1
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Kosakowski HL, Saadon-Grosman N, Du J, Eldaief MC, Buckner RL. Human striatal association megaclusters. J Neurophysiol 2024; 131:1083-1100. [PMID: 38505898 DOI: 10.1152/jn.00387.2023] [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: 10/19/2023] [Revised: 03/13/2024] [Accepted: 03/13/2024] [Indexed: 03/21/2024] Open
Abstract
The striatum receives projections from multiple regions of the cerebral cortex consistent with the role of the basal ganglia in diverse motor, affective, and cognitive functions. Within the striatum, the caudate receives projections from association cortex, including multiple distinct regions of prefrontal cortex. Building on recent insights about the details of how juxtaposed cortical networks are specialized for distinct aspects of higher-order cognition, we revisited caudate organization using within-individual precision neuroimaging initially in two intensively scanned individuals (each scanned 31 times). Results revealed that the caudate has side-by-side regions that are coupled to at least five distinct distributed association networks, paralleling the organization observed in the cerebral cortex. We refer to these spatial groupings of regions as striatal association megaclusters. Correlation maps from closely juxtaposed seed regions placed within the megaclusters recapitulated the five distinct cortical networks, including their multiple spatially distributed regions. Striatal association megaclusters were explored in 15 additional participants (each scanned at least 8 times), finding that their presence generalizes to new participants. Analysis of the laterality of the regions within the megaclusters further revealed that they possess asymmetries paralleling their cortical counterparts. For example, caudate regions linked to the language network were left lateralized. These results extend the general notion of parallel specialized basal ganglia circuits with the additional discovery that, even within the caudate, there is fine-grained separation of multiple distinct higher-order networks that reflects the organization and lateralization found in the cerebral cortex.NEW & NOTEWORTHY An individualized precision neuroimaging approach reveals juxtaposed zones of the caudate that are coupled with five distinct networks in association cortex. The organization of these caudate zones recapitulates organization observed in the cerebral cortex and extends the notion of specialized basal ganglia circuits.
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Affiliation(s)
- Heather L Kosakowski
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States
| | - Noam Saadon-Grosman
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States
| | - Jingnan Du
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States
| | - Mark C Eldaief
- Department of Psychiatry, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Department of Neurology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
| | - Randy L Buckner
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States
- Department of Psychiatry, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, United States
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2
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Du J, DiNicola LM, Angeli PA, Saadon-Grosman N, Sun W, Kaiser S, Ladopoulou J, Xue A, Yeo BTT, Eldaief MC, Buckner RL. Organization of the human cerebral cortex estimated within individuals: networks, global topography, and function. J Neurophysiol 2024; 131:1014-1082. [PMID: 38489238 DOI: 10.1152/jn.00308.2023] [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: 08/16/2023] [Revised: 01/18/2024] [Accepted: 02/16/2024] [Indexed: 03/17/2024] Open
Abstract
The cerebral cortex is populated by specialized regions that are organized into networks. Here we estimated networks from functional MRI (fMRI) data in intensively sampled participants. The procedure was developed in two participants (scanned 31 times) and then prospectively applied to 15 participants (scanned 8-11 times). Analysis of the networks revealed a global organization. Locally organized first-order sensory and motor networks were surrounded by spatially adjacent second-order networks that linked to distant regions. Third-order networks possessed regions distributed widely throughout association cortex. Regions of distinct third-order networks displayed side-by-side juxtapositions with a pattern that repeated across multiple cortical zones. We refer to these as supra-areal association megaclusters (SAAMs). Within each SAAM, two candidate control regions were adjacent to three separate domain-specialized regions. Response properties were explored with task data. The somatomotor and visual networks responded to body movements and visual stimulation, respectively. Second-order networks responded to transients in an oddball detection task, consistent with a role in orienting to salient events. The third-order networks, including distinct regions within each SAAM, showed two levels of functional specialization. Regions linked to candidate control networks responded to working memory load across multiple stimulus domains. The remaining regions dissociated across language, social, and spatial/episodic processing domains. These results suggest that progressively higher-order networks nest outward from primary sensory and motor cortices. Within the apex zones of association cortex, there is specialization that repeatedly divides domain-flexible from domain-specialized regions. We discuss implications of these findings, including how repeating organizational motifs may emerge during development.NEW & NOTEWORTHY The organization of cerebral networks was estimated within individuals with intensive, repeat sampling of fMRI data. A hierarchical organization emerged in each individual that delineated first-, second-, and third-order cortical networks. Regions of distinct third-order association networks consistently exhibited side-by-side juxtapositions that repeated across multiple cortical zones, with clear and robust functional specialization among the embedded regions.
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Affiliation(s)
- Jingnan Du
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States
| | - Lauren M DiNicola
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States
| | - Peter A Angeli
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States
| | - Noam Saadon-Grosman
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States
| | - Wendy Sun
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States
| | - Stephanie Kaiser
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States
| | - Joanna Ladopoulou
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States
| | - Aihuiping Xue
- Centre for Sleep & Cognition and Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
| | - B T Thomas Yeo
- Centre for Sleep & Cognition and Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
| | - Mark C Eldaief
- Department of Psychiatry, Massachusetts General Hospital, Charlestown, Massachusetts, United States
| | - Randy L Buckner
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States
- Department of Psychiatry, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, United States
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3
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Norberg J, McMains S, Persson J, Mitchell JP. Frontotemporal contributions to social and non-social semantic judgements. J Neuropsychol 2024; 18:66-80. [PMID: 37255262 DOI: 10.1111/jnp.12328] [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: 10/26/2022] [Revised: 05/22/2023] [Accepted: 05/23/2023] [Indexed: 06/01/2023]
Abstract
Semantic judgements involve the use of general knowledge about the world in specific situations. Such judgements are typically associated with activity in a number of brain regions that include the left inferior frontal gyrus (IFG). However, previous studies showed activity in brain regions associated with mentalizing, including the right temporoparietal junction (TPJ), in semantic judgements that involved social knowledge. The aim of the present study was to investigate if social and non-social semantic judgements are dissociated using a combination of fMRI and repetitive TMS. To study this, we asked participants to estimate the percentage of exemplars in a given category that shared a specified attribute. Categories could be either social (i.e., stereotypes) or non-social (i.e., object categories). As expected, fMRI results (n = 26) showed enhanced activity in the left IFG that was specific to non-social semantic judgements. However, statistical evidence did not support that repetitive TMS stimulation (n = 19) to this brain region specifically disrupted non-social semantic judgements. Also as expected, the right TPJ showed enhanced activity to social semantic judgements. However, statistical evidence did not support that repetitive TMS stimulation to this brain region specifically disrupted social semantic judgements. It is possible that the causal networks involved in social and non-social semantic judgements may be more complex than expected.
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Affiliation(s)
- Joakim Norberg
- Harvard University, Cambridge, Massachusetts, USA
- Uppsala University, Uppsala, Sweden
- Örebro University, Örebro, Sweden
| | | | - Jonas Persson
- Örebro University, Örebro, Sweden
- Karolinska Institute, Stockholm, Sweden
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4
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Saha DK, Silva RF, Baker BT, Saha R, Calhoun VD. dcSBM: A federated constrained source-based morphometry approach for multivariate brain structure mapping. Hum Brain Mapp 2023; 44:5892-5905. [PMID: 37837630 PMCID: PMC10619413 DOI: 10.1002/hbm.26483] [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: 05/16/2023] [Revised: 08/09/2023] [Accepted: 08/29/2023] [Indexed: 10/16/2023] Open
Abstract
The examination of multivariate brain morphometry patterns has gained attention in recent years, especially for their powerful exploratory capabilities in the study of differences between patients and controls. Among the many existing methods and tools for the analysis of brain anatomy based on structural magnetic resonance imaging data, data-driven source-based morphometry (SBM) focuses on the exploratory detection of such patterns. Here, we implement a semi-blind extension of SBM, called constrained source-based morphometry (constrained SBM), which enables the extraction of maximally independent reference-alike sources using the constrained independent component analysis (ICA) approach. To do this, we combine SBM with a set of reference components covering the full brain, derived from a large independent data set (UKBiobank), to provide a fully automated SBM framework. This also allows us to implement a federated version of constrained SBM (cSBM) to allow analysis of data that is not locally accessible. In our proposed decentralized constrained source-based morphometry (dcSBM), the original data never leaves the local site. Each site operates constrained ICA on its private local data using a common distributed computation platform. Next, an aggregator/master node aggregates the results estimated from each local site and applies statistical analysis to estimate the significance of the sources. Finally, we utilize two additional multisite patient data sets to validate our model by comparing the resulting group difference estimates from both cSBM and dcSBM.
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Affiliation(s)
- Debbrata K. Saha
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - Rogers F. Silva
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - Bradley T. Baker
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - Rekha Saha
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - Vince D. Calhoun
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
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5
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Kosakowski HL, Saadon-Grosman N, Du J, Eldaief ME, Buckner RL. Human Striatal Association Megaclusters. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.03.560666. [PMID: 37873093 PMCID: PMC10592903 DOI: 10.1101/2023.10.03.560666] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
The striatum receives projections from multiple regions of the cerebral cortex consistent with its role in diverse motor, affective, and cognitive functions. Supporting cognitive functions, the caudate receives projections from cortical association regions. Building on recent insights about the details of how multiple cortical networks are specialized for distinct aspects of higher-order cognition, we revisited caudate organization using within-individual precision neuroimaging (n=2, each participant scanned 31 times). Detailed analysis revealed that the caudate has side-by-side zones that are coupled to at least Give distinct distributed association networks, paralleling the specialization observed in the cerebral cortex. Examining correlation maps from closely juxtaposed seed regions in the caudate recapitulated the Give distinct cerebral networks including their multiple spatially distributed regions. These results extend the general notion of parallel specialized basal ganglia circuits, with the additional discovery that even within the caudate, there is Gine-grained separation of multiple distinct higher-order networks.
