1
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Al-Khalil K, Bell RP, Towe SL, Cohen JR, Gadde S, Mu J, Hall SA, Meade CS. Hub disruption in HIV disease and cocaine use: A connectomics analysis of brain function. Drug Alcohol Depend 2024; 263:112416. [PMID: 39197360 DOI: 10.1016/j.drugalcdep.2024.112416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 07/08/2024] [Accepted: 08/07/2024] [Indexed: 09/01/2024]
Abstract
BACKGROUND Cocaine use (CU) is prevalent in people with HIV (PWH). Both conditions are linked to changes in cognitive functioning and neural network topology. The current study utilizes graph theory to investigate functional connectomics associated with HIV and CU, focusing on disruption of densely connected nodes called hubs. METHODS Resting state functional magnetic resonance imaging (fMRI) from 206 adults (ages 22-55 years) were analyzed. A HIV x CU factorial design was implemented with participants in four groups: HIV+CU (n= 41), HIV only (n= 88), CU only (n= 36), and controls (n= 41). Functional connectomes were constructed, and thresholded graph metrics were calculated. Network centrality metrics - betweenness centrality (BC), participation coefficient (PC), and within module degree (WD) - were quantified into hub disruption indices (HDI). For each index, a 2×2 ANCOVA was performed controlling for education. RESULTS Participants were 68 % male and 74 % African-American with a mean age of 44.4 years. HIV and CU were associated with hub disruption in all three indices. Interactions were significant for HDI-PC and HDI-WD, such that HIV disease was associated with greater hub disruption among participants without CU, but not among participants with CU. Overall, lower global cognitive functioning was associated with greater hub disruption on all three indices. CONCLUSIONS Widespread hub disruption was evident in HIV disease and CU, highlighting topological reorganization in both diseases with neurocognitive effects. Hub-related measures inform functional connectivity disruptions in HIV disease and CU, particularly with respect to changes in network topology throughout the connectome.
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Affiliation(s)
- Kareem Al-Khalil
- Duke University School of Medicine, Department of Psychiatry and Behavioral Sciences, 2400 Pratt Street, Durham, NC 27705, USA.
| | - Ryan P Bell
- Duke University School of Medicine, Department of Psychiatry and Behavioral Sciences, 2400 Pratt Street, Durham, NC 27705, USA; Wake Forest University, School of Medicine, 475 Vine Street, Winston-Salem, NC 27101, USA.
| | - Sheri L Towe
- Duke University School of Medicine, Department of Psychiatry and Behavioral Sciences, 2400 Pratt Street, Durham, NC 27705, USA; Wake Forest University, School of Medicine, 475 Vine Street, Winston-Salem, NC 27101, USA.
| | - Jessica R Cohen
- University of North Carolina at Chapel Hill, Department of Psychology and Neuroscience, 100 E. Franklin Street Suite 200, Chapel Hill, NC 27599, USA.
| | - Syam Gadde
- Duke University Medical Center, Brain Imaging Analysis Center, 40 Duke Medicine Cir #414, Durham, NC 27710, USA.
| | - James Mu
- Duke University School of Medicine, Department of Psychiatry and Behavioral Sciences, 2400 Pratt Street, Durham, NC 27705, USA.
| | - Shana A Hall
- Duke University School of Medicine, Department of Psychiatry and Behavioral Sciences, 2400 Pratt Street, Durham, NC 27705, USA.
| | - Christina S Meade
- Duke University School of Medicine, Department of Psychiatry and Behavioral Sciences, 2400 Pratt Street, Durham, NC 27705, USA; Duke University Medical Center, Brain Imaging Analysis Center, 40 Duke Medicine Cir #414, Durham, NC 27710, USA; Wake Forest University, School of Medicine, 475 Vine Street, Winston-Salem, NC 27101, USA.
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2
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Tian Y, Peng XR, Tang Z, Long Z, Xie C, Lei X. Enhanced diversity on connector hubs following sleep deprivation: Evidence from diffusion and functional magnetic resonance imaging. Neuroimage 2024; 299:120837. [PMID: 39241898 DOI: 10.1016/j.neuroimage.2024.120837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 04/08/2024] [Accepted: 09/03/2024] [Indexed: 09/09/2024] Open
Abstract
Sleep deprivation has been demonstrated to exert widespread and intricate impacts on the brain network. The human brain network is a modular network composed of interconnected nodes. This network consists of provincial hubs and connector hubs, with provincial hubs having diverse connectivities within their own modules, while connector hubs distribute their connectivities across different modules. The latter is crucial for integrating information from various modules and ensuring the normal functioning of the modular brain. However, there has been a lack of systematic investigation into the impact of sleep deprivation on brain connector hubs. In this study, we utilized functional connectivity from resting-state functional magnetic resonance imaging, as well as structural connectivity from diffusion-weighted imaging, to systematically explore the variation of connector hub properties in the cerebral cortex after one night of sleep deprivation. The normalized participation coefficients (PCnorm) were utilized to identify connector hubs. In both the functional and structural networks, connector hubs exhibited a significant increase in average PCnorm, indicating the diversity enhancement of the connector hub following sleep deprivation. This enhancement is associated with increased network cost, reduced modularity, and decreased small-worldness, but enhanced global efficiency. This may potentially signify a compensatory mechanism within the brain following sleep deprivation. The significantly affected connector hubs were primarily observed in both the Control Network and Salience Network. We believe that the observed results reflect the increasing demand on the brain to invest more effort at preventing performance deterioration after sleep loss, in exchange for increased communication efficiency, especially involving systems responsible for neural resource allocation and cognitive control. These results have been replicated in an independent dataset. In conclusion, this study has enhanced our understanding of the compensatory mechanism in the brain response to sleep deprivation. This compensation is characterized by an enhancement in the connector hubs responsible for inter-modular communication, especially those related to neural resource and cognitive control. As a result, this compensation comes with a higher network cost but leads to an improvement in global communication efficiency, akin to a more random-like network manner.
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Affiliation(s)
- Yun Tian
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality (Ministry of Education), Chongqing 400715, China
| | - Xue-Rui Peng
- Faculty of Psychology, Technische Universität Dresden, Dresden, Germany
| | - Zihan Tang
- Key Laboratory of Cognition and Personality (Ministry of Education), Chongqing 400715, China
| | - Zhiliang Long
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality (Ministry of Education), Chongqing 400715, China
| | - Chao Xie
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Xu Lei
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality (Ministry of Education), Chongqing 400715, China.
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3
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Fotiadis P, Parkes L, Davis KA, Satterthwaite TD, Shinohara RT, Bassett DS. Structure-function coupling in macroscale human brain networks. Nat Rev Neurosci 2024; 25:688-704. [PMID: 39103609 DOI: 10.1038/s41583-024-00846-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/03/2024] [Indexed: 08/07/2024]
Abstract
Precisely how the anatomical structure of the brain gives rise to a repertoire of complex functions remains incompletely understood. A promising manifestation of this mapping from structure to function is the dependency of the functional activity of a brain region on the underlying white matter architecture. Here, we review the literature examining the macroscale coupling between structural and functional connectivity, and we establish how this structure-function coupling (SFC) can provide more information about the underlying workings of the brain than either feature alone. We begin by defining SFC and describing the computational methods used to quantify it. We then review empirical studies that examine the heterogeneous expression of SFC across different brain regions, among individuals, in the context of the cognitive task being performed, and over time, as well as its role in fostering flexible cognition. Last, we investigate how the coupling between structure and function is affected in neurological and psychiatric conditions, and we report how aberrant SFC is associated with disease duration and disease-specific cognitive impairment. By elucidating how the dynamic relationship between the structure and function of the brain is altered in the presence of neurological and psychiatric conditions, we aim to not only further our understanding of their aetiology but also establish SFC as a new and sensitive marker of disease symptomatology and cognitive performance. Overall, this Review collates the current knowledge regarding the regional interdependency between the macroscale structure and function of the human brain in both neurotypical and neuroatypical individuals.
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Affiliation(s)
- Panagiotis Fotiadis
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Anaesthesiology, University of Michigan, Ann Arbor, MI, USA.
| | - Linden Parkes
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Kathryn A Davis
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing & Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing & Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA, USA.
- Santa Fe Institute, Santa Fe, NM, USA.
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4
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Peng X, Trambaiolli LR, Choi EY, Lehman JF, Linn G, Russ BE, Schroeder CE, Haber SN, Liu H. Cross-species striatal hubs: linking anatomy to resting-state connectivity. Neuroimage 2024:120866. [PMID: 39322095 DOI: 10.1016/j.neuroimage.2024.120866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2024] [Revised: 08/13/2024] [Accepted: 09/23/2024] [Indexed: 09/27/2024] Open
Abstract
Corticostriatal connections are essential for motivation, cognition, and behavioral flexibility. There is broad interest in using resting-state functional magnetic resonance imaging (rs-fMRI) to link circuit dysfunction in these connections with neuropsychiatric disorders. In this paper, we used tract-tracing data from non-human primates (NHPs) to assess the likelihood of monosynaptic connections being represented in rs-fMRI data of NHPs and humans. We also demonstrated that existing hub locations in the anatomical data can be identified in the rs-fMRI data from both species. To characterize this in detail, we mapped the complete striatal projection zones from 27 tract-tracer injections located in the orbitofrontal cortex (OFC), dorsal anterior cingulate cortex (dACC), ventromedial prefrontal cortex (vmPFC), ventrolateral PFC (vlPFC), and dorsal PFC (dPFC) of macaque monkeys. Rs-fMRI seeds at the same regions of NHP and homologous regions of human brains showed connectivity maps in the striatum mostly consistent with those observed in the tracer data. We then examined the location of overlap in striatal projection zones. The medial rostral dorsal caudate connected with all five frontocortical regions evaluated in this study in both modalities (tract-tracing and rs-fMRI) and species (NHP and human). Other locations in the caudate also presented an overlap of four frontocortical regions, suggesting the existence of different locations with lower levels of input diversity. Small retrograde tracer injections and rs-fMRI seeds in the striatum confirmed these cortical input patterns. This study sets the ground for future studies evaluating rs-fMRI in clinical samples to measure anatomical corticostriatal circuit dysfunction and identify connectional hubs to provide more specific treatment targets for neurological and psychiatric disorders.
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Affiliation(s)
- Xiaolong Peng
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, USA
| | - Lucas R Trambaiolli
- McLean Hospital, Harvard Medical School, Boston, USA; University of Rochester School of Medicine & Dentistry, Rochester, USA
| | - Eun Young Choi
- Department of Neurosurgery, Stanford University, Stanford, USA
| | - Julia F Lehman
- University of Rochester School of Medicine & Dentistry, Rochester, USA
| | - Gary Linn
- Nathan S. Kline Institute for Psychiatric Research, Orangeburg, USA
| | - Brian E Russ
- Nathan S. Kline Institute for Psychiatric Research, Orangeburg, USA
| | | | - Suzanne N Haber
- McLean Hospital, Harvard Medical School, Boston, USA; University of Rochester School of Medicine & Dentistry, Rochester, USA.
| | - Hesheng Liu
- Changping Laboratory, Beijing, China; Biomedical Pioneering Innovation Center, Peking University, Beijing, China.
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5
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Stampacchia S, Asadi S, Tomczyk S, Ribaldi F, Scheffler M, Lövblad KO, Pievani M, Fall AB, Preti MG, Unschuld PG, Van De Ville D, Blanke O, Frisoni GB, Garibotto V, Amico E. Fingerprints of brain disease: connectome identifiability in Alzheimer's disease. Commun Biol 2024; 7:1169. [PMID: 39294332 PMCID: PMC11411139 DOI: 10.1038/s42003-024-06829-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 09/03/2024] [Indexed: 09/20/2024] Open
Abstract
Functional connectivity patterns in the human brain, like the friction ridges of a fingerprint, can uniquely identify individuals. Does this "brain fingerprint" remain distinct even during Alzheimer's disease (AD)? Using fMRI data from healthy and pathologically ageing subjects, we find that individual functional connectivity profiles remain unique and highly heterogeneous during mild cognitive impairment and AD. However, the patterns that make individuals identifiable change with disease progression, revealing a reconfiguration of the brain fingerprint. Notably, connectivity shifts towards functional system connections in AD and lower-order cognitive functions in early disease stages. These findings emphasize the importance of focusing on individual variability rather than group differences in AD studies. Individual functional connectomes could be instrumental in creating personalized models of AD progression, predicting disease course, and optimizing treatments, paving the way for personalized medicine in AD management.
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Affiliation(s)
- Sara Stampacchia
- Neuro-X Institute and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland.
| | - Saina Asadi
- Department of Radiology and Medical Informatics, Geneva University Neurocenter, University of Geneva, Geneva, Switzerland
| | - Szymon Tomczyk
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
| | - Federica Ribaldi
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Department of Rehabilitation and Geriatrics, Geneva University Hospitals, Geneva, Switzerland
| | - Max Scheffler
- Division of Radiology, Geneva University Hospitals, Geneva, Switzerland
| | - Karl-Olof Lövblad
- Department of Radiology and Medical Informatics, Geneva University Neurocenter, University of Geneva, Geneva, Switzerland
- Neurodiagnostic and Neurointerventional Division, Geneva University Hospitals, Geneva, Switzerland
| | - Michela Pievani
- Lab of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Aïda B Fall
- Faculty of Medicine, University of Geneva, Geneva, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Department of Psychiatry, Geneva University Hospitals, Geneva, Switzerland
| | - Maria Giulia Preti
- Neuro-X Institute and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Radiology and Medical Informatics, Geneva University Neurocenter, University of Geneva, Geneva, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Paul G Unschuld
- Division of Geriatric Psychiatry, University Hospitals of Geneva (HUG), 1226, Thônex, Switzerland
- Department of Psychiatry, University of Geneva (UniGE), 1205, Geneva, Switzerland
| | - Dimitri Van De Ville
- Neuro-X Institute and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Radiology and Medical Informatics, Geneva University Neurocenter, University of Geneva, Geneva, Switzerland
| | - Olaf Blanke
- Neuro-X Institute and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Giovanni B Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Department of Rehabilitation and Geriatrics, Geneva University Hospitals, Geneva, Switzerland
| | - Valentina Garibotto
- Department of Radiology and Medical Informatics, Geneva University Neurocenter, University of Geneva, Geneva, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland
| | - Enrico Amico
- Neuro-X Institute and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland.
- School of Mathematics, University of Birmingham, Birmingham, UK.
- Centre for Human Brain Health, University of Birmingham, Birmingham, UK.
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6
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Chen P, Yang H, Zheng X, Jia H, Hao J, Xu X, Li C, He X, Chen R, Okubo TS, Cui Z. Group-common and individual-specific effects of structure-function coupling in human brain networks with graph neural networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.22.568257. [PMID: 38045396 PMCID: PMC10690242 DOI: 10.1101/2023.11.22.568257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
The human cerebral cortex is organized into functionally segregated but synchronized regions bridged by the structural connectivity of white matter pathways. While structure-function coupling has been implicated in cognitive development and neuropsychiatric disorders, it remains unclear to what extent the structure-function coupling reflects a group-common characteristic or varies across individuals, at both the global and regional brain levels. By leveraging two independent, high-quality datasets, we found that the graph neural network accurately predicted unseen individuals' functional connectivity from structural connectivity, reflecting a strong structure-function coupling. This coupling was primarily driven by network topology and was substantially stronger than that of the correlation approaches. Moreover, we observed that structure-function coupling was dominated by group-common effects, with subtle yet significant individual-specific effects. The regional group and individual effects of coupling were hierarchically organized across the cortex along a sensorimotor-association axis, with lower group and higher individual effects in association cortices. These findings emphasize the importance of considering both group and individual effects in understanding cortical structure-function coupling, suggesting insights into interpreting individual differences of the coupling and informing connectivity-guided therapeutics.
