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Wang M, Deng Y, Liu Y, Suo T, Guo B, Eickhoff SB, Xu J, Rao H. The common and distinct brain basis associated with adult and adolescent risk-taking behavior: Evidence from the neuroimaging meta-analysis. Neurosci Biobehav Rev 2024; 160:105607. [PMID: 38428473 DOI: 10.1016/j.neubiorev.2024.105607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 02/25/2024] [Accepted: 02/26/2024] [Indexed: 03/03/2024]
Abstract
Risk-taking is a common, complex, and multidimensional behavior construct that has significant implications for human health and well-being. Previous research has identified the neural mechanisms underlying risk-taking behavior in both adolescents and adults, yet the differences between adolescents' and adults' risk-taking in the brain remain elusive. This study firstly employs a comprehensive meta-analysis approach that includes 73 adult and 20 adolescent whole-brain experiments, incorporating observations from 1986 adults and 789 adolescents obtained from online databases, including Web of Science, PubMed, ScienceDirect, Google Scholar and Neurosynth. It then combines functional decoding methods to identify common and distinct brain regions and corresponding psychological processes associated with risk-taking behavior in these two cohorts. The results indicated that the neural bases underlying risk-taking behavior in both age groups are situated within the cognitive control, reward, and sensory networks. Subsequent contrast analysis revealed that adolescents and adults risk-taking engaged frontal pole within the fronto-parietal control network (FPN), but the former recruited more ventrolateral area and the latter recruited more dorsolateral area. Moreover, adolescents' risk-taking evoked brain area activity within the ventral attention network (VAN) and the default mode network (DMN) compared with adults, consistent with the functional decoding analyses. These findings provide new insights into the similarities and disparities of risk-taking neural substrates underlying different age cohorts, supporting future neuroimaging research on the dynamic changes of risk-taking.
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Affiliation(s)
- Mengmeng Wang
- Center for Magnetic Resonance Imaging Research & Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China; Business School, NingboTech University, Ningbo, China
| | - Yao Deng
- Center for Magnetic Resonance Imaging Research & Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China; State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Yingying Liu
- Center for Magnetic Resonance Imaging Research & Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China
| | | | - Bowen Guo
- Center for Magnetic Resonance Imaging Research & Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain and Behavior (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Jing Xu
- Center for Magnetic Resonance Imaging Research & Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China.
| | - Hengyi Rao
- Center for Magnetic Resonance Imaging Research & Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China; Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
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2
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Fan L, Li Y, Zhao X, Huang ZG, Liu T, Wang J. Dynamic nonreversibility view of intrinsic brain organization and brain dynamic analysis of repetitive transcranial magnitude stimulation. Cereb Cortex 2024; 34:bhae098. [PMID: 38494890 DOI: 10.1093/cercor/bhae098] [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: 01/14/2024] [Revised: 02/20/2024] [Accepted: 02/21/2024] [Indexed: 03/19/2024] Open
Abstract
Intrinsic neural activities are characterized as endless spontaneous fluctuation over multiple time scales. However, how the intrinsic brain organization changes over time under local perturbation remains an open question. By means of statistical physics, we proposed an approach to capture whole-brain dynamics based on estimating time-varying nonreversibility and k-means clustering of dynamic varying nonreversibility patterns. We first used synthetic fMRI to investigate the effects of window parameters on the temporal variability of varying nonreversibility. Second, using real test-retest fMRI data, we examined the reproducibility, reliability, biological, and physiological correlation of the varying nonreversibility substates. Finally, using repetitive transcranial magnetic stimulation-fMRI data, we investigated the modulation effects of repetitive transcranial magnetic stimulation on varying nonreversibility substate dynamics. The results show that: (i) as window length increased, the varying nonreversibility variance decreased, while the sliding step almost did not alter it; (ii) the global high varying nonreversibility states and low varying nonreversibility states were reproducible across multiple datasets and different window lengths; and (iii) there were increased low varying nonreversibility states and decreased high varying nonreversibility states when the left frontal lobe was stimulated, but not the occipital lobe. Taken together, these results provide a thermodynamic equilibrium perspective of intrinsic brain organization and reorganization under local perturbation.