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Affiliation(s)
- Heather L Kosakowski
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Noam Saadon-Grosman
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Jingnan Du
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Mark E Eldaief
- Department of Psychiatry, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Department of Neurology, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Randy L Buckner
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Department of Psychiatry, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
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6
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James SN, Manning EN, Storey M, Nicholas JM, Coath W, Keuss SE, Cash DM, Lane CA, Parker T, Keshavan A, Buchanan SM, Wagen A, Harris M, Malone I, Lu K, Needham LP, Street R, Thomas D, Dickson J, Murray-Smith H, Wong A, Freiberger T, Crutch SJ, Fox NC, Richards M, Barkhof F, Sudre CH, Barnes J, Schott JM. Neuroimaging, clinical and life course correlates of normal-appearing white matter integrity in 70-year-olds. Brain Commun 2023; 5:fcad225. [PMID: 37680671 PMCID: PMC10481255 DOI: 10.1093/braincomms/fcad225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 05/30/2023] [Accepted: 08/17/2023] [Indexed: 09/09/2023] Open
Abstract
We investigate associations between normal-appearing white matter microstructural integrity in cognitively normal ∼70-year-olds and concurrently measured brain health and cognition, demographics, genetics and life course cardiovascular health. Participants born in the same week in March 1946 (British 1946 birth cohort) underwent PET-MRI around age 70. Mean standardized normal-appearing white matter integrity metrics (fractional anisotropy, mean diffusivity, neurite density index and orientation dispersion index) were derived from diffusion MRI. Linear regression was used to test associations between normal-appearing white matter metrics and (i) concurrent measures, including whole brain volume, white matter hyperintensity volume, PET amyloid and cognition; (ii) the influence of demographic and genetic predictors, including sex, childhood cognition, education, socio-economic position and genetic risk for Alzheimer's disease (APOE-ɛ4); (iii) systolic and diastolic blood pressure and cardiovascular health (Framingham Heart Study Cardiovascular Risk Score) across adulthood. Sex interactions were tested. Statistical significance included false discovery rate correction (5%). Three hundred and sixty-two participants met inclusion criteria (mean age 70, 49% female). Higher white matter hyperintensity volume was associated with lower fractional anisotropy [b = -0.09 (95% confidence interval: -0.11, -0.06), P < 0.01], neurite density index [b = -0.17 (-0.22, -0.12), P < 0.01] and higher mean diffusivity [b = 0.14 (-0.10, -0.17), P < 0.01]; amyloid (in men) was associated with lower fractional anisotropy [b = -0.04 (-0.08, -0.01), P = 0.03)] and higher mean diffusivity [b = 0.06 (0.01, 0.11), P = 0.02]. Framingham Heart Study Cardiovascular Risk Score in later-life (age 69) was associated with normal-appearing white matter {lower fractional anisotropy [b = -0.06 (-0.09, -0.02) P < 0.01], neurite density index [b = -0.10 (-0.17, -0.03), P < 0.01] and higher mean diffusivity [b = 0.09 (0.04, 0.14), P < 0.01]}. Significant sex interactions (P < 0.05) emerged for midlife cardiovascular health (age 53) and normal-appearing white matter at 70: marginal effect plots demonstrated, in women only, normal-appearing white matter was associated with higher midlife Framingham Heart Study Cardiovascular Risk Score (lower fractional anisotropy and neurite density index), midlife systolic (lower fractional anisotropy, neurite density index and higher mean diffusivity) and diastolic (lower fractional anisotropy and neurite density index) blood pressure and greater blood pressure change between 43 and 53 years (lower fractional anisotropy and neurite density index), independently of white matter hyperintensity volume. In summary, poorer normal-appearing white matter microstructural integrity in ∼70-year-olds was associated with measures of cerebral small vessel disease, amyloid (in males) and later-life cardiovascular health, demonstrating how normal-appearing white matter can provide additional information to overt white matter disease. Our findings further show that greater 'midlife' cardiovascular risk and higher blood pressure were associated with poorer normal-appearing white matter microstructural integrity in females only, suggesting that women's brains may be more susceptible to the effects of midlife blood pressure and cardiovascular health.
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Affiliation(s)
- Sarah-Naomi James
- MRC Unit for Lifelong Health and Ageing at UCL, Institute of Cardiovascular Science, University College London, London, UK
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Emily N Manning
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Mathew Storey
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Jennifer M Nicholas
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | - William Coath
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Sarah E Keuss
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - David M Cash
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Christopher A Lane
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Thomas Parker
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Ashvini Keshavan
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Sarah M Buchanan
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Aaron Wagen
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
- Centre for Medical Image Computing, University College London, London, UK
| | - Mathew Harris
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Ian Malone
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Kirsty Lu
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Louisa P Needham
- MRC Unit for Lifelong Health and Ageing at UCL, Institute of Cardiovascular Science, University College London, London, UK
| | - Rebecca Street
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - David Thomas
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, London, UK
| | - John Dickson
- Institute of Nuclear Medicine, University College London Hospitals Foundation Trust, London, UK
| | - Heidi Murray-Smith
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCL, Institute of Cardiovascular Science, University College London, London, UK
| | - Tamar Freiberger
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Sebastian J Crutch
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Nick C Fox
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Marcus Richards
- MRC Unit for Lifelong Health and Ageing at UCL, Institute of Cardiovascular Science, University College London, London, UK
| | - Frederik Barkhof
- Centre for Medical Image Computing, University College London, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Carole H Sudre
- MRC Unit for Lifelong Health and Ageing at UCL, Institute of Cardiovascular Science, University College London, London, UK
- Centre for Medical Image Computing, University College London, London, UK
- School of Biomedical Engineering, King’s College, London, UK
| | - Josephine Barnes
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Jonathan M Schott
- MRC Unit for Lifelong Health and Ageing at UCL, Institute of Cardiovascular Science, University College London, London, UK
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
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7
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Du J, DiNicola LM, Angeli PA, Saadon-Grosman N, Sun W, Kaiser S, Ladopoulou J, Xue A, Yeo BTT, Eldaief MC, Buckner RL. Within-Individual Organization of the Human Cerebral Cortex: Networks, Global Topography, and Function. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.08.552437. [PMID: 37609246 PMCID: PMC10441314 DOI: 10.1101/2023.08.08.552437] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
The human cerebral cortex is populated by specialized regions that are organized into networks. Here we estimated networks using a Multi-Session Hierarchical Bayesian Model (MS-HBM) applied to intensively sampled within-individual functional MRI (fMRI) data. The network estimation procedure was initially developed and tested in two participants (each scanned 31 times) and then prospectively applied to 15 new participants (each scanned 8 to 11 times). Detailed analysis of the networks revealed a global organization. Locally organized first-order sensory and motor networks were surrounded by spatially adjacent second-order networks that also linked to distant regions. Third-order networks each possessed regions distributed widely throughout association cortex. Moreover, regions of distinct third-order networks displayed side-by-side juxtapositions with a pattern that repeated similarly across multiple cortical zones. We refer to these as Supra-Areal Association Megaclusters (SAAMs). Within each SAAM, two candidate control regions were typically adjacent to three separate domain-specialized regions. Independent task data were analyzed to explore functional response properties. The somatomotor and visual first-order networks responded to body movements and visual stimulation, respectively. A subset of the second-order networks responded to transients in an oddball detection task, consistent with a role in orienting to salient or novel events. The third-order networks, including distinct regions within each SAAM, showed two levels of functional specialization. Regions linked to candidate control networks responded to working memory load across multiple stimulus domains. The remaining regions within each SAAM did not track working memory load but rather dissociated across language, social, and spatial / episodic processing domains. These results support a model of the cerebral cortex in which progressively higher-order networks nest outwards from primary sensory and motor cortices. Within the apex zones of association cortex there is specialization of large-scale networks that divides domain-flexible from domain-specialized regions repeatedly across parietal, temporal, and prefrontal cortices. We discuss implications of these findings including how repeating organizational motifs may emerge during development.
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Affiliation(s)
- Jingnan Du
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Lauren M DiNicola
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Peter A Angeli
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Noam Saadon-Grosman
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Wendy Sun
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Stephanie Kaiser
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Joanna Ladopoulou
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Aihuiping Xue
- Centre for Sleep & Cognition & Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
| | - B T Thomas Yeo
- Centre for Sleep & Cognition & Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
| | - Mark C Eldaief
- Department of Psychiatry, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Randy L Buckner
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Department of Psychiatry, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
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8
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Butterfield J, Post A, Karton C, Robidoux MA, Gilchrist M, Hoshizaki TB. A video analysis examination of the frequency and type of head impacts for player positions in youth ice hockey and FE estimation of their impact severity. Sports Biomech 2023:1-17. [PMID: 36911883 DOI: 10.1080/14763141.2023.2186941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/14/2023]
Abstract
This research employed head impact frequency and frequency of estimated strain to analyse the influence of player position on brain trauma in U15 and U18 youth ice hockey. The methods involved a video analysis of 30 U15 and 30 U18 games where frequency, type of head impact event, and player position during impact was recorded. These impacts were then simulated in the laboratory using physical reconstructions and finite element modelling to determine the brain strains for each impact category. U15 forwards experienced significantly higher head impact frequencies (139) when compared to defenceman (50), with goalies showing the lowest frequency (6) (p < 0.05). U18 forwards experienced significantly higher head impact frequencies (220) when compared to defenceman (92), with goalies having the least frequent head impacts (4) (p < 0.05). The U15 forwards had a significantly higher frequency of head impacts at the very low and med strains and the U18s had higher frequency of head impacts for the very low and low level strains (p < 0.05). Game rule changes and equipment innovation may be considered to mitigate the increased risk faced by forwards compared to other positions in U15 and U18 youth ice hockey.
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Affiliation(s)
| | - Andrew Post
- Human Kinetics, University of Ottawa, Ottawa, Ontario, Canada.,School of Mechanical and Materials Engineering, University College Dublin, Dublin, Ireland
| | - Clara Karton
- Human Kinetics, University of Ottawa, Ottawa, Ontario, Canada
| | | | - Michael Gilchrist
- Human Kinetics, University of Ottawa, Ottawa, Ontario, Canada.,School of Mechanical and Materials Engineering, University College Dublin, Dublin, Ireland
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9
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Mathies F, Apostolova I, Dierck L, Jacobi J, Kuen K, Sauer M, Schenk M, Klutmann S, Forgács A, Buchert R. Multiple-pinhole collimators improve intra- and between-rater agreement and the certainty of the visual interpretation in dopamine transporter SPECT. EJNMMI Res 2022; 12:51. [PMID: 35976493 PMCID: PMC9385910 DOI: 10.1186/s13550-022-00923-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 08/08/2022] [Indexed: 11/10/2022] Open
Abstract
Background Multiple-pinhole (MPH) collimators improve the resolution–sensitivity trade-off compared to parallel-hole collimators. This study evaluated the impact of MPH collimators on intra- and between-rater agreement, and on the certainty of visual interpretation in dopamine transporter (DAT)-SPECT. Methods The study included 71 patients (62.1 ± 12.7 y). Two SPECT acquisitions were performed in randomized order after a single injection of 182 ± 9 MBq 123I-FP-CIT, one with MPH and one with low-energy–high-resolution–high-sensitivity (LEHRHS) collimators. MPH projections were reconstructed with an iterative 3d Monte Carlo algorithm. LEHRHS projections were reconstructed with filtered backprojection (FBP) or with ordered-subsets expectation–maximization and resolution recovery (OSEM). Images were visually evaluated twice by three independent raters with respect to presence/absence of Parkinson-typical reduction of striatal 123I-FP-CIT uptake using a Likert 6-score (− 3 = clearly normal, …, 3 = clearly reduced). In case of intra-rater discrepancy, an intra-rater consensus was obtained. Intra- and between-rater agreement with respect to the Likert score (6-score and dichotomized score) was characterized by Cohen’s kappa. Results Intra-rater kappa of visual scoring of MPH/LEHRHS-OSEM/LEHRHS-FBP images was 0.84 ± 0.12/0.73 ± 0.06/0.73 ± 0.08 (6-score, mean of three raters) and 1.00 ± 0.00/0.96 ± 0.04/0.97 ± 0.03 (dichotomized score). Between-rater kappa of visual scoring (intra-rater consensus) of MPH/LEHRHS-OSEM/LEHRHS-FBP images was 0.70 ± 0.06/0.63 ± 0.08/0.48 ± 0.05 (6-score, mean of three pairs of raters) and 1.00 ± 0.00/0.92 ± 0.04/0.90 ± 0.06 (dichotomized score). There was a decrease of (negative) Likert scores in normal DAT-SPECT by 0.87 ± 0.18 points from the LEHRHS-OSEM to the MPH setting. The (positive) Likert scores of reduced DAT-SPECT did not change on average. Conclusions MPH collimators improve intra- and between-rater agreement as well as the certainty of the visual interpretation of DAT-SPECT. Supplementary Information The online version contains supplementary material available at 10.1186/s13550-022-00923-w.
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Affiliation(s)
- Franziska Mathies
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany
| | - Ivayla Apostolova
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany
| | - Lena Dierck
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany
| | - Janin Jacobi
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany
| | - Katja Kuen
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany
| | - Markus Sauer
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany
| | - Michael Schenk
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany
| | - Susanne Klutmann
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany
| | | | - Ralph Buchert
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany.
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10
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Reading intervention and neuroplasticity: A systematic review and meta-analysis of brain changes associated with reading intervention. Neurosci Biobehav Rev 2021; 132:465-494. [PMID: 34856223 DOI: 10.1016/j.neubiorev.2021.11.011] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Revised: 11/05/2021] [Accepted: 11/08/2021] [Indexed: 11/22/2022]
Abstract
Behavioral research supports the efficacy of intervention for reading disability, but the brain mechanisms underlying improvement in reading are not well understood. Here, we review 39 neuroimaging studies of reading intervention to characterize links between reading improvement and changes in the brain. We report evidence of changes in activation, connectivity, and structure within the reading network, and right hemisphere, frontal and sub-cortical regions. Our meta-analysis of changes in brain activation from pre- to post- reading intervention in eight studies did not yield any significant effects. Methodological heterogeneity among studies may contribute to the lack of significant meta-analytic findings. Based on our qualitative synthesis, we propose that brain changes in response to intervention should be considered in terms of interactions among distributed cognitive, linguistic and sensory systems, rather than via a "normalized" vs. "compensatory" dichotomy. Further empirical research is needed to identify effects of moderating factors such as features of intervention programs, neuroimaging tasks, and individual differences among participants.
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11
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Meliambro J, Karton C, Cournoyer J, Post A, Hoshizaki TB, Gilchrist MD. Comparison of head impact frequency and magnitude in youth tackle football and ice hockey. Comput Methods Biomech Biomed Engin 2021; 25:936-951. [PMID: 34615414 DOI: 10.1080/10255842.2021.1987420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Repetitive head impacts are a growing concern for youth and adolescent contact sport athletes as they have been linked to long term negative brain health outcomes. Of all contact sports, tackle football and ice hockey have been reported to have the highest incidence of head or brain injury however, each sporting environment is unique with distinct rules and regulations regarding contact and collisions. The purpose of this research was to measure and compare the head impact frequency and estimated magnitude of brain tissue strain, amongst youth tackle football and ice hockey players during game play. Head impact frequency was documented by video analysis of youth tackle football and ice hockey game play. Impact magnitude was determined through physical laboratory reconstructions and finite element modelling to estimate brain tissue strains. Tackle football demonstrated significantly higher impact frequency (P < 0.01) and magnitude of estimated brain tissue strains (P < 0.01) compared to ice hockey. A significantly higher number of higher strain head impacts were documented in tackle football when compared to ice hockey (P < 0.01). These differences suggest that youth football players may experience increased frequency and magnitude of estimated brain tissue strains in comparison to youth hockey.
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Affiliation(s)
- Julia Meliambro
- School of Human Kinetics, University of Ottawa, Ottawa, Canada
| | - Clara Karton
- School of Human Kinetics, University of Ottawa, Ottawa, Canada
| | - Janie Cournoyer
- School of Human Kinetics, University of Ottawa, Ottawa, Canada
| | - Andrew Post
- School of Human Kinetics, University of Ottawa, Ottawa, Canada.,School of Mechanical & Materials Engineering, University College Dublin, Dublin, Ireland
| | | | - Michael D Gilchrist
- School of Mechanical & Materials Engineering, University College Dublin, Dublin, Ireland
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12
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Paret C, Niedtfeld I, Lotter T, Wunder A, Grimm S, Mennes M, Okell T, Beckmann C, Schmahl C. Single-Dose Effects of Citalopram on Neural Responses to Affective Stimuli in Borderline Personality Disorder: A Randomized Clinical Trial. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2021; 6:837-845. [PMID: 33607327 DOI: 10.1016/j.bpsc.2021.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 01/22/2021] [Accepted: 02/05/2021] [Indexed: 10/22/2022]
Abstract
BACKGROUND Psychiatric medication that has a soothing effect on limbic responses to affective stimuli could improve affective instability symptoms as observed in borderline personality disorder (BPD). The objective of this study was to investigate whether citalopram versus placebo reduces the response of the affective neural circuitry during an emotional challenge. METHODS A total of 30 female individuals with a BPD diagnosis participated in a placebo-controlled, double-blind crossover trial design. Three hours after oral drug intake, individuals with BPD viewed affective pictures while undergoing functional magnetic resonance imaging. Blood oxygen level-dependent responses to images of negative affective scenes and faces showing negative emotional expressions were assessed in regions of interest (amygdala, anterior cingulate cortex, anterior insula, dorsolateral prefrontal cortex). Blood perfusion at rest was assessed with arterial spin labeling. RESULTS The neural response to pictures showing negative affective scenes was not significantly affected by citalopram (n = 23). Citalopram significantly reduced the amygdala response to pictures of faces with negative affective expressions (n = 25, treatment difference left hemisphere: -0.06 ± 0.16, p < .05; right hemisphere: -0.06 ± 0.17, p < .05). We observed no significant effects of citalopram on the other regions. The drug did not significantly alter blood perfusion at rest. CONCLUSIONS Citalopram can alter the amygdala response to affective stimuli in BPD, which is characterized by overly responsive affective neural circuitry.
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Affiliation(s)
- Christian Paret
- Department of Psychosomatic Medicine and Psychotherapy, Central Institute of Mental Health Mannheim, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany; Sagol Brain Institute, Wohl Institute for Advanced Imaging, Tel-Aviv Sourasky Medical Center and School of Psychological Sciences, Tel-Aviv University, Israel.
| | - Inga Niedtfeld
- Department of Psychosomatic Medicine and Psychotherapy, Central Institute of Mental Health Mannheim, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany
| | - Tobias Lotter
- Department of Psychosomatic Medicine and Psychotherapy, Central Institute of Mental Health Mannheim, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany
| | - Andreas Wunder
- Translational Medicine and Clinical Pharmacology, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
| | - Simone Grimm
- MSB Medical School Berlin, Hochschule für Gesundheit und Medizin, Berlin, Germany
| | | | - Thomas Okell
- SBGneuro Ltd., Oxford, United Kingdom; Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | | | - Christian Schmahl
- Department of Psychosomatic Medicine and Psychotherapy, Central Institute of Mental Health Mannheim, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany.