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7
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Lumaca M, Keller PE, Baggio G, Pando-Naude V, Bajada CJ, Martinez MA, Hansen JH, Ravignani A, Joe N, Vuust P, Vulić K, Sandberg K. Frontoparietal network topology as a neural marker of musical perceptual abilities. Nat Commun 2024; 15:8160. [PMID: 39289390 PMCID: PMC11408523 DOI: 10.1038/s41467-024-52479-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Accepted: 09/05/2024] [Indexed: 09/19/2024] Open
Abstract
Why are some individuals more musical than others? Neither cognitive testing nor classical localizationist neuroscience alone can provide a complete answer. Here, we test how the interplay of brain network organization and cognitive function delivers graded perceptual abilities in a distinctively human capacity. We analyze multimodal magnetic resonance imaging, cognitive, and behavioral data from 200+ participants, focusing on a canonical working memory network encompassing prefrontal and posterior parietal regions. Using graph theory, we examine structural and functional frontoparietal network organization in relation to assessments of musical aptitude and experience. Results reveal a positive correlation between perceptual abilities and the integration efficiency of key frontoparietal regions. The linkage between functional networks and musical abilities is mediated by working memory processes, whereas structural networks influence these abilities through sensory integration. Our work lays the foundation for future investigations into the neurobiological roots of individual differences in musicality.
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Affiliation(s)
- M Lumaca
- Center for Music in the Brain, Department of Clinical Medicine, Health, Aarhus University & The Royal Academy of Music Aarhus/Aalborg, Aarhus, Denmark.
| | - P E Keller
- Center for Music in the Brain, Department of Clinical Medicine, Health, Aarhus University & The Royal Academy of Music Aarhus/Aalborg, Aarhus, Denmark
- The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Penrith, Australia
| | - G Baggio
- Language Acquisition and Language Processing Lab, Norwegian University of Science and Technology, Trondheim, Norway
| | - V Pando-Naude
- Center for Music in the Brain, Department of Clinical Medicine, Health, Aarhus University & The Royal Academy of Music Aarhus/Aalborg, Aarhus, Denmark
| | - C J Bajada
- Department of Physiology and Biochemistry, Faculty of Medicine and Surgery, University of Malta / University of Malta Magnetic Resonance Imaging Research Platform, Msida, Malta
| | - M A Martinez
- Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Health, Aarhus University, Aarhus, Denmark
| | - J H Hansen
- Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Health, Aarhus University, Aarhus, Denmark
| | - A Ravignani
- Center for Music in the Brain, Department of Clinical Medicine, Health, Aarhus University & The Royal Academy of Music Aarhus/Aalborg, Aarhus, Denmark
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
| | - N Joe
- Center for Music in the Brain, Department of Clinical Medicine, Health, Aarhus University & The Royal Academy of Music Aarhus/Aalborg, Aarhus, Denmark
| | - P Vuust
- Center for Music in the Brain, Department of Clinical Medicine, Health, Aarhus University & The Royal Academy of Music Aarhus/Aalborg, Aarhus, Denmark
| | - K Vulić
- Department for Human Neuroscience, Institute for Medical Research, University of Belgrade, Belgrade, Serbia
| | - K Sandberg
- Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Health, Aarhus University, Aarhus, Denmark
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8
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Liu H, Jing J, Jiang J, Wen W, Zhu W, Li Z, Pan Y, Cai X, Liu C, Zhou Y, Meng X, Wang Y, Li H, Jiang Y, Zheng H, Wang S, Niu H, Kochan N, Brodaty H, Wei T, Sachdev PS, Fan Y, Liu T, Wang Y. Exploring the link between brain topological resilience and cognitive performance in the context of aging and vascular risk factors: A cross-ethnicity population-based study. Sci Bull (Beijing) 2024; 69:2735-2744. [PMID: 38664095 DOI: 10.1016/j.scib.2024.04.018] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Revised: 02/08/2024] [Accepted: 04/07/2024] [Indexed: 09/09/2024]
Abstract
Brain aging is typically associated with a significant decline in cognitive performance. Vascular risk factors (VRF) and subsequent atherosclerosis (AS) play a major role in this process. Brain resilience reflects the brain's ability to withstand external perturbations, but the relationship of brain resilience with cognition during the aging process remains unclear. Here, we investigated how brain topological resilience (BTR) is associated with cognitive performance in the face of aging and vascular risk factors. We used data from two cross-ethnicity community cohorts, PolyvasculaR Evaluation for Cognitive Impairment and Vascular Events (PRECISE, n = 2220) and Sydney Memory and Ageing Study (MAS, n = 246). We conducted an attack simulation on brain structural networks based on k-shell decomposition and node degree centrality. BTR was defined based on changes in the size of the largest subgroup of the network during the simulation process. Subsequently, we explored the negative correlations of BTR with age, VRF, and AS, and its positive correlation with cognitive performance. Furthermore, using structural equation modeling (SEM), we constructed path models to analyze the directional dependencies among these variables, demonstrating that aging, AS, and VRF affect cognition by disrupting BTR. Our results also indicated the specificity of this metric, independent of brain volume. Overall, these findings underscore the supportive role of BTR on cognition during aging and highlight its potential application as an imaging marker for objective assessment of brain cognitive performance.
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Affiliation(s)
- Hao Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191 , China
| | - Jing Jing
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China.
| | - Jiyang Jiang
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney NSW 2031, Australia; Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW Medicine, Sydney NSW 2052, Australia
| | - Wei Wen
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney NSW 2031, Australia; Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW Medicine, Sydney NSW 2052, Australia
| | - Wanlin Zhu
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Zixiao Li
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Yuesong Pan
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Xueli Cai
- Department of Neurology, Lishui Hospital, Zhejiang University School of Medicine, Lishui 323000, China
| | - Chang Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191 , China
| | - Yijun Zhou
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191 , China
| | - Xia Meng
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Yilong Wang
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Hao Li
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China
| | - Yong Jiang
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Huaguang Zheng
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Suying Wang
- Cerebrovascular Research Lab, Lishui Hospital, Zhejiang University School of Medicine, Lishui 323000, China
| | - Haijun Niu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191 , China
| | - Nicole Kochan
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney NSW 2031, Australia; Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW Medicine, Sydney NSW 2052, Australia
| | - Henry Brodaty
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney NSW 2031, Australia; Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW Medicine, Sydney NSW 2052, Australia
| | - Tiemin Wei
- Department of Cardiology, Lishui Hospital, Zhejiang University School of Medicine, Lishui 323000, China
| | - Perminder S Sachdev
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney NSW 2031, Australia; Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW Medicine, Sydney NSW 2052, Australia
| | - Yubo Fan
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191 , China
| | - Tao Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191 , China.
| | - Yongjun Wang
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China.
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9
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Winter-Hjelm N, Sikorski P, Sandvig A, Sandvig I. Engineered cortical microcircuits for investigations of neuroplasticity. LAB ON A CHIP 2024. [PMID: 39264326 DOI: 10.1039/d4lc00546e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/13/2024]
Abstract
Recent advances in neural engineering have opened new ways to investigate the impact of topology on neural network function. Leveraging microfluidic technologies, it is possible to establish modular circuit motifs that promote both segregation and integration of information processing in the engineered neural networks, similar to those observed in vivo. However, the impact of the underlying topologies on network dynamics and response to pathological perturbation remains largely unresolved. In this work, we demonstrate the utilization of microfluidic platforms with 12 interconnected nodes to structure modular, cortical engineered neural networks. By implementing geometrical constraints inspired by a Tesla valve within the connecting microtunnels, we additionally exert control over the direction of axonal outgrowth between the nodes. Interfacing these platforms with nanoporous microelectrode arrays reveals that the resulting laminar cortical networks exhibit pronounced segregated and integrated functional dynamics across layers, mirroring key elements of the feedforward, hierarchical information processing observed in the neocortex. The multi-nodal configuration also facilitates selective perturbation of individual nodes within the networks. To illustrate this, we induced hypoxia, a key factor in the pathogenesis of various neurological disorders, in well-connected nodes within the networks. Our findings demonstrate that such perturbations induce ablation of information flow across the hypoxic node, while enabling the study of plasticity and information processing adaptations in neighboring nodes and neural communication pathways. In summary, our presented model system recapitulates fundamental attributes of the microcircuit organization of neocortical neural networks, rendering it highly pertinent for preclinical neuroscience research. This model system holds promise for yielding new insights into the development, topological organization, and neuroplasticity mechanisms of the neocortex across the micro- and mesoscale level, in both healthy and pathological conditions.
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Affiliation(s)
- Nicolai Winter-Hjelm
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Norway.
| | - Pawel Sikorski
- Department of Physics, Faculty of Natural Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Axel Sandvig
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Norway.
- Department of Neurology and Clinical Neurophysiology, St. Olavs University Hospital, Trondheim, Norway
| | - Ioanna Sandvig
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Norway.
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Klahn AL, Thompson WH, Momoh I, Abé C, Liberg B, Landén M. Provincial and connector qualities of somatosensory brain network hubs in bipolar disorder. Cereb Cortex 2024; 34:bhae366. [PMID: 39270674 PMCID: PMC11398877 DOI: 10.1093/cercor/bhae366] [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: 06/24/2024] [Revised: 08/15/2024] [Accepted: 08/24/2024] [Indexed: 09/15/2024] Open
Abstract
Brain network hubs are highly connected brain regions serving as important relay stations for information integration. Recent studies have linked mental disorders to impaired hub function. Provincial hubs mainly integrate information within their own brain network, while connector hubs share information between different brain networks. This study used a novel time-varying analysis to investigate whether hubs aberrantly follow the trajectory of other brain networks than their own. The aim was to characterize brain hub functioning in clinically remitted bipolar patients. We analyzed resting-state functional magnetic resonance imaging data from 96 euthymic individuals with bipolar disorder and 61 healthy control individuals. We characterized different hub qualities within the somatomotor network. We found that the somatomotor network comprised mainly provincial hubs in healthy controls. Conversely, in bipolar disorder patients, hubs in the primary somatosensory cortex displayed weaker provincial and stronger connector hub function. Furthermore, hubs in bipolar disorder showed weaker allegiances with their own brain network and followed the trajectories of the limbic, salience, dorsal attention, and frontoparietal network. We suggest that these hub aberrancies contribute to previously shown functional connectivity alterations in bipolar disorder and may thus constitute the neural substrate to persistently impaired sensory integration despite clinical remission.
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Affiliation(s)
- Anna Luisa Klahn
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, Blå Stråket 15, 413 45 Gothenburg, Sweden
- Department of Chemistry and Molecular Biology, Faculty of Science, University of Gothenburg, Medicinaregatan 7B, 413 90 Gothenburg, Sweden
| | - William Hedley Thompson
- Department of Applied Information Technology, Forskningsgången 6, 417 56 Gothenburg University, Gothenburg, Sweden
- Centre for Cognitive and Computation Neuropsychiatry, Karolinska Institutet, Retzius väg 8, 171 65 Stockholm, Sweden
- Department of Clinical Neuroscience, Karolinska Institutet, Retzius väg 8, 171 65 Stockholm, Sweden
| | - Imiele Momoh
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, Blå Stråket 15, 413 45 Gothenburg, Sweden
| | - Christoph Abé
- Centre for Cognitive and Computation Neuropsychiatry, Karolinska Institutet, Retzius väg 8, 171 65 Stockholm, Sweden
- Department of Clinical Neuroscience, Karolinska Institutet, Retzius väg 8, 171 65 Stockholm, Sweden
| | - Benny Liberg
- Department of Clinical Neuroscience, Karolinska Institutet, Retzius väg 8, 171 65 Stockholm, Sweden
| | - Mikael Landén
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, Blå Stråket 15, 413 45 Gothenburg, Sweden
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, 171 65 Stockholm, Sweden
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11
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Huang BK, Zhou JH, Deng Y, Li CH, Ning BL, Ye ZY, Huang XC, Zhao MM, Dong D, Liu M, Zhang DL, Fu WB. Perceived stress and brain connectivity in subthreshold depression: Insights from eyes-closed and eyes-open states. Brain Res 2024; 1838:148947. [PMID: 38657887 DOI: 10.1016/j.brainres.2024.148947] [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: 03/14/2024] [Revised: 04/09/2024] [Accepted: 04/16/2024] [Indexed: 04/26/2024]
Abstract
Perceived stress is an acknowledged risk factor for subthreshold depression (StD), and fluctuations in perceived stress are thought to disrupt the harmony of brain networks essential for emotional and cognitive functioning. This study aimed to elucidate the relationship between eye-open (EO) and eye-closed (EC) states, perceived stress, and StD. We recruited 27 individuals with StD and 33 healthy controls, collecting resting state fMRI data under both EC and EO conditions. We combined intrinsic connectivity and seed-based functional connectivity analyses to construct the functional network and explore differences between EC and EO conditions. Graph theory analysis revealed weakened connectivity strength in the right superior frontal gyrus (SFG) and right median cingulate and paracingulate gyrus (MCC) among participants with StD, suggesting an important role for these regions in the stress-related emotions dysregulation. Notably, altered SFG connectivity was observed to significantly relate to perceived stress levels in StD, and the SFG connection emerges as a neural mediator potentially influencing the relationship between perceived stress and StD. These findings highlight the role of SFG and MCC in perceived stress and suggest that understanding EC and EO states in relation to these regions is important in the neurobiological framework of StD. This may offer valuable perspectives for early prevention and intervention strategies in mental health disorders.
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Affiliation(s)
- Bin-Kun Huang
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, Guangzhou 510631, China; School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China
| | - Jun-He Zhou
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, Guangzhou 510631, China; School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China; Department of Acupuncture and Moxibustion, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 510000, China
| | - Ying Deng
- The Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou 510000, China
| | - Chang-Hong Li
- College of Teacher Education, Guangdong University of Education, Guangzhou 510303, China
| | - Bai-Le Ning
- Department of Acupuncture and Moxibustion, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 510000, China
| | - Zi-Yu Ye
- Acupuncture and Rehabilitation Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou 510000, China
| | - Xi-Chang Huang
- The Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou 510000, China
| | - Mi-Mi Zhao
- Acupuncture and Rehabilitation Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou 510000, China
| | - Dian Dong
- Acupuncture and Rehabilitation Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou 510000, China
| | - Ming Liu
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, Guangzhou 510631, China; School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China
| | - De-Long Zhang
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, Guangzhou 510631, China; School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China.
| | - Wen-Bin Fu
- Department of Acupuncture and Moxibustion, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 510000, China.
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12
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Hechler A, Kuchling J, Müller-Jensen L, Klag J, Paul F, Prüss H, Finke C. Hippocampal hub failure is linked to long-term memory impairment in anti-NMDA-receptor encephalitis: insights from structural connectome graph theoretical network analysis. J Neurol 2024; 271:5886-5898. [PMID: 38977462 PMCID: PMC11377655 DOI: 10.1007/s00415-024-12545-4] [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: 03/12/2024] [Revised: 06/22/2024] [Accepted: 06/26/2024] [Indexed: 07/10/2024]
Abstract
BACKGROUND Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is characterized by distinct structural and functional brain alterations, predominantly affecting the medial temporal lobes and the hippocampus. Structural connectome analysis with graph-based investigations of network properties allows for an in-depth characterization of global and local network changes and their relationship with clinical deficits in NMDAR encephalitis. METHODS Structural networks from 61 NMDAR encephalitis patients in the post-acute stage (median time from acute hospital discharge: 18 months) and 61 age- and sex-matched healthy controls (HC) were analyzed using diffusion-weighted imaging (DWI)-based probabilistic anatomically constrained tractography and volumetry of a selection of subcortical and white matter brain volumes was performed. We calculated global, modular, and nodal graph measures with special focus on default-mode network, medial temporal lobe, and hippocampus. Pathologically altered metrics were investigated regarding their potential association with clinical course, disease severity, and cognitive outcome. RESULTS Patients with NMDAR encephalitis showed regular global graph metrics, but bilateral reductions of hippocampal node strength (left: p = 0.049; right: p = 0.013) and increased node strength of right precuneus (p = 0.013) compared to HC. Betweenness centrality was decreased for left-sided entorhinal cortex (p = 0.042) and left caudal middle frontal gyrus (p = 0.037). Correlation analyses showed a significant association between reduced left hippocampal node strength and verbal long-term memory impairment (p = 0.021). We found decreased left (p = 0.013) and right (p = 0.001) hippocampal volumes that were associated with hippocampal node strength (left p = 0.009; right p < 0.001). CONCLUSIONS Focal network property changes of the medial temporal lobes indicate hippocampal hub failure that is associated with memory impairment in NMDAR encephalitis at the post-acute stage, while global structural network properties remain unaltered. Graph theory analysis provides new pathophysiological insight into structural network changes and their association with persistent cognitive deficits in NMDAR encephalitis.
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Affiliation(s)
- André Hechler
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
- TUM-Neuroimaging Center, Technische Universitaet Muenchen, Munich, Germany
| | - Joseph Kuchling
- Department of Neurology and Experimental Neurology, Charité, Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, 10117, Berlin, Germany
- Experimental and Clinical Research Center, Max Delbrueck Center for Molecular Medicine and Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Leonie Müller-Jensen
- Department of Neurology and Experimental Neurology, Charité, Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, 10117, Berlin, Germany
| | - Johanna Klag
- Department of Neurology and Experimental Neurology, Charité, Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, 10117, Berlin, Germany
| | - Friedemann Paul
- Department of Neurology and Experimental Neurology, Charité, Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, 10117, Berlin, Germany
- Experimental and Clinical Research Center, Max Delbrueck Center for Molecular Medicine and Charité, Universitätsmedizin Berlin, Berlin, Germany
- Neurocure Cluster of Excellence, NeuroCure Clinical Research Center, Charité, Berlin Institute of Health, Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Harald Prüss
- Department of Neurology and Experimental Neurology, Charité, Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, 10117, Berlin, Germany
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Berlin, Germany
| | - Carsten Finke
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany.
- Department of Neurology and Experimental Neurology, Charité, Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, 10117, Berlin, Germany.
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13
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Manivelan SV, Sabarathinam S, Thamilmaran K, Manimehan I. Investigation of transient extreme events in a mutually coupled star network of theoretical Brusselator system. CHAOS (WOODBURY, N.Y.) 2024; 34:091102. [PMID: 39298342 DOI: 10.1063/5.0232021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2024] [Accepted: 09/02/2024] [Indexed: 09/21/2024]
Abstract
In this article, we present evidence of a distinct class of extreme events that occur during the transient chaotic state within network modeling using the Brusselator with a mutually coupled star network. We analyze the phenomenon of transient extreme events in the network by focusing on the lifetimes of chaotic states. These events are identified through the finite-time Lyapunov exponent and quantified using threshold and statistical methods, including the probability distribution function (PDF), generalized extreme value (GEV) distribution, and return period plots. We also evaluate the transitions of these extreme events by examining the average synchronization error and the system's energy function. Our findings, validated across networks of various sizes, demonstrate consistent patterns and behaviors, contributing to a deeper understanding of transient extreme events in complex networks.
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Affiliation(s)
- S V Manivelan
- Department of Physics, M. R. Government Arts College (Affiliated to Bharathidasan University, Tiruchirappalli), Mannargudi 614 001, Tamilnadu, India
| | - S Sabarathinam
- Laboratory of Complex Systems Modeling and Control, Faculty of Computer Science, National Research University, Higher School of Economics (HSE), Moscow 109028, Russia
| | - K Thamilmaran
- Centre for Computational Modeling, Chennai Institute of Technology, Chennai 600 069, Tamilnadu, India
| | - I Manimehan
- Department of Physics, M. R. Government Arts College (Affiliated to Bharathidasan University, Tiruchirappalli), Mannargudi 614 001, Tamilnadu, India
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14
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Del Mauro G, Li Y, Wang Z. Global brain connectivity: Test-retest stability and association with biological and neurocognitive variables. J Neurosci Methods 2024; 409:110205. [PMID: 38914376 DOI: 10.1016/j.jneumeth.2024.110205] [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: 01/08/2024] [Revised: 06/03/2024] [Accepted: 06/21/2024] [Indexed: 06/26/2024]
Abstract
BACKGROUND Global brain connectivity (GBC) enables measuring brain regions' functional connectivity strength at rest by computing the average correlation between each brain voxel's time-series and that of all other voxels. NEW METHOD We used resting-state fMRI (rs-fMRI) data of young adult participants from the Human Connectome Project (HCP) dataset to explore the test-retest stability of GBC, the brain regions with higher or lower GBC, as well as the associations of this measure with age, sex, and fluid intelligence. GBC was computed by considering separately the positive and negative correlation coefficients (positive GBC and negative GBC). RESULTS Test-retest stability was higher for positive compared to negative GBC. Areas with higher GBC were located in the default mode network, insula, and visual areas, while regions with lower GBC were in subcortical regions, temporal cortex, and cerebellum. Higher age was related to global reduction of positive GBC. Males displayed higher positive GBC in the whole brain. Fluid intelligence was associated to increased positive GBC in fronto-parietal, occipital and temporal regions. COMPARISON WITH EXISTING METHOD Compared to previous works, this study adopted a larger sample size and tested GBC stability using data from different rs-fMRI sessions. Moreover, these associations were examined by testing positive and negative GBC separately. CONCLUSIONS Lower stability for negative compared to positive GBC suggests that negative correlations may reflect less stable couplings between brain regions. Our findings indicate a greater importance of positive compared to negative GBC for the associations of functional connectivity strength with biological and neurocognitive variables.
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Affiliation(s)
- Gianpaolo Del Mauro
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, 670 W Baltimore St, HSF III, Baltimore, MD 21202, United States
| | - Yiran Li
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, 670 W Baltimore St, HSF III, Baltimore, MD 21202, United States
| | - Ze Wang
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, 670 W Baltimore St, HSF III, Baltimore, MD 21202, United States.
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15
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Asad Z, Fakheir Y, Abukhaled Y, Khalil R. Implications of altered pyramidal cell morphology on clinical symptoms of neurodevelopmental disorders. Eur J Neurosci 2024; 60:4877-4892. [PMID: 39054743 DOI: 10.1111/ejn.16484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 05/26/2024] [Accepted: 07/13/2024] [Indexed: 07/27/2024]
Abstract
The prevalence of pyramidal cells (PCs) in the mammalian cerebral cortex underscore their value as they play a crucial role in various brain functions, ranging from cognition, sensory processing, to motor output. PC morphology significantly influences brain connectivity and plays a critical role in maintaining normal brain function. Pathological alterations to PC morphology are thought to contribute to the aetiology of neurodevelopmental disorders such as autism spectrum disorder (ASD) and schizophrenia. This review explores the relationship between abnormalities in PC morphology in key cortical areas and the clinical manifestations in schizophrenia and ASD. We focus largely on human postmortem studies and provide evidence that dendritic segment length, complexity and spine density are differentially affected in these disorders. These morphological alterations can lead to disruptions in cortical connectivity, potentially contributing to the cognitive and behavioural deficits observed in these disorders. Furthermore, we highlight the importance of investigating the functional and structural characteristics of PCs in these disorders to illuminate the underlying pathogenesis and stimulate further research in this area.
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Affiliation(s)
- Zummar Asad
- School of Medicine, Royal College of Surgeons in Ireland (RCSI), Dublin, Ireland
| | - Yara Fakheir
- College of Medicine and Health Sciences, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Yara Abukhaled
- College of Medicine and Health Sciences, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Reem Khalil
- Department of Biology, Chemistry and Environmental Sciences, American University of Sharjah, Sharjah, United Arab Emirates
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16
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Li J, Jin S, Li Z, Zeng X, Yang Y, Luo Z, Xu X, Cui Z, Liu Y, Wang J. Morphological Brain Networks of White Matter: Mapping, Evaluation, Characterization, and Application. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2400061. [PMID: 39005232 PMCID: PMC11425219 DOI: 10.1002/advs.202400061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 06/27/2024] [Indexed: 07/16/2024]
Abstract
Although white matter (WM) accounts for nearly half of adult brain, its wiring diagram is largely unknown. Here, an approach is developed to construct WM networks by estimating interregional morphological similarity based on structural magnetic resonance imaging. It is found that morphological WM networks showed nontrivial topology, presented good-to-excellent test-retest reliability, accounted for phenotypic interindividual differences in cognition, and are under genetic control. Through integration with multimodal and multiscale data, it is further showed that morphological WM networks are able to predict the patterns of hamodynamic coherence, metabolic synchronization, gene co-expression, and chemoarchitectonic covariance, and associated with structural connectivity. Moreover, the prediction followed WM functional connectomic hierarchy for the hamodynamic coherence, is related to genes enriched in the forebrain neuron development and differentiation for the gene co-expression, and is associated with serotonergic system-related receptors and transporters for the chemoarchitectonic covariance. Finally, applying this approach to multiple sclerosis and neuromyelitis optica spectrum disorders, it is found that both diseases exhibited morphological dysconnectivity, which are correlated with clinical variables of patients and are able to diagnose and differentiate the diseases. Altogether, these findings indicate that morphological WM networks provide a reliable and biologically meaningful means to explore WM architecture in health and disease.
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Affiliation(s)
- Junle Li
- Institute for Brain Research and RehabilitationSouth China Normal UniversityGuangzhou510631China
| | - Suhui Jin
- Institute for Brain Research and RehabilitationSouth China Normal UniversityGuangzhou510631China
| | - Zhen Li
- Institute for Brain Research and RehabilitationSouth China Normal UniversityGuangzhou510631China
| | - Xiangli Zeng
- Institute for Brain Research and RehabilitationSouth China Normal UniversityGuangzhou510631China
| | - Yuping Yang
- Institute for Brain Research and RehabilitationSouth China Normal UniversityGuangzhou510631China
| | - Zhenzhen Luo
- Institute for Brain Research and RehabilitationSouth China Normal UniversityGuangzhou510631China
| | - Xiaoyu Xu
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijing100875China
- Chinese Institute for Brain ResearchBeijing102206China
| | - Zaixu Cui
- Chinese Institute for Brain ResearchBeijing102206China
| | - Yaou Liu
- Department of RadiologyBeijing Tiantan HospitalBeijing100070China
| | - Jinhui Wang
- Institute for Brain Research and RehabilitationSouth China Normal UniversityGuangzhou510631China
- Key Laboratory of BrainCognition and Education SciencesMinistry of EducationGuangzhou510631China
- Center for Studies of Psychological ApplicationSouth China Normal UniversityGuangzhou510631China
- Guangdong Key Laboratory of Mental Health and Cognitive ScienceSouth China Normal UniversityGuangzhou510631China
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17
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Požar R, Martin T, Giordani B, Kavcic V. Enhanced functional brain network integration in mild cognitive impairment during cognitive task performance: A compensatory mechanism or a result of neural disinhibition? Eur J Neurosci 2024. [PMID: 39180174 DOI: 10.1111/ejn.16511] [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: 06/03/2024] [Revised: 07/16/2024] [Accepted: 08/06/2024] [Indexed: 08/26/2024]
Abstract
Although previous studies have observed increased global network integration during tasks in persons with mild cognitive impairment (MCI), the association between this integration and actual task performance has remained unexplored. Understanding this link is crucial for uncovering the underlying mechanism behind these changes in network integration and their potential role in MCI. Here, to find such a link, we investigated brain network integration derived from electroencephalography recordings during a visual motion discrimination task in older adults with MCI and those with normal cognition. We focused on a critical period just before stimulus presentation, which is known to be important for task performance. Our results revealed that during this period, MCI patients exhibited increased network integration compared to controls. Interestingly, increased integration was associated with worse task performance in the MCI group, suggesting it was not beneficial. No such association was found in the control group. Notably, this difference existed despite similar overall task performance between the groups. This suboptimal integration pattern during the cognitive task might reflect network de-differentiation due to disinhibition in MCI patients. Collectively, our study highlights the potential of analysing network integration during tasks to identify cognitive impairment and suggest a distinct role for network integration in MCI patients compared with healthy controls.
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Affiliation(s)
- Rok Požar
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Koper, Slovenia
- Andrej Marušič Institute, University of Primorska, Koper, Slovenia
- Physics and Mechanics, Institute of Mathematics, Ljubljana, Slovenia
| | - Tim Martin
- Kennesaw State University, Kennesaw, Georgia, USA
| | - Bruno Giordani
- Michigan Alzheimer's Disease Research Center, Ann Arbor, Michigan, USA
- University of Michigan, Ann Arbor, Michigan, USA
| | - Voyko Kavcic
- Wayne State University, Institute of Gerontology, Detroit, Michigan, USA
- International Institute of Applied Gerontology, Ljubljana, Slovenia
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18
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Osaki T, Delepine C, Osako Y, Kranz D, Levin A, Nelson C, Fagiolini M, Sur M. Early differential impact of MeCP2 mutations on functional networks in Rett syndrome patient-derived human cerebral organoids. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.10.607464. [PMID: 39149328 PMCID: PMC11326256 DOI: 10.1101/2024.08.10.607464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Human cerebral organoids derived from induced pluripotent stem cells can recapture early developmental processes and reveal changes involving neurodevelopmental disorders. Mutations in the X-linked methyl-CpG binding protein 2 (MECP2) gene are associated with Rett syndrome, and disease severity varies depending on the location and type of mutation. Here, we focused on neuronal activity in Rett syndrome patient-derived organoids, analyzing two types of MeCP2 mutations - a missense mutation (R306C) and a truncating mutation (V247X) - using calcium imaging with three-photon microscopy. Compared to isogenic controls, we found abnormal neuronal activity in Rett organoids and altered network function based on graph theoretic analyses, with V247X mutations impacting functional responses and connectivity more severely than R306C mutations. These changes paralleled EEG data obtained from patients with comparable mutations. Labeling DLX promoter-driven inhibitory neurons demonstrated differences in activity and functional connectivity of inhibitory and excitatory neurons in the two types of mutation. Transcriptomic analyses revealed HDAC2-associated impairment in R306C organoids and decreased GABAA receptor expression in excitatory neurons in V247X organoids. These findings demonstrate mutation-specific mechanisms of vulnerability in Rett syndrome and suggest targeted strategies for their treatment.
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Affiliation(s)
- Tatsuya Osaki
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Chloe Delepine
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Yuma Osako
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Devorah Kranz
- Boston Children’s Hospital, Brookline, MA 02445, USA
- Harvard University, Cambridge, MA 02139, USA
| | - April Levin
- Boston Children’s Hospital, Brookline, MA 02445, USA
| | - Charles Nelson
- Boston Children’s Hospital, Brookline, MA 02445, USA
- Harvard University, Cambridge, MA 02139, USA
| | - Michela Fagiolini
- Boston Children’s Hospital, Brookline, MA 02445, USA
- Harvard University, Cambridge, MA 02139, USA
| | - Mriganka Sur
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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Shang G, Zhou T, Yu X, Yan X, He K, Liu B, Feng Z, Xu J, Zhang Y, Yu X. Chronic hypercortisolism disrupts the principal functional gradient in Cushing's disease: A multi-scale connectomics and transcriptomics study. Neuroimage Clin 2024; 43:103652. [PMID: 39146836 PMCID: PMC11367515 DOI: 10.1016/j.nicl.2024.103652] [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: 05/12/2024] [Revised: 07/22/2024] [Accepted: 08/06/2024] [Indexed: 08/17/2024]
Abstract
Cushing's disease (CD) represents a state of cortisol excess, serving as a model to investigate the effects of prolonged hypercortisolism on functional brain. Potential alterations in the functional connectome of the brain may explain frequently reported cognitive deficits and affective disorders in CD patients. This study aims to elucidate the effects of chronic hypercortisolism on the principal functional gradient, which represents a hierarchical architecture with gradual transitions across cognitive processes, by integrating connectomics and transcriptomics approaches. Utilizing resting-state functional magnetic resonance imaging data from 140 participants (86 CD patients, 54 healthy controls) recruited at a single center, we explored the alterations in the principal gradient in CD patients. Further, we thoroughly explored the underlying associative mechanisms of the observed characteristic alterations with cognitive function domains, biological attributes, and neuropsychiatric representations, as well as gene expression profiles. Compared to healthy controls, CD patients demonstrated changes in connectome patterns in both primary and higher-order networks, exhibiting an overall converged trend along the principal gradient axis. The gradient values in CD patients' right prefrontal cortex and bilateral sensorimotor cortices exhibited a significant correlation with cortisol levels. Moreover, the cortical regions showing gradient alterations were principally associated with sensory information processing and higher-cognitive functions, as well as correlated with the gene expression patterns which involved synaptic components and function. The findings suggest that converged alterations in the principal gradient in CD patients may mediate the relationship between hypercortisolism and cognitive impairments, potentially involving genes regulating synaptic components and function.
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Affiliation(s)
- Guosong Shang
- Department of Neurosurgery, The First Medical Centre of Chinese PLA General Hospital, Beijing, China; Chinese PLA Medical School, Beijing, China
| | - Tao Zhou
- Department of Neurosurgery, The First Medical Centre of Chinese PLA General Hospital, Beijing, China; Neurosurgery Institute, Chinese PLA General Hospital, Beijing, China
| | - Xiaoteng Yu
- Department of Urology, Peking University First Hospital, Beijing, China; Institute of Urology, Peking University, National Urological Cancer Center, Beijing, China
| | - Xinyuan Yan
- Department of Psychiatry, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Kunyu He
- Department of Neurosurgery, The First Medical Centre of Chinese PLA General Hospital, Beijing, China; Chinese PLA Medical School, Beijing, China
| | - Bin Liu
- Department of Neurosurgery, The First Medical Centre of Chinese PLA General Hospital, Beijing, China; Chinese PLA Medical School, Beijing, China
| | - Zhebin Feng
- Department of Neurosurgery, PLA 942 Hospital, Yinchuan, Ningxia, China
| | - Junpeng Xu
- Department of Neurosurgery, The First Medical Centre of Chinese PLA General Hospital, Beijing, China; Chinese PLA Medical School, Beijing, China
| | - Yanyang Zhang
- Department of Neurosurgery, The First Medical Centre of Chinese PLA General Hospital, Beijing, China; Neurosurgery Institute, Chinese PLA General Hospital, Beijing, China.
| | - Xinguang Yu
- Department of Neurosurgery, The First Medical Centre of Chinese PLA General Hospital, Beijing, China; Chinese PLA Medical School, Beijing, China; Neurosurgery Institute, Chinese PLA General Hospital, Beijing, China.
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20
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Smith NR, Ameen S, Miller SN, Kasper JM, Schwarz JM, Hommel JD, Borzou A. The neuroanatomical organization of the hypothalamus is driven by spatial and topological efficiency. Front Syst Neurosci 2024; 18:1417346. [PMID: 39165582 PMCID: PMC11334159 DOI: 10.3389/fnsys.2024.1417346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2024] [Accepted: 07/18/2024] [Indexed: 08/22/2024] Open
Abstract
The hypothalamus in the mammalian brain is responsible for regulating functions associated with survival and reproduction representing a complex set of highly interconnected, yet anatomically and functionally distinct, sub-regions. It remains unclear what factors drive the spatial organization of sub-regions within the hypothalamus. One potential factor may be structural connectivity of the network that promotes efficient function with well-connected sub-regions placed closer together geometrically, i.e., the strongest axonal signal transferred through the shortest geometrical distance. To empirically test for such efficiency, we use hypothalamic data derived from the Allen Mouse Brain Connectivity Atlas, which provides a structural connectivity map of mouse brain regions derived from a series of viral tracing experiments. Using both cost function minimization and comparison with a weighted, sphere-packing ensemble, we demonstrate that the sum of the distances between hypothalamic sub-regions are not close to the minimum possible distance, consistent with prior whole brain studies. However, if such distances are weighted by the inverse of the magnitude of the connectivity, their sum is among the lowest possible values. Specifically, the hypothalamus appears within the top 94th percentile of neural efficiencies of randomly packed configurations and within one standard deviation of the median efficiency when packings are optimized for maximal neural efficiency. Our results, therefore, indicate that a combination of geometrical and topological constraints help govern the structure of the hypothalamus.
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Affiliation(s)
- Nathan R. Smith
- Center for Addiction Sciences and Therapeutics, Department of Pharmacology and Toxicology, University of Texas Medical Branch, Galveston, TX, United States
| | - Shabeeb Ameen
- Physics Department and BioInspired Institute, Syracuse University, Syracuse, NY, United States
| | - Sierra N. Miller
- Center for Addiction Sciences and Therapeutics, Department of Pharmacology and Toxicology, University of Texas Medical Branch, Galveston, TX, United States
| | - James M. Kasper
- Center for Addiction Sciences and Therapeutics, Department of Pharmacology and Toxicology, University of Texas Medical Branch, Galveston, TX, United States
| | - Jennifer M. Schwarz
- Physics Department and BioInspired Institute, Syracuse University, Syracuse, NY, United States
- Indian Creek Farm, Ithaca, NY, United States
| | - Jonathan D. Hommel
- Center for Addiction Sciences and Therapeutics, Department of Pharmacology and Toxicology, University of Texas Medical Branch, Galveston, TX, United States
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21
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Markett S, Boeken OJ, Wudarczyk OA. Multimodal imaging investigation of structural rich club alterations in Alzheimer's disease and mild cognitive impairment: Amyloid deposition, structural atrophy, and functional activation differences. Eur J Neurosci 2024; 60:4169-4181. [PMID: 38779858 DOI: 10.1111/ejn.16384] [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: 09/13/2023] [Revised: 04/03/2024] [Accepted: 04/22/2024] [Indexed: 05/25/2024]
Abstract
Alzheimer's disease (AD) is characterized by significant cerebral dysfunction, including increased amyloid deposition, gray matter atrophy, and changes in brain function. The involvement of highly connected network hubs, known as the "rich club," in the pathology of the disease remains inconclusive despite previous research efforts. In this study, we aimed to systematically assess the link between the rich club and AD using a multimodal neuroimaging approach. We employed network analyses of diffusion magnetic resonance imaging (MRI), longitudinal assessments of gray matter atrophy, amyloid deposition measurements using positron emission tomography (PET) imaging, and meta-analytic data on functional activation differences. Our study focused on evaluating the role of both the structural brain network's core and extended rich club regions in individuals with mild cognitive impairment (MCI) and those diagnosed with AD. Our findings revealed that structural rich club regions exhibited accelerated gray matter atrophy and increased amyloid deposition in both MCI and AD. Importantly, these regions remained unaffected by altered functional activation patterns observed outside the core rich club regions. These results shed light on the connection between two major AD biomarkers and the rich club, providing valuable insights into AD as a potential disconnection syndrome.
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Affiliation(s)
| | - Ole J Boeken
- Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Neurology and Experimental Neurology, Charité-Universitätsmedizin, Berlin, Germany
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22
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Forbes CE. On the neural networks of self and other bias and their role in emergent social interactions. Cortex 2024; 177:113-129. [PMID: 38848651 DOI: 10.1016/j.cortex.2024.05.002] [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: 06/01/2023] [Revised: 02/09/2024] [Accepted: 05/14/2024] [Indexed: 06/09/2024]
Abstract
Extensive research has documented the brain networks that play an integral role in bias, or the alteration and filtration of information processing in a manner that fundamentally favors an individual. The roots of bias, whether self- or other-oriented, are a complex constellation of neural and psychological processes that start at the most fundamental levels of sensory processing. From the millisecond information is received in the brain it is filtered at various levels and through various brain networks in relation to extant intrinsic activity to provide individuals with a perception of reality that complements and satisfies the conscious perceptions they have for themselves and the cultures in which they were reared. The products of these interactions, in turn, are dynamically altered by the introduction of others, be they friends or strangers who are similar or different in socially meaningful ways. While much is known about the various ways that basic biases alter specific aspects of neural function to support various forms of bias, the breadth and scope of the phenomenon remains entirely unclear. The purpose of this review is to examine the brain networks that shape (i.e., bias) the self-concept and how interactions with similar (ingroup) compared to dissimilar (outgroup) others alter these network (and subsequent interpersonal) interactions in fundamental ways. Throughout, focus is placed on an emerging understanding of the brain as a complex system, which suggests that many of these network interactions likely occur on a non-linear scale that blurs the lines between network hierarchies.
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Affiliation(s)
- Chad E Forbes
- Social Neuroscience Laboratory, Department of Psychology, Florida Atlantic University, Boca Raton, FL, USA; Florida Atlantic University Stiles-Nicholson Brain Institute, USA.
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23
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Yang ZC, Xue BW, Song XY, Yin CD, Yeh FC, Li G, Deng ZH, Sun SJ, Hou ZG, Xie J. Connectomic insights into the impact of 1p/19q co-deletion in dominant hemisphere insular glioma patients. Front Neurosci 2024; 18:1283518. [PMID: 39135733 PMCID: PMC11317282 DOI: 10.3389/fnins.2024.1283518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Accepted: 07/10/2024] [Indexed: 08/15/2024] Open
Abstract
Objectives This study aimed to elucidate the influences of 1p/19q co-deletion on structural connectivity alterations in patients with dominant hemisphere insular diffuse gliomas. Methods We incorporated 32 cases of left insular gliomas and 20 healthy controls for this study. Using diffusion MRI, we applied correlational tractography, differential tractography, and graph theoretical analysis to explore the potential connectivity associated with 1p/19q co-deletion. Results The study revealed that the quantitative anisotropy (QA) of key deep medial fiber tracts, including the anterior thalamic radiation, superior thalamic radiation, fornix, and cingulum, had significant negative associations with 1p/19q co-deletion (FDR = 4.72 × 10-5). These tracts are crucial in maintaining the integrity of brain networks. Differential analysis further supported these findings (FWER-corrected p < 0.05). The 1p/19q non-co-deletion group exhibited significantly higher clustering coefficients (FDR-corrected p < 0.05) and reduced betweenness centrality (FDR-corrected p < 0.05) in regions around the tumor compared to HC group. Graph theoretical analysis indicated that non-co-deletion patients had increased local clustering and decreased betweenness centrality in peritumoral brain regions compared to co-deletion patients and healthy controls (FDR-corrected p < 0.05). Additionally, despite not being significant through correction, patients with 1p/19q co-deletion exhibited lower trends in weighted average clustering coefficient, transitivity, small worldness, and global efficiency, while showing higher tendencies in weighted path length compared to patients without the co-deletion. Conclusion The findings of this study underline the significant role of 1p/19q co-deletion in altering structural connectivity in insular glioma patients. These alterations in brain networks could have profound implications for the neural functionality in patients with dominant hemisphere insular gliomas.
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Affiliation(s)
- Zuo-cheng Yang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Bo-wen Xue
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xin-yu Song
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Chuan-dong Yin
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Fang-cheng Yeh
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States
| | - Gen Li
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zheng-hai Deng
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Sheng-jun Sun
- Neuroimaging Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zong-gang Hou
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jian Xie
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
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24
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Parkes L, Kim JZ, Stiso J, Brynildsen JK, Cieslak M, Covitz S, Gur RE, Gur RC, Pasqualetti F, Shinohara RT, Zhou D, Satterthwaite TD, Bassett DS. A network control theory pipeline for studying the dynamics of the structural connectome. Nat Protoc 2024:10.1038/s41596-024-01023-w. [PMID: 39075309 DOI: 10.1038/s41596-024-01023-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 05/16/2024] [Indexed: 07/31/2024]
Abstract
Network control theory (NCT) is a simple and powerful tool for studying how network topology informs and constrains the dynamics of a system. Compared to other structure-function coupling approaches, the strength of NCT lies in its capacity to predict the patterns of external control signals that may alter the dynamics of a system in a desired way. An interesting development for NCT in the neuroscience field is its application to study behavior and mental health symptoms. To date, NCT has been validated to study different aspects of the human structural connectome. NCT outputs can be monitored throughout developmental stages to study the effects of connectome topology on neural dynamics and, separately, to test the coherence of empirical datasets with brain function and stimulation. Here, we provide a comprehensive pipeline for applying NCT to structural connectomes by following two procedures. The main procedure focuses on computing the control energy associated with the transitions between specific neural activity states. The second procedure focuses on computing average controllability, which indexes nodes' general capacity to control the dynamics of the system. We provide recommendations for comparing NCT outputs against null network models, and we further support this approach with a Python-based software package called 'network control theory for python'. The procedures in this protocol are appropriate for users with a background in network neuroscience and experience in dynamical systems theory.
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Affiliation(s)
- Linden Parkes
- Department of Psychiatry, Rutgers University, Piscataway, NJ, USA.
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Jason Z Kim
- Department of Physics, Cornell University, Ithaca, NY, USA
| | - Jennifer Stiso
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Julia K Brynildsen
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Matthew Cieslak
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sydney Covitz
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Raquel E Gur
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ruben C Gur
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Fabio Pasqualetti
- Department of Mechanical Engineering, University of California, Riverside, Riverside, CA, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, Philadelphia, PA, USA
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dale Zhou
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Theodore D Satterthwaite
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, Philadelphia, PA, USA
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA, USA
- Santa Fe Institute, Santa Fe, NM, USA
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25
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Teles M, Maximo JO, Lahti AC, Kraguljac NV. Topological Perturbations in the Functional Connectome Support the Deficit/Non-deficit Distinction in Antipsychotic Medication-Naïve First Episode Psychosis Patients. Schizophr Bull 2024; 50:839-847. [PMID: 38666705 PMCID: PMC11283198 DOI: 10.1093/schbul/sbae054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
BACKGROUND Heterogeneity in the etiology, pathophysiology, and clinical features of schizophrenia challenges clinicians and researchers. A helpful approach could be stratifying patients according to the presence or absence of clinical features of the deficit syndrome (DS). DS is characterized by enduring and primary negative symptoms, a clinically less heterogeneous subtype of the illness, and patients with features of DS are thought to present abnormal brain network characteristics, however, this idea has received limited attention. We investigated functional brain network topology in patients displaying deficit features and those who do not. DESIGN We applied graph theory analytics to resting-state functional magnetic resonance imaging data of 61 antipsychotic medication-naïve first episode psychosis patients, 18 DS and 43 non-deficit schizophrenia (NDS), and 72 healthy controls (HC). We quantified small-worldness, global and nodal efficiency measures, shortest path length, nodal local efficiency, and synchronization and contrasted them among the 3 groups. RESULTS DS presented decreased network integration and segregation compared to HC and NDS. DS showed lower global efficiency, longer global path lengths, and lower global local efficiency. Nodal efficiency was lower and the shortest path length was longer in DS in default mode, ventral attention, dorsal attention, frontoparietal, limbic, somatomotor, and visual networks compared to HC. Compared to NDS, DS showed lower efficiency and longer shortest path length in default mode, limbic, somatomotor, and visual networks. CONCLUSIONS Our data supports increasing evidence, based on topological perturbations of the functional connectome, that deficit syndrome may be a distinct form of the illness.
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Affiliation(s)
- Matheus Teles
- Department of Psychology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Jose Omar Maximo
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Adrienne Carol Lahti
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Nina Vanessa Kraguljac
- Department of Psychiatry and Behavioral Health, The Ohio State University, Columbus, OH, USA
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26
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Lin Q, Jin S, Yin G, Li J, Asgher U, Qiu S, Wang J. Cortical Morphological Networks Differ Between Gyri and Sulci. Neurosci Bull 2024:10.1007/s12264-024-01262-7. [PMID: 39044060 DOI: 10.1007/s12264-024-01262-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 03/28/2024] [Indexed: 07/25/2024] Open
Abstract
This study explored how the human cortical folding pattern composed of convex gyri and concave sulci affected single-subject morphological brain networks, which are becoming an important method for studying the human brain connectome. We found that gyri-gyri networks exhibited higher morphological similarity, lower small-world parameters, and lower long-term test-retest reliability than sulci-sulci networks for cortical thickness- and gyrification index-based networks, while opposite patterns were observed for fractal dimension-based networks. Further behavioral association analysis revealed that gyri-gyri networks and connections between gyral and sulcal regions significantly explained inter-individual variance in Cognition and Motor domains for fractal dimension- and sulcal depth-based networks. Finally, the clinical application showed that only sulci-sulci networks exhibited morphological similarity reductions in major depressive disorder for cortical thickness-, fractal dimension-, and gyrification index-based networks. Taken together, these findings provide novel insights into the constraint of the cortical folding pattern to the network organization of the human brain.
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Affiliation(s)
- Qingchun Lin
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, 510631, China
| | - Suhui Jin
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, 510631, China
| | - Guole Yin
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, 510631, China
| | - Junle Li
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, 510631, China
| | - Umer Asgher
- Department of Air Transport, Faculty of Transportation Sciences, Czech Technical University in Prague (CTU), Prague, 128 00, Czech Republic
- School of Interdisciplinary Engineering and Sciences (SINES), National University of Science and Technology (NUST), Islamabad, 44000, Pakistan
| | - Shijun Qiu
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510405, China
| | - Jinhui Wang
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, 510631, China.
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Guangzhou, 510631, China.
- Center for Studies of Psychological Application, South China Normal University, Guangzhou, 510631, China.
- Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, 510631, China.
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27
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Chen R, Zhao R, Li H, Xu X, Li M, Zhao Z, Sun C, Wang G, Wu D. Development of the Fetal Brain Corticocortical Structural Network during the Second-to-Third Trimester Based on Diffusion MRI. J Neurosci 2024; 44:e1567232024. [PMID: 38844343 PMCID: PMC11255424 DOI: 10.1523/jneurosci.1567-23.2024] [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: 08/14/2023] [Revised: 05/08/2024] [Accepted: 05/31/2024] [Indexed: 07/19/2024] Open
Abstract
During the second-to-third trimester, the neuronal pathways of the fetal brain experience rapid development, resulting in the complex architecture of the interwired network at birth. While diffusion MRI-based tractography has been employed to study the prenatal development of structural connectivity network (SCN) in preterm neonatal and postmortem fetal brains, the in utero development of SCN in the normal fetal brain remains largely unknown. In this study, we utilized in utero dMRI data from human fetuses of both sexes between 26 and 38 gestational weeks to investigate the developmental trajectories of the fetal brain SCN, focusing on intrahemispheric connections. Our analysis revealed significant increases in global efficiency, mean local efficiency, and clustering coefficient, along with significant decrease in shortest path length, while small-worldness persisted during the studied period, revealing balanced network integration and segregation. Widespread short-ranged connectivity strengthened significantly. The nodal strength developed in a posterior-to-anterior and medial-to-lateral order, reflecting a spatiotemporal gradient in cortical network connectivity development. Moreover, we observed distinct lateralization patterns in the fetal brain SCN. Globally, there was a leftward lateralization in network efficiency, clustering coefficient, and small-worldness. The regional lateralization patterns in most language, motor, and visual-related areas were consistent with prior knowledge, except for Wernicke's area, indicating lateralized brain wiring is an innate property of the human brain starting from the fetal period. Our findings provided a comprehensive view of the development of the fetal brain SCN and its lateralization, as a normative template that may be used to characterize atypical development.
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Affiliation(s)
- Ruike Chen
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310027, P. R. China
| | - Ruoke Zhao
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310027, P. R. China
| | - Haotian Li
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310027, P. R. China
| | - Xinyi Xu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310027, P. R. China
| | - Mingyang Li
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310027, P. R. China
| | - Zhiyong Zhao
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310027, P. R. China
| | - Cong Sun
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing 100730, P. R. China
| | - Guangbin Wang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, P. R. China
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310027, P. R. China
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28
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Calazans MAA, Ferreira FABS, Santos FAN, Madeiro F, Lima JB. Machine Learning and Graph Signal Processing Applied to Healthcare: A Review. Bioengineering (Basel) 2024; 11:671. [PMID: 39061753 PMCID: PMC11273494 DOI: 10.3390/bioengineering11070671] [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: 05/27/2024] [Revised: 06/20/2024] [Accepted: 06/26/2024] [Indexed: 07/28/2024] Open
Abstract
Signal processing is a very useful field of study in the interpretation of signals in many everyday applications. In the case of applications with time-varying signals, one possibility is to consider them as graphs, so graph theory arises, which extends classical methods to the non-Euclidean domain. In addition, machine learning techniques have been widely used in pattern recognition activities in a wide variety of tasks, including health sciences. The objective of this work is to identify and analyze the papers in the literature that address the use of machine learning applied to graph signal processing in health sciences. A search was performed in four databases (Science Direct, IEEE Xplore, ACM, and MDPI), using search strings to identify papers that are in the scope of this review. Finally, 45 papers were included in the analysis, the first being published in 2015, which indicates an emerging area. Among the gaps found, we can mention the need for better clinical interpretability of the results obtained in the papers, that is not to restrict the results or conclusions simply to performance metrics. In addition, a possible research direction is the use of new transforms. It is also important to make new public datasets available that can be used to train the models.
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Affiliation(s)
| | - Felipe A. B. S. Ferreira
- Unidade Acadêmica do Cabo de Santo Agostinho, Universidade Federal Rural de Pernambuco, Cabo de Santo Agostinho 54518-430, Brazil;
| | - Fernando A. N. Santos
- Institute for Advanced Studies, Universiteit van Amsterdam, 1012 WP Amsterdam, The Netherlands;
| | - Francisco Madeiro
- Escola Politécnica de Pernambuco, Universidade de Pernambuco, Recife 50720-001, Brazil;
| | - Juliano B. Lima
- Centro de Tecnologia e Geociências, Universidade Federal de Pernambuco, Recife 50670-901, Brazil;
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Huang S, De Brigard F, Cabeza R, Davis SW. Connectivity analyses for task-based fMRI. Phys Life Rev 2024; 49:139-156. [PMID: 38728902 PMCID: PMC11116041 DOI: 10.1016/j.plrev.2024.04.012] [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: 04/25/2024] [Accepted: 04/29/2024] [Indexed: 05/12/2024]
Abstract
Functional connectivity is conventionally defined by measuring the similarity between brain signals from two regions. The technique has become widely adopted in the analysis of functional magnetic resonance imaging (fMRI) data, where it has provided cognitive neuroscientists with abundant information on how brain regions interact to support complex cognition. However, in the past decade the notion of "connectivity" has expanded in both the complexity and heterogeneity of its application to cognitive neuroscience, resulting in greater difficulty of interpretation, replication, and cross-study comparisons. In this paper, we begin with the canonical notions of functional connectivity and then introduce recent methodological developments that either estimate some alternative form of connectivity or extend the analytical framework, with the hope of bringing better clarity for cognitive neuroscience researchers.
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Affiliation(s)
- Shenyang Huang
- Department of Psychology and Neuroscience, Duke University, Durham, NC 27708, United States; Center for Cognitive Neuroscience, Duke University, Durham, NC 27708, United States.
| | - Felipe De Brigard
- Department of Psychology and Neuroscience, Duke University, Durham, NC 27708, United States; Center for Cognitive Neuroscience, Duke University, Durham, NC 27708, United States; Department of Philosophy, Duke University, Durham, NC 27708, United States
| | - Roberto Cabeza
- Department of Psychology and Neuroscience, Duke University, Durham, NC 27708, United States; Center for Cognitive Neuroscience, Duke University, Durham, NC 27708, United States
| | - Simon W Davis
- Department of Psychology and Neuroscience, Duke University, Durham, NC 27708, United States; Department of Philosophy, Duke University, Durham, NC 27708, United States; Department of Neurology, Duke University School of Medicine, Durham, NC 27708, United States
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30
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van den Heuvel MP, Seoane SL. Support for network theories of schizophrenia. Nat Rev Neurol 2024; 20:381-382. [PMID: 38580849 DOI: 10.1038/s41582-024-00956-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2024]
Affiliation(s)
- Martijn P van den Heuvel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, the Netherlands.
- Department of Child Psychiatry, Section Complex Trait Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam UMC, Amsterdam, the Netherlands.
| | - Sara L Seoane
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, the Netherlands
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31
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Zamani J, Jafadideh AT. Predicting the Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using Graph Frequency Bands and Functional Connectivity-Based Features. RESEARCH SQUARE 2024:rs.3.rs-4549428. [PMID: 38947050 PMCID: PMC11213162 DOI: 10.21203/rs.3.rs-4549428/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Accurate prediction of the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is crucial for disease management. Machine learning techniques have demonstrated success in classifying AD and MCI cases, particularly with the use of resting-state functional magnetic resonance imaging (rs-fMRI) data.This study utilized three years of rs-fMRI data from the ADNI, involving 142 patients with stable MCI (sMCI) and 136 with progressive MCI (pMCI). Graph signal processing was applied to filter rs-fMRI data into low, middle, and high frequency bands. Connectivity-based features were derived from both filtered and unfiltered data, resulting in a comprehensive set of 100 features, including global graph metrics, minimum spanning tree (MST) metrics, triadic interaction metrics, hub tendency metrics, and the number of links. Feature selection was enhanced using particle swarm optimization (PSO) and simulated annealing (SA). A support vector machine (SVM) with a radial basis function (RBF) kernel and a 10-fold cross-validation setup were employed for classification. The proposed approach demonstrated superior performance, achieving optimal accuracy with minimal feature utilization. When PSO selected five features, SVM exhibited accuracy, specificity, and sensitivity rates of 77%, 70%, and 83%, respectively. The identified features were as follows: (Mean of clustering coefficient, Mean of strength)/Radius/(Mean Eccentricity, and Modularity) from low/middle/high frequency bands of graph. The study highlights the efficacy of the proposed framework in identifying individuals at risk of AD development using a parsimonious feature set. This approach holds promise for advancing the precision of MCI to AD progression prediction, aiding in early diagnosis and intervention strategies.
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Affiliation(s)
- Jafar Zamani
- Department of Psychiatry and Behavioral Sciences, Stanford University, California, USA
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32
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Yu Y, Zhang X, Nitsche MA, Vicario CM, Qi F. Does a single session of transcranial direct current stimulation enhance both physical and psychological performance in national- or international-level athletes? A systematic review. Front Physiol 2024; 15:1365530. [PMID: 38962069 PMCID: PMC11220198 DOI: 10.3389/fphys.2024.1365530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 05/29/2024] [Indexed: 07/05/2024] Open
Abstract
Some studies showed that a single session of transcranial direct current stimulation (tDCS) has the potential of modulating motor performance in healthy and athletes. To our knowledge, previously published systematic reviews have neither comprehensively investigated the effects of tDCS on athletic performance in both physical and psychological parameters nor investigated the effects of tDCS on high-level athletes. We examined all available research testing a single session of tDCS on strength, endurance, sport-specific performance, emotional states and cognitive performance for better application in competition and pre-competition trainings of national- or international-level athletes. A systematic search was conducted in PubMed, Web of Science, EBSCO, Embase, and Scopus up until to June 2023. Studies were eligible when participants had sports experience at a minimum of state and national level competitions, underwent a single session of tDCS without additional interventions, and received either sham tDCS or no interventions in the control groups. A total of 20 experimental studies (224 participants) were included from 18 articles. The results showed that a single tDCS session improved both physical and psychological parameters in 12 out of the 18 studies. Of these, six refer to the application of tDCS on the motor system (motor cortex, premotor cortex, cerebellum), five on dorsolateral prefrontal cortex and two on temporal cortex. The most sensitive to tDCS are strength, endurance, and emotional states, improved in 67%, 75%, and 75% of studies, respectively. Less than half of the studies showed improvement in sport-specific tasks (40%) and cognitive performance (33%). We suggest that tDCS is an effective tool that can be applied to competition and pre-competition training to improve athletic performance in national- or international-level athletes. Further research would explore various parameters (type of sports, brain regions, stimulation protocol, athlete level, and test tasks) and neural mechanistic studies in improving efficacy of tDCS interventions. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022326989, identifier CRD42022326989.
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Affiliation(s)
- Ying Yu
- Key Laboratory of Sport Training of General Administration of Sport of China, Beijing Sport University, Beijing, China
- Sports, Exercise and Brain Sciences Laboratory, Beijing Sport University, Beijing, China
| | - Xinbi Zhang
- Key Laboratory of Sport Training of General Administration of Sport of China, Beijing Sport University, Beijing, China
- Sports, Exercise and Brain Sciences Laboratory, Beijing Sport University, Beijing, China
| | - Michael A. Nitsche
- Department of Psychology and Neurosciences, Leibniz Research Centre for Working Environment and Human Factors, Dortmund, Germany
- University Clinic of Psychiatry and Psychotherapy and University Clinic of Child and Adolescent Psychiatry and Psychotherapy, Protestant Hospital of Bethel Foundation, University Hospital OWL, Bielefeld University, Bielefeld, Germany
| | - Carmelo M. Vicario
- Department of Cognitive Sciences, Psychology, Education and Cultural Studies, University of Messina, Messina, Italy
| | - Fengxue Qi
- Key Laboratory of Sport Training of General Administration of Sport of China, Beijing Sport University, Beijing, China
- Sports, Exercise and Brain Sciences Laboratory, Beijing Sport University, Beijing, China
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33
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Wiafe SL, Asante NO, Calhoun VD, Faghiri A. Studying time-resolved functional connectivity via communication theory: on the complementary nature of phase synchronization and sliding window Pearson correlation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.12.598720. [PMID: 38915498 PMCID: PMC11195172 DOI: 10.1101/2024.06.12.598720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Time-resolved functional connectivity (trFC) assesses the time-resolved coupling between brain regions using functional magnetic resonance imaging (fMRI) data. This study aims to compare two techniques used to estimate trFC, to investigate their similarities and differences when applied to fMRI data. These techniques are the sliding window Pearson correlation (SWPC), an amplitude-based approach, and phase synchronization (PS), a phase-based technique. To accomplish our objective, we used resting-state fMRI data from the Human Connectome Project (HCP) with 827 subjects (repetition time: 0.7s) and the Function Biomedical Informatics Research Network (fBIRN) with 311 subjects (repetition time: 2s), which included 151 schizophrenia patients and 160 controls. Our simulations reveal distinct strengths in two connectivity methods: SWPC captures high-magnitude, low-frequency connectivity, while PS detects low-magnitude, high-frequency connectivity. Stronger correlations between SWPC and PS align with pronounced fMRI oscillations. For fMRI data, higher correlations between SWPC and PS occur with matched frequencies and smaller SWPC window sizes (~30s), but larger windows (~88s) sacrifice clinically relevant information. Both methods identify a schizophrenia-associated brain network state but show different patterns: SWPC highlights low anti-correlations between visual, subcortical, auditory, and sensory-motor networks, while PS shows reduced positive synchronization among these networks. In sum, our findings underscore the complementary nature of SWPC and PS, elucidating their respective strengths and limitations without implying the superiority of one over the other.
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Affiliation(s)
- Sir-Lord Wiafe
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Nana O. Asante
- ETH Zürich, Zürich, Rämistrasse 101, Switzerland
- Ashesi University, 1 University Avenue Berekuso, Ghana
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Ashkan Faghiri
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
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Taylor HP, Thung KH, Huynh KM, Lin W, Ahmad S, Yap PT. Functional Hierarchy of the Human Neocortex from Cradle to Grave. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.14.599109. [PMID: 38915694 PMCID: PMC11195193 DOI: 10.1101/2024.06.14.599109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Recent evidence indicates that the organization of the human neocortex is underpinned by smooth spatial gradients of functional connectivity (FC). These gradients provide crucial insight into the relationship between the brain's topographic organization and the texture of human cognition. However, no studies to date have charted how intrinsic FC gradient architecture develops across the entire human lifespan. In this work, we model developmental trajectories of the three primary gradients of FC using a large, high-quality, and temporally-dense functional MRI dataset spanning from birth to 100 years of age. The gradient axes, denoted as sensorimotor-association (SA), visual-somatosensory (VS), and modulation-representation (MR), encode crucial hierarchical organizing principles of the brain in development and aging. By tracking their evolution throughout the human lifespan, we provide the first ever comprehensive low-dimensional normative reference of global FC hierarchical architecture. We observe significant age-related changes in global network features, with global markers of hierarchical organization increasing from birth to early adulthood and decreasing thereafter. During infancy and early childhood, FC organization is shaped by primary sensory processing, dense short-range connectivity, and immature association and control hierarchies. Functional differentiation of transmodal systems supported by long-range coupling drives a convergence toward adult-like FC organization during late childhood, while adolescence and early adulthood are marked by the expansion and refinement of SA and MR hierarchies. While gradient topographies remain stable during late adulthood and aging, we observe decreases in global gradient measures of FC differentiation and complexity from 30 to 100 years. Examining cortical microstructure gradients alongside our functional gradients, we observed that structure-function gradient coupling undergoes differential lifespan trajectories across multiple gradient axes.
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Affiliation(s)
- Hoyt Patrick Taylor
- Department of Computer Science, University of North Carolina, Chapel Hill, U.S.A
| | - Kim-Han Thung
- Department of Radiology, University of North Carolina, Chapel Hill, U.S.A
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, U.S.A
| | - Khoi Minh Huynh
- Department of Radiology, University of North Carolina, Chapel Hill, U.S.A
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, U.S.A
| | - Weili Lin
- Department of Radiology, University of North Carolina, Chapel Hill, U.S.A
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, U.S.A
| | - Sahar Ahmad
- Department of Radiology, University of North Carolina, Chapel Hill, U.S.A
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, U.S.A
| | - Pew-Thian Yap
- Department of Radiology, University of North Carolina, Chapel Hill, U.S.A
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, U.S.A
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Fujimoto SH, Fujimoto A, Elorette C, Seltzer A, Andraka E, Verma G, Janssen WGM, Fleysher L, Folloni D, Choi KS, Russ BE, Mayberg HS, Rudebeck PH. Deep brain stimulation induces white matter remodeling and functional changes to brain-wide networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.13.598710. [PMID: 38915600 PMCID: PMC11195276 DOI: 10.1101/2024.06.13.598710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Deep brain stimulation (DBS) is an emerging therapeutic option for treatment resistant neurological and psychiatric disorders, most notably depression. Despite this, little is known about the anatomical and functional mechanisms that underlie this therapy. Here we targeted stimulation to the white matter adjacent to the subcallosal anterior cingulate cortex (SCC-DBS) in macaques, modeling the location in the brain proven effective for depression. We demonstrate that SCC-DBS has a selective effect on white matter macro- and micro-structure in the cingulum bundle distant to where stimulation was delivered. SCC-DBS also decreased functional connectivity between subcallosal and posterior cingulate cortex, two areas linked by the cingulum bundle and implicated in depression. Our data reveal that white matter remodeling as well as functional effects contribute to DBS's therapeutic efficacy.
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Affiliation(s)
- Satoka H. Fujimoto
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai; New York, NY 10029, USA
- Lipschultz Center for Cognitive Neuroscience, Icahn School of Medicine at Mount Sinai; New York, NY 10029, USA
| | - Atsushi Fujimoto
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai; New York, NY 10029, USA
- Lipschultz Center for Cognitive Neuroscience, Icahn School of Medicine at Mount Sinai; New York, NY 10029, USA
| | - Catherine Elorette
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai; New York, NY 10029, USA
- Lipschultz Center for Cognitive Neuroscience, Icahn School of Medicine at Mount Sinai; New York, NY 10029, USA
| | - Adela Seltzer
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai; New York, NY 10029, USA
- Lipschultz Center for Cognitive Neuroscience, Icahn School of Medicine at Mount Sinai; New York, NY 10029, USA
| | - Emma Andraka
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai; New York, NY 10029, USA
- Lipschultz Center for Cognitive Neuroscience, Icahn School of Medicine at Mount Sinai; New York, NY 10029, USA
| | - Gaurav Verma
- Nash Family Center for Advanced Circuit Therapeutics, Mount Sinai West Hospital; New York, NY 10019, USA
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai; New York, NY 10029, USA
| | - William GM Janssen
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai; New York, NY 10029, USA
| | - Lazar Fleysher
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai; New York, NY 10029, USA
| | - Davide Folloni
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai; New York, NY 10029, USA
- Lipschultz Center for Cognitive Neuroscience, Icahn School of Medicine at Mount Sinai; New York, NY 10029, USA
| | - Ki Sueng Choi
- Nash Family Center for Advanced Circuit Therapeutics, Mount Sinai West Hospital; New York, NY 10019, USA
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai; New York, NY 10029, USA
| | - Brian E. Russ
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai; New York, NY 10029, USA
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute; Orangeburg, NY 10962, USA
- Department of Psychiatry, New York University at Langone; New York, NY 10016, USA
| | - Helen S. Mayberg
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai; New York, NY 10029, USA
- Nash Family Center for Advanced Circuit Therapeutics, Mount Sinai West Hospital; New York, NY 10019, USA
| | - Peter H. Rudebeck
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai; New York, NY 10029, USA
- Lipschultz Center for Cognitive Neuroscience, Icahn School of Medicine at Mount Sinai; New York, NY 10029, USA
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36
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Chen G, Wang W, Wu H, Zhao X, Kang X, Ren J, Zhang J, Sun Y, He J, Sun S, Zhong Z, Shang D, Fan M, Cheng J, Zhang D, Su C, Lin J. Disrupted topological properties of structural brain networks present a glutamatergic neuropathophysiology in people with narcolepsy. Sleep 2024; 47:zsae002. [PMID: 38173348 DOI: 10.1093/sleep/zsae002] [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: 07/26/2023] [Revised: 11/18/2023] [Indexed: 01/05/2024] Open
Abstract
STUDY OBJECTIVES Growing evidences have documented various abnormalities of the white matter bundles in people with narcolepsy. We sought to evaluate topological properties of brain structural networks, and their association with symptoms and neuropathophysiological features in people with narcolepsy. METHODS Diffusion tensor imaging was conducted for people with narcolepsy (n = 30) and matched healthy controls as well as symptoms assessment. Structural connectivity for each participant was generated to analyze global and regional topological properties and their correlations with narcoleptic features. Further human brain transcriptome was extracted and spatially registered for connectivity vulnerability. Genetic functional enrichment analysis was performed and further clarified using in vivo emission computed tomography data. RESULTS A wide and dramatic decrease in structural connectivities was observed in people with narcolepsy, with descending network degree and global efficiency. These metrics were not only correlated with sleep latency and awakening features, but also reflected alterations of sleep macrostructure in people with narcolepsy. Network-based statistics identified a small hyperenhanced subnetwork of cingulate gyrus that was closely related to rapid eye movement sleep behavior disorder (RBD) in narcolepsy. Further imaging genetics analysis suggested glutamatergic signatures were responsible for the preferential vulnerability of connectivity alterations in people with narcolepsy, while additional PET/SPECT data verified that structural alteration was significantly correlated with metabotropic glutamate receptor 5 (mGlutR5) and N-methyl-D-aspartate receptor (NMDA). CONCLUSIONS People with narcolepsy endured a remarkable decrease in the structural architecture, which was not only closely related to narcolepsy symptoms but also glutamatergic signatures.
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Affiliation(s)
- Guoyan Chen
- Department of Neurology, The Second Affiliated Hospital of Air Force Medical University, Xi'an, China
| | - Wen Wang
- Department of Radiology, The Second Affiliated Hospital of Air Force Medical University, Xi'an, China
| | - Haoyang Wu
- Basic Medicine School, Air Force Medical University, Xi'an, China
| | - Xianchao Zhao
- Department of Neurology, The Second Affiliated Hospital of Air Force Medical University, Xi'an, China
| | - Xiaopeng Kang
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Jiafeng Ren
- Department of Neurology, The Second Affiliated Hospital of Air Force Medical University, Xi'an, China
| | - Jun Zhang
- Department of Neurology, The Second Affiliated Hospital of Air Force Medical University, Xi'an, China
| | - Yingzhi Sun
- Department of Radiology, The Second Affiliated Hospital of Air Force Medical University, Xi'an, China
| | - Jiaxiu He
- Department of Neurology, The Second Affiliated Hospital of Air Force Medical University, Xi'an, China
| | - Shihui Sun
- Department of Neurology, The Second Affiliated Hospital of Air Force Medical University, Xi'an, China
| | - Zhao Zhong
- Department of Neurology, The Second Affiliated Hospital of Air Force Medical University, Xi'an, China
| | - Danqing Shang
- Department of Neurology, The Second Affiliated Hospital of Air Force Medical University, Xi'an, China
| | - Mengmeng Fan
- Department of Neurology, The Second Affiliated Hospital of Air Force Medical University, Xi'an, China
| | - Jinxiang Cheng
- Department of Neurology, The Second Affiliated Hospital of Air Force Medical University, Xi'an, China
| | - Dan Zhang
- Department of Neurology, The Second Affiliated Hospital of Air Force Medical University, Xi'an, China
| | - Changjun Su
- Department of Neurology, The Second Affiliated Hospital of Air Force Medical University, Xi'an, China
| | - Jiaji Lin
- Department of Neurology, The Second Affiliated Hospital of Air Force Medical University, Xi'an, China
- Department of Radiology, Chinese PLA General Hospital/Medical School of Chinese PLA, Beijing, China
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37
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Arichi T. Characterizing Large-Scale Human Circuit Development with In Vivo Neuroimaging. Cold Spring Harb Perspect Biol 2024; 16:a041496. [PMID: 38438187 PMCID: PMC11146311 DOI: 10.1101/cshperspect.a041496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2024]
Abstract
Large-scale coordinated patterns of neural activity are crucial for the integration of information in the human brain and to enable complex and flexible human behavior across the life span. Through recent advances in noninvasive functional magnetic resonance imaging (fMRI) methods, it is now possible to study this activity and how it emerges in the living fetal brain across the second half of human gestation. This work has demonstrated that functional activity in the fetal brain has several features in keeping with highly organized networks of activity, which are undergoing a highly programmed and rapid sequence of development before birth, in which long-range connections emerge and core features of the mature functional connectome (such as hub regions and a gradient organization) are established. In this review, the findings of these studies are summarized, their relationship to the known changes in developmental neurobiology is considered, and considerations for future work in the context of limitations to the fMRI approach are presented.
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Affiliation(s)
- Tomoki Arichi
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London SE1 7EH, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King's College London, New Hunt's House, Guy's Campus, London SE1 1UL, United Kingdom
- Children's Neurosciences, Evelina London Children's Hospital, Guy's and St Thomas' NHS Foundation Trust, London SE1 7EH, United Kingdom
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38
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Kóbor A, Janacsek K, Hermann P, Zavecz Z, Varga V, Csépe V, Vidnyánszky Z, Kovács G, Nemeth D. Finding Pattern in the Noise: Persistent Implicit Statistical Knowledge Impacts the Processing of Unpredictable Stimuli. J Cogn Neurosci 2024; 36:1239-1264. [PMID: 38683699 DOI: 10.1162/jocn_a_02173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Abstract
Humans can extract statistical regularities of the environment to predict upcoming events. Previous research recognized that implicitly acquired statistical knowledge remained persistent and continued to influence behavior even when the regularities were no longer present in the environment. Here, in an fMRI experiment, we investigated how the persistence of statistical knowledge is represented in the brain. Participants (n = 32) completed a visual, four-choice, RT task consisting of statistical regularities. Two types of blocks constantly alternated with one another throughout the task: predictable statistical regularities in one block type and unpredictable ones in the other. Participants were unaware of the statistical regularities and their changing distribution across the blocks. Yet, they acquired the statistical regularities and showed significant statistical knowledge at the behavioral level not only in the predictable blocks but also in the unpredictable ones, albeit to a smaller extent. Brain activity in a range of cortical and subcortical areas, including early visual cortex, the insula, the right inferior frontal gyrus, and the right globus pallidus/putamen contributed to the acquisition of statistical regularities. The right insula, inferior frontal gyrus, and hippocampus as well as the bilateral angular gyrus seemed to play a role in maintaining this statistical knowledge. The results altogether suggest that statistical knowledge could be exploited in a relevant, predictable context as well as transmitted to and retrieved in an irrelevant context without a predictable structure.
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Affiliation(s)
- Andrea Kóbor
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Hungary
| | - Karolina Janacsek
- Centre of Thinking and Learning, Institute for Lifecourse Development, School of Human Sciences, University of Greenwich, United Kingdom
- ELTE Eötvös Loránd University, Hungary
| | - Petra Hermann
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Hungary
| | | | - Vera Varga
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Hungary
- University of Pannonia, Hungary
| | - Valéria Csépe
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Hungary
- University of Pannonia, Hungary
| | - Zoltán Vidnyánszky
- Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Hungary
| | | | - Dezso Nemeth
- INSERM, CRNL U1028 UMR5292, France
- ELTE Eötvös Loránd University & HUN-REN Research Centre for Natural Sciences, Hungary
- University of Atlántico Medio, Spain
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39
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Alshehri A, Koussis N, Al-Iedani O, Khormi I, Lea R, Ramadan S, Lechner-Scott J. Improvement of the thalamocortical white matter network in people with stable treated relapsing-remitting multiple sclerosis over time. NMR IN BIOMEDICINE 2024; 37:e5119. [PMID: 38383137 DOI: 10.1002/nbm.5119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 12/28/2023] [Accepted: 01/18/2024] [Indexed: 02/23/2024]
Abstract
Advanced imaging techniques (tractography) enable the mapping of white matter (WM) pathways and the understanding of brain connectivity patterns. We combined tractography with a network-based approach to examine WM microstructure on a network level in people with relapsing-remitting multiple sclerosis (pw-RRMS) and healthy controls (HCs) over 2 years. Seventy-six pw-RRMS matched with 43 HCs underwent clinical assessments and 3T MRI scans at baseline (BL) and 2-year follow-up (2-YFU). Probabilistic tractography was performed, accounting for the effect of lesions, producing connectomes of 25 million streamlines. Network differences in fibre density across pw-RRMS and HCs at BL and 2-YFU were quantified using network-based statistics (NBS). Longitudinal network differences in fibre density were quantified using NBS in pw-RRMS, and were tested for correlations with disability, cognition and fatigue scores. Widespread network reductions in fibre density were found in pw-RRMS compared with HCs at BL in cortical regions, with more reductions detected at 2-YFU. Pw-RRMS had reduced fibre density at BL in the thalamocortical network compared to 2-YFU. This effect appeared after correction for age, was robust across different thresholds, and did not correlate with lesion volume or disease duration. Pw-RRMS demonstrated a robust and long-distance improvement in the thalamocortical WM network, regardless of age, disease burden, duration or therapy, suggesting a potential locus of neuroplasticity in MS. This network's role over the disease's lifespan and its potential implications in prognosis and treatment warrants further investigation.
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Affiliation(s)
- Abdulaziz Alshehri
- School of Health Sciences, College of Health, Medicine and Wellbeing, University of Newcastle, Callaghan, NSW, Australia
- Hunter Medical Research Institute, New Lambton Heights, NSW, Australia
- Department of Radiology, King Fahd University Hospital, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Nikitas Koussis
- Hunter Medical Research Institute, New Lambton Heights, NSW, Australia
- School of Psychological Sciences, College of Engineering, Science and Environment, University of Newcastle, Callaghan, NSW, Australia
| | - Oun Al-Iedani
- Hunter Medical Research Institute, New Lambton Heights, NSW, Australia
- School of Biomedical Sciences and Pharmacy, College of Health, Medicine and Wellbeing, University of Newcastle, Callaghan, NSW, Australia
| | - Ibrahim Khormi
- School of Health Sciences, College of Health, Medicine and Wellbeing, University of Newcastle, Callaghan, NSW, Australia
- Hunter Medical Research Institute, New Lambton Heights, NSW, Australia
- College of Applied Medical Sciences, University of Jeddah, Jeddah, Saudi Arabia
| | - Rodney Lea
- Hunter Medical Research Institute, New Lambton Heights, NSW, Australia
| | - Saadallah Ramadan
- School of Health Sciences, College of Health, Medicine and Wellbeing, University of Newcastle, Callaghan, NSW, Australia
- Hunter Medical Research Institute, New Lambton Heights, NSW, Australia
| | - Jeannette Lechner-Scott
- Hunter Medical Research Institute, New Lambton Heights, NSW, Australia
- Department of Neurology, John Hunter Hospital, New Lambton Heights, NSW, Australia
- School of Medicine and Public Health, College of Health, Medicine and Wellbeing, University of Newcastle, Callaghan, NSW, Australia
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Sibilia F, Jost-Mousseau C, Banaschewski T, Barker GJ, Büchel C, Desrivières S, Flor H, Grigis A, Garavan H, Gowland P, Heinz A, Ittermann B, Martinot JL, Martinot MLP, Artiges E, Nees F, Orfanos DP, Poustka L, Millenet S, Fröhner JH, Smolka MN, Walter H, Whelan R, Schumann G, Bokde AL. The relationship between negative life events and cortical structural connectivity in adolescents. IBRO Neurosci Rep 2024; 16:201-210. [PMID: 38348392 PMCID: PMC10859284 DOI: 10.1016/j.ibneur.2024.01.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 01/27/2024] [Indexed: 02/15/2024] Open
Abstract
Adolescence is a crucial period for physical and psychological development. The impact of negative life events represents a risk factor for the onset of neuropsychiatric disorders. This study aims to investigate the relationship between negative life events and structural brain connectivity, considering both graph theory and connectivity strength. A group (n = 487) of adolescents from the IMAGEN Consortium was divided into Low and High Stress groups. Brain networks were extracted at an individual level, based on morphological similarity between grey matter regions with regions defined using an atlas-based region of interest (ROI) approach. Between-group comparisons were performed with global and local graph theory measures in a range of sparsity levels. The analysis was also performed in a larger sample of adolescents (n = 976) to examine linear correlations between stress level and network measures. Connectivity strength differences were investigated with network-based statistics. Negative life events were not found to be a factor influencing global network measures at any sparsity level. At local network level, between-group differences were found in centrality measures of the left somato-motor network (a decrease of betweenness centrality was seen at sparsity 5%), of the bilateral central visual and the left dorsal attention network (increase of degree at sparsity 10% at sparsity 30% respectively). Network-based statistics analysis showed an increase in connectivity strength in the High stress group in edges connecting the dorsal attention, limbic and salience networks. This study suggests negative life events alone do not alter structural connectivity globally, but they are associated to connectivity properties in areas involved in emotion and attention.
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Affiliation(s)
- Francesca Sibilia
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Coline Jost-Mousseau
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
- Paris Institute of Technology for Life, Food and Environmental Sciences, Paris, France
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
| | - Gareth J. Barker
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, United Kingdom
| | - Christian Büchel
- University Medical Centre Hamburg-Eppendorf, House W34, 3.OG, Martinistr. 52, 20246, Hamburg, Germany
| | - Sylvane Desrivières
- Medical Research Council - Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, United Kingdom
| | - Herta Flor
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
- Department of Psychology, School of Social Sciences, University of Mannheim, 68131 Mannheim, Germany
| | - Antoine Grigis
- NeuroSpin, CEA, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France
| | - Hugh Garavan
- Departments of Psychiatry and Psychology, University of Vermont, 05405 Burlington, VT, USA
| | - Penny Gowland
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
| | - Andreas Heinz
- Charité – Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany
| | - Bernd Ittermann
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Jean-Luc Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Saclay, University Paris Descartes – Sorbonne Paris Cité; and Maison de Solenn, Paris, France
| | - Marie-Laure Paillère Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Saclay, University Paris Descartes; and AP-HP.Sorbonne Université, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France
| | - Eric Artiges
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Sud, University Paris Descartes - Sorbonne Paris Cité; and Psychiatry Department 91G16, Orsay Hospital, France
| | - Frauke Nees
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
| | | | - Luise Poustka
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, von-Siebold-Str. 5, 37075, Göttingen, Germany
| | - Sabina Millenet
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
| | - Juliane H. Fröhner
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Michael N. Smolka
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Henrik Walter
- Charité – Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany
| | - Robert Whelan
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Ireland
| | - Gunter Schumann
- Medical Research Council - Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, United Kingdom
| | - Arun L.W. Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - IMAGEN Consortium
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
- Paris Institute of Technology for Life, Food and Environmental Sciences, Paris, France
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, United Kingdom
- University Medical Centre Hamburg-Eppendorf, House W34, 3.OG, Martinistr. 52, 20246, Hamburg, Germany
- Medical Research Council - Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, United Kingdom
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
- Department of Psychology, School of Social Sciences, University of Mannheim, 68131 Mannheim, Germany
- NeuroSpin, CEA, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France
- Departments of Psychiatry and Psychology, University of Vermont, 05405 Burlington, VT, USA
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
- Charité – Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Saclay, University Paris Descartes – Sorbonne Paris Cité; and Maison de Solenn, Paris, France
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Saclay, University Paris Descartes; and AP-HP.Sorbonne Université, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Sud, University Paris Descartes - Sorbonne Paris Cité; and Psychiatry Department 91G16, Orsay Hospital, France
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, von-Siebold-Str. 5, 37075, Göttingen, Germany
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Ireland
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Gutiérrez-de Pablo V, Poza J, Maturana-Candelas A, Rodríguez-González V, Tola-Arribas MÁ, Cano M, Hoshi H, Shigihara Y, Hornero R, Gómez C. Exploring the disruptions of the neurophysiological organization in Alzheimer's disease: An integrative approach. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 250:108197. [PMID: 38688139 DOI: 10.1016/j.cmpb.2024.108197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 12/20/2023] [Accepted: 04/21/2024] [Indexed: 05/02/2024]
Abstract
BACKGROUND AND OBJECTIVE Alzheimer's disease (AD) is a neurological disorder that impairs brain functions associated with cognition, memory, and behavior. Noninvasive neurophysiological techniques like magnetoencephalography (MEG) and electroencephalography (EEG) have shown promise in reflecting brain changes related to AD. These techniques are usually assessed at two levels: local activation (spectral, nonlinear, and dynamic properties) and global synchronization (functional connectivity, frequency-dependent network, and multiplex network organization characteristics). Nonetheless, the understanding of the organization formed by the existing relationships between these levels, henceforth named neurophysiological organization, remains unexplored. This work aims to assess the alterations AD causes in the resting-state neurophysiological organization. METHODS To that end, three datasets from healthy controls (HC) and patients with dementia due to AD were considered: MEG database (55 HC and 87 patients with AD), EEG1 database (51 HC and 100 patients with AD), and EEG2 database (45 HC and 82 patients with AD). To explore the alterations induced by AD in the relationships between several features extracted from M/EEG data, association networks (ANs) were computed. ANs are graphs, useful to quantify and visualize the intricate relationships between multiple features. RESULTS Our results suggested a disruption in the neurophysiological organization of patients with AD, exhibiting a greater inclination towards the local activation level; and a significant decrease in the complexity and diversity of the ANs (p-value ¡ 0.05, Mann-Whitney U-test, Bonferroni correction). This effect might be due to a shift of the neurophysiological organization towards more regular configurations, which may increase its vulnerability. Moreover, our findings support the crucial role played by the local activation level in maintaining the stability of the neurophysiological organization. Classification performance exhibited accuracy values of 83.91%, 73.68%, and 72.65% for MEG, EEG1, and EEG2 databases, respectively. CONCLUSION This study introduces a novel, valuable methodology able to integrate parameters characterize different properties of the brain activity and to explore the intricate organization of the neurophysiological organization at different levels. It was noted that AD increases susceptibility to changes in functional neural organization, suggesting a greater ease in the development of severe impairments. Therefore, ANs could facilitate a deeper comprehension of the complex interactions in brain function from a global standpoint.
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Affiliation(s)
- Víctor Gutiérrez-de Pablo
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Spain.
| | - Jesús Poza
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Spain; IMUVA, Instituto de Investigación en Matemáticas, University of Valladolid, Spain
| | - Aarón Maturana-Candelas
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Spain
| | - Víctor Rodríguez-González
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Spain
| | - Miguel Ángel Tola-Arribas
- CIBER-BBN, Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Spain; Department of Neurology, Río Hortega University Hospital, Valladolid, Spain
| | - Mónica Cano
- Department of Clinical Neurophysiology, Río Hortega University Hospital, Valladolid, Spain
| | - Hideyuki Hoshi
- Precision Medicine Centre, Hokuto Hospital, Obihiro, Japan
| | | | - Roberto Hornero
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Spain; IMUVA, Instituto de Investigación en Matemáticas, University of Valladolid, Spain
| | - Carlos Gómez
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Spain
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Qu J, Tian M, Zhu R, Song C, Wu Y, Xu G, Liu Y, Wang D. Aberrant dynamic functional network connectivity in progressive supranuclear palsy. Neurobiol Dis 2024; 195:106493. [PMID: 38579913 DOI: 10.1016/j.nbd.2024.106493] [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: 01/10/2024] [Revised: 03/07/2024] [Accepted: 04/02/2024] [Indexed: 04/07/2024] Open
Abstract
BACKGROUND The clinical symptoms of progressive supranuclear palsy (PSP) may be mediated by aberrant dynamic functional network connectivity (dFNC). While earlier research has found altered functional network connections in PSP patients, the majority of those studies have concentrated on static functional connectivity. Nevertheless, in this study, we sought to evaluate the modifications in dynamic characteristics and establish the correlation between these disease-related changes and clinical variables. METHODS In our study, we conducted a study on 53 PSP patients and 65 normal controls. Initially, we employed a group independent component analysis (ICA) to derive resting-state networks (RSNs), while employing a sliding window correlation approach to produce dFNC matrices. The K-means algorithm was used to cluster these matrices into distinct dynamic states, and then state analysis was subsequently employed to analyze the dFNC and temporal metrics between the two groups. Finally, we made a correlation analysis. RESULTS PSP patients showed increased connectivity strength between medulla oblongata (MO) and visual network (VN) /cerebellum network (CBN) and decreased connections were found between default mode network (DMN) and VN/CBN, subcortical cortex network (SCN) and CBN. In addition, PSP patients spend less fraction time and shorter dwell time in a diffused state, especially the MO and SCN. Finally, the fraction time and mean dwell time in the distributed connectivity state (state 2) is negatively correlated with duration, bulbar and oculomotor symptoms. DISCUSSION Our findings were that the altered connectivity was mostly concentrated in the CBN and MO. In addition, PSP patients had different temporal dynamics, which were associated with bulbar and oculomotor symptoms in PSPRS. It suggest that variations in dynamic functional network connectivity properties may represent an essential neurological mechanism in PSP.
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Affiliation(s)
- Junyu Qu
- Department of Radiology, Qilu Hospital of Shandong University; Qilu Medical Imaging Institute of Shandong University, Ji'nan, China
| | - Min Tian
- Department of Neurology, Qilu Hospital of Shandong University, Ji'nan, China
| | - Rui Zhu
- Department of Radiology, Qilu Hospital of Shandong University; Qilu Medical Imaging Institute of Shandong University, Ji'nan, China
| | - Chengyuan Song
- Department of Neurology, Qilu Hospital of Shandong University, Ji'nan, China
| | - Yongsheng Wu
- Department of Radiology, Qilu Hospital of Shandong University; Qilu Medical Imaging Institute of Shandong University, Ji'nan, China
| | - Guihua Xu
- Department of Radiology, Qilu Hospital of Shandong University; Qilu Medical Imaging Institute of Shandong University, Ji'nan, China
| | - Yiming Liu
- Department of Neurology, Qilu Hospital of Shandong University, Ji'nan, China.
| | - Dawei Wang
- Department of Radiology, Qilu Hospital of Shandong University; Qilu Medical Imaging Institute of Shandong University, Ji'nan, China; Research Institute of Shandong University: Magnetic Field-free Medicine & Functional Imaging, Ji'nan, China; Shandong Key Laboratory: Magnetic Field-free Medicine & Functional Imaging (MF), Ji'nan, China.
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Qian Y, Han J, Yang R, Chen W, Lei Z, Zheng Z. Emergence of chimeralike oscillation modes in excitable complex networks with preferentially cutting-rewiring operation. CHAOS (WOODBURY, N.Y.) 2024; 34:063143. [PMID: 38934725 DOI: 10.1063/5.0167251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 06/05/2024] [Indexed: 06/28/2024]
Abstract
In this paper, the preferentially cutting-rewiring operation (PCRO) consisting of the cutting procedure and the rewiring procedure is proposed and is applied on an excitable Erdös-Rényi random network (EERRN), by which the structure of the initially homogeneous network changes dramatically, and lots of common leaves (CLs) are formed between the two hubs. Subsequently, besides the single-mode oscillations that can be usually observed in homogeneous excitable systems, a new kind of multi-mode oscillations composed of synchronous and asynchronous parts can self-organize to emerge, which are similar to the coherent and incoherent clusters in traditional chimera states and are consequently named as the chimeralike oscillation modes (CLOMs). Importantly, by utilizing the dominant phase-advanced driving method, both the mechanisms for the formation and the emergence of CLOMs in EERRNs with PCRO are well explained, among which the CL is exposed to play a key role in forming the CLOMs. Furthermore, the PCRO-induced CLOM phenomena can also be observed in other paradigmatic network models or with other paradigmatic excitable dynamics, which definitely confirms that the PCRO is an universal method in inducing the CLOMs in excitable complex networks. Our contributions may shed lights on a new perspective of the emergence of CLOMs in complex systems and would have great impacts in related fields.
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Affiliation(s)
- Yu Qian
- College of Physics and Optoelectronic Technology, Baoji University of Arts and Sciences, Baoji 721007, China
| | - Jing Han
- College of Physics and Optoelectronic Technology, Baoji University of Arts and Sciences, Baoji 721007, China
| | - Runru Yang
- College of Physics and Optoelectronic Technology, Baoji University of Arts and Sciences, Baoji 721007, China
| | - Wentao Chen
- School of Systems Science, Beijing Normal University, Beijing 100875, China
| | - Zhao Lei
- College of Physics and Optoelectronic Technology, Baoji University of Arts and Sciences, Baoji 721007, China
| | - Zhigang Zheng
- Institute of Systems Science, Huaqiao University, Xiamen 361021, China
- College of Information Science and Engineering, Huaqiao University, Xiamen 361021, China
- School of Mathematical Sciences, Huaqiao University, Quanzhou 362021, China
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Zhang J, Luo Y, Zhong L, Liu H, Yang Z, Weng A, Zhang Y, Zhang W, Yan Z, Xu J, Liu G, Peng K, Ou Z. Topological alterations in white matter anatomical networks in cervical dystonia. BMC Neurol 2024; 24:179. [PMID: 38802755 PMCID: PMC11129473 DOI: 10.1186/s12883-024-03682-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2024] [Accepted: 05/17/2024] [Indexed: 05/29/2024] Open
Abstract
BACKGROUND Accumulating neuroimaging evidence indicates that patients with cervical dystonia (CD) have changes in the cortico-subcortical white matter (WM) bundle. However, whether these patients' WM structural networks undergo reorganization remains largely unclear. We aimed to investigate topological changes in large-scale WM structural networks in patients with CD compared to healthy controls (HCs), and explore the network changes associated with clinical manifestations. METHODS Diffusion tensor imaging (DTI) was conducted in 30 patients with CD and 30 HCs, and WM network construction was based on the BNA-246 atlas and deterministic tractography. Based on the graph theoretical analysis, global and local topological properties were calculated and compared between patients with CD and HCs. Then, the AAL-90 atlas was used for the reproducibility analyses. In addition, the relationship between abnormal topological properties and clinical characteristics was analyzed. RESULTS Compared with HCs, patients with CD showed changes in network segregation and resilience, characterized by increased local efficiency and assortativity, respectively. In addition, a significant decrease of network strength was also found in patients with CD relative to HCs. Validation analyses using the AAL-90 atlas similarly showed increased assortativity and network strength in patients with CD. No significant correlations were found between altered network properties and clinical characteristics in patients with CD. CONCLUSION Our findings show that reorganization of the large-scale WM structural network exists in patients with CD. However, this reorganization is attributed to dystonia-specific abnormalities or hyperkinetic movements that need further identification.
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Affiliation(s)
- Jiana Zhang
- Department of Neurology, The First Affiliated Hospital, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, Sun Yat-sen University, Guangzhou, 510080, China
| | - Yuhan Luo
- Department of Neurology, The First Affiliated Hospital, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, Sun Yat-sen University, Guangzhou, 510080, China
| | - Linchang Zhong
- Department of Medical Imaging, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Huiming Liu
- Department of Medical Imaging, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Zhengkun Yang
- Department of Neurology, The First Affiliated Hospital, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, Sun Yat-sen University, Guangzhou, 510080, China
| | - Ai Weng
- Department of Neurology, The First Affiliated Hospital, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, Sun Yat-sen University, Guangzhou, 510080, China
| | - Yue Zhang
- Department of Neurology, The First Affiliated Hospital, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, Sun Yat-sen University, Guangzhou, 510080, China
| | - Weixi Zhang
- Department of Neurology, The First Affiliated Hospital, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, Sun Yat-sen University, Guangzhou, 510080, China
| | - Zhicong Yan
- Department of Neurology, The First Affiliated Hospital, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, Sun Yat-sen University, Guangzhou, 510080, China
| | - Jinping Xu
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Gang Liu
- Department of Neurology, The First Affiliated Hospital, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, Sun Yat-sen University, Guangzhou, 510080, China
| | - Kangqiang Peng
- Department of Medical Imaging, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China.
| | - Zilin Ou
- Department of Neurology, The First Affiliated Hospital, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, Sun Yat-sen University, Guangzhou, 510080, China.
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Mashour GA. Anesthesia and the neurobiology of consciousness. Neuron 2024; 112:1553-1567. [PMID: 38579714 PMCID: PMC11098701 DOI: 10.1016/j.neuron.2024.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 03/05/2024] [Accepted: 03/06/2024] [Indexed: 04/07/2024]
Abstract
In the 19th century, the discovery of general anesthesia revolutionized medical care. In the 21st century, anesthetics have become indispensable tools to study consciousness. Here, I review key aspects of the relationship between anesthesia and the neurobiology of consciousness, including interfaces of sleep and anesthetic mechanisms, anesthesia and primary sensory processing, the effects of anesthetics on large-scale functional brain networks, and mechanisms of arousal from anesthesia. I discuss the implications of the data derived from the anesthetized state for the science of consciousness and then conclude with outstanding questions, reflections, and future directions.
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Affiliation(s)
- George A Mashour
- Center for Consciousness Science, Department of Anesthesiology, Department of Pharmacology, Neuroscience Graduate Program, University of Michigan Medical School, Ann Arbor, MI 48109, USA.
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Guo R, Wu J, Zheng Y, Lin X, Zhuang Z, Yin J, Lin Z, Xie L, Ma S. Graph Theory Further Revealed Visual Spatial Working Memory Impairment in Patients with Inflammatory Bowel Disease. J Inflamm Res 2024; 17:2811-2823. [PMID: 38737113 PMCID: PMC11088826 DOI: 10.2147/jir.s462268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Accepted: 04/25/2024] [Indexed: 05/14/2024] Open
Abstract
Background Inflammatory Bowel Disease (IBD) patients may experience cognitive impairments in Visuospatial Working Memory (VSWM), significantly impacting their quality of life. However, the mechanisms underlying these impairments remain poorly understood. Methods We studied functional MRI and graph theory analysis to investigate changes in functional connectivity networks during the Mental Rotation Task (MRT) in IBD patients. Twenty IBD patients (13 males, 7 females; mean age = 34.95 ± 13.80 years; mean disease duration = 2.43 ± 2.37 years) participated in the study. Exclusion criteria encompassed recent use of analgesics, 5-Aminosalicylate, corticosteroids, or immunosuppressants within the past three months. Additionally, we recruited 20 age-, gender-, and education-matched healthy controls for comparison. Results Compared to a control group, IBD patients exhibited significantly longer reaction times and reduced accuracy during the MRT. Our analysis revealed abnormalities in multiple nodal attributes within the functional connectivity network, particularly in regions such as the bilateral orbitofrontal cortex, right supplementary motor area, bilateral parahippocampal gyrus, and bilateral anterior temporal lobe. We observed that the nodal efficiency in the left temporal pole is negatively correlated with Red Blood Cell Distribution Width (RDW) and positively correlated with response time of MRT. Conclusion Our findings revealed notable abnormalities in multiple node attributes among IBD patients during MRT, providing evidence of cognitive impairments in VSWM in IBD patients. This study found RDW maybe can serve as a clinical indicator for predicting early VSWM impairment in patients with IBD.
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Affiliation(s)
- Ruiwei Guo
- Department of Radiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, 515041, People’s Republic of China
- Laboratory of Medical Molecular Imaging, The First Affiliated Hospital of Shantou University Medical College, Shantou, 515041, People’s Republic of China
| | - Jin Wu
- Department of Radiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, 515041, People’s Republic of China
- Laboratory of Medical Molecular Imaging, The First Affiliated Hospital of Shantou University Medical College, Shantou, 515041, People’s Republic of China
| | - Yanmin Zheng
- Department of Radiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, 515041, People’s Republic of China
- Laboratory of Medical Molecular Imaging, The First Affiliated Hospital of Shantou University Medical College, Shantou, 515041, People’s Republic of China
| | - Xiaona Lin
- Department of Radiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, 515041, People’s Republic of China
- Laboratory of Medical Molecular Imaging, The First Affiliated Hospital of Shantou University Medical College, Shantou, 515041, People’s Republic of China
| | - Zelin Zhuang
- Department of Radiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, 515041, People’s Republic of China
- Laboratory of Medical Molecular Imaging, The First Affiliated Hospital of Shantou University Medical College, Shantou, 515041, People’s Republic of China
| | - Jingjing Yin
- Department of Radiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, 515041, People’s Republic of China
- Laboratory of Medical Molecular Imaging, The First Affiliated Hospital of Shantou University Medical College, Shantou, 515041, People’s Republic of China
| | - Zhirong Lin
- Department of Radiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, 515041, People’s Republic of China
| | - Lei Xie
- Department of Radiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, 515041, People’s Republic of China
- Laboratory of Medical Molecular Imaging, The First Affiliated Hospital of Shantou University Medical College, Shantou, 515041, People’s Republic of China
| | - Shuhua Ma
- Department of Radiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, 515041, People’s Republic of China
- Laboratory of Medical Molecular Imaging, The First Affiliated Hospital of Shantou University Medical College, Shantou, 515041, People’s Republic of China
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Liang Q, Ma J, Chen X, Lin Q, Shu N, Dai Z, Lin Y. A Hybrid Routing Pattern in Human Brain Structural Network Revealed By Evolutionary Computation. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:1895-1909. [PMID: 38194401 DOI: 10.1109/tmi.2024.3351907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
Abstract
The human brain functional connectivity network (FCN) is constrained and shaped by the communication processes in the structural connectivity network (SCN). The underlying communication mechanism thus becomes a critical issue for understanding the formation and organization of the FCN. A number of communication models supported by different routing strategies have been proposed, with shortest path (SP), random diffusion (DIF), and spatial navigation (NAV) as the most typical, respectively requiring network global knowledge, local knowledge, and both for path seeking. Yet these models all assumed every brain region to use one routing strategy uniformly, ignoring convergent evidence that supports the regional heterogeneity in both terms of biological substrates and functional roles. In this regard, the current study developed a hybrid communication model that allowed each brain region to choose a routing strategy from SP, DIF, and NAV independently. A genetic algorithm was designed to uncover the underlying region-wise hybrid routing strategy (namely HYB). The HYB was found to outperform the three typical routing strategies in predicting FCN and facilitating robust communication. Analyses on HYB further revealed that brain regions in lower-order functional modules inclined to route signals using global knowledge, while those in higher-order functional modules preferred DIF that requires only local knowledge. Compared to regions that used global knowledge for routing, regions using DIF had denser structural connections, participated in more functional modules, but played a less dominant role within modules. Together, our findings further evidenced that hybrid routing underpins efficient SCN communication and locally heterogeneous structure-function coupling.
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Xie K, Royer J, Larivière S, Rodriguez-Cruces R, Frässle S, Cabalo DG, Ngo A, DeKraker J, Auer H, Tavakol S, Weng Y, Abdallah C, Arafat T, Horwood L, Frauscher B, Caciagli L, Bernasconi A, Bernasconi N, Zhang Z, Concha L, Bernhardt BC. Atypical connectome topography and signal flow in temporal lobe epilepsy. Prog Neurobiol 2024; 236:102604. [PMID: 38604584 DOI: 10.1016/j.pneurobio.2024.102604] [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: 06/26/2023] [Revised: 12/18/2023] [Accepted: 04/07/2024] [Indexed: 04/13/2024]
Abstract
Temporal lobe epilepsy (TLE) is the most common pharmaco-resistant epilepsy in adults. While primarily associated with mesiotemporal pathology, recent evidence suggests that brain alterations in TLE extend beyond the paralimbic epicenter and impact macroscale function and cognitive functions, particularly memory. Using connectome-wide manifold learning and generative models of effective connectivity, we examined functional topography and directional signal flow patterns between large-scale neural circuits in TLE at rest. Studying a multisite cohort of 95 patients with TLE and 95 healthy controls, we observed atypical functional topographies in the former group, characterized by reduced differentiation between sensory and transmodal association cortices, with most marked effects in bilateral temporo-limbic and ventromedial prefrontal cortices. These findings were consistent across all study sites, present in left and right lateralized patients, and validated in a subgroup of patients with histopathological validation of mesiotemporal sclerosis and post-surgical seizure freedom. Moreover, they were replicated in an independent cohort of 30 TLE patients and 40 healthy controls. Further analyses demonstrated that reduced differentiation related to decreased functional signal flow into and out of temporolimbic cortical systems and other brain networks. Parallel analyses of structural and diffusion-weighted MRI data revealed that topographic alterations were independent of TLE-related cortical thinning but partially mediated by white matter microstructural changes that radiated away from paralimbic circuits. Finally, we found a strong association between the degree of functional alterations and behavioral markers of memory dysfunction. Our work illustrates the complex landscape of macroscale functional imbalances in TLE, which can serve as intermediate markers bridging microstructural changes and cognitive impairment.
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Affiliation(s)
- Ke Xie
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Jessica Royer
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada; Analytical Neurophysiology Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Sara Larivière
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Raul Rodriguez-Cruces
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Stefan Frässle
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Donna Gift Cabalo
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Alexander Ngo
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Jordan DeKraker
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Hans Auer
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Shahin Tavakol
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Yifei Weng
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Chifaou Abdallah
- Analytical Neurophysiology Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Thaera Arafat
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Linda Horwood
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada; Analytical Neurophysiology Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Birgit Frauscher
- Analytical Neurophysiology Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada; Department of Neurology, Duke University School of Medicine and Department of Biomedical Engineering, Duke University Pratt School of Engineering, Durham, NC 27705, USA
| | - Lorenzo Caciagli
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Inselspital, Sleep-Wake-Epilepsy-Center, Bern University Hospital, University of Bern, Bern, Switzerland; Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London WC1N 3 BG, United Kingdom
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Zhiqiang Zhang
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Luis Concha
- Institute of Neurobiology, Universidad Nacional Autónoma de Mexico (UNAM), Queretaro, Mexico
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada.
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Wu H, Dai W, Hong Z, Qin Y, Yang M, Wang B, Liao J. Higher-order sensorimotor circuit of the whole-brain functional network involved in pruritus regulation in atopic dermatitis. J Eur Acad Dermatol Venereol 2024; 38:873-882. [PMID: 38069553 DOI: 10.1111/jdv.19691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 11/09/2023] [Indexed: 04/26/2024]
Abstract
BACKGROUND Little is known about the neural mechanisms underlying pruritus regulation in Atopic dermatitis (AD). OBJECTIVE To investigate the functional changes of the resting-state whole brain network of AD participants and the mechanisms by which they were involved in pruritus regulation. METHOD Based on the functional magnetic resonance imaging data from 19 AD participants and 37 healthy controls (HC), a graph-theoretical measure of degree centrality (DC) conjoined with a voxel-level seed-based functional connectivity (FC) method was used to identify abnormal higher-order nodes and the functionally relevant circuit in AD participants compared to healthy controls (HC). RESULTS Of 64 participants screened, 19 AD participants (12M/7F, median [IQR] age, 27 [14] years) and 36 HCs (13M/23F, median [IQR] age, 20 [1] years) were enrolled. DC values of the left superior frontal gyrus (LSFG) increased in AD participants and exhibited a negative correlation with the SCORAD score (r = -0.561, p = 0.012) compared with HC. In the FC analysis with LSFG as the seed, FC values of several sensory and motor regions increased in AD participants, highly overlapping with the anatomical distribution of the inferior fronto-occipital fascicle (IFOF). AD participants with severe pruritus exhibited lower levels of DC (T = -2.316, p = 0.033) and FC between the LSFG and left insula (T = -2.203, p = 0.042) than those with mild-to- moderate pruritus. CONCLUSIONS AND RELEVANCE LSFG was involved in pruritus regulation in AD by forming a high-order sensorimotor circuit through the IFOF, a white matter fascicle that proved to provide multimodal integration in motor control and sensory information processing. These results offer more mechanism-guided treatment targets for severe pruritus in AD.
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Affiliation(s)
- Haishan Wu
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, China National Technology Institute on Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Wenyu Dai
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, China National Technology Institute on Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Zhaoyi Hong
- Department of Dermatology, Second Xiangya Hospital, Hunan Key Laboratory of Medical Epigenomics, Central South University, Changsha, Hunan, China
| | - Yue Qin
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, China National Technology Institute on Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Min Yang
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, China National Technology Institute on Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Bolun Wang
- Scientific Research Program of Hunan Provincial Health Commission, Department of Radiology, Clinical Research Center for Medical Imaging in Hunan Province, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Jieyue Liao
- Department of Dermatology, Second Xiangya Hospital, Hunan Key Laboratory of Medical Epigenomics, Central South University, Changsha, Hunan, China
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50
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Flinkenflügel K, Gruber M, Meinert S, Thiel K, Winter A, Goltermann J, Usemann P, Brosch K, Stein F, Thomas-Odenthal F, Wroblewski A, Pfarr JK, David FS, Beins EC, Grotegerd D, Hahn T, Leehr EJ, Dohm K, Bauer J, Forstner AJ, Nöthen MM, Jamalabadi H, Straube B, Alexander N, Jansen A, Witt SH, Rietschel M, Nenadić I, van den Heuvel MP, Kircher T, Repple J, Dannlowski U. The interplay between polygenic score for tumor necrosis factor-α, brain structural connectivity, and processing speed in major depression. Mol Psychiatry 2024:10.1038/s41380-024-02577-7. [PMID: 38693319 DOI: 10.1038/s41380-024-02577-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 04/15/2024] [Accepted: 04/19/2024] [Indexed: 05/03/2024]
Abstract
Reduced processing speed is a core deficit in major depressive disorder (MDD) and has been linked to altered structural brain network connectivity. Ample evidence highlights the involvement of genetic-immunological processes in MDD and specific depressive symptoms. Here, we extended these findings by examining associations between polygenic scores for tumor necrosis factor-α blood levels (TNF-α PGS), structural brain connectivity, and processing speed in a large sample of MDD patients. Processing speed performance of n = 284 acutely depressed, n = 177 partially and n = 198 fully remitted patients, and n = 743 healthy controls (HC) was estimated based on five neuropsychological tests. Network-based statistic was used to identify a brain network associated with processing speed. We employed general linear models to examine the association between TNF-α PGS and processing speed. We investigated whether network connectivity mediates the association between TNF-α PGS and processing speed. We identified a structural network positively associated with processing speed in the whole sample. We observed a significant negative association between TNF-α PGS and processing speed in acutely depressed patients, whereas no association was found in remitted patients and HC. The mediation analysis revealed that brain connectivity partially mediated the association between TNF-α PGS and processing speed in acute MDD. The present study provides evidence that TNF-α PGS is associated with decreased processing speed exclusively in patients with acute depression. This association was partially mediated by structural brain connectivity. Using multimodal data, the current findings advance our understanding of cognitive dysfunction in MDD and highlight the involvement of genetic-immunological processes in its pathomechanisms.
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Grants
- DA1151/5-1, DA1151/5-2, DA1151/11‑1 DA1151/6-1 Deutsche Forschungsgemeinschaft (German Research Foundation)
- HA7070/2-2, HA7070/3, HA7070/4 Deutsche Forschungsgemeinschaft (German Research Foundation)
- NO 246/10-1, NO 246/10-2 Deutsche Forschungsgemeinschaft (German Research Foundation)
- STR 1146/18-1 Deutsche Forschungsgemeinschaft (German Research Foundation)
- JA 1890/7-1, JA 1890/7-2 Deutsche Forschungsgemeinschaft (German Research Foundation)
- WI 3439/3-1, WI 3439/3-2 Deutsche Forschungsgemeinschaft (German Research Foundation)
- RI 908/11-1, RI 908/11-2 Deutsche Forschungsgemeinschaft (German Research Foundation)
- KI 588/14-1, KI 588/14-2, KI 588/22-1 Deutsche Forschungsgemeinschaft (German Research Foundation)
- ERC-COG 101001062, VIDI-452-16-015 Nederlandse Organisatie voor Wetenschappelijk Onderzoek (Netherlands Organisation for Scientific Research)
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Affiliation(s)
- Kira Flinkenflügel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Marius Gruber
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Goethe University, Frankfurt, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Institute for Translational Neuroscience, University of Münster, Münster, Germany
| | - Katharina Thiel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Alexandra Winter
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Janik Goltermann
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Paula Usemann
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
| | - Florian Thomas-Odenthal
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
| | - Adrian Wroblewski
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
| | - Julia-Katharina Pfarr
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
| | - Friederike S David
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Eva C Beins
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Tim Hahn
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Elisabeth J Leehr
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Katharina Dohm
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Jochen Bauer
- Department of Radiology, University of Münster, Münster, Germany
| | - Andreas J Forstner
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
- Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Jülich, Germany
- Center for Human Genetics, University of Marburg, Marburg, Germany
| | - Markus M Nöthen
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Hamidreza Jamalabadi
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
| | - Benjamin Straube
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
| | - Nina Alexander
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
| | - Andreas Jansen
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
- Core-Facility Brainimaging, Faculty of Medicine, University of Marburg, Marburg, Germany
| | - Stephanie H Witt
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
| | - Martijn P van den Heuvel
- Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Department of Child Psychiatry, Amsterdam University Medical Center, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg, Marburg, Germany
| | - Jonathan Repple
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Goethe University, Frankfurt, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany.
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