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Affiliation(s)
- Liming Fan
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Institute of Health and Rehabilitation Science, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
| | - Youjun Li
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Institute of Health and Rehabilitation Science, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
| | - Xingjian Zhao
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Institute of Health and Rehabilitation Science, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
| | - Zi-Gang Huang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Institute of Health and Rehabilitation Science, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
| | - Tian Liu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Institute of Health and Rehabilitation Science, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
| | - Jue Wang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Institute of Health and Rehabilitation Science, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
- The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi 710049, China
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3
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Jauny G, Mijalkov M, Canal-Garcia A, Volpe G, Pereira J, Eustache F, Hinault T. Linking structural and functional changes during aging using multilayer brain network analysis. Commun Biol 2024; 7:239. [PMID: 38418523 PMCID: PMC10902297 DOI: 10.1038/s42003-024-05927-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Accepted: 02/16/2024] [Indexed: 03/01/2024] Open
Abstract
Brain structure and function are intimately linked, however this association remains poorly understood and the complexity of this relationship has remained understudied. Healthy aging is characterised by heterogenous levels of structural integrity changes that influence functional network dynamics. Here, we use the multilayer brain network analysis on structural (diffusion weighted imaging) and functional (magnetoencephalography) data from the Cam-CAN database. We found that the level of similarity of connectivity patterns between brain structure and function in the parietal and temporal regions (alpha frequency band) is associated with cognitive performance in healthy older individuals. These results highlight the impact of structural connectivity changes on the reorganisation of functional connectivity associated with the preservation of cognitive function, and provide a mechanistic understanding of the concepts of brain maintenance and compensation with aging. Investigation of the link between structure and function could thus represent a new marker of individual variability, and of pathological changes.
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Affiliation(s)
- Gwendolyn Jauny
- Normandie Univ, UNICAEN, PSL Université Paris, EPHE, Inserm, U1077, CHU de Caen, Centre Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14000, Caen, France
| | - Mite Mijalkov
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Anna Canal-Garcia
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Giovanni Volpe
- Department of Physics, Goteborg University, Goteborg, Sweden
| | - Joana Pereira
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Francis Eustache
- Normandie Univ, UNICAEN, PSL Université Paris, EPHE, Inserm, U1077, CHU de Caen, Centre Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14000, Caen, France
| | - Thomas Hinault
- Normandie Univ, UNICAEN, PSL Université Paris, EPHE, Inserm, U1077, CHU de Caen, Centre Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14000, Caen, France.
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4
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Zimmermann MLM, Breedt LC, Centeno EGZ, Reijneveld JC, Santos FAN, Stam CJ, van Lingen MR, Schoonheim MM, Hillebrand A, Douw L. The relationship between pathological brain activity and functional network connectivity in glioma patients. J Neurooncol 2024; 166:523-533. [PMID: 38308803 PMCID: PMC10876827 DOI: 10.1007/s11060-024-04577-7] [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: 10/03/2023] [Accepted: 01/17/2024] [Indexed: 02/05/2024]
Abstract
PURPOSE Glioma is associated with pathologically high (peri)tumoral brain activity, which relates to faster progression. Functional connectivity is disturbed locally and throughout the entire brain, associating with symptomatology. We, therefore, investigated how local activity and network measures relate to better understand how the intricate relationship between the tumor and the rest of the brain may impact disease and symptom progression. METHODS We obtained magnetoencephalography in 84 de novo glioma patients and 61 matched healthy controls. The offset of the power spectrum, a proxy of neuronal activity, was calculated for 210 cortical regions. We calculated patients' regional deviations in delta, theta and lower alpha network connectivity as compared to controls, using two network measures: clustering coefficient (local connectivity) and eigenvector centrality (integrative connectivity). We then tested group differences in activity and connectivity between (peri)tumoral, contralateral homologue regions, and the rest of the brain. We also correlated regional offset to connectivity. RESULTS As expected, patients' (peri)tumoral activity was pathologically high, and patients showed higher clustering and lower centrality than controls. At the group-level, regionally high activity related to high clustering in controls and patients alike. However, within-patient analyses revealed negative associations between regional deviations in brain activity and clustering, such that pathologically high activity coincided with low network clustering, while regions with 'normal' activity levels showed high network clustering. CONCLUSION Our results indicate that pathological activity and connectivity co-localize in a complex manner in glioma. This insight is relevant to our understanding of disease progression and cognitive symptomatology.
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Affiliation(s)
- Mona L M Zimmermann
- Anatomy and Neurosciences, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
- Amsterdam Neuroscience, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
- Cancer Center Amsterdam, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
| | - Lucas C Breedt
- Anatomy and Neurosciences, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Eduarda G Z Centeno
- Anatomy and Neurosciences, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Univ. Bordeaux, CNRS, IMN, UMR 5293, Bordeaux, France
| | - Jaap C Reijneveld
- Department of Neurology, Stichting Epilepsie Instellingen Nederland, Heemstede, The Netherlands
| | - Fernando A N Santos
- Anatomy and Neurosciences, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Dutch Institute for Emergent Phenomena (DIEP), Institute for Advanced Studies, University of Amsterdam, Amsterdam, The Netherlands
| | - Cornelis J Stam
- Clinical Neurophysiology and MEG Center, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Marike R van Lingen
- Anatomy and Neurosciences, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Menno M Schoonheim
- Anatomy and Neurosciences, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Amsterdam Neuroscience, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Linda Douw
- Anatomy and Neurosciences, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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5
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Kotlarz P, Lankinen K, Hakonen M, Turpin T, Polimeni JR, Ahveninen J. Multilayer Network Analysis across Cortical Depths in Resting-State 7T fMRI. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.23.573208. [PMID: 38187540 PMCID: PMC10769454 DOI: 10.1101/2023.12.23.573208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
In graph theory, "multilayer networks" represent systems involving several interconnected topological levels. A neuroscience example is the hierarchy of connections between different cortical depths or "lamina". This hierarchy is becoming non-invasively accessible in humans using ultra-high-resolution functional MRI (fMRI). Here, we applied multilayer graph theory to examine functional connectivity across different cortical depths in humans, using 7T fMRI (1-mm3 voxels; 30 participants). Blood oxygenation level dependent (BOLD) signals were derived from five depths between the white matter and pial surface. We then compared networks where the inter-regional connections were limited to a single cortical depth only ("layer-by-layer matrices") to those considering all possible connections between regions and cortical depths ("multilayer matrix"). We utilized global and local graph theory features that quantitatively characterize network attributes such as network composition, nodal centrality, path-based measures, and hub segregation. Detecting functional differences between cortical depths was improved using multilayer connectomics compared to the layer-by-layer versions. Superficial aspects of the cortex dominated information transfer and deeper aspects clustering. These differences were largest in frontotemporal and limbic brain regions. fMRI functional connectivity across different cortical depths may contain neurophysiologically relevant information. Multilayer connectomics could provide a methodological framework for studies on how information flows across this hierarchy.
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Affiliation(s)
- Parker Kotlarz
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Kaisu Lankinen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Maria Hakonen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | | | - Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jyrki Ahveninen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
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6
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van Lingen MR, Breedt LC, Geurts JJG, Hillebrand A, Klein M, Kouwenhoven MCM, Kulik SD, Reijneveld JC, Stam CJ, De Witt Hamer PC, Zimmermann MLM, Santos FAN, Douw L. The longitudinal relation between executive functioning and multilayer network topology in glioma patients. Brain Imaging Behav 2023; 17:425-435. [PMID: 37067658 PMCID: PMC10435610 DOI: 10.1007/s11682-023-00770-w] [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] [Accepted: 03/28/2023] [Indexed: 04/18/2023]
Abstract
Many patients with glioma, primary brain tumors, suffer from poorly understood executive functioning deficits before and/or after tumor resection. We aimed to test whether frontoparietal network centrality of multilayer networks, allowing for integration across multiple frequencies, relates to and predicts executive functioning in glioma. Patients with glioma (n = 37) underwent resting-state magnetoencephalography and neuropsychological tests assessing word fluency, inhibition, and set shifting before (T1) and one year after tumor resection (T2). We constructed binary multilayer networks comprising six layers, with each layer representing frequency-specific functional connectivity between source-localized time series of 78 cortical regions. Average frontoparietal network multilayer eigenvector centrality, a measure for network integration, was calculated at both time points. Regression analyses were used to investigate associations with executive functioning. At T1, lower multilayer integration (p = 0.017) and epilepsy (p = 0.006) associated with poorer set shifting (adj. R2 = 0.269). Decreasing multilayer integration (p = 0.022) and not undergoing chemotherapy at T2 (p = 0.004) related to deteriorating set shifting over time (adj. R2 = 0.283). No significant associations were found for word fluency or inhibition, nor did T1 multilayer integration predict changes in executive functioning. As expected, our results establish multilayer integration of the frontoparietal network as a cross-sectional and longitudinal correlate of executive functioning in glioma patients. However, multilayer integration did not predict postoperative changes in executive functioning, which together with the fact that this correlate is also found in health and other diseases, limits its specific clinical relevance in glioma.
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Affiliation(s)
- Marike R van Lingen
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, de Boelelaan 1108, Amsterdam, the Netherlands.
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands.
- Amsterdam Neuroscience, Systems & Network Neurosciences, Amsterdam, the Netherlands.
