establish test-retest reliability as a minimum standard for methods development in functional connectomics. Specifically, CoRR has aggregated 1,629 typical individuals’ resting state fMRI (rfMRI) data (5,093 rfMRI scans) from 18 international sites, and is openly sharing them via the International Data-sharing Neuroimaging Initiative (INDI). To allow researchers to generate various estimates of reliability and reproducibility, a variety of data acquisition procedures and experimental designs are included. Similarly, to enable users to assess the impact of commonly encountered artifacts (for example, motion) on characterizations of inter-individual variation, datasets of varying quality are included.
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