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13
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Xue A, Kong R, Yang Q, Eldaief MC, Angeli PA, DiNicola LM, Braga RM, Buckner RL, Yeo BTT. The detailed organization of the human cerebellum estimated by intrinsic functional connectivity within the individual. J Neurophysiol 2020; 125:358-384. [PMID: 33427596 PMCID: PMC7948146 DOI: 10.1152/jn.00561.2020] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
Distinct regions of the cerebellum connect to separate regions of the cerebral cortex forming a complex topography. Although cerebellar organization has been examined in group-averaged data, study of individuals provides an opportunity to discover features that emerge at a higher spatial resolution. Here, functional connectivity MRI was used to examine the cerebellum of two intensively sampled individuals (each scanned 31 times). Connectivity to somatomotor cortex showed the expected crossed laterality and topography of the body maps. A surprising discovery was connectivity to the primary visual cortex along the vermis with evidence for representation of the central field. Within the hemispheres, each individual displayed a hierarchical progression from the inverted anterior lobe somatomotor map through to higher-order association zones. The hierarchy ended at Crus I/II and then progressed in reverse order through to the upright somatomotor map in the posterior lobe. Evidence for a third set of networks was found in the most posterior extent of the cerebellum. Detailed analysis of the higher-order association networks revealed robust representations of two distinct networks linked to the default network, multiple networks linked to cognitive control, as well as a separate representation of a language network. Although idiosyncratic spatial details emerged between subjects, each network could be detected in both individuals, and seed regions placed within the cerebellum recapitulated the full extent of the spatially specific cerebral networks. The observation of multiple networks in juxtaposed regions at the Crus I/II apex confirms the importance of this zone to higher-order cognitive function and reveals new organizational details.NEW & NOTEWORTHY Stable, within-individual maps of cerebellar organization reveal orderly macroscale representations of the cerebral cortex with local juxtaposed zones representing distinct networks. In addition, individuals reveal idiosyncratic organizational features.
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Affiliation(s)
- Aihuiping Xue
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.,Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore, Singapore.,N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
| | - Ru Kong
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.,Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore, Singapore.,N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
| | - Qing Yang
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.,Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore, Singapore.,N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
| | - Mark C Eldaief
- Department of Psychiatry, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Peter A Angeli
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts
| | - Lauren M DiNicola
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts
| | - Rodrigo M Braga
- Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Randy L Buckner
- Department of Psychiatry, Massachusetts General Hospital, Charlestown, Massachusetts.,Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.,Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore, Singapore.,N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, Singapore
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14
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Braga RM, DiNicola LM, Becker HC, Buckner RL. Situating the left-lateralized language network in the broader organization of multiple specialized large-scale distributed networks. J Neurophysiol 2020; 124:1415-1448. [PMID: 32965153 PMCID: PMC8356783 DOI: 10.1152/jn.00753.2019] [Citation(s) in RCA: 91] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Using procedures optimized to explore network organization within the individual, the topography of a candidate language network was characterized and situated within the broader context of adjacent networks. The candidate network was first identified using functional connectivity and replicated across individuals, acquisition tasks, and analytical methods. In addition to classical language regions near the perisylvian cortex and temporal pole, regions were also observed in dorsal posterior cingulate, midcingulate, and anterior superior frontal and inferior temporal cortex. The candidate network was selectively activated when processing meaningful (as contrasted with nonword) sentences, whereas spatially adjacent networks showed minimal or even decreased activity. Results were replicated and triplicated across two prospectively acquired cohorts. Examined in relation to adjacent networks, the topography of the language network was found to parallel the motif of other association networks, including the transmodal association networks linked to theory of mind and episodic remembering (often collectively called the default network). The several networks contained juxtaposed regions in multiple association zones. Outside of these juxtaposed higher-order networks, we further noted a distinct frontotemporal network situated between language regions and a frontal orofacial motor region and a temporal auditory region. A possibility is that these functionally related sensorimotor regions might anchor specialization of neighboring association regions that develop into a language network. What is most striking is that the canonical language network appears to be just one of multiple similarly organized, differentially specialized distributed networks that populate the evolutionarily expanded zones of human association cortex. NEW & NOTEWORTHY This research shows that a language network can be identified within individuals using functional connectivity. Organizational details reveal that the language network shares a common spatial motif with other association networks, including default and frontoparietal control networks. The language network is activated by language task demands, whereas closely juxtaposed networks are not, suggesting that similarly organized but differentially specialized distributed networks populate association cortex.
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Affiliation(s)
- Rodrigo M Braga
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts.,Department of Neurology and Neurological Sciences, Stanford University, Stanford, California.,The Computational, Cognitive, and Clinical Neuroimaging Laboratory, Hammersmith Hospital Campus, Imperial College London, London, United Kingdom.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Lauren M DiNicola
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts
| | - Hannah C Becker
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts
| | - Randy L Buckner
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Department of Radiology, Harvard Medical School, Boston, Massachusetts.,Department of Psychiatry, Massachusetts General Hospital, Charlestown, Massachusetts
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15
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Tecklenburg K, Forgács A, Apostolova I, Lehnert W, Klutmann S, Csirik J, Garutti E, Buchert R. Performance evaluation of a novel multi-pinhole collimator for dopamine transporter SPECT. Phys Med Biol 2020; 65:165015. [PMID: 32369781 DOI: 10.1088/1361-6560/ab9067] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
There is a tradeoff between spatial resolution and count sensitivity in SPECT with conventional collimators. Multi-pinhole (MPH) collimator technology has potential for concurrent improvement of resolution and sensitivity in clinical SPECT of 'small' organs. This study evaluated a novel MPH collimator specifically designed for dopamine transporter (DAT) SPECT with a triple-head SPECT camera. Count sensitivity was measured with a 99mTc point source placed on the lattice points of a 1 cm grid covering the whole field-of-view (FOV). Spatial resolution was assessed with a Derenzo type hot rod phantom. An anthropomorphic striatum phantom was scanned with total activity representative of a typical patient scan and different striatum-to-background activity concentration ratios. Recovery of striatum-to-background contrast was assessed by the contrast-recovery-coefficient. Measurements were repeated with double-head SPECT with fan-beam or low-energy-high-resolution-high-sensitivity (LEHRHS) collimators. A patient referred to DAT SPECT because of suspicion of Parkinson's disease was scanned with both LEHRHS and MPH collimators after a single tracer injection. The axial MPH sensitivity profile was approximately symmetrical around its peak, although it was shifted 7 cm towards the patient to simplify positioning. Peak sensitivity of the triple-head MPH system in the center of the FOV was 620 cps MBq-1 compared to 225 cps MBq-1 for the double-head fan-beam system. Sensitivity of the MPH system decreased towards the edges of the FOV. The full width of the sensitivity profile at 200 cps MBq-1 was 21 cm transaxially and 11 cm axially. In MPH SPECT of the Derenzo phantom all rods with ≥ 5 mm diameter were clearly visible. MPH SPECT improved striatal contrast recovery by ≥ 20% compared to fan-beam SPECT. The patient scan demonstrated good image quality of MPH SPECT with almost PET-like delineation of putamen and caudate nucleus. SPECT with dedicated MPH collimators provides considerable improvement of the resolution-sensitivity tradeoff in DAT SPECT compared to SPECT with fan-beam or LEHRHS collimators.
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Affiliation(s)
- K Tecklenburg
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany. Institute of Experimental Physics, Faculty of Mathematics, Informatics and Natural Sciences, University of Hamburg, Hamburg, Germany
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16
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Post A, Karton C, Thevenot O, Hoshizaki TB, Robidoux M, Gilchrist MD. Comparison of frequency and magnitude of head impacts experienced by Peewee boys and girls in games of youth ice hockey. Comput Methods Biomech Biomed Engin 2020; 24:1-13. [PMID: 32787715 DOI: 10.1080/10255842.2020.1805442] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
In youth ice hockey, girls are reported to suffer more concussions than boys, peaking around 13-14 years old, which may be related to differences in the level of brain trauma experienced by the players. The purpose of this research was to describe the differences in brain trauma characteristics, specifically the magnitude and frequency of head impacts between Peewee boys and girls from playing ice hockey. Thirty games of Peewee boys and Peewee girl's ice hockey were recorded to document the head impact events. These events were reconstructed using physical and computational techniques to estimate the strain to the brain tissue. The results found that Peewee boys experienced more head impacts than girls, specifically from the shoulder, ice, boards, and fist/punches (p < 0.05). The boys also experienced more medium strain category impacts (p < 0.05). These results identify that Peewee boys and girls engage in ice hockey differently, which affects the risk of brain trauma likely to be encountered while during game play, suggesting that the increased rate of concussion for girls may not be related to impact magnitudes within the sport but increased reporting of symptoms of concussion or gender differences in brain tissue response to an impact.
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Affiliation(s)
- Andrew Post
- Human Kinetics, University of Ottawa, Ottawa, Canada.,School of Mechanical & Materials Engineering, University College Dublin, Dublin, Ireland
| | - Clara Karton
- Human Kinetics, University of Ottawa, Ottawa, Canada
| | | | | | | | - Michael D Gilchrist
- Human Kinetics, University of Ottawa, Ottawa, Canada.,School of Mechanical & Materials Engineering, University College Dublin, Dublin, Ireland
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17
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Dawson L, Koncan D, Post A, Zemek R, Gilchrist MD, Marshall S, Hoshizaki TB. Biomechanical Comparison of Real World Concussive Impacts in Children, Adolescents, and Adults. J Biomech Eng 2020; 142:1072288. [PMID: 31891370 DOI: 10.1115/1.4045808] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2019] [Indexed: 11/08/2022]
Abstract
Accidental falls occur to people of all ages, with some resulting in concussive injury. At present, it is unknown whether children and adolescents are at a comparable risk of sustaining a concussion compared to adults. This study reconstructed the impact kinematics of concussive falls for children, adolescents, and adults and simulated the associated brain tissue deformations. Patients included in this study were diagnosed with a concussion as defined by the Zurich Consensus guidelines. Eleven child, 10 adolescent, and 11 adult falls were simulated using mathematical dynamic models(MADYMO), with three ellipsoid pedestrian models sized to each age group. Laboratory impact reconstruction was conducted using Hybrid III head forms, with finite element model simulations of the brain tissue response using recorded impact kinematics from the reconstructions. The results of the child group showed lower responses than the adolescent group for impact variables of impact velocity, peak linear acceleration, and peak rotational acceleration but no statistical differences existed for any other groups. Finite element model simulations showed the child group to have lower strain values than both the adolescent and adult groups. There were no statistical differences between the adolescent and adult groups for any variables examined in this study. With the cases included in this study, young children sustained concussive injuries at lower modeled brain strains than adolescents and adults, supporting the theory that children may be more susceptible to concussive impacts than adolescents or adults.
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Affiliation(s)
- Lauren Dawson
- Department of Pediatrics, Division of Emergency Medicine, Children's Hospital of Eastern Ontario Research Institute, 401 Smyth Ave- Rm R139, Ottawa, ON K1H 8L1, Canada; Human Kinetics, University of Ottawa, Ottawa, ON K1N 6N5, Canada
| | - David Koncan
- Human Kinetics, University of Ottawa, Ottawa, ON K1N 6N5, Canada
| | - Andrew Post
- Department of Pediatrics, Division of Emergency Medicine, Children's Hospital of Eastern Ontario Research Institute, 401 Smyth Ave- Rm R139, Ottawa, ON K1H 8L1, Canada; Human Kinetics, University of Ottawa, Ottawa, ON K1N 6N5, Canada
| | - Roger Zemek
- Department of Pediatrics, Division of Emergency Medicine, Children's Hospital of Eastern Ontario Research Institute, 401 Smyth Ave- Rm R139, Ottawa, ON K1H 8L1, Canada
| | - Michael D Gilchrist
- School of Mechanical & Materials Engineering, University College Dublin, Dublin 4, Ireland
| | - Shawn Marshall
- Department Head, Physical Medicine and Rehabilitation Bruyere Continuing Care, Ottawa Hospital Research Institute, Ottawa, ON K1Y 4E9, Canada
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18
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Chen W, Post A, Karton C, Gilchrist MD, Robidoux M, Hoshizaki TB. A comparison of frequency and magnitude of head impacts between Pee Wee And Bantam youth ice hockey. Sports Biomech 2020; 22:728-751. [PMID: 32538288 DOI: 10.1080/14763141.2020.1754450] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
The purpose of this research was to compare the frequency and magnitude of head impact events between Pee Wee and Bantam ice hockey players. Videos of Pee Wee and Bantam boys' ice hockey were analysed to determine the frequency and type of head impact events. The head impact events were then reconstructed in the laboratory using physical and finite element models to determine the magnitude of strain in the brain tissues. The results showed that Pee Wee boys experienced more head impacts from elbows and boards, while Bantam players had more head impacts to the glass. Pee Wee and Bantam players experienced similar frequency and magnitudes of very low, low, and medium and above (med+) levels of strain to the brain. This research suggests to ice hockey leagues and coaches that to reduce the incidence of these levels of brain trauma, consideration must be given to either reducing the level of contact along the boards or the removal of body checking. In addition, companies who innovate in ice hockey should develop protective devices and equipment strategies that aim to reduce the risk of head injury from shoulder and glass impacts for Bantam players.
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Affiliation(s)
- Wesley Chen
- Human Kinetics, University of Ottawa, Ottawa, Ontario, Canada
| | - Andrew Post
- Human Kinetics, University of Ottawa, Ottawa, Ontario, Canada
- School of Mechanical and Materials Engineering, University College Dublin, Dublin, Ireland
| | - Clara Karton
- Human Kinetics, University of Ottawa, Ottawa, Ontario, Canada
| | - Michael D. Gilchrist
- School of Mechanical and Materials Engineering, University College Dublin, Dublin, Ireland
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19
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Cerebellar Contributions to Proactive and Reactive Control in the Stop Signal Task: A Systematic Review and Meta-Analysis of Functional Magnetic Resonance Imaging Studies. Neuropsychol Rev 2020; 30:362-385. [DOI: 10.1007/s11065-020-09432-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2018] [Accepted: 02/17/2020] [Indexed: 01/10/2023]
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20
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Rochefort C, Cournoyer J, Post A, Hoshizaki TB, Zemek R, Sveistrup H. Brain tissue strain and balance impairments in children following a concussion: An exploratory study. JOURNAL OF CONCUSSION 2019. [DOI: 10.1177/2059700219889233] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Background Balance impairments present in approximately 30% of concussion cases. Biomechanical reconstructions model the degree and location of brain tissue strain associated with injury. The objective was to examine the relationship between the magnitude and location of brain tissue strain and balance impairment following a concussion. Methods Children one month post-concussion (n = 33) and non-injured children (n = 33) completed two balance conditions while standing on a Wii Balance Board that recorded the centre of pressure during (i) double-leg stance with eyes closed (EC) and (ii) dual-task (DT) combining double-leg stance while completing a cognitive task. Injury reconstructions were performed for 10 of the concussed participants. A 5th percentile Hybrid III headform was used to obtain linear and rotational acceleration time-curves of the head impact. These data were input in the University College Dublin Brain Trauma Model (UCDBTM) to calculate maximum principal strains and cumulative strain damage values at 10% (CSDM-10) and 20% (CSDM-20) for different brain regions. Correlations between balance and reconstruction variables were calculated. Results Out of the 10 reconstructed cases, six participants had impaired balance on the EC condition, six had impaired balance on the DT condition and four had impaired balance on both the EC and DT conditions. For maximum principal strain values, correlations with balance variables ranged from −0.0190 to 0.394 for the DT condition and from −0.225 and 0.152 for the EC condition. For CSDM-10 values, correlations with balance variables ranged from 0.280 to 0.386 for the DT condition and from −0.103 to 0.252 for the EC condition. For CSDM-20 values, correlations with balance variables ranged from 0.0629 to 0.289 for the DT condition and from −0.353 to −0.155 for the EC condition. Conclusions Although a subset of the concussed participants continued to show balance impairments, no association was established between the presence of balance impairment and the magnitude and/or location of brain tissue strain. Maintaining balance is a complex process integrated into multiple subcortical regions, white matter tracts and cranial nerves, which were not represented in the brain model, and as a result the UCDBTM may not be sensitive to damage in these areas.
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Affiliation(s)
- Coralie Rochefort
- School of Human Kinetics, Faculty of Health Sciences, University of Ottawa, Ottawa, Canada
| | - Janie Cournoyer
- School of Human Kinetics, Faculty of Health Sciences, University of Ottawa, Ottawa, Canada
| | - Andrew Post
- School of Human Kinetics, Faculty of Health Sciences, University of Ottawa, Ottawa, Canada
| | - T Blaine Hoshizaki
- School of Human Kinetics, Faculty of Health Sciences, University of Ottawa, Ottawa, Canada
| | - Roger Zemek
- Children’s Hospital of Eastern Ontario Research Institute, Ottawa, Canada
| | - Heidi Sveistrup
- School of Human Kinetics, Faculty of Health Sciences, University of Ottawa, Ottawa, Canada
- School of Rehabilitation Sciences, Faculty of Health Sciences, University of Ottawa, Ottawa, Canada
- Bruyère Research Institute, Ottawa, Canada
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21
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Braga RM, Van Dijk KRA, Polimeni JR, Eldaief MC, Buckner RL. Parallel distributed networks resolved at high resolution reveal close juxtaposition of distinct regions. J Neurophysiol 2019; 121:1513-1534. [PMID: 30785825 PMCID: PMC6485740 DOI: 10.1152/jn.00808.2018] [Citation(s) in RCA: 75] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
Examination of large-scale distributed networks within the individual reveals details of cortical network organization that are absent in group-averaged studies. One recent discovery is that a distributed transmodal network, often referred to as the “default network,” comprises two closely interdigitated networks, only one of which is coupled to posterior parahippocampal cortex. Not all studies of individuals have identified the same networks, and questions remain about the degree to which the two networks are separate, particularly within regions hypothesized to be interconnected hubs. In this study we replicate the observation of network separation across analytical (seed-based connectivity and parcellation) and data projection (volume and surface) methods in two individuals each scanned 31 times. Additionally, three individuals were examined with high-resolution (7T; 1.35 mm) functional magnetic resonance imaging to gain further insight into the anatomical details. The two networks were identified with separate regions localized to adjacent portions of the cortical ribbon, sometimes inside the same sulcus. Midline regions previously implicated as hubs revealed near complete spatial separation of the two networks, displaying a complex spatial topography in the posterior cingulate and precuneus. The network coupled to parahippocampal cortex also revealed a separate region directly within the hippocampus, at or near the subiculum. These collective results support that the default network is composed of at least two spatially juxtaposed networks. Fine spatial details and juxtapositions of the two networks can be identified within individuals at high resolution, providing insight into the network organization of association cortex and placing further constraints on interpretation of group-averaged neuroimaging data. NEW & NOTEWORTHY Recent evidence has emerged that canonical large-scale networks such as the “default network” fractionate into parallel distributed networks when defined within individuals. This research uses high-resolution imaging to show that the networks possess juxtapositions sometimes evident inside the same sulcus and within regions that have been previously hypothesized to be network hubs. Distinct circumscribed regions of one network were also resolved in the hippocampal formation, at or near the parahippocampal cortex and subiculum.
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Affiliation(s)
- Rodrigo M Braga
- Department of Psychology, Center for Brain Science, Harvard University , Cambridge, Massachusetts.,The Computational, Cognitive & Clinical Neuroimaging Laboratory, Hammersmith Hospital Campus, Imperial College London , London , United Kingdom.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Koene R A Van Dijk
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Department of Radiology, Harvard Medical School , Boston, Massachusetts.,Division of Health Sciences and Technology, Massachusetts Institute of Technology , Cambridge, Massachusetts
| | - Mark C Eldaief
- Department of Psychology, Center for Brain Science, Harvard University , Cambridge, Massachusetts.,Department of Psychiatry, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Randy L Buckner
- Department of Psychology, Center for Brain Science, Harvard University , Cambridge, Massachusetts.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Department of Radiology, Harvard Medical School , Boston, Massachusetts.,Department of Psychiatry, Massachusetts General Hospital, Charlestown, Massachusetts
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22
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Volz S, Callaghan MF, Josephs O, Weiskopf N. Maximising BOLD sensitivity through automated EPI protocol optimisation. Neuroimage 2018; 189:159-170. [PMID: 30593904 PMCID: PMC6435104 DOI: 10.1016/j.neuroimage.2018.12.052] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Revised: 12/23/2018] [Accepted: 12/24/2018] [Indexed: 11/23/2022] Open
Abstract
Gradient echo echo-planar imaging (GE EPI) is used for most fMRI studies but can suffer substantially from image distortions and BOLD sensitivity (BS) loss due to susceptibility-induced magnetic field inhomogeneities. While there are various post-processing methods for correcting image distortions, signal dropouts cannot be recovered and therefore need to be addressed at the data acquisition stage. Common approaches for reducing susceptibility-related BS loss in selected brain areas are: z-shimming, inverting the phase encoding (PE) gradient polarity, optimizing the slice tilt and increasing spatial resolution. The optimization of these parameters can be based on atlases derived from multiple echo-planar imaging (EPI) acquisitions. However, this requires resource and time, which imposes a practical limitation on the range over which parameters can be optimised meaning that the chosen settings may still be sub-optimal. To address this issue, we have developed an automated method that can be used to optimize across a large parameter space. It is based on numerical signal simulations of the BS loss predicted by physical models informed by a large database of magnetic field (B0) maps acquired on a broad cohort of participants. The advantage of our simulation-based approach compared to previous methods is that it saves time and expensive measurements and allows for optimizing EPI protocols by incorporating a broad range of factors, including different resolutions, echo times or slice orientations. To verify the numerical optimisation, results are compared to those from an earlier study and to experimental BS measurements carried out in six healthy volunteers. Significant BOLD sensitivity increase by optimization of z-shim, gradient polarity and slice tilt. Method based on numerical signal simulations informed by large database of magnetic field maps. Saves time and expensive measurements. Automated and flexible optimization of multiple EPI parameter settings.
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Affiliation(s)
- Steffen Volz
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, University College London, London, UK.
| | - Martina F Callaghan
- Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, University College London, London, UK
| | - Oliver Josephs
- Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, University College London, London, UK
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, University College London, London, UK
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23
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Imhof MA, Schmälzle R, Renner B, Schupp HT. How real-life health messages engage our brains: Shared processing of effective anti-alcohol videos. Soc Cogn Affect Neurosci 2018; 12:1188-1196. [PMID: 28402568 PMCID: PMC5490672 DOI: 10.1093/scan/nsx044] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2016] [Accepted: 03/20/2017] [Indexed: 11/14/2022] Open
Abstract
Health communication via mass media is an important strategy when targeting risky drinking, but many questions remain about how health messages are processed and how they unfold their effects within receivers. Here we examine how the brains of young adults-a key target group for alcohol prevention-'tune in' to real-life health prevention messages about risky alcohol use. In a first study, a large sample of authentic public service announcements (PSAs) targeting the risks of alcohol was characterized using established measures of message effectiveness. In the main study, we used inter-subject correlation analysis of fMRI data to examine brain responses to more and less effective PSAs in a sample of young adults. We find that more effective messages command more similar responses within widespread brain regions, including the dorsomedial prefrontal cortex, insulae and precuneus. In previous research, these regions have been related to processing narratives, emotional stimuli, self-relevance and attention towards salient stimuli. The present study thus suggests that more effective health prevention messages have greater 'neural reach', i.e. they engage the brains of audience members' more widely. This work outlines a promising strategy for assessing the effects of health communication at a neural level.
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Affiliation(s)
- Martin A Imhof
- Department of Psychology, University of Konstanz, 78457 Konstanz, Germany
| | - Ralf Schmälzle
- Department of Psychology, University of Konstanz, 78457 Konstanz, Germany.,Department of Communication, Michigan State University, East Lansing, MI 48824, USA
| | - Britta Renner
- Department of Psychology, University of Konstanz, 78457 Konstanz, Germany
| | - Harald T Schupp
- Department of Psychology, University of Konstanz, 78457 Konstanz, Germany
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24
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Shi Y, Vannesjo SJ, Miller KL, Clare S. Template-based field map prediction for rapid whole brain B 0 shimming. Magn Reson Med 2017; 80:171-180. [PMID: 29193340 PMCID: PMC5900895 DOI: 10.1002/mrm.27020] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Revised: 10/29/2017] [Accepted: 10/30/2017] [Indexed: 11/21/2022]
Abstract
Purpose In typical MRI protocols, time is spent acquiring a field map to calculate the shim settings for best image quality. We propose a fast template‐based field map prediction method that yields near‐optimal shims without measuring the field. Methods The template‐based prediction method uses prior knowledge of the B0 distribution in the human brain, based on a large database of field maps acquired from different subjects, together with subject‐specific structural information from a quick localizer scan. The shimming performance of using the template‐based prediction is evaluated in comparison to a range of potential fast shimming methods. Results Static B0 shimming based on predicted field maps performed almost as well as shimming based on individually measured field maps. In experimental evaluations at 7 T, the proposed approach yielded a residual field standard deviation in the brain of on average 59 Hz, compared with 50 Hz using measured field maps and 176 Hz using no subject‐specific shim. Conclusions This work demonstrates that shimming based on predicted field maps is feasible. The field map prediction accuracy could potentially be further improved by generating the template from a subset of subjects, based on parameters such as head rotation and body mass index. Magn Reson Med 80:171–180, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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Affiliation(s)
- Yuhang Shi
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom
| | - S Johanna Vannesjo
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom
| | - Stuart Clare
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom
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25
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Parallel Interdigitated Distributed Networks within the Individual Estimated by Intrinsic Functional Connectivity. Neuron 2017; 95:457-471.e5. [PMID: 28728026 PMCID: PMC5519493 DOI: 10.1016/j.neuron.2017.06.038] [Citation(s) in RCA: 335] [Impact Index Per Article: 47.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2017] [Revised: 04/28/2017] [Accepted: 06/23/2017] [Indexed: 12/16/2022]
Abstract
Certain organizational features of brain networks present in the individual are lost when central tendencies are examined in the group. Here we investigated the detailed network organization of four individuals each scanned 24 times using MRI. We discovered that the distributed network known as the default network is comprised of two separate networks possessing adjacent regions in eight or more cortical zones. A distinction between the networks is that one is coupled to the hippocampal formation while the other is not. Further exploration revealed that these two networks were juxtaposed with additional networks that themselves fractionate group-defined networks. The collective networks display a repeating spatial progression in multiple cortical zones, suggesting that they are embedded within a broad macroscale gradient. Regions contributing to the newly defined networks are spatially variable across individuals and adjacent to distinct networks, raising issues for network estimation in group-averaged data and applied endeavors, including targeted neuromodulation. Within-individual characterization of brain networks reveals new spatial details Group-defined networks fractionate into distinct parallel networks in individuals Parallel networks possess closely juxtaposed regions in numerous cortical zones Networks share a conserved motif that may be organized along a macroscale gradient
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26
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Post A, Hoshizaki TB, Zemek R, Gilchrist MD, Koncan D, Dawson L, Chen W, Ledoux AA. Pediatric concussion: biomechanical differences between outcomes of transient and persistent (> 4 weeks) postconcussion symptoms. J Neurosurg Pediatr 2017; 19:641-651. [PMID: 28347202 DOI: 10.3171/2016.11.peds16383] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Currently, little is known about the biomechanics of head impact for concussion in youths (ages 5 to 18 years). Even less is known about the biomechanical characteristics and variables related to head impacts that may be useful in differentiating between transient and persistent postconcussion symptoms in a youth population. The purpose of this research was to examine the differences in biomechanics of youth head impact for transient postconcussion symptoms (TPCSs) and persistent postconcussion symptoms (PPCSs) by using data from a hospital population. METHODS In a laboratory setting and using physical, computational, and finite element models, the authors reconstructed falling events in a large cohort of patients who had sustained a brain injury that resulted in transient or persistent postconcussion symptoms. The falling events and resulting concussions for the TPCS and PPCS patient groups were analyzed in terms of force, energy, peak resultant linear and rotational accelerations, and maximum principal strain in the gray and white matter of the brain, as well as measurements of cumulative strain damage. RESULTS The results indicated that there were no significant differences between the groups for any of the variables analyzed. CONCLUSIONS With methods derived for use in an adult population, the magnitudes of peak linear acceleration for the youth data set were determined to be above the 50% risk of injury. The youth data set showed higher brain tissue strain responses for lower energy and impact velocities than measured in adults, suggesting that youths are at higher risk of concussive injury at lower event severities. A trend shown by some variables indicated that larger magnitudes of response were associated with PPCSs, but no single measurement variable consistently differentiated between the TPCS and PPCS groups. It is possible that using the biomechanics of head and brain responses to predict a subjective symptom load may not be appropriate. To enhance future biomechanical analyses, further investigations should include the use of quantifiable measures of brain injury linked to clinical outcomes and possible confounding factors such as history of brain injury and patient predisposition.
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Affiliation(s)
- Andrew Post
- Children's Hospital of Eastern Ontario Research Institute, Ottawa.,Human Kinetics, University of Ottawa, Ontario, Canada ; and
| | | | - Roger Zemek
- Children's Hospital of Eastern Ontario Research Institute, Ottawa
| | - Michael D Gilchrist
- School of Mechanical & Materials Engineering, University College Dublin, Ireland
| | - David Koncan
- Human Kinetics, University of Ottawa, Ontario, Canada ; and
| | - Lauren Dawson
- Human Kinetics, University of Ottawa, Ontario, Canada ; and
| | - Wesley Chen
- Human Kinetics, University of Ottawa, Ontario, Canada ; and
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27
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Lane CA, Parker TD, Cash DM, Macpherson K, Donnachie E, Murray-Smith H, Barnes A, Barker S, Beasley DG, Bras J, Brown D, Burgos N, Byford M, Jorge Cardoso M, Carvalho A, Collins J, De Vita E, Dickson JC, Epie N, Espak M, Henley SMD, Hoskote C, Hutel M, Klimova J, Malone IB, Markiewicz P, Melbourne A, Modat M, Schrag A, Shah S, Sharma N, Sudre CH, Thomas DL, Wong A, Zhang H, Hardy J, Zetterberg H, Ourselin S, Crutch SJ, Kuh D, Richards M, Fox NC, Schott JM. Study protocol: Insight 46 - a neuroscience sub-study of the MRC National Survey of Health and Development. BMC Neurol 2017; 17:75. [PMID: 28420323 PMCID: PMC5395844 DOI: 10.1186/s12883-017-0846-x] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2017] [Accepted: 03/21/2017] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Increasing age is the biggest risk factor for dementia, of which Alzheimer's disease is the commonest cause. The pathological changes underpinning Alzheimer's disease are thought to develop at least a decade prior to the onset of symptoms. Molecular positron emission tomography and multi-modal magnetic resonance imaging allow key pathological processes underpinning cognitive impairment - including β-amyloid depostion, vascular disease, network breakdown and atrophy - to be assessed repeatedly and non-invasively. This enables potential determinants of dementia to be delineated earlier, and therefore opens a pre-symptomatic window where intervention may prevent the onset of cognitive symptoms. METHODS/DESIGN This paper outlines the clinical, cognitive and imaging protocol of "Insight 46", a neuroscience sub-study of the MRC National Survey of Health and Development. This is one of the oldest British birth cohort studies and has followed 5362 individuals since their birth in England, Scotland and Wales during one week in March 1946. These individuals have been tracked in 24 waves of data collection incorporating a wide range of health and functional measures, including repeat measures of cognitive function. Now aged 71 years, a small fraction have overt dementia, but estimates suggest that ~1/3 of individuals in this age group may be in the preclinical stages of Alzheimer's disease. Insight 46 is recruiting 500 study members selected at random from those who attended a clinical visit at 60-64 years and on whom relevant lifecourse data are available. We describe the sub-study design and protocol which involves a prospective two time-point (0, 24 month) data collection covering clinical, neuropsychological, β-amyloid positron emission tomography and magnetic resonance imaging, biomarker and genetic information. Data collection started in 2015 (age 69) and aims to be completed in 2019 (age 73). DISCUSSION Through the integration of data on the socioeconomic environment and on physical, psychological and cognitive function from 0 to 69 years, coupled with genetics, structural and molecular imaging, and intensive cognitive and neurological phenotyping, Insight 46 aims to identify lifetime factors which influence brain health and cognitive ageing, with particular focus on Alzheimer's disease and cerebrovascular disease. This will provide an evidence base for the rational design of disease-modifying trials.
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Affiliation(s)
- Christopher A. Lane
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Thomas D. Parker
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Dave M. Cash
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Kirsty Macpherson
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Elizabeth Donnachie
- Leonard Wolfson Experimental Neurology Centre, Institute of Neurology, University College London, London, UK
| | - Heidi Murray-Smith
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Anna Barnes
- Institute of Nuclear Medicine, University College London Hospitals, London, UK
| | - Suzie Barker
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Daniel G. Beasley
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Jose Bras
- Department of Molecular Neuroscience, Institute of Neurology, University College London, London, UK
- Department of Medical Sciences and Institute of Biomedicine - iBiMED, University of Aveiro, Aveiro, Portugal
| | - David Brown
- Institute of Nuclear Medicine, University College London Hospitals, London, UK
| | - Ninon Burgos
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | | | - M. Jorge Cardoso
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Ana Carvalho
- Institute of Nuclear Medicine, University College London Hospitals, London, UK
| | - Jessica Collins
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Enrico De Vita
- Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, Institute of Neurology, University College London, London, UK
| | - John C. Dickson
- Institute of Nuclear Medicine, University College London Hospitals, London, UK
| | - Norah Epie
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Miklos Espak
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Susie M. D. Henley
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Chandrashekar Hoskote
- Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
| | - Michael Hutel
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Jana Klimova
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Ian B. Malone
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Pawel Markiewicz
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Andrew Melbourne
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Marc Modat
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Anette Schrag
- Department of Clinical Neuroscience, Institute of Neurology, University College London, London, UK
| | - Sachit Shah
- Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, Institute of Neurology, University College London, London, UK
| | - Nikhil Sharma
- MRC Unit for Lifelong Health and Ageing at UCL, London, UK
- National Hospital for Neurology and Neurosurgery, London, UK
| | - Carole H. Sudre
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - David L. Thomas
- Leonard Wolfson Experimental Neurology Centre, Institute of Neurology, University College London, London, UK
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, Institute of Neurology, University College London, London, UK
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCL, London, UK
| | - Hui Zhang
- Department of Computer Science and Centre for Medical Image Computing, University College London, London, UK
| | - John Hardy
- Reta Lila Weston Research Laboratories, Department of Molecular Neuroscience, Institute of Neurology, University College London, London, UK
| | - Henrik Zetterberg
- Department of Molecular Neuroscience, Institute of Neurology, University College London, London, UK
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Sebastien Ourselin
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Sebastian J. Crutch
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Diana Kuh
- MRC Unit for Lifelong Health and Ageing at UCL, London, UK
| | | | - Nick C. Fox
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Jonathan M. Schott
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
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Post A, Hoshizaki TB, Gilchrist MD, Koncan D, Dawson L, Chen W, Ledoux AA, Zemek R, _ _. A comparison in a youth population between those with and without a history of concussion using biomechanical reconstruction. J Neurosurg Pediatr 2017; 19:502-510. [PMID: 28128703 DOI: 10.3171/2016.10.peds16449] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Concussion is a common topic of research as a result of the short- and long-term effects it can have on the affected individual. Of particular interest is whether previous concussions can lead to a biomechanical susceptibility, or vulnerability, to incurring further head injuries, particularly for youth populations. The purpose of this research was to compare the impact biomechanics of a concussive event in terms of acceleration and brain strains of 2 groups of youths: those who had incurred a previous concussion and those who had not. It was hypothesized that the youths with a history of concussion would have lower-magnitude biomechanical impact measures than those who had never suffered a previous concussion. METHODS Youths who had suffered a concussion were recruited from emergency departments across Canada. This pool of patients was then separated into 2 categories based on their history of concussion: those who had incurred 1 or more previous concussions, and those who had never suffered a concussion. The impact event that resulted in the brain injury was reconstructed biomechanically using computational, physical, and finite element modeling techniques. The output of the events was measured in biomechanical parameters such as energy, force, acceleration, and brain tissue strain to determine if those patients who had a previous concussion sustained a brain injury at lower magnitudes than those who had no previously reported concussion. RESULTS The results demonstrated that there was no biomechanical variable that could distinguish between the concussion groups with a history of concussion versus no history of concussion. CONCLUSIONS The results suggest that there is no measureable biomechanical vulnerability to head impact related to a history of concussions in this youth population. This may be a reflection of the long time between the previous concussion and the one reconstructed in the laboratory, where such a long period has been associated with recovery from injury.
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Affiliation(s)
- Andrew Post
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Canada
- Human Kinetics, University of Ottawa, Canada; and
| | | | - Michael D. Gilchrist
- School of Mechanical & Materials Engineering, University College Dublin, Ireland
| | - David Koncan
- Human Kinetics, University of Ottawa, Canada; and
| | | | - Wesley Chen
- Human Kinetics, University of Ottawa, Canada; and
| | - Andrée-Anne Ledoux
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Canada
| | - Roger Zemek
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Canada
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Miller KL, Alfaro-Almagro F, Bangerter NK, Thomas DL, Yacoub E, Xu J, Bartsch AJ, Jbabdi S, Sotiropoulos SN, Andersson JLR, Griffanti L, Douaud G, Okell TW, Weale P, Dragonu I, Garratt S, Hudson S, Collins R, Jenkinson M, Matthews PM, Smith SM. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat Neurosci 2016; 19:1523-1536. [PMID: 27643430 PMCID: PMC5086094 DOI: 10.1038/nn.4393] [Citation(s) in RCA: 978] [Impact Index Per Article: 122.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2016] [Accepted: 08/25/2016] [Indexed: 01/17/2023]
Abstract
Medical imaging has enormous potential for early disease prediction, but is impeded by the difficulty and expense of acquiring data sets before symptom onset. UK Biobank aims to address this problem directly by acquiring high-quality, consistently acquired imaging data from 100,000 predominantly healthy participants, with health outcomes being tracked over the coming decades. The brain imaging includes structural, diffusion and functional modalities. Along with body and cardiac imaging, genetics, lifestyle measures, biological phenotyping and health records, this imaging is expected to enable discovery of imaging markers of a broad range of diseases at their earliest stages, as well as provide unique insight into disease mechanisms. We describe UK Biobank brain imaging and present results derived from the first 5,000 participants' data release. Although this covers just 5% of the ultimate cohort, it has already yielded a rich range of associations between brain imaging and other measures collected by UK Biobank.
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Affiliation(s)
- Karla L Miller
- Oxford Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Oxford, UK
| | - Fidel Alfaro-Almagro
- Oxford Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Oxford, UK
| | - Neal K Bangerter
- Department of Electrical Engineering, Brigham Young University, Provo, USA
| | - David L Thomas
- Institute of Neurology, University College London, London, UK
| | - Essa Yacoub
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, USA
| | - Junqian Xu
- Icahn School of Medicine at Mount Sinai, New York, USA
| | | | - Saad Jbabdi
- Oxford Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Oxford, UK
| | | | - Jesper LR Andersson
- Oxford Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Oxford, UK
| | - Ludovica Griffanti
- Oxford Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Oxford, UK
| | - Gwenaëlle Douaud
- Oxford Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Oxford, UK
| | - Thomas W Okell
- Oxford Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Oxford, UK
| | | | | | | | | | - Rory Collins
- UK Biobank, Stockport, UK
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Mark Jenkinson
- Oxford Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Oxford, UK
| | - Paul M Matthews
- Division of Brain Sciences, Department of Medicine, Imperial College London, London, UK
| | - Stephen M Smith
- Oxford Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Oxford, UK
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30
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Cameron Craddock R, S Margulies D, Bellec P, Nolan Nichols B, Alcauter S, A Barrios F, Burnod Y, J Cannistraci C, Cohen-Adad J, De Leener B, Dery S, Downar J, Dunlop K, R Franco A, Seligman Froehlich C, J Gerber A, S Ghosh S, J Grabowski T, Hill S, Sólon Heinsfeld A, Matthew Hutchison R, Kundu P, R Laird A, Liew SL, J Lurie D, G McLaren D, Meneguzzi F, Mennes M, Mesmoudi S, O'Connor D, H Pasaye E, Peltier S, Poline JB, Prasad G, Fraga Pereira R, Quirion PO, Rokem A, S Saad Z, Shi Y, C Strother S, Toro R, Q Uddin L, D Van Horn J, W Van Meter J, C Welsh R, Xu T. Brainhack: a collaborative workshop for the open neuroscience community. Gigascience 2016; 5:16. [PMID: 27042293 PMCID: PMC4818387 DOI: 10.1186/s13742-016-0121-x] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2016] [Accepted: 03/15/2016] [Indexed: 11/10/2022] Open
Abstract
Brainhack events offer a novel workshop format with participant-generated content that caters to the rapidly growing open neuroscience community. Including components from hackathons and unconferences, as well as parallel educational sessions, Brainhack fosters novel collaborations around the interests of its attendees. Here we provide an overview of its structure, past events, and example projects. Additionally, we outline current innovations such as regional events and post-conference publications. Through introducing Brainhack to the wider neuroscience community, we hope to provide a unique conference format that promotes the features of collaborative, open science.
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Affiliation(s)
- R Cameron Craddock
- The Neuro Bureau, Leipzig, 04317 Germany ; Computational Neuroimaging Lab, Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York, 10962 USA ; Center for the Developing Brain, Child Mind Institute, New York, New York, 10022 USA
| | - Daniel S Margulies
- The Neuro Bureau, Leipzig, 04317 Germany ; Max Planck Research Group for Neuroanatomy & Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, 04103 Germany
| | - Pierre Bellec
- The Neuro Bureau, Leipzig, 04317 Germany ; Département d'Informatique et de Recherche Opérationnelle, Université de Montréal, Montréal, Québec H3W 1W5, Canada ; Functional Neuroimaging Unit, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Québec H3W 1W5, Canada
| | - B Nolan Nichols
- The Neuro Bureau, Leipzig, 04317 Germany ; Center for Health Sciences, SRI International, Menlo Park, California, 94025 USA ; Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California, 94305 USA
| | - Sarael Alcauter
- Instituto De Neurobiología, Universidad Nacional Autónoma de México, Querétaro, 76203 México
| | - Fernando A Barrios
- Instituto De Neurobiología, Universidad Nacional Autónoma de México, Querétaro, 76203 México
| | - Yves Burnod
- Laboratoire d'Imagerie Biomédicale, Sorbonne Universités, UPMC Université Paris 06, Paris, 75005 France ; Institut des Systèmes Complexes de Paris-Île-de-France, Paris, 75013 France
| | - Christopher J Cannistraci
- Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, 10029 USA
| | - Julien Cohen-Adad
- Functional Neuroimaging Unit, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Québec H3W 1W5, Canada ; Institute of Biomedical Engineering, Ecole Polytechnique de Montréal, Montréal, Québec H3T 1J4, Canada
| | - Benjamin De Leener
- Institute of Biomedical Engineering, Ecole Polytechnique de Montréal, Montréal, Québec H3T 1J4, Canada
| | - Sebastien Dery
- McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, Quebec H3A 2B4, Canada
| | - Jonathan Downar
- MRI-Guided rTMS Clinic, University Health Network, Toronto, Ontario M5T 2S8, Canada ; Department of Psychiatry, University Health Network, University of Toronto, Toronto, Ontario M5T 2S8, Canada ; Institute of Medical Sciences, University of Toronto, Toronto, Ontario M5S 1A8, Canada
| | - Katharine Dunlop
- MRI-Guided rTMS Clinic, University Health Network, Toronto, Ontario M5T 2S8, Canada ; Institute of Medical Sciences, University of Toronto, Toronto, Ontario M5S 1A8, Canada
| | - Alexandre R Franco
- The Neuro Bureau, Leipzig, 04317 Germany ; Faculdade de Engenharia, PUCRS, Porto Alegre, 90619 Brazil ; Instituto do Cérebro do Rio Grande do Sul, PUCRS, Porto Alegre, 90610 Brazil ; Faculdade de Medicina, PUCRS, Porto Alegre, 90619 Brazil
| | - Caroline Seligman Froehlich
- The Neuro Bureau, Leipzig, 04317 Germany ; Computational Neuroimaging Lab, Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York, 10962 USA
| | - Andrew J Gerber
- New York State Psychiatric Institute, New York, New York, 10032 USA ; Division of Child and Adolescent Psychiatry, Department of Psychiatry, Columbia University, New York, New York, 10032 USA
| | - Satrajit S Ghosh
- The Neuro Bureau, Leipzig, 04317 Germany ; McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139 USA ; Department of Otology and Laryngology, Harvard Medical School, Boston, Massachusetts, 02115 USA
| | - Thomas J Grabowski
- Department of Radiology, University of Washington, Seattle, Washington, 98105 USA ; Department of Neurology, University of Washington, Seattle, Washington, 98105 USA
| | - Sean Hill
- International Neuroinformatics Coordinating Facility, Stockholm, 171 77 Sweden ; Karolinska Institutet, Stockholm, 171 77 Sweden
| | | | - R Matthew Hutchison
- The Neuro Bureau, Leipzig, 04317 Germany ; Center for Brain Science, Harvard University, Cambridge, Massachusetts, 02138 USA
| | - Prantik Kundu
- The Neuro Bureau, Leipzig, 04317 Germany ; Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, 10029 USA
| | - Angela R Laird
- Department of Physics, Florida International University, Miami, Florida, 33199 USA
| | - Sook-Lei Liew
- The Neuro Bureau, Leipzig, 04317 Germany ; Chan Division of Occupational Science and Occupational Therapy, Division of Physical Therapy and Biokinesiology, Department of Neurology, University of Southern California, Los Angeles, California, 90033 USA ; USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, Canada, 90033 USA
| | - Daniel J Lurie
- Department of Psychology,, University of California, Berkeley, California, 94720 USA
| | - Donald G McLaren
- The Neuro Bureau, Leipzig, 04317 Germany ; Biospective, Inc., Montréal,, Québec H4P 1K6, Canada ; Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, 02114, USA
| | | | - Maarten Mennes
- The Neuro Bureau, Leipzig, 04317 Germany ; Radboud University Nijmegen, Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Nijmegen, 6525 EN The Netherlands
| | - Salma Mesmoudi
- Institut des Systèmes Complexes de Paris-Île-de-France, Paris, 75013 France ; Sorbonne Universités, Paris-1 Université, Equipement d'Excellence MATRICE, Paris, 75005, France
| | - David O'Connor
- Center for the Developing Brain, Child Mind Institute, New York, New York, 10022 USA
| | - Erick H Pasaye
- Instituto De Neurobiología, Universidad Nacional Autónoma de México, Querétaro, 76203 México
| | - Scott Peltier
- Functional MRI Laboratory, University of Michigan, Ann Arbor, Michigan, 48109 USA
| | - Jean-Baptiste Poline
- Helen Wills Neuroscience Institute, University of California, Berkeley, California, 94720 USA ; Henry H. Wheeler Jr. Brain Imaging Center, University of California, Berkeley, California, 94709 USA
| | - Gautam Prasad
- Laboratory of Neuro Imaging, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of University of Southern California, Los Angeles, California, 90033 USA
| | | | - Pierre-Olivier Quirion
- Functional Neuroimaging Unit, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Québec H3W 1W5, Canada
| | - Ariel Rokem
- The University of Washington eScience Institute, Seattle, Washington, 98195 USA
| | - Ziad S Saad
- Scientific and Statistical Computing Core, National Institute of Mental Health, Bethesda, Maryland, 20892 USA
| | - Yonggang Shi
- Laboratory of Neuro Imaging, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of University of Southern California, Los Angeles, California, 90033 USA
| | - Stephen C Strother
- Institute of Medical Sciences, University of Toronto, Toronto, Ontario M5S 1A8, Canada ; Rotman Research Institute, Baycrest Hospital, Toronto, Ontario M6A 2E1, Canada ; Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5G 1L7, Canada
| | - Roberto Toro
- The Neuro Bureau, Leipzig, 04317 Germany ; Human Genetics and Cognitive Functions Unit, Institut Pasteur, Paris, 75015 France ; Unité Mixte de Recherche 3571, Genes, Synapses and Cognition, Centre National de la Recherche Scientifique, Institut Pasteur, Paris, 75015 France
| | - Lucina Q Uddin
- The Neuro Bureau, Leipzig, 04317 Germany ; Department of Psychology, University of Miami, Coral Gables, Florida, 33124 USA ; Neuroscience Program, University of Miami Miller School of Medicine, Miami, Florida, 33136 USA
| | - John D Van Horn
- USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, Canada, 90033 USA
| | - John W Van Meter
- Center for Functional and Molecular Imaging, Georgetown University Medical Center, Washington,, 20007 DC USA
| | - Robert C Welsh
- Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, 48109 USA ; Department of Radiology,, University of Michigan, Ann Arbor, Michigan, 48109 USA
| | - Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, New York, 10022 USA
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