- Cancer Center Amsterdam, Amsterdam, the Netherlands.
| | - Lucas C Breedt
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, de Boelelaan 1108, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Systems & Network Neurosciences, Amsterdam, the Netherlands
| | - Jeroen J G Geurts
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, de Boelelaan 1108, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Systems & Network Neurosciences, Amsterdam, the Netherlands
| | - Arjan Hillebrand
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Systems & Network Neurosciences, Amsterdam, the Netherlands
- Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Martin Klein
- Department of Medical Psychology, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Mathilde C M Kouwenhoven
- Department of Neurology, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Shanna D Kulik
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, de Boelelaan 1108, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Systems & Network Neurosciences, Amsterdam, the Netherlands
| | - Jaap C Reijneveld
- Department of Neurology, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, the Netherlands
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, the Netherlands
- Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Cornelis J Stam
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Systems & Network Neurosciences, Amsterdam, the Netherlands
- Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Philip C De Witt Hamer
- Department of Neurosurgery, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Mona L M Zimmermann
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, de Boelelaan 1108, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Systems & Network Neurosciences, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Fernando A N Santos
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, de Boelelaan 1108, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Systems & Network Neurosciences, Amsterdam, the Netherlands
- Institute of Advanced Studies, University of Amsterdam, Amsterdam, the Netherlands
| | - Linda Douw
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, de Boelelaan 1108, Amsterdam, the Netherlands.
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands.
- Amsterdam Neuroscience, Systems & Network Neurosciences, Amsterdam, the Netherlands.
- Cancer Center Amsterdam, Amsterdam, the Netherlands.
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7
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Parsons N, Irimia A, Amgalan A, Ugon J, Morgan K, Shelyag S, Hocking A, Poudel G, Caeyenberghs K. Structural-functional connectivity bandwidth predicts processing speed in mild traumatic brain Injury: A multiplex network analysis. Neuroimage Clin 2023; 38:103428. [PMID: 37167841 PMCID: PMC10196722 DOI: 10.1016/j.nicl.2023.103428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 04/17/2023] [Accepted: 05/01/2023] [Indexed: 05/13/2023]
Abstract
An emerging body of work has revealed alterations in structural (SC) and functional (FC) brain connectivity following mild TBI (mTBI), with mixed findings. However, these studies seldom integrate complimentary neuroimaging modalities within a unified framework. Multilayer network analysis is an emerging technique to uncover how white matter organization enables functional communication. Using our novel graph metric (SC-FC Bandwidth), we quantified the information capacity of synchronous brain regions in 53 mild TBI patients (46 females; age mean = 40.2 years (y), σ = 16.7 (y), range: 18-79 (y). Diffusion MRI and resting state fMRI were administered at the acute and chronic post-injury intervals. Moreover, participants completed a cognitive task to measure processing speed (30 Seconds and Counting Task; 30-SACT). Processing speed was significantly increased at the chronic, relative to the acute post-injury intervals (p = <0.001). Nonlinear principal components of direct (t = -1.84, p = 0.06) and indirect SC-FC Bandwidth (t = 3.86, p = <0.001) predicted processing speed with a moderate effect size (R2 = 0.43, p < 0.001), while controlling for age. A subnetwork of interhemispheric edges with increased SC-FC Bandwidth was identified at the chronic, relative to the acute mTBI post-injury interval (pFDR = 0.05). Increased interhemispheric SC-FC Bandwidth of this network corresponded with improved processing speed at the chronic post-injury interval (partial r = 0.32, p = 0.02). Our findings revealed that mild TBI results in complex reorganization of brain connectivity optimized for maximum information flow, supporting improved cognitive performance as a compensatory mechanism. Moving forward, this measurement may complement clinical assessment as an objective marker of mTBI recovery.
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Affiliation(s)
- Nicholas Parsons
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Melbourne, VIC, Australia; BrainCast Neurotechnologies, Australia; School of Psychological Sciences, Faculty of Medicine, Nursing and Health Sciences, Monash University, Australia.
| | - Andrei Irimia
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Anar Amgalan
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Julien Ugon
- School of Information Technology, Faculty of Science Engineering Built Environment, Deakin University, Melbourne, VIC, Australia
| | - Kerri Morgan
- School of Information Technology, Faculty of Science Engineering Built Environment, Deakin University, Melbourne, VIC, Australia
| | - Sergiy Shelyag
- School of Information Technology, Faculty of Science Engineering Built Environment, Deakin University, Melbourne, VIC, Australia
| | - Alex Hocking
- School of Information Technology, Faculty of Science Engineering Built Environment, Deakin University, Melbourne, VIC, Australia
| | - Govinda Poudel
- BrainCast Neurotechnologies, Australia; Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, VIC, Australia
| | - Karen Caeyenberghs
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Melbourne, VIC, Australia
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