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Liu H, Xia Y, Hua L, Sun H, Yan R, Yao Z, Qin J. Brain network communication in remission: a comparative study of bipolar and unipolar depression. J Psychiatr Res 2025; 186:1-8. [PMID: 40203489 DOI: 10.1016/j.jpsychires.2025.03.057] [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: 01/16/2025] [Revised: 03/04/2025] [Accepted: 03/30/2025] [Indexed: 04/11/2025]
Abstract
Distinguishing between unipolar depression (UD) and bipolar disorder (BD) during periods of remission presents a significant clinical challenge. To mitigate the potential confounding effects of depressive episodes, our study compares the white matter networks of individuals with UD and BD in remission, aiming to explore the differentiation between these two affective disorders. Our cohort included 69 individuals with remitted UD, 55 with remitted BD, and 78 healthy controls (HC). We employed diffusion tensor imaging (DTI) to assess the white matter (WM) network. Additionally, we utilized a comprehensive set of connectome and five communication models to characterize the alterations within the whole-brain WM network. Compared to HC, both UD and BD patients showed reduced connectivity in the frontal orbital region, with BD patients exhibiting a more pronounced decrease. BD patients demonstrated superior navigation ability and higher shortest path metric values in key brain region connections compared to UD. Conversely, UD patients showed greater diffusion efficiency in certain brain regions. Communicability and search information analyses revealed distinct patterns of connectivity between the two patient groups, with potential implications for emotion regulation and information processing. Our findings highlight distinct brain connectivity patterns in BD and UD during remission, suggesting that these patterns could serve as neuroimaging biomarkers for differentiating between the two disorders. The study provides insights into the enduring effects of mood disorders on brain connectivity and has potential clinical implications for diagnosis and treatment.
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Affiliation(s)
- Haiyan Liu
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Yi Xia
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Lingling Hua
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Hao Sun
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Rui Yan
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Zhijian Yao
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.
| | - Jiaolong Qin
- The Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China.
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2
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Wang S, Qin JL, Yang LL, Ji YY, Huang HX, Gao XS, Zhou ZM, Guo ZR, Wu Y, Tian L, Ni HJ, Zhou ZH. Structural network communication differences in drug-naive depressed adolescents with non-suicidal self-injury and suicide attempts. World J Psychiatry 2025; 15:102706. [DOI: 10.5498/wjp.v15.i5.102706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2024] [Revised: 01/26/2025] [Accepted: 02/14/2025] [Indexed: 04/30/2025] Open
Abstract
BACKGROUND Depression, non-suicidal self-injury (NSSI), and suicide attempts (SA) often co-occur during adolescence and are associated with long-term adverse health outcomes. Unfortunately, neural mechanisms underlying self-injury and SA are poorly understood in depressed adolescents but likely relate to the structural abnormalities in brain regions.
AIM To investigate structural network communication within large-scale brain networks in adolescents with depression.
METHODS We constructed five distinct network communication models to evaluate structural network efficiency at the whole-brain level in adolescents with depression. Diffusion magnetic resonance imaging data were acquired from 32 healthy controls and 85 depressed adolescents, including 17 depressed adolescents without SA or NSSI (major depressive disorder group), 27 depressed adolescents with NSSI but no SA (NSSI group), and 41 depressed adolescents with SA and NSSI (NSSI + SA group).
RESULTS Significant differences in structural network communication were observed across the four groups, involving spatially widespread brain regions, particularly encompassing cortico-cortical connections (e.g., dorsal posterior cingulate gyrus and the right ventral posterior cingulate gyrus; connections based on precentral gyrus) and cortico-subcortical circuits (e.g., the nucleus accumbens-frontal circuit). In addition, we examined whether compromised communication efficiency was linked to clinical symptoms in the depressed adolescents. We observed significant correlations between network communication efficiencies and clinical scale scores derived from depressed adolescents with NSSI and SA.
CONCLUSION This study provides evidence of structural network communication differences in depressed adolescents with NSSI and SA, highlighting impaired neuroanatomical communication efficiency as a potential contributor to their symptoms. These findings offer new insights into the pathophysiological mechanisms underlying the comorbidity of NSSI and SA in adolescent depression.
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Affiliation(s)
- Shuai Wang
- School of Wuxi Medicine, Nanjing Medical University, Wuxi 214000, Jiangsu Province, China
- Department of Clinical Psychology, The Affiliated Mental Health Center of Jiangnan University, Wuxi Central Rehabilitation Hospital, Wuxi 214000, Jiangsu Province, China
| | - Jiao-Long Qin
- Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210000, Jiangsu Province, China
| | - Lian-Lian Yang
- School of Medicine, Jiangnan University, Wuxi 214000, Jiangsu Province, China
| | - Ying-Ying Ji
- Department of Rehabilitation Medicine, The Affiliated Mental Health Center of Jiangnan University, Wuxi Central Rehabilitation Hospital, Wuxi 214000, Jiangsu Province, China
| | - Hai-Xia Huang
- Department of Medical Imaging, Huadong Sanatorium, Wuxi 214000, Jiangsu Province, China
| | - Xiao-Shan Gao
- School of Wuxi Medicine, Nanjing Medical University, Wuxi 214000, Jiangsu Province, China
| | - Zi-Mo Zhou
- School of Medicine, Jiangnan University, Wuxi 214000, Jiangsu Province, China
| | - Zhen-Ru Guo
- School of Medicine, Jiangnan University, Wuxi 214000, Jiangsu Province, China
| | - Ye Wu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210000, Jiangsu Province, China
| | - Lin Tian
- Department of Psychiatry, The Affiliated Mental Health Center of Jiangnan University, Wuxi Central Rehabilitation Hospital, Wuxi 214000, Jiangsu Province, China
| | - Huang-Jing Ni
- School of Computer Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210000, Jiangsu Province, China
| | - Zhen-He Zhou
- School of Wuxi Medicine, Nanjing Medical University, Wuxi 214000, Jiangsu Province, China
- Department of Psychiatry, The Affiliated Mental Health Center of Jiangnan University, Wuxi Central Rehabilitation Hospital, Wuxi 214000, Jiangsu Province, China
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3
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Han MJ, Oh Y, Ann Y, Kang S, Baeg E, Hong SJ, Sohn H, Kim SG. Whole-brain effective connectivity of the sensorimotor system using 7 T fMRI with electrical microstimulation in non-human primates. Prog Neurobiol 2025; 250:102760. [PMID: 40280291 DOI: 10.1016/j.pneurobio.2025.102760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2024] [Revised: 03/11/2025] [Accepted: 04/14/2025] [Indexed: 04/29/2025]
Abstract
The sensorimotor system is a crucial interface between the brain and the environment, and it is endowed with multiple computational mechanisms that enable efficient behaviors. For example, predictive processing via an efference copy of a motor command has been proposed as one of the key computations used to compensate for the sensory consequence of movement. However, the neural pathways underlying this process remain unclear, particularly regarding whether the M1-to-S1 pathway plays a dominant role in predictive processing and how its influence compares to that of other pathways. In this study, we present a causally inferable input-output map of the sensorimotor effective connectivity that we made by combining ultrahigh-field functional MRI, electrical microstimulation of the S1/M1 cortex, and dynamic causal modeling for the whole sensorimotor network in anesthetized primates. We investigated how motor signals from M1 are transmitted to S1 at the circuit level, either via direct cortico-cortical projections or indirectly via subcortical structures such as the thalamus. Across different stimulation conditions, we observed a robust asymmetric connectivity from M1 to S1 that was also the most prominent output from M1. In the thalamus, we identified distinct activations: M1 stimulation showed connections to the anterior part of ventral thalamic nuclei, whereas S1 was linked to the more posterior regions of the ventral thalamic nuclei. These findings suggest that the cortico-cortical projection from M1 to S1, rather than the cortico-thalamic loop, plays a dominant role in transmitting movement-related information. Together, our detailed dissection of the sensorimotor circuitry underscores the importance of M1-to-S1 connectivity in sensorimotor coordination.
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Affiliation(s)
- Min-Jun Han
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea; Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
| | - Younghyun Oh
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea; Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
| | - Yejin Ann
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea; Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea
| | - Sangyun Kang
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea
| | - Eunha Baeg
- Department of Nano-bioengineering, Incheon National University, Incheon, Republic of Korea; Center for Brain-Machine Interface, Incheon National University, Incheon, Republic of Korea
| | - Seok Jun Hong
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea; Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea; Center for the Developing Brain, Child Mind Institute, NY, United States
| | - Hansem Sohn
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea; Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea.
| | - Seong-Gi Kim
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea; Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea.
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4
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Fakhar K, Hadaeghi F, Seguin C, Dixit S, Messé A, Zamora-López G, Misic B, Hilgetag CC. A general framework for characterizing optimal communication in brain networks. eLife 2025; 13:RP101780. [PMID: 40244650 PMCID: PMC12005722 DOI: 10.7554/elife.101780] [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] [Indexed: 04/18/2025] Open
Abstract
Efficient communication in brain networks is foundational for cognitive function and behavior. However, how communication efficiency is defined depends on the assumed model of signaling dynamics, e.g., shortest path signaling, random walker navigation, broadcasting, and diffusive processes. Thus, a general and model-agnostic framework for characterizing optimal neural communication is needed. We address this challenge by assigning communication efficiency through a virtual multi-site lesioning regime combined with game theory, applied to large-scale models of human brain dynamics. Our framework quantifies the exact influence each node exerts over every other, generating optimal influence maps given the underlying model of neural dynamics. These descriptions reveal how communication patterns unfold if regions are set to maximize their influence over one another. Comparing these maps with a variety of brain communication models showed that optimal communication closely resembles a broadcasting regime in which regions leverage multiple parallel channels for information dissemination. Moreover, we found that the brain's most influential regions are its rich-club, exploiting their topological vantage point by broadcasting across numerous pathways that enhance their reach even if the underlying connections are weak. Altogether, our work provides a rigorous and versatile framework for characterizing optimal brain communication, and uncovers the most influential brain regions, and the topological features underlying their influence.
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Affiliation(s)
- Kayson Fakhar
- MRC Cognition and Brain Sciences Unit, University of CambridgeCambridgeUnited Kingdom
- Institute of Computational Neuroscience, University Medical Center Eppendorf-Hamburg, Hamburg University, Hamburg Center of NeuroscienceHamburgGermany
| | - Fatemeh Hadaeghi
- Institute of Computational Neuroscience, University Medical Center Eppendorf-Hamburg, Hamburg University, Hamburg Center of NeuroscienceHamburgGermany
| | - Caio Seguin
- Department of Psychological and Brain Sciences, Indiana UniversityBloomingtonUnited States
| | - Shrey Dixit
- Institute of Computational Neuroscience, University Medical Center Eppendorf-Hamburg, Hamburg University, Hamburg Center of NeuroscienceHamburgGermany
- Department of Psychology, Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- International Max Planck Research School on Cognitive NeuroimagingBarcelonaSpain
| | - Arnaud Messé
- Institute of Computational Neuroscience, University Medical Center Eppendorf-Hamburg, Hamburg University, Hamburg Center of NeuroscienceHamburgGermany
| | - Gorka Zamora-López
- Center for Brain and Cognition, Pompeu Fabra UniversityBarcelonaSpain
- Department of Information and Communication Technologies, Pompeu Fabra UniversityBarcelonaSpain
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill UniversityMontréalCanada
| | - Claus C Hilgetag
- Institute of Computational Neuroscience, University Medical Center Eppendorf-Hamburg, Hamburg University, Hamburg Center of NeuroscienceHamburgGermany
- Department of Health Sciences, Boston UniversityBostonUnited States
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5
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Momi D, Wang Z, Parmigiani S, Mikulan E, Bastiaens SP, Oveisi MP, Kadak K, Gaglioti G, Waters AC, Hill S, Pigorini A, Keller CJ, Griffiths JD. Stimulation mapping and whole-brain modeling reveal gradients of excitability and recurrence in cortical networks. Nat Commun 2025; 16:3222. [PMID: 40185725 PMCID: PMC11971347 DOI: 10.1038/s41467-025-58187-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Accepted: 03/11/2025] [Indexed: 04/07/2025] Open
Abstract
The human brain exhibits a modular and hierarchical structure, spanning low-order sensorimotor to high-order cognitive/affective systems. What is the mechanistic significance of this organization for brain dynamics and information processing properties? We investigated this question using rare simultaneous multimodal electrophysiology (stereotactic and scalp electroencephalography - EEG) recordings in 36 patients with drug-resistant focal epilepsy during presurgical intracerebral electrical stimulation (iES) (323 stimulation sessions). Our analyses revealed an anatomical gradient of excitability across the cortex, with stronger iES-evoked EEG responses in high-order compared to low-order regions. Mathematical modeling further showed that this variation in excitability levels results from a differential dependence on recurrent feedback from non-stimulated regions across the anatomical hierarchy, and could be extinguished by suppressing those connections in-silico. High-order brain regions/networks thus show an activity pattern characterized by more inter-network functional integration than low-order ones, which manifests as a spatial gradient of excitability that is emergent from, and causally dependent on, the underlying hierarchical network structure. These findings offer new insights into how hierarchical brain organization influences cognitive functions and could inform strategies for targeted neuromodulation therapies.
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Affiliation(s)
- Davide Momi
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, Canada.
- Department of Psychiatry and Behavioral Sciences, Stanford University Medical Center, Stanford, CA, USA.
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA.
| | - Zheng Wang
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Sara Parmigiani
- Department of Psychiatry and Behavioral Sciences, Stanford University Medical Center, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Ezequiel Mikulan
- Department of Health Sciences, Università degli studi di Milano, Milan, Italy
| | - Sorenza P Bastiaens
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
- Institute of Medical Science, University of Toronto, Toronto, Canada
| | - Mohammad P Oveisi
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, Canada
| | - Kevin Kadak
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
- Institute of Medical Science, University of Toronto, Toronto, Canada
| | - Gianluca Gaglioti
- Department of Biomedical and Clinical Sciences "L.Sacco", Università degli Studi di Milano, Milan, Italy
| | - Allison C Waters
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sean Hill
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
- Institute of Medical Science, University of Toronto, Toronto, Canada
- Department of Psychiatry, University of Toronto, Toronto, Canada
| | - Andrea Pigorini
- Department of Biomedical, Surgical and Dental Sciences, Università degli Studi di Milano, Milan, Italy
- UOC Maxillo-facial Surgery and dentistry, Fondazione IRCCS Cà Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Corey J Keller
- Department of Psychiatry and Behavioral Sciences, Stanford University Medical Center, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Veterans Affairs Palo Alto Healthcare System, Palo Alto, CA, USA
| | - John D Griffiths
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
- Institute of Medical Science, University of Toronto, Toronto, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, Canada
- Department of Psychiatry, University of Toronto, Toronto, Canada
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6
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Paquola C, Garber M, Frässle S, Royer J, Zhou Y, Tavakol S, Rodriguez-Cruces R, Cabalo DG, Valk S, Eickhoff SB, Margulies DS, Evans A, Amunts K, Jefferies E, Smallwood J, Bernhardt BC. The architecture of the human default mode network explored through cytoarchitecture, wiring and signal flow. Nat Neurosci 2025; 28:654-664. [PMID: 39875581 PMCID: PMC11893468 DOI: 10.1038/s41593-024-01868-0] [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: 11/23/2023] [Accepted: 12/06/2024] [Indexed: 01/30/2025]
Abstract
The default mode network (DMN) is implicated in many aspects of complex thought and behavior. Here, we leverage postmortem histology and in vivo neuroimaging to characterize the anatomy of the DMN to better understand its role in information processing and cortical communication. Our results show that the DMN is cytoarchitecturally heterogenous, containing cytoarchitectural types that are variably specialized for unimodal, heteromodal and memory-related processing. Studying diffusion-based structural connectivity in combination with cytoarchitecture, we found the DMN contains regions receptive to input from sensory cortex and a core that is relatively insulated from environmental input. Finally, analysis of signal flow with effective connectivity models showed that the DMN is unique amongst cortical networks in balancing its output across the levels of sensory hierarchies. Together, our study establishes an anatomical foundation from which accounts of the broad role the DMN plays in human brain function and cognition can be developed.
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Affiliation(s)
- Casey Paquola
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montréal, Quebec, Canada.
- Institute for Neuroscience and Medicine (INM-7), Forschungszentrum Jülich, Jülich, Germany.
| | - Margaret Garber
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montréal, Quebec, Canada
| | - Stefan Frässle
- Translational Neuromodeling Unit (TNU), University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Jessica Royer
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montréal, Quebec, Canada
| | - Yigu Zhou
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montréal, Quebec, Canada
| | - Shahin Tavakol
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montréal, Quebec, Canada
| | - Raul Rodriguez-Cruces
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montréal, Quebec, Canada
| | - Donna Gift Cabalo
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montréal, Quebec, Canada
| | - Sofie Valk
- Institute for Neuroscience and Medicine (INM-7), Forschungszentrum Jülich, Jülich, Germany
- Max Planck Institute for Cognitive and Brain Sciences, Leipzig, Germany
- Institute for Systems Neuroscience, Heinrich Heine Universistät Dusseldorf, Dusseldorf, Germany
| | - Simon B Eickhoff
- Institute for Neuroscience and Medicine (INM-7), Forschungszentrum Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Heinrich Heine Universistät Dusseldorf, Dusseldorf, Germany
| | - Daniel S Margulies
- Integrative Neuroscience & Cognition Center (INCC - UMR 8002), University of Paris, Centre national de la recherche scientifique (CNRS), Paris, France
| | - Alan Evans
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montréal, Quebec, Canada
| | - Katrin Amunts
- Institute for Neuroscience and Medicine (INM-1), Forschungszentrum Jülich, Jülich, Germany
| | | | | | - Boris C Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montréal, Quebec, Canada
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7
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Wang Z, Yang Y, Huang Z, Zhao W, Su K, Zhu H, Yin D. Exploring the transmission of cognitive task information through optimal brain pathways. PLoS Comput Biol 2025; 21:e1012870. [PMID: 40053566 PMCID: PMC11957563 DOI: 10.1371/journal.pcbi.1012870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2024] [Revised: 03/18/2025] [Accepted: 02/12/2025] [Indexed: 03/09/2025] Open
Abstract
Understanding the large-scale information processing that underlies complex human cognition is the central goal of cognitive neuroscience. While emerging activity flow models demonstrate that cognitive task information is transferred by interregional functional or structural connectivity, graph-theory-based models typically assume that neural communication occurs via the shortest path of brain networks. However, whether the shortest path is the optimal route for empirical cognitive information transmission remains unclear. Based on a large-scale activity flow mapping framework, we found that the performance of activity flow prediction with the shortest path was significantly lower than that with the direct path. The shortest path routing was superior to other network communication strategies, including search information, path ensembles, and navigation. Intriguingly, the shortest path outperformed the direct path in activity flow prediction when the physical distance constraint and asymmetric routing contribution were simultaneously considered. This study not only challenges the shortest path assumption through empirical network models but also suggests that cognitive task information routing is constrained by the spatial and functional embedding of the brain network.
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Affiliation(s)
- Zhengdong Wang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Yifeixue Yang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Ziyi Huang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Wanyun Zhao
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Kaiqiang Su
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Hengcheng Zhu
- Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Dazhi Yin
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
- Shanghai Changning Mental Health Center, Shanghai, China
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8
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Greaves MD, Novelli L, Mansour L S, Zalesky A, Razi A. Structurally informed models of directed brain connectivity. Nat Rev Neurosci 2025; 26:23-41. [PMID: 39663407 DOI: 10.1038/s41583-024-00881-3] [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: 10/30/2024] [Indexed: 12/13/2024]
Abstract
Understanding how one brain region exerts influence over another in vivo is profoundly constrained by models used to infer or predict directed connectivity. Although such neural interactions rely on the anatomy of the brain, it remains unclear whether, at the macroscale, structural (or anatomical) connectivity provides useful constraints on models of directed connectivity. Here, we review the current state of research on this question, highlighting a key distinction between inference-based effective connectivity and prediction-based directed functional connectivity. We explore the methods via which structural connectivity has been integrated into directed connectivity models: through prior distributions, fixed parameters in state-space models and inputs to structure learning algorithms. Although the evidence suggests that integrating structural connectivity substantially improves directed connectivity models, assessments of reliability and out-of-sample validity are lacking. We conclude this Review with a strategy for future research that addresses current challenges and identifies opportunities for advancing the integration of structural and directed connectivity to ultimately improve understanding of the brain in health and disease.
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Affiliation(s)
- Matthew D Greaves
- School of Psychological Sciences, Monash University, Clayton, Victoria, Australia.
- Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia.
| | - Leonardo Novelli
- School of Psychological Sciences, Monash University, Clayton, Victoria, Australia
- Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia
| | - Sina Mansour L
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Psychiatry, The University of Melbourne, Parkville, Victoria, Australia
| | - Andrew Zalesky
- Department of Psychiatry, The University of Melbourne, Parkville, Victoria, Australia
| | - Adeel Razi
- School of Psychological Sciences, Monash University, Clayton, Victoria, Australia.
- Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia.
- Wellcome Centre for Human Neuroimaging, University College London, London, UK.
- CIFAR Azrieli Global Scholars Program, CIFAR, Toronto, Ontario, Canada.
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9
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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; 19:3721-3749. [PMID: 39075309 PMCID: PMC12039364 DOI: 10.1038/s41596-024-01023-w] [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: 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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10
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Liu Y, Sundman MH, Ugonna C, Chen YCA, Green JM, Haaheim LG, Siu HM, Chou YH. Reproducible routes: reliably navigating the connectome to enrich personalized brain stimulation strategies. Front Hum Neurosci 2024; 18:1477049. [PMID: 39568548 PMCID: PMC11576443 DOI: 10.3389/fnhum.2024.1477049] [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: 08/06/2024] [Accepted: 10/24/2024] [Indexed: 11/22/2024] Open
Abstract
Non-invasive brain stimulation (NIBS) technologies, such as repetitive transcranial magnetic stimulation (rTMS), offer significant therapeutic potential for a growing number of neuropsychiatric conditions. Concurrent with the expansion of this field is the swift evolution of rTMS methodologies, including approaches to optimize stimulation site planning. Traditional targeting methods, foundational to early successes in the field and still widely employed today, include using scalp-based heuristics or integrating structural MRI co-registration to align the transcranial magnetic stimulation (TMS) coil with anatomical landmarks. Recent evidence, however, supports refining and personalizing stimulation sites based on the target's structural and/or functional connectivity profile. These connectomic approaches harness the network-wide neuromodulatory effects of rTMS to reach deeper brain structures while also enabling a greater degree of personalization by accounting for heterogenous network topology. In this study, we acquired baseline multimodal magnetic resonance (MRI) at two time points to evaluate the reliability and reproducibility of distinct connectome-based strategies for stimulation site planning. Specifically, we compared the intra-individual difference between the optimal stimulation sites generated at each time point for (1) functional connectivity (FC) guided targets derived from resting-state functional MRI and (2) structural connectivity (SC) guided targets derived from diffusion tensor imaging. Our findings suggest superior reproducibility of SC-guided targets. We emphasize the necessity for further research to validate these findings across diverse patient populations, thereby advancing the personalization of rTMS treatments.
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Affiliation(s)
- Yilin Liu
- Brain Imaging and TMS Laboratory, Department of Psychology, University of Arizona, Tucson, AZ, United States
| | - Mark H Sundman
- Brain Imaging and TMS Laboratory, Department of Psychology, University of Arizona, Tucson, AZ, United States
| | - Chidi Ugonna
- Department of Biomedical Engineering, University of Arizona, Tucson, AZ, United States
| | - Yu-Chin Allison Chen
- Brain Imaging and TMS Laboratory, Department of Psychology, University of Arizona, Tucson, AZ, United States
| | - Jacob M Green
- Brain Imaging and TMS Laboratory, Department of Psychology, University of Arizona, Tucson, AZ, United States
| | - Lisbeth G Haaheim
- Brain Imaging and TMS Laboratory, Department of Psychology, University of Arizona, Tucson, AZ, United States
| | - Hannah M Siu
- Brain Imaging and TMS Laboratory, Department of Psychology, University of Arizona, Tucson, AZ, United States
| | - Ying-Hui Chou
- Brain Imaging and TMS Laboratory, Department of Psychology, University of Arizona, Tucson, AZ, United States
- Evelyn F. McKnight Brain Institute, Arizona Center on Aging, BIO5 Institute, University of Arizona, Tucson, AZ, United States
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11
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Genc S, Schiavi S, Chamberland M, Tax CMW, Raven EP, Daducci A, Jones DK. Developmental differences in canonical cortical networks: Insights from microstructure-informed tractography. Netw Neurosci 2024; 8:946-964. [PMID: 39355444 PMCID: PMC11424039 DOI: 10.1162/netn_a_00378] [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: 12/10/2023] [Accepted: 04/09/2024] [Indexed: 10/03/2024] Open
Abstract
In response to a growing interest in refining brain connectivity assessments, this study focuses on integrating white matter fiber-specific microstructural properties into structural connectomes. Spanning ages 8-19 years in a developmental sample, it explores age-related patterns of microstructure-informed network properties at both local and global scales. First, the diffusion-weighted signal fraction associated with each tractography-reconstructed streamline was constructed. Subsequently, the convex optimization modeling for microstructure-informed tractography (COMMIT) approach was employed to generate microstructure-informed connectomes from diffusion MRI data. To complete the investigation, network characteristics within eight functionally defined networks (visual, somatomotor, dorsal attention, ventral attention, limbic, fronto-parietal, default mode, and subcortical networks) were evaluated. The findings underscore a consistent increase in global efficiency across child and adolescent development within the visual, somatomotor, and default mode networks (p < 0.005). Additionally, mean strength exhibits an upward trend in the somatomotor and visual networks (p < 0.001). Notably, nodes within the dorsal and ventral visual pathways manifest substantial age-dependent changes in local efficiency, aligning with existing evidence of extended maturation in these pathways. The outcomes strongly support the notion of a prolonged developmental trajectory for visual association cortices. This study contributes valuable insights into the nuanced dynamics of microstructure-informed brain connectivity throughout different developmental stages.
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Affiliation(s)
- Sila Genc
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
- Neuroscience Advanced Clinical Imaging Service (NACIS), Department of Neurosurgery, The Royal Children’s Hospital, Parkville, Victoria, Australia
- Developmental Imaging, Clinical Sciences, Murdoch Children’s Research Institute, Parkville, Victoria, Australia
| | - Simona Schiavi
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
- Department of Computer Science, University of Verona, Italy
- ASG Superconductors, Genova, Italy
| | - Maxime Chamberland
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
- Eindhoven University of Technology, Department of Mathematics and Computer Science, Eindhoven, Netherlands
| | - Chantal M. W. Tax
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, United Kingdom
| | - Erika P. Raven
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
- Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | | | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
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12
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Benozzo D, Baggio G, Baron G, Chiuso A, Zampieri S, Bertoldo A. Analyzing asymmetry in brain hierarchies with a linear state-space model of resting-state fMRI data. Netw Neurosci 2024; 8:965-988. [PMID: 39355437 PMCID: PMC11424037 DOI: 10.1162/netn_a_00381] [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: 01/09/2024] [Accepted: 05/02/2024] [Indexed: 10/03/2024] Open
Abstract
This study challenges the traditional focus on zero-lag statistics in resting-state functional magnetic resonance imaging (rsfMRI) research. Instead, it advocates for considering time-lag interactions to unveil the directionality and asymmetries of the brain hierarchy. Effective connectivity (EC), the state matrix in dynamical causal modeling (DCM), is a commonly used metric for studying dynamical properties and causal interactions within a linear state-space system description. Here, we focused on how time-lag statistics are incorporated within the framework of DCM resulting in an asymmetric EC matrix. Our approach involves decomposing the EC matrix, revealing a steady-state differential cross-covariance matrix that is responsible for modeling information flow and introducing time-irreversibility. Specifically, the system's dynamics, influenced by the off-diagonal part of the differential covariance, exhibit a curl steady-state flow component that breaks detailed balance and diverges the dynamics from equilibrium. Our empirical findings indicate that the EC matrix's outgoing strengths correlate with the flow described by the differential cross covariance, while incoming strengths are primarily driven by zero-lag covariance, emphasizing conditional independence over directionality.
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Affiliation(s)
- Danilo Benozzo
- Information Engineering Department, University of Padova, Padova, Italy
| | - Giacomo Baggio
- Information Engineering Department, University of Padova, Padova, Italy
| | - Giorgia Baron
- Information Engineering Department, University of Padova, Padova, Italy
| | - Alessandro Chiuso
- Information Engineering Department, University of Padova, Padova, Italy
| | - Sandro Zampieri
- Information Engineering Department, University of Padova, Padova, Italy
| | - Alessandra Bertoldo
- Information Engineering Department, University of Padova, Padova, Italy
- Padova Neuroscience Center, University of Padova, Padova, Italy
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13
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Kim CY, Park Y, Namgung JY, Park Y, Park BY. The macroscale routing mechanism of structural brain connectivity related to body mass index. Hum Brain Mapp 2024; 45:e70019. [PMID: 39230183 PMCID: PMC11372826 DOI: 10.1002/hbm.70019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 08/05/2024] [Accepted: 08/20/2024] [Indexed: 09/05/2024] Open
Abstract
Understanding the brain's mechanisms in individuals with obesity is important for managing body weight. Prior neuroimaging studies extensively investigated alterations in brain structure and function related to body mass index (BMI). However, how the network communication among the large-scale brain networks differs across BMI is underinvestigated. This study used diffusion magnetic resonance imaging of 290 young adults to identify links between BMI and brain network mechanisms. Navigation efficiency, a measure of network routing, was calculated from the structural connectivity computed using diffusion tractography. The sensory and frontoparietal networks indicated positive associations between navigation efficiency and BMI. The neurotransmitter association analysis identified that serotonergic and dopaminergic receptors, as well as opioid and norepinephrine systems, were related to BMI-related alterations in navigation efficiency. The transcriptomic analysis found that genes associated with network routing across BMI overlapped with genes enriched in excitatory and inhibitory neurons, specifically, gene enrichments related to synaptic transmission and neuron projection. Our findings suggest a valuable insight into understanding BMI-related alterations in brain network routing mechanisms and the potential underlying cellular biology, which might be used as a foundation for BMI-based weight management.
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Affiliation(s)
- Chae Yeon Kim
- Department of Data Science, Inha University, Incheon, South Korea
| | - Yunseo Park
- Department of Data Science, Inha University, Incheon, South Korea
| | | | - Yeongjun Park
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea
| | - Bo-Yong Park
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea
- Research Center for Small Businesses Ecosystem, Inha University, Incheon, South Korea
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14
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Klug S, Murgaš M, Godbersen GM, Hacker M, Lanzenberger R, Hahn A. Synaptic signaling modeled by functional connectivity predicts metabolic demands of the human brain. Neuroimage 2024; 295:120658. [PMID: 38810891 DOI: 10.1016/j.neuroimage.2024.120658] [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: 01/15/2024] [Revised: 04/22/2024] [Accepted: 05/27/2024] [Indexed: 05/31/2024] Open
Abstract
PURPOSE The human brain is characterized by interacting large-scale functional networks fueled by glucose metabolism. Since former studies could not sufficiently clarify how these functional connections shape glucose metabolism, we aimed to provide a neurophysiologically-based approach. METHODS 51 healthy volunteers underwent simultaneous PET/MRI to obtain BOLD functional connectivity and [18F]FDG glucose metabolism. These multimodal imaging proxies of fMRI and PET were combined in a whole-brain extension of metabolic connectivity mapping. Specifically, functional connectivity of all brain regions were used as input to explain glucose metabolism of a given target region. This enabled the modeling of postsynaptic energy demands by incoming signals from distinct brain regions. RESULTS Functional connectivity input explained a substantial part of metabolic demands but with pronounced regional variations (34 - 76%). During cognitive task performance this multimodal association revealed a shift to higher network integration compared to resting state. In healthy aging, a dedifferentiation (decreased segregated/modular structure of the brain) of brain networks during rest was observed. Furthermore, by including data from mRNA maps, [11C]UCB-J synaptic density and aerobic glycolysis (oxygen-to-glucose index from PET data), we show that whole-brain functional input reflects non-oxidative, on-demand metabolism of synaptic signaling. The metabolically-derived directionality of functional inputs further marked them as top-down predictions. In addition, the approach uncovered formerly hidden networks with superior efficiency through metabolically informed network partitioning. CONCLUSIONS Applying multimodal imaging, we decipher a crucial part of the metabolic and neurophysiological basis of functional connections in the brain as interregional on-demand synaptic signaling fueled by anaerobic metabolism. The observed task- and age-related effects indicate promising future applications to characterize human brain function and clinical alterations.
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Affiliation(s)
- Sebastian Klug
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria; Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria
| | - Matej Murgaš
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria; Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria
| | - Godber M Godbersen
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria; Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria
| | - Marcus Hacker
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria; Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria
| | - Andreas Hahn
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria; Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria.
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15
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Tanner J, Faskowitz J, Teixeira AS, Seguin C, Coletta L, Gozzi A, Mišić B, Betzel RF. A multi-modal, asymmetric, weighted, and signed description of anatomical connectivity. Nat Commun 2024; 15:5865. [PMID: 38997282 PMCID: PMC11245624 DOI: 10.1038/s41467-024-50248-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 07/01/2024] [Indexed: 07/14/2024] Open
Abstract
The macroscale connectome is the network of physical, white-matter tracts between brain areas. The connections are generally weighted and their values interpreted as measures of communication efficacy. In most applications, weights are either assigned based on imaging features-e.g. diffusion parameters-or inferred using statistical models. In reality, the ground-truth weights are unknown, motivating the exploration of alternative edge weighting schemes. Here, we explore a multi-modal, regression-based model that endows reconstructed fiber tracts with directed and signed weights. We find that the model fits observed data well, outperforming a suite of null models. The estimated weights are subject-specific and highly reliable, even when fit using relatively few training samples, and the networks maintain a number of desirable features. In summary, we offer a simple framework for weighting connectome data, demonstrating both its ease of implementation while benchmarking its utility for typical connectome analyses, including graph theoretic modeling and brain-behavior associations.
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Affiliation(s)
- Jacob Tanner
- Cognitive Science Program, Indiana University, Bloomington, IN, USA
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Andreia Sofia Teixeira
- LASIGE, Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal
| | - Caio Seguin
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | | | - Alessandro Gozzi
- Functional Neuroimaging Lab, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems, Rovereto, Italy
| | - Bratislav Mišić
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Richard F Betzel
- Cognitive Science Program, Indiana University, Bloomington, IN, USA.
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA.
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA.
- Program in Neuroscience, Indiana University, Bloomington, IN, USA.
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16
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Namgung JY, Park Y, Park Y, Kim CY, Park BY. Diffusion time-related structure-function coupling reveals differential association with inter-individual variations in body mass index. Neuroimage 2024; 291:120590. [PMID: 38548036 DOI: 10.1016/j.neuroimage.2024.120590] [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: 09/12/2023] [Revised: 03/11/2024] [Accepted: 03/25/2024] [Indexed: 04/13/2024] Open
Abstract
Body mass index (BMI) is an indicator of obesity, and recent neuroimaging studies have demonstrated that inter-individual variations in BMI are associated with altered brain structure and function. However, the mechanism underlying the alteration of structure-function correspondence according to BMI is under-investigated. In this study, we studied structural and functional connectivity derived from diffusion MRI tractography and inter-regional correlations of functional MRI time series, respectively. We combined the structural and functional connectivity information using the Riemannian optimization approach. First, the low-dimensional principal eigenvectors (i.e., gradients) of the structural connectivity were generated by applying diffusion map embedding with varying diffusion times. A transformation was identified so that the structural and functional embeddings share the same coordinate system, and subsequently, the functional connectivity matrix was simulated. Then, we generated gradients from the simulated functional connectivity matrix. We found the most apparent cortical hierarchical organization differentiating between low-level sensory and higher-order transmodal regions in the middle of the diffusion time, indicating that the hierarchical organization of the brain may reflect the intermediate mechanisms of mono- and polysynaptic communications. Associations between the functional gradients and BMI were strongest when the hierarchical structure was the most evident. Moreover, the gradient-BMI association map was related to the microstructural features, and the findings indicated that the BMI-related structure-function coupling was significantly associated with brain microstructure, particularly in higher-order transmodal areas. Finally, transcriptomic association analysis revealed the potential biological underpinnings specifying gene enrichment in the striatum, hypothalamus, and cortical cells. Our findings provide evidence that structure-function correspondence is strongly coupled with BMI when hierarchical organization is the most apparent and that the associations are related to the multiscale properties of the brain, leading to an advanced understanding of the neural mechanisms related to BMI.
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Affiliation(s)
| | - Yeongjun Park
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea
| | - Yunseo Park
- Department of Data Science, Inha University, Incheon, Republic of Korea
| | - Chae Yeon Kim
- Department of Data Science, Inha University, Incheon, Republic of Korea
| | - Bo-Yong Park
- Department of Data Science, Inha University, Incheon, Republic of Korea; Department of Statistics and Data Science, Inha University, Incheon, Republic of Korea; Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea.
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17
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Luppi AI, Olbrich E, Finn C, Suárez LE, Rosas FE, Mediano PA, Jost J. Quantifying synergy and redundancy between networks. CELL REPORTS. PHYSICAL SCIENCE 2024; 5:101892. [PMID: 38720789 PMCID: PMC11077508 DOI: 10.1016/j.xcrp.2024.101892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 01/18/2024] [Accepted: 03/01/2024] [Indexed: 05/12/2024]
Abstract
Understanding how different networks relate to each other is key for understanding complex systems. We introduce an intuitive yet powerful framework to disentangle different ways in which networks can be (dis)similar and complementary to each other. We decompose the shortest paths between nodes as uniquely contributed by one source network, or redundantly by either, or synergistically by both together. Our approach considers the networks' full topology, providing insights at multiple levels of resolution: from global statistics to individual paths. Our framework is widely applicable across scientific domains, from public transport to brain networks. In humans and 124 other species, we demonstrate the prevalence of unique contributions by long-range white-matter fibers in structural brain networks. Across species, efficient communication also relies on significantly greater synergy between long-range and short-range fibers than expected by chance. Our framework could find applications for designing network systems or evaluating existing ones.
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Affiliation(s)
- Andrea I. Luppi
- Division of Anaesthesia and Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- St John’s College, University of Cambridge, Cambridge, UK
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Eckehard Olbrich
- Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany
| | - Conor Finn
- Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany
| | - Laura E. Suárez
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Fernando E. Rosas
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK
- Department of Informatics, University of Sussex, Brighton, UK
- Centre for Complexity Science, Imperial College London, London, UK
- Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, London, UK
| | | | - Jürgen Jost
- Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany
- ScaDS.AI, Leipzig University, Leipzig, Germany
- Santa Fe Institute, Santa Fe, NM, USA
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18
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Popp JL, Thiele JA, Faskowitz J, Seguin C, Sporns O, Hilger K. Structural-functional brain network coupling predicts human cognitive ability. Neuroimage 2024; 290:120563. [PMID: 38492685 DOI: 10.1016/j.neuroimage.2024.120563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 10/14/2023] [Accepted: 03/01/2024] [Indexed: 03/18/2024] Open
Abstract
Individual differences in general cognitive ability (GCA) have a biological basis within the structure and function of the human brain. Network neuroscience investigations revealed neural correlates of GCA in structural as well as in functional brain networks. However, whether the relationship between structural and functional networks, the structural-functional brain network coupling (SC-FC coupling), is related to individual differences in GCA remains an open question. We used data from 1030 adults of the Human Connectome Project, derived structural connectivity from diffusion weighted imaging, functional connectivity from resting-state fMRI, and assessed GCA as a latent g-factor from 12 cognitive tasks. Two similarity measures and six communication measures were used to model possible functional interactions arising from structural brain networks. SC-FC coupling was estimated as the degree to which these measures align with the actual functional connectivity, providing insights into different neural communication strategies. At the whole-brain level, higher GCA was associated with higher SC-FC coupling, but only when considering path transitivity as neural communication strategy. Taking region-specific variations in the SC-FC coupling strategy into account and differentiating between positive and negative associations with GCA, allows for prediction of individual cognitive ability scores in a cross-validated prediction framework (correlation between predicted and observed scores: r = 0.25, p < .001). The same model also predicts GCA scores in a completely independent sample (N = 567, r = 0.19, p < .001). Our results propose structural-functional brain network coupling as a neurobiological correlate of GCA and suggest brain region-specific coupling strategies as neural basis of efficient information processing predictive of cognitive ability.
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Affiliation(s)
- Johanna L Popp
- Department of Psychology I, Würzburg University, Marcusstr. 9-11, Würzburg D 97070, Germany.
| | - Jonas A Thiele
- Department of Psychology I, Würzburg University, Marcusstr. 9-11, Würzburg D 97070, Germany
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, 1101 E. 10th St., Bloomington 47405-7007, IN, USA
| | - Caio Seguin
- Department of Psychological and Brain Sciences, Indiana University, 1101 E. 10th St., Bloomington 47405-7007, IN, USA
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, 1101 E. 10th St., Bloomington 47405-7007, IN, USA
| | - Kirsten Hilger
- Department of Psychology I, Würzburg University, Marcusstr. 9-11, Würzburg D 97070, Germany.
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19
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Yoo S, Jang Y, Hong SJ, Park H, Valk SL, Bernhardt BC, Park BY. Whole-brain structural connectome asymmetry in autism. Neuroimage 2024; 288:120534. [PMID: 38340881 DOI: 10.1016/j.neuroimage.2024.120534] [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: 09/08/2023] [Revised: 01/28/2024] [Accepted: 02/07/2024] [Indexed: 02/12/2024] Open
Abstract
Autism spectrum disorder is a common neurodevelopmental condition that manifests as a disruption in sensory and social skills. Although it has been shown that the brain morphology of individuals with autism is asymmetric, how this differentially affects the structural connectome organization of each hemisphere remains under-investigated. We studied whole-brain structural connectivity-based brain asymmetry in individuals with autism using diffusion magnetic resonance imaging obtained from the Autism Brain Imaging Data Exchange initiative. By leveraging dimensionality reduction techniques, we constructed low-dimensional representations of structural connectivity and calculated their asymmetry index. Comparing the asymmetry index between individuals with autism and neurotypical controls, we found atypical structural connectome asymmetry in the sensory and default-mode regions, particularly showing weaker asymmetry towards the right hemisphere in autism. Network communication provided topological underpinnings by demonstrating that the inferior temporal cortex and limbic and frontoparietal regions showed reduced global network communication efficiency and decreased send-receive network navigation in the inferior temporal and lateral visual cortices in individuals with autism. Finally, supervised machine learning revealed that structural connectome asymmetry could be used as a measure for predicting communication-related autistic symptoms and nonverbal intelligence. Our findings provide insights into macroscale structural connectome alterations in autism and their topological underpinnings.
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Affiliation(s)
- Seulki Yoo
- Convergence Research Institute, Sungkyunkwan University, Suwon, Republic of Korea
| | - Yurim Jang
- Artificial Intelligence Convergence Research Center, Inha University, Incheon, Republic of Korea
| | - Seok-Jun Hong
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea; Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea
| | - Hyunjin Park
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea; School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
| | - Sofie L Valk
- Forschungszentrum Julich, Germany; Max Planck Institute for Cognitive and Brain Sciences, Leipzig, Germany; Systems Neuroscience, Heinrich Heine University, Duesseldorf, Germany
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Bo-Yong Park
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea; Department of Data Science, Inha University, Incheon, Republic of Korea; Department of Statistics and Data Science, Inha University, Incheon, Republic of Korea.
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20
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Cai G, Li X, Lin SS, Chen SJ, Rodgers NC, Koning KM, Bi D, Liu AP. Matrix confinement modulates 3D spheroid sorting and burst-like collective migration. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.23.549940. [PMID: 37546827 PMCID: PMC10401934 DOI: 10.1101/2023.07.23.549940] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
While it is known that cells with differential adhesion tend to segregate and preferentially sort, the physical forces governing sorting and invasion in heterogeneous tumors remain poorly understood. To investigate this, we tune matrix confinement, mimicking changes in the stiffness and confinement of the tumor microenvironment, to explore how physical confinement influences individual and collective cell migration in 3D spheroids. High levels of confinement lead to cell sorting while reducing matrix confinement triggers the collective fluidization of cell motion. Cell sorting, which depends on cell-cell adhesion, is crucial to this phenomenon. Burst-like migration does not occur for spheroids that have not undergone sorting, regardless of the degree of matrix confinement. Using computational Self-Propelled Voronoi modeling, we show that spheroid sorting and invasion into the matrix depend on the balance between cell-generated forces and matrix resistance. The findings support a model where matrix confinement modulates 3D spheroid sorting and unjamming in an adhesion-dependent manner, providing insights into the mechanisms of cell sorting and migration in the primary tumor and toward distant metastatic sites.
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Affiliation(s)
- Grace Cai
- Applied Physics Program, University of Michigan, Ann Arbor, MI, USA
| | - Xinzhi Li
- Department of Physics, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Shan-Shan Lin
- Institute of Molecular Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Samuel J. Chen
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Nicole C. Rodgers
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Katherine M. Koning
- Cellular and Molecular Biology Program, University of Michigan, Ann Arbor, MI, USA
| | - Dapeng Bi
- Department of Physics, Northeastern University, Boston, MA, USA
| | - Allen P. Liu
- Applied Physics Program, University of Michigan, Ann Arbor, MI, USA
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA
- Cellular and Molecular Biology Program, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
- Department of Biophysics, University of Michigan, Ann Arbor, MI, USA
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21
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Puxeddu MG, Faskowitz J, Seguin C, Yovel Y, Assaf Y, Betzel R, Sporns O. Relation of connectome topology to brain volume across 103 mammalian species. PLoS Biol 2024; 22:e3002489. [PMID: 38315722 PMCID: PMC10868790 DOI: 10.1371/journal.pbio.3002489] [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: 05/10/2023] [Revised: 02/15/2024] [Accepted: 01/08/2024] [Indexed: 02/07/2024] Open
Abstract
The brain connectome is an embedded network of anatomically interconnected brain regions, and the study of its topological organization in mammals has become of paramount importance due to its role in scaffolding brain function and behavior. Unlike many other observable networks, brain connections incur material and energetic cost, and their length and density are volumetrically constrained by the skull. Thus, an open question is how differences in brain volume impact connectome topology. We address this issue using the MaMI database, a diverse set of mammalian connectomes reconstructed from 201 animals, covering 103 species and 12 taxonomy orders, whose brain size varies over more than 4 orders of magnitude. Our analyses focus on relationships between volume and modular organization. After having identified modules through a multiresolution approach, we observed how connectivity features relate to the modular structure and how these relations vary across brain volume. We found that as the brain volume increases, modules become more spatially compact and dense, comprising more costly connections. Furthermore, we investigated how spatial embedding shapes network communication, finding that as brain volume increases, nodes' distance progressively impacts communication efficiency. We identified modes of variation in network communication policies, as smaller and bigger brains show higher efficiency in routing- and diffusion-based signaling, respectively. Finally, bridging network modularity and communication, we found that in larger brains, modular structure imposes stronger constraints on network signaling. Altogether, our results show that brain volume is systematically related to mammalian connectome topology and that spatial embedding imposes tighter restrictions on larger brains.
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Affiliation(s)
- Maria Grazia Puxeddu
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana, United States of America
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana, United States of America
| | - Caio Seguin
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana, United States of America
| | - Yossi Yovel
- School of Neurobiology, Biochemistry and Biophysics, Tel Aviv University, Tel Aviv, Israel
| | - Yaniv Assaf
- School of Neurobiology, Biochemistry and Biophysics, Tel Aviv University, Tel Aviv, Israel
| | - Richard Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana, United States of America
- Program in Neuroscience, Indiana University, Bloomington, Indiana, United States of America
- Program in Cognitive Science, Indiana University, Bloomington, Indiana, United States of America
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana, United States of America
- Program in Neuroscience, Indiana University, Bloomington, Indiana, United States of America
- Program in Cognitive Science, Indiana University, Bloomington, Indiana, United States of America
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22
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Kumar PR, Jha RK, Katti A. Brain tissue segmentation in neurosurgery: a systematic analysis for quantitative tractography approaches. Acta Neurol Belg 2024; 124:1-15. [PMID: 36609837 DOI: 10.1007/s13760-023-02170-9] [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: 07/14/2022] [Accepted: 12/31/2022] [Indexed: 01/09/2023]
Abstract
Diffusion magnetic resonance imaging (dMRI) is a cutting-edge imaging method that provides a macro-scale in vivo map of the white matter pathways in the brain. The measurement of brain microstructure and the enhancement of tractography rely heavily on dMRI tissue segmentation. Anatomical MRI technique (e.g., T1- and T2-weighted imaging) is the most widely used method for segmentation in dMRI. In comparison to anatomical MRI, dMRI suffers from higher image distortions, lower image quality, and making inter-modality registration more difficult. The dMRI tractography study of brain connectivity has become a major part of the neuroimaging landscape in recent years. In this research, we provide a high-level overview of the methods used to segment several brain tissues types, including grey and white matter and cerebrospinal fluid, to enable quantitative studies of structural connectivity in the brain in health and illness. In the first part of our review, we discuss the three main phases in the quantitative analysis of tractography, which are correction, segmentation, and quantification. Methodological possibilities are described for each phase, along with their popularity and potential benefits and drawbacks. After that, we will look at research that used quantitative tractography approaches to examine the white and grey matter of the brain, with an emphasis on neurodevelopment, ageing, neurological illnesses, mental disorders, and neurosurgery as possible applications. Even though there have been substantial advancements in methodological technology and the spectrum of applications, there is still no consensus regarding the "optimal" approach in the quantitative analysis of tractography. As a result, researchers should tread carefully when interpreting the findings of quantitative analysis of tractography.
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Affiliation(s)
- Puranam Revanth Kumar
- Department of Electronics and Communication Engineering, IcfaiTech (Faculty of Science and Technology), IFHE University, Hyderabad, 501203, India.
| | - Rajesh Kumar Jha
- Department of Electronics and Communication Engineering, IcfaiTech (Faculty of Science and Technology), IFHE University, Hyderabad, 501203, India
| | - Amogh Katti
- Department of Computer Science and Engineering, Gitam School of Technology, GITAM University, Hyderabad, 502329, India
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23
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Park BY, Benkarim O, Weber CF, Kebets V, Fett S, Yoo S, Martino AD, Milham MP, Misic B, Valk SL, Hong SJ, Bernhardt BC. Connectome-wide structure-function coupling models implicate polysynaptic alterations in autism. Neuroimage 2024; 285:120481. [PMID: 38043839 DOI: 10.1016/j.neuroimage.2023.120481] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Revised: 11/29/2023] [Accepted: 12/01/2023] [Indexed: 12/05/2023] Open
Abstract
Autism spectrum disorder (ASD) is one of the most common neurodevelopmental diagnoses. Although incompletely understood, structural and functional network alterations are increasingly recognized to be at the core of the condition. We utilized multimodal imaging and connectivity modeling to study structure-function coupling in ASD and probed mono- and polysynaptic mechanisms on structurally-governed network function. We examined multimodal magnetic resonance imaging data in 80 ASD and 61 neurotypical controls from the Autism Brain Imaging Data Exchange (ABIDE) II initiative. We predicted intrinsic functional connectivity from structural connectivity data in each participant using a Riemannian optimization procedure that varies the times that simulated signals can unfold along tractography-derived personalized connectomes. In both ASD and neurotypical controls, we observed improved structure-function prediction at longer diffusion time scales, indicating better modeling of brain function when polysynaptic mechanisms are accounted for. Prediction accuracy differences (∆prediction accuracy) were marked in transmodal association systems, such as the default mode network, in both neurotypical controls and ASD. Differences were, however, lower in ASD in a polysynaptic regime at higher simulated diffusion times. We compared regional differences in ∆prediction accuracy between both groups to assess the impact of polysynaptic communication on structure-function coupling. This analysis revealed that between-group differences in ∆prediction accuracy followed a sensory-to-transmodal cortical hierarchy, with an increased gap between controls and ASD in transmodal compared to sensory/motor systems. Multivariate associative techniques revealed that structure-function differences reflected inter-individual differences in autistic symptoms and verbal as well as non-verbal intelligence. Our network modeling approach sheds light on atypical structure-function coupling in autism, and suggests that polysynaptic network mechanisms are implicated in the condition and that these can help explain its wide range of associated symptoms.
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Affiliation(s)
- Bo-Yong Park
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada; Department of Data Science, Inha University, Incheon, South Korea; Department of Statistics and Data Science, Inha University, Incheon, South Korea; Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea.
| | - Oualid Benkarim
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Clara F Weber
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Valeria Kebets
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Serena Fett
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Seulki Yoo
- Convergence Research Institute, Sungkyunkwan University, Suwon, South Korea
| | - Adriana Di Martino
- Center for the Developing Brain, Child Mind Institute, New York, United States
| | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York, United States
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Sofie L Valk
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Seok-Jun Hong
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada; Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea; Center for the Developing Brain, Child Mind Institute, New York, United States; Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada.
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24
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Bazinet V, Hansen JY, Misic B. Towards a biologically annotated brain connectome. Nat Rev Neurosci 2023; 24:747-760. [PMID: 37848663 DOI: 10.1038/s41583-023-00752-3] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/20/2023] [Indexed: 10/19/2023]
Abstract
The brain is a network of interleaved neural circuits. In modern connectomics, brain connectivity is typically encoded as a network of nodes and edges, abstracting away the rich biological detail of local neuronal populations. Yet biological annotations for network nodes - such as gene expression, cytoarchitecture, neurotransmitter receptors or intrinsic dynamics - can be readily measured and overlaid on network models. Here we review how connectomes can be represented and analysed as annotated networks. Annotated connectomes allow us to reconceptualize architectural features of networks and to relate the connection patterns of brain regions to their underlying biology. Emerging work demonstrates that annotated connectomes help to make more veridical models of brain network formation, neural dynamics and disease propagation. Finally, annotations can be used to infer entirely new inter-regional relationships and to construct new types of network that complement existing connectome representations. In summary, biologically annotated connectomes offer a compelling way to study neural wiring in concert with local biological features.
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Affiliation(s)
- Vincent Bazinet
- Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada
| | - Justine Y Hansen
- Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada
| | - Bratislav Misic
- Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada.
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25
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Silchenko AN, Hoffstaedter F, Eickhoff SB. Impact of sample size and regression of tissue-specific signals on effective connectivity within the core default mode network. Hum Brain Mapp 2023; 44:5858-5870. [PMID: 37713540 PMCID: PMC10619387 DOI: 10.1002/hbm.26481] [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: 04/09/2023] [Revised: 07/24/2023] [Accepted: 08/28/2023] [Indexed: 09/17/2023] Open
Abstract
Interactions within brain networks are inherently directional, which are inaccessible to classical functional connectivity estimates from resting-state functional magnetic resonance imaging (fMRI) but can be detected using spectral dynamic causal modeling (DCM). The sample size and unavoidable presence of nuisance signals during fMRI measurement are the two important factors influencing the stability of group estimates of connectivity parameters. However, most recent studies exploring effective connectivity (EC) have been conducted with small sample sizes and minimally pre-processed datasets. We explore the impact of these two factors by analyzing clean resting-state fMRI data from 330 unrelated subjects from the Human Connectome Project database. We demonstrate that both the stability of the model selection procedures and the inference of connectivity parameters are highly dependent on the sample size. The minimum sample size required for stable DCM is approximately 50, which may explain the variability of the DCM results reported so far. We reveal a stable pattern of EC within the core default mode network computed for large sample sizes and demonstrate that the use of subject-specific thresholded whole-brain masks for tissue-specific signals regression enhances the detection of weak connections.
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Affiliation(s)
- Alexander N. Silchenko
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM‐7)Research Center JülichJülichGermany
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM‐7)Research Center JülichJülichGermany
- Institute of Systems Neuroscience, Medical FacultyHeinrich Heine University DüsseldorfDüsseldorfGermany
| | - Simon B. Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM‐7)Research Center JülichJülichGermany
- Institute of Systems Neuroscience, Medical FacultyHeinrich Heine University DüsseldorfDüsseldorfGermany
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26
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Qu Y, Wang P, Yao H, Wang D, Song C, Yang H, Zhang Z, Chen P, Kang X, Du K, Fan L, Zhou B, Han T, Yu C, Zhang X, Zuo N, Jiang T, Zhou Y, Liu B, Han Y, Lu J, Liu Y. Reproducible Abnormalities and Diagnostic Generalizability of White Matter in Alzheimer's Disease. Neurosci Bull 2023; 39:1533-1543. [PMID: 37014553 PMCID: PMC10533766 DOI: 10.1007/s12264-023-01041-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 11/29/2022] [Indexed: 04/05/2023] Open
Abstract
Alzheimer's disease (AD) is associated with the impairment of white matter (WM) tracts. The current study aimed to verify the utility of WM as the neuroimaging marker of AD with multisite diffusion tensor imaging datasets [321 patients with AD, 265 patients with mild cognitive impairment (MCI), 279 normal controls (NC)], a unified pipeline, and independent site cross-validation. Automated fiber quantification was used to extract diffusion profiles along tracts. Random-effects meta-analyses showed a reproducible degeneration pattern in which fractional anisotropy significantly decreased in the AD and MCI groups compared with NC. Machine learning models using tract-based features showed good generalizability among independent site cross-validation. The diffusion metrics of the altered regions and the AD probability predicted by the models were highly correlated with cognitive ability in the AD and MCI groups. We highlighted the reproducibility and generalizability of the degeneration pattern of WM tracts in AD.
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Affiliation(s)
- Yida Qu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Pan Wang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin University, Tianjin, 300222, China
| | - Hongxiang Yao
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin University, Tianjin, 300222, China
| | - Dawei Wang
- Department of Radiology, Department of Epidemiology and Health Statistics, School of Public Health, Qilu Hospital of Shandong University, Ji'nan, 250063, China
| | - Chengyuan Song
- Department of Neurology, Qilu Hospital of Shandong University, Ji'nan, 250063, China
| | - Hongwei Yang
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
| | - Zengqiang Zhang
- Branch of Chinese, PLA General Hospital, Sanya, 572022, China
| | - Pindong Chen
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xiaopeng Kang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Kai Du
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Lingzhong Fan
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Bo Zhou
- Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100089, China
| | - Tong Han
- Department of Radiology, Tianjin Huanhu Hospital, Tianjin, 300222, China
| | - Chunshui Yu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Xi Zhang
- Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100089, China
| | - Nianming Zuo
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Tianzi Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yuying Zhou
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin University, Tianjin, 300222, China
| | - Bing Liu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Lab of Cognition Neuroscience & Learning, Beijing Normal University, Beijing, 100091, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
- Beijing Institute of Geriatrics, Beijing, 100053, China
- National Clinical Research Center for Geriatric Disorders, Beijing, 100053, China
- Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, 100053, China
| | - Jie Lu
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China.
| | - Yong Liu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China.
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27
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Milisav F, Bazinet V, Iturria-Medina Y, Misic B. Resolving inter-regional communication capacity in the human connectome. Netw Neurosci 2023; 7:1051-1079. [PMID: 37781139 PMCID: PMC10473316 DOI: 10.1162/netn_a_00318] [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: 10/11/2022] [Accepted: 04/03/2023] [Indexed: 10/03/2023] Open
Abstract
Applications of graph theory to the connectome have inspired several models of how neural signaling unfolds atop its structure. Analytic measures derived from these communication models have mainly been used to extract global characteristics of brain networks, obscuring potentially informative inter-regional relationships. Here we develop a simple standardization method to investigate polysynaptic communication pathways between pairs of cortical regions. This procedure allows us to determine which pairs of nodes are topologically closer and which are further than expected on the basis of their degree. We find that communication pathways delineate canonical functional systems. Relating nodal communication capacity to meta-analytic probabilistic patterns of functional specialization, we also show that areas that are most closely integrated within the network are associated with higher order cognitive functions. We find that these regions' proclivity towards functional integration could naturally arise from the brain's anatomical configuration through evenly distributed connections among multiple specialized communities. Throughout, we consider two increasingly constrained null models to disentangle the effects of the network's topology from those passively endowed by spatial embedding. Altogether, the present findings uncover relationships between polysynaptic communication pathways and the brain's functional organization across multiple topological levels of analysis and demonstrate that network integration facilitates cognitive integration.
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Affiliation(s)
- Filip Milisav
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Vincent Bazinet
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Yasser Iturria-Medina
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
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28
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Seguin C, Sporns O, Zalesky A. Brain network communication: concepts, models and applications. Nat Rev Neurosci 2023; 24:557-574. [PMID: 37438433 DOI: 10.1038/s41583-023-00718-5] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/08/2023] [Indexed: 07/14/2023]
Abstract
Understanding communication and information processing in nervous systems is a central goal of neuroscience. Over the past two decades, advances in connectomics and network neuroscience have opened new avenues for investigating polysynaptic communication in complex brain networks. Recent work has brought into question the mainstay assumption that connectome signalling occurs exclusively via shortest paths, resulting in a sprawling constellation of alternative network communication models. This Review surveys the latest developments in models of brain network communication. We begin by drawing a conceptual link between the mathematics of graph theory and biological aspects of neural signalling such as transmission delays and metabolic cost. We organize key network communication models and measures into a taxonomy, aimed at helping researchers navigate the growing number of concepts and methods in the literature. The taxonomy highlights the pros, cons and interpretations of different conceptualizations of connectome signalling. We showcase the utility of network communication models as a flexible, interpretable and tractable framework to study brain function by reviewing prominent applications in basic, cognitive and clinical neurosciences. Finally, we provide recommendations to guide the future development, application and validation of network communication models.
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Affiliation(s)
- Caio Seguin
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Victoria, Australia.
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA.
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
- Program in Neuroscience, Indiana University, Bloomington, IN, USA
- Program in Cognitive Science, Indiana University, Bloomington, IN, USA
- Indiana University Network Science Institute, Indiana University, Bloomington, IN, USA
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Victoria, Australia
- Department of Biomedical Engineering, Melbourne School of Engineering, University of Melbourne, Melbourne, Victoria, Australia
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29
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Liu ZQ, Shafiei G, Baillet S, Misic B. Spatially heterogeneous structure-function coupling in haemodynamic and electromagnetic brain networks. Neuroimage 2023; 278:120276. [PMID: 37451374 DOI: 10.1016/j.neuroimage.2023.120276] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 07/04/2023] [Accepted: 07/11/2023] [Indexed: 07/18/2023] Open
Abstract
The relationship between structural and functional connectivity in the brain is a key question in connectomics. Here we quantify patterns of structure-function coupling across the neocortex, by comparing structural connectivity estimated using diffusion MRI with functional connectivity estimated using both neurophysiological (MEG-based) and haemodynamic (fMRI-based) recordings. We find that structure-function coupling is heterogeneous across brain regions and frequency bands. The link between structural and functional connectivity is generally stronger in multiple MEG frequency bands compared to resting state fMRI. Structure-function coupling is greater in slower and intermediate frequency bands compared to faster frequency bands. We also find that structure-function coupling systematically follows the archetypal sensorimotor-association hierarchy, as well as patterns of laminar differentiation, peaking in granular layer IV. Finally, structure-function coupling is better explained using structure-informed inter-regional communication metrics than using structural connectivity alone. Collectively, these results place neurophysiological and haemodynamic structure-function relationships in a common frame of reference and provide a starting point for a multi-modal understanding of structure-function coupling in the brain.
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Affiliation(s)
- Zhen-Qi Liu
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Golia Shafiei
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sylvain Baillet
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada.
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30
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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. Using network control theory to study the dynamics of the structural connectome. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.23.554519. [PMID: 37662395 PMCID: PMC10473719 DOI: 10.1101/2023.08.23.554519] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Network control theory (NCT) is a simple and powerful tool for studying how network topology informs and constrains dynamics. 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 dynamics in a desired way. We have extensively developed and validated the application of NCT to the human structural connectome. Through these efforts, we have studied (i) how different aspects of connectome topology affect neural dynamics, (ii) whether NCT outputs cohere with empirical data on brain function and stimulation, and (iii) how NCT outputs vary across development and correlate with behavior and mental health symptoms. In this protocol, we introduce a framework for applying NCT to structural connectomes following two main pathways. Our primary pathway focuses on computing the control energy associated with transitioning between specific neural activity states. Our second pathway focuses on computing average controllability, which indexes nodes' general capacity to control dynamics. We also provide recommendations for comparing NCT outputs against null network models. Finally, we support this protocol with a Python-based software package called network control theory for python (nctpy).
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Affiliation(s)
- Linden Parkes
- Department of Bioengineering, University of Pennsylvania, PA 19104, USA
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, Rutgers University, Piscataway, NJ 08854, USA
| | - Jason Z Kim
- Department of Physics, Cornell University, Ithaca, NY 14853, USA
| | - Jennifer Stiso
- Department of Bioengineering, University of Pennsylvania, PA 19104, USA
| | | | - Matthew Cieslak
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sydney Covitz
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Raquel E Gur
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ruben C Gur
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Fabio Pasqualetti
- Department of Mechanical Engineering, University of California, Riverside, Riverside, CA 92521, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, Philadelphia, PA 19104, USA
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Dale Zhou
- Department of Bioengineering, University of Pennsylvania, PA 19104, USA
| | - Theodore D Satterthwaite
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, Philadelphia, PA 19104, USA
- Department of Electrical and Systems Engineering, University of Pennsylvania, PA 19104, USA
- Department of Physics and Astronomy, University of Pennsylvania, PA 19104, USA
- Santa Fe Institute, Santa Fe, NM 87501, USA
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Puri P, Wu ST, Su CY, Aljadeff J. Shallow networks run deep: Peripheral preprocessing facilitates odor classification. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.23.550211. [PMID: 37546820 PMCID: PMC10401955 DOI: 10.1101/2023.07.23.550211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
The mammalian brain implements sophisticated sensory processing algorithms along multilayered ('deep') neural-networks. Strategies that insects use to meet similar computational demands, while relying on smaller nervous systems with shallow architectures, remain elusive. Using Drosophila as a model, we uncover the algorithmic role of odor preprocessing by a shallow network of compartmentalized olfactory receptor neurons. Each compartment operates as a ratiometric unit for specific odor-mixtures. This computation arises from a simple mechanism: electrical coupling between two differently-sized neurons. We demonstrate that downstream synaptic connectivity is shaped to optimally leverage amplification of a hedonic value signal in the periphery. Furthermore, peripheral preprocessing is shown to markedly improve novel odor classification in a higher brain center. Together, our work highlights a far-reaching functional role of the sensory periphery for downstream processing. By elucidating the implementation of powerful computations by a shallow network, we provide insights into general principles of efficient sensory processing algorithms.
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Affiliation(s)
- Palka Puri
- Department of Physics, University of California San Diego, La Jolla, CA, 92093, USA
| | - Shiuan-Tze Wu
- Department of Neurobiology, University of California San Diego, La Jolla, CA, 92093, USA
| | - Chih-Ying Su
- Department of Neurobiology, University of California San Diego, La Jolla, CA, 92093, USA
| | - Johnatan Aljadeff
- Department of Neurobiology, University of California San Diego, La Jolla, CA, 92093, USA
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32
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Ackerman JE, Adjei-Sowah E, Korcari A, Muscat SN, Nichols AE, Buckley MR, Loiselle AE. Identification of Periostin as a critical niche for myofibroblast dynamics and fibrosis during tendon healing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.21.550090. [PMID: 37502924 PMCID: PMC10370208 DOI: 10.1101/2023.07.21.550090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Tendon injuries are a major clinical problem, with poor patient outcomes caused by abundant scar tissue deposition during healing. Myofibroblasts play a critical role in the initial restoration of structural integrity after injury. However, persistent myofibroblast activity drives the transition to fibrotic scar tissue formation. As such, disrupting myofibroblast persistence is a key therapeutic target. While myofibroblasts are typically defined by the presence of αSMA+ stress fibers, αSMA is expressed in other cell types including the vasculature. As such, modulation of myofibroblast dynamics via disruption of αSMA expression is not a translationally tenable approach. Recent work has demonstrated that Periostin-lineage (PostnLin) cells are a precursor for cardiac fibrosis-associated myofibroblasts. In contrast to this, here we show that PostnLin cells contribute to a transient αSMA+ myofibroblast population that is required for functional tendon healing, and that Periostin forms a supportive matrix niche that facilitates myofibroblast differentiation and persistence. Collectively, these data identify the Periostin matrix niche as a critical regulator of myofibroblast fate and persistence that could be targeted for therapeutic manipulation to facilitate regenerative tendon healing.
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Affiliation(s)
- Jessica E. Ackerman
- Center for Musculoskeletal Research, University of Rochester Medical Center, Rochester, NY
- Current affiliation: NDORMS, University of Oxford, Oxford, United Kingdom
| | - Emmanuela Adjei-Sowah
- Center for Musculoskeletal Research, University of Rochester Medical Center, Rochester, NY
- Department of Biomedical Engineering, University of Rochester, Rochester, NY
| | - Antonion Korcari
- Center for Musculoskeletal Research, University of Rochester Medical Center, Rochester, NY
- Department of Biomedical Engineering, University of Rochester, Rochester, NY
| | - Samantha N. Muscat
- Center for Musculoskeletal Research, University of Rochester Medical Center, Rochester, NY
- Department of Pathology & Laboratory Medicine, University of Rochester Medical Center, Rochester, NY
| | - Anne E.C. Nichols
- Center for Musculoskeletal Research, University of Rochester Medical Center, Rochester, NY
- Department of Orthopaedics & Physical Performance, University of Rochester Medical Center, Rochester, NY
| | - Mark R. Buckley
- Center for Musculoskeletal Research, University of Rochester Medical Center, Rochester, NY
- Department of Biomedical Engineering, University of Rochester, Rochester, NY
| | - Alayna E. Loiselle
- Center for Musculoskeletal Research, University of Rochester Medical Center, Rochester, NY
- Department of Biomedical Engineering, University of Rochester, Rochester, NY
- Department of Pathology & Laboratory Medicine, University of Rochester Medical Center, Rochester, NY
- Department of Orthopaedics & Physical Performance, University of Rochester Medical Center, Rochester, NY
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33
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Jia M, Sayed K, Kapetanaki MG, Dion W, Rosas L, Irfan S, Valenzi E, Mora AL, Lafyatis RA, Rojas M, Zhu B, Benos PV. LEF1 isoforms regulate cellular senescence and aging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.20.549883. [PMID: 37502913 PMCID: PMC10370160 DOI: 10.1101/2023.07.20.549883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Background The study of aging and its mechanisms, such as cellular senescence, has provided valuable insights into age-related pathologies, thus contributing to their prevention and treatment. The current abundance of high throughput data combined with the surge of robust analysis algorithms has facilitated novel ways of identifying underlying pathways that may drive these pathologies. Methods With the focus on identifying key regulators of lung aging, we performed comparative analyses of transcriptional profiles of aged versus young human subjects and mice, focusing on the common age-related changes in the transcriptional regulation in lung macrophages, T cells, and B immune cells. Importantly, we validated our findings in cell culture assays and human lung samples. Results We identified Lymphoid Enhancer Binding Factor 1 (LEF1) as an important age-associated regulator of gene expression in all three cell types across different tissues and species. Follow-up experiments showed that the differential expression of long and short LEF1 isoforms is a key regulatory mechanism of cellular senescence. Further examination of lung tissue from patients with Idiopathic Pulmonary Fibrosis (IPF), an age-related disease with strong ties to cellular senescence, we demonstrated a stark dysregulation of LEF1. Conclusions Collectively, our results suggest that the LEF1 is a key factor of aging, and its differential regulation is associated with human and murine cellular senescence.
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Affiliation(s)
- Minxue Jia
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Joint Carnegie Mellon University-University of Pittsburgh Ph.D. Program in Computational Biology, Pittsburgh, Pennsylvania, USA
| | - Khaled Sayed
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Department of Epidemiology, University of Florida, Gainesville, Florida, USA
| | - Maria G. Kapetanaki
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - William Dion
- Aging Institute of UPMC, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Lorena Rosas
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, The Ohio State University, Columbus, Ohio, USA
| | - Saad Irfan
- Aging Institute of UPMC, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Eleanor Valenzi
- Department of Rheumatology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Ana L. Mora
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, The Ohio State University, Columbus, Ohio, USA
| | - Robert A. Lafyatis
- Department of Rheumatology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Mauricio Rojas
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, The Ohio State University, Columbus, Ohio, USA
| | - Bokai Zhu
- Aging Institute of UPMC, University of Pittsburgh, Pittsburgh, Pennsylvania
- Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Pittsburgh Liver Research Center, University of Pittsburgh, Pennsylvania, USA
| | - Panayiotis V. Benos
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Joint Carnegie Mellon University-University of Pittsburgh Ph.D. Program in Computational Biology, Pittsburgh, Pennsylvania, USA
- Department of Epidemiology, University of Florida, Gainesville, Florida, USA
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34
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Zorlu N, Bayrakçı A, Karakılıç M, Zalesky A, Seguin C, Tian Y, Gülyüksel F, Yalınçetin B, Oral E, Gelal F, Bora E. Abnormal Structural Network Communication Reflects Cognitive Deficits in Schizophrenia. Brain Topogr 2023; 36:294-304. [PMID: 36971857 DOI: 10.1007/s10548-023-00954-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 03/04/2023] [Indexed: 03/28/2023]
Abstract
Schizophrenia has long been thought to be a disconnection syndrome and several previous studies have reported widespread abnormalities in white matter tracts in individuals with schizophrenia. Furthermore, reductions in structural connectivity may also impair communication between anatomically unconnected pairs of brain regions, potentially impacting global signal traffic in the brain. Therefore, we used different communication models to examine direct and indirect structural connections (polysynaptic) communication in large-scale brain networks in schizophrenia. Diffusion-weighted magnetic resonance imaging scans were acquired from 62 patients diagnosed with schizophrenia and 35 controls. In this study, we used five network communication models including, shortest paths, navigation, diffusion, search information and communicability to examine polysynaptic communication in large-scale brain networks in schizophrenia. We showed less efficient communication between spatially widespread brain regions particulary encompassing cortico-subcortical basal ganglia network in schizophrenia group relative to controls. Then, we also examined whether reduced communication efficiency was related to clinical symptoms in schizophrenia group. Among different measures of communication efficiency, only navigation efficiency was associated with global cognitive impairment across multiple cognitive domains including verbal learning, processing speed, executive functions and working memory, in individuals with schizophrenia. We did not find any association between communication efficiency measures and positive or negative symptoms within the schizophrenia group. Our findings are important for improving our mechanistic understanding of neurobiological process underlying cognitive symptoms in schizophrenia.
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35
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Seguin C, Jedynak M, David O, Mansour S, Sporns O, Zalesky A. Communication dynamics in the human connectome shape the cortex-wide propagation of direct electrical stimulation. Neuron 2023; 111:1391-1401.e5. [PMID: 36889313 DOI: 10.1016/j.neuron.2023.01.027] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 11/28/2022] [Accepted: 01/30/2023] [Indexed: 03/09/2023]
Abstract
Communication between gray matter regions underpins all facets of brain function. We study inter-areal communication in the human brain using intracranial EEG recordings, acquired following 29,055 single-pulse direct electrical stimulations in a total of 550 individuals across 20 medical centers (average of 87 ± 37 electrode contacts per subject). We found that network communication models-computed on structural connectivity inferred from diffusion MRI-can explain the causal propagation of focal stimuli, measured at millisecond timescales. Building on this finding, we show that a parsimonious statistical model comprising structural, functional, and spatial factors can accurately and robustly predict cortex-wide effects of brain stimulation (R2=46% in data from held-out medical centers). Our work contributes toward the biological validation of concepts in network neuroscience and provides insight into how connectome topology shapes polysynaptic inter-areal signaling. We anticipate that our findings will have implications for research on neural communication and the design of brain stimulation paradigms.
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Affiliation(s)
- Caio Seguin
- Melbourne Neuropsychiatry Centre, The University of Melbourne and Melbourne Health, Melbourne, VIC, Australia; Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA.
| | - Maciej Jedynak
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
| | - Olivier David
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
| | - Sina Mansour
- Melbourne Neuropsychiatry Centre, The University of Melbourne and Melbourne Health, Melbourne, VIC, Australia; Department of Biomedical Engineering, Melbourne School of Engineering, The University of Melbourne, Melbourne, VIC, Australia
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA; Cognitive Science Program, Indiana University, Bloomington, IN, USA; Program in Neuroscience, Indiana University, Bloomington, IN, USA; Network Science Institute, Indiana University, Bloomington, IN, USA
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, The University of Melbourne and Melbourne Health, Melbourne, VIC, Australia; Department of Biomedical Engineering, Melbourne School of Engineering, The University of Melbourne, Melbourne, VIC, Australia
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36
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Nenning KH, Xu T, Franco AR, Swallow K, Tambini A, Margulies DS, Smallwood J, Colcombe SJ, Milham MP. Omnipresence of the sensorimotor-association axis topography in the human connectome. Neuroimage 2023; 272:120059. [PMID: 37001835 DOI: 10.1016/j.neuroimage.2023.120059] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 03/04/2023] [Accepted: 03/27/2023] [Indexed: 04/03/2023] Open
Abstract
Low-dimensional representations are increasingly used to study meaningful organizational principles within the human brain. Most notably, the sensorimotor-association axis consistently explains the most variance in the human connectome as its so-called principal gradient, suggesting that it represents a fundamental organizational principle. While recent work indicates these low dimensional representations are relatively robust, they are limited by modeling only certain aspects of the functional connectivity structure. To date, the majority of studies have restricted these approaches to the strongest connections in the brain, treating weaker or negative connections as noise despite evidence of meaningful structure among them. The present work examines connectivity gradients of the human connectome across a full range of connectivity strengths and explores the implications for outcomes of individual differences, identifying potential dependencies on thresholds and opportunities to improve prediction tasks. Interestingly, the sensorimotor-association axis emerged as the principal gradient of the human connectome across the entire range of connectivity levels. Moreover, the principal gradient of connections at intermediate strengths encoded individual differences, better followed individual-specific anatomical features, and was also more predictive of intelligence. Taken together, our results add to evidence of the sensorimotor-association axis as a fundamental principle of the brain's functional organization, since it is evident even in the connectivity structure of more lenient connectivity thresholds. These more loosely coupled connections further appear to contain valuable and potentially important information that could be used to improve our understanding of individual differences, diagnosis, and the prediction of treatment outcomes.
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37
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Graham DJ. Nine insights from internet engineering that help us understand brain network communication. FRONTIERS IN COMPUTER SCIENCE 2023. [DOI: 10.3389/fcomp.2022.976801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
Philosophers have long recognized the value of metaphor as a tool that opens new avenues of investigation. By seeing brains as having the goal of representation, the computer metaphor in its various guises has helped systems neuroscience approach a wide array of neuronal behaviors at small and large scales. Here I advocate a complementary metaphor, the internet. Adopting this metaphor shifts our focus from computing to communication, and from seeing neuronal signals as localized representational elements to seeing neuronal signals as traveling messages. In doing so, we can take advantage of a comparison with the internet's robust and efficient routing strategies to understand how the brain might meet the challenges of network communication. I lay out nine engineering strategies that help the internet solve routing challenges similar to those faced by brain networks. The internet metaphor helps us by reframing neuronal activity across the brain as, in part, a manifestation of routing, which may, in different parts of the system, resemble the internet more, less, or not at all. I describe suggestive evidence consistent with the brain's use of internet-like routing strategies and conclude that, even if empirical data do not directly implicate internet-like routing, the metaphor is valuable as a reference point for those investigating the difficult problem of network communication in the brain and in particular the problem of routing.
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38
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Imms P, Clemente A, Deutscher E, Radwan AM, Akhlaghi H, Beech P, Wilson PH, Irimia A, Poudel G, Domínguez Duque JF, Caeyenberghs K. Exploring personalized structural connectomics for moderate to severe traumatic brain injury. Netw Neurosci 2023; 7:160-183. [PMID: 37334004 PMCID: PMC10270710 DOI: 10.1162/netn_a_00277] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 09/06/2022] [Indexed: 10/03/2023] Open
Abstract
Graph theoretical analysis of the structural connectome has been employed successfully to characterize brain network alterations in patients with traumatic brain injury (TBI). However, heterogeneity in neuropathology is a well-known issue in the TBI population, such that group comparisons of patients against controls are confounded by within-group variability. Recently, novel single-subject profiling approaches have been developed to capture inter-patient heterogeneity. We present a personalized connectomics approach that examines structural brain alterations in five chronic patients with moderate to severe TBI who underwent anatomical and diffusion magnetic resonance imaging. We generated individualized profiles of lesion characteristics and network measures (including personalized graph metric GraphMe plots, and nodal and edge-based brain network alterations) and compared them against healthy reference cases (N = 12) to assess brain damage qualitatively and quantitatively at the individual level. Our findings revealed alterations of brain networks with high variability between patients. With validation and comparison to stratified, normative healthy control comparison cohorts, this approach could be used by clinicians to formulate a neuroscience-guided integrative rehabilitation program for TBI patients, and for designing personalized rehabilitation protocols based on their unique lesion load and connectome.
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Affiliation(s)
- Phoebe Imms
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Adam Clemente
- Healthy Brain and Mind Research Centre, School of Behavioural, Health, and Human Sciences, Faculty of Health Sciences, Australian Catholic University, Fitzroy, Victoria, Australia
| | - Evelyn Deutscher
- Cognitive Neuroscience Unit, School of Psychology, Faculty of Health, Deakin University, Burwood, Victoria, Australia
| | - Ahmed M. Radwan
- KU Leuven, Department of Imaging and Pathology, Translational MRI, Leuven, Belgium
| | - Hamed Akhlaghi
- Emergency Department, St. Vincent’s Hospital (Melbourne), Faculty of Health, Deakin University, Melbourne, Victoria, Australia
| | - Paul Beech
- Department of Radiology and Nuclear Medicine, The Alfred Hospital, Melbourne, Victoria, Australia
| | - Peter H. Wilson
- Healthy Brain and Mind Research Centre, School of Behavioural, Health, and Human Sciences, Faculty of Health Sciences, Australian Catholic University, Fitzroy, Victoria, Australia
| | - Andrei Irimia
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
- Corwin D. Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
- Department of Quantitative and Computational Biology, Dana and David Dornsife College of Arts and Sciences, University of Southern California, Los Angeles, CA, USA
| | - Govinda Poudel
- Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, Victoria, Australia
| | - Juan F. Domínguez Duque
- Cognitive Neuroscience Unit, School of Psychology, Faculty of Health, Deakin University, Burwood, Victoria, Australia
| | - Karen Caeyenberghs
- Cognitive Neuroscience Unit, School of Psychology, Faculty of Health, Deakin University, Burwood, Victoria, Australia
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39
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Parkes L, Kim JZ, Stiso J, Calkins ME, Cieslak M, Gur RE, Gur RC, Moore TM, Ouellet M, Roalf DR, Shinohara RT, Wolf DH, Satterthwaite TD, Bassett DS. Asymmetric signaling across the hierarchy of cytoarchitecture within the human connectome. SCIENCE ADVANCES 2022; 8:eadd2185. [PMID: 36516263 PMCID: PMC9750154 DOI: 10.1126/sciadv.add2185] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 11/10/2022] [Indexed: 05/30/2023]
Abstract
Cortical variations in cytoarchitecture form a sensory-fugal axis that shapes regional profiles of extrinsic connectivity and is thought to guide signal propagation and integration across the cortical hierarchy. While neuroimaging work has shown that this axis constrains local properties of the human connectome, it remains unclear whether it also shapes the asymmetric signaling that arises from higher-order topology. Here, we used network control theory to examine the amount of energy required to propagate dynamics across the sensory-fugal axis. Our results revealed an asymmetry in this energy, indicating that bottom-up transitions were easier to complete compared to top-down. Supporting analyses demonstrated that asymmetries were underpinned by a connectome topology that is wired to support efficient bottom-up signaling. Lastly, we found that asymmetries correlated with differences in communicability and intrinsic neuronal time scales and lessened throughout youth. Our results show that cortical variation in cytoarchitecture may guide the formation of macroscopic connectome topology.
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Affiliation(s)
- Linden Parkes
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jason Z. Kim
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jennifer Stiso
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Monica E. Calkins
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, University of Pennsylvania and Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Matthew Cieslak
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, University of Pennsylvania and Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Raquel E. Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, University of Pennsylvania and Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, Philadelphia, PA 19104, USA
- Department of Radiology, Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Ruben C. Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, University of Pennsylvania and Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, Philadelphia, PA 19104, USA
- Department of Radiology, Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Tyler M. Moore
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, University of Pennsylvania and Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Mathieu Ouellet
- Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - David R. Roalf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, University of Pennsylvania and Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Russell T. Shinohara
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Daniel H. Wolf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Theodore D. Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, University of Pennsylvania and Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Dani S. Bassett
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, Philadelphia, PA 19104, USA
- Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Physics and Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
- Santa Fe Institute, Santa Fe, NM 87501, USA
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40
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The impact of aging on human brain network target controllability. Brain Struct Funct 2022; 227:3001-3015. [DOI: 10.1007/s00429-022-02584-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 10/09/2022] [Indexed: 11/27/2022]
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41
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Liu ZQ, Betzel RF, Misic B. Benchmarking functional connectivity by the structure and geometry of the human brain. Netw Neurosci 2022; 6:937-949. [PMID: 36875010 PMCID: PMC9976650 DOI: 10.1162/netn_a_00236] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 01/18/2022] [Indexed: 11/04/2022] Open
Abstract
The brain's structural connectivity supports the propagation of electrical impulses, manifesting as patterns of coactivation, termed functional connectivity. Functional connectivity emerges from the underlying sparse structural connections, particularly through polysynaptic communication. As a result, functional connections between brain regions without direct structural links are numerous, but their organization is not completely understood. Here we investigate the organization of functional connections without direct structural links. We develop a simple, data-driven method to benchmark functional connections with respect to their underlying structural and geometric embedding. We then use this method to reweigh and reexpress functional connectivity. We find evidence of unexpectedly strong functional connectivity among distal brain regions and within the default mode network. We also find unexpectedly strong functional connectivity at the apex of the unimodal-transmodal hierarchy. Our results suggest that both phenomena-functional modules and functional hierarchies-emerge from functional interactions that transcend the underlying structure and geometry. These findings also potentially explain recent reports that structural and functional connectivity gradually diverge in transmodal cortex. Collectively, we show how structural connectivity and geometry can be used as a natural frame of reference with which to study functional connectivity patterns in the brain.
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Affiliation(s)
- Zhen-Qi Liu
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Richard F. Betzel
- Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
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42
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Madan Mohan V, Banerjee A. A perturbative approach to study information communication in brain networks. Netw Neurosci 2022; 6:1275-1295. [PMID: 38800461 PMCID: PMC11117119 DOI: 10.1162/netn_a_00260] [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: 02/21/2022] [Accepted: 06/15/2022] [Indexed: 05/29/2024] Open
Abstract
How communication among neuronal ensembles shapes functional brain dynamics is a question of fundamental importance to neuroscience. Communication in the brain can be viewed as a product of the interaction of node activities with the structural network over which these activities flow. The study of these interactions is, however, restricted by the difficulties in describing the complex dynamics of the brain. There is thus a need to develop methods to study these network-dynamical interactions and how they impact information flow, without having to ascertain dynamics a priori or resort to restrictive analytical approaches. Here, we adapt a recently established network analysis method based on perturbations, it to a neuroscientific setting to study how information flow in the brain can raise from properties of underlying structure. For proof-of-concept, we apply the approach on in silico whole-brain models. We expound on the functional implications of the distributions of metrics that capture network-dynamical interactions, termed net influence and flow. We also study the network-dynamical interactions at the level of resting-state networks. An attractive feature of this method is its simplicity, which allows a direct translation to an experimental or clinical setting, such as for identifying targets for stimulation studies or therapeutic interventions.
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43
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Zahnert F, Kräling G, Melms L, Belke M, Kleinholdermann U, Timmermann L, Hirsch M, Jansen A, Mross P, Menzler K, Habermehl L, Knake S. Diffusion magnetic resonance imaging connectome features are predictive of functional lateralization of semantic processing in the anterior temporal lobes. Hum Brain Mapp 2022; 44:496-508. [PMID: 36098483 PMCID: PMC9842893 DOI: 10.1002/hbm.26074] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 07/22/2022] [Accepted: 08/18/2022] [Indexed: 01/25/2023] Open
Abstract
Assessment of regional language lateralization is crucial in many scenarios, but not all populations are suited for its evaluation via task-functional magnetic resonance imaging (fMRI). In this study, the utility of structural connectome features for the classification of language lateralization in the anterior temporal lobes (ATLs) was investigated. Laterality indices for semantic processing in the ATL were computed from task-fMRI in 1038 subjects from the Human Connectome Project who were labeled as stronger rightward lateralized (RL) or stronger leftward to bilaterally lateralized (LL) in a data-driven approach. Data of unrelated subjects (n = 432) were used for further analyses. Structural connectomes were generated from diffusion-MRI tractography, and graph theoretical metrics (node degree, betweenness centrality) were computed. A neural network (NN) and a random forest (RF) classifier were trained on these metrics to classify subjects as RL or LL. After classification, comparisons of network measures were conducted via permutation testing. Degree-based classifiers produced significant above-chance predictions both during cross-validation (NN: AUC-ROC[CI] = 0.68[0.64-0.73], accuracy[CI] = 68.34%[63-73.2%]; RF: AUC-ROC[CI] = 0.7[0.66-0.73], accuracy[CI] = 64.81%[60.9-68.5]) and testing (NN: AUC-ROC[CI] = 0.69[0.53-0.84], accuracy[CI] = 68.09[53.2-80.9]; RF: AUC-ROC[CI] = 0.68[0.53-0.84], accuracy[CI] = 68.09[55.3-80.9]). Comparison of network metrics revealed small effects of increased node degree within the right posterior middle temporal gyrus (pMTG) in subjects with RL, while degree was decreased in the right posterior cingulate cortex (PCC). Above-chance predictions of functional language lateralization in the ATL are possible based on diffusion-MRI connectomes alone. Increased degree within the right pMTG as a right-sided homologue of a known semantic hub, and decreased hubness of the right PCC may form a structural basis for rightward-lateralized semantic processing.
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Affiliation(s)
- Felix Zahnert
- Epilepsy Center Hesse, Department for NeurologyUniversity Hospital Marburg, Philipps University MarburgMarburgGermany
| | - Gunter Kräling
- Department of Medical TechnologyUniversity Hospital MarburgMarburgGermany
| | - Leander Melms
- Institute for Artificial IntelligenceUniversity Hospital Marburg, Philipps University MarburgMarburgGermany
| | - Marcus Belke
- Epilepsy Center Hesse, Department for NeurologyUniversity Hospital Marburg, Philipps University MarburgMarburgGermany,LOEWE Center for Personalized Translational Epilepsy Research (CePTER)Goethe‐University FrankfurtFrankfurt Am MainGermany
| | - Urs Kleinholdermann
- Department for NeurologyUniversity Hospital Marburg, Philipps University MarburgMarburgGermany
| | - Lars Timmermann
- Department for NeurologyUniversity Hospital Marburg, Philipps University MarburgMarburgGermany,Center for Mind, Brain and Behavior (CMBB)Philipps‐University MarburgMarburgGermany,Core Facility Brainimaging, Faculty of MedicineUniversity of MarburgMarburgGermany
| | - Martin Hirsch
- Institute for Artificial IntelligenceUniversity Hospital Marburg, Philipps University MarburgMarburgGermany
| | - Andreas Jansen
- Center for Mind, Brain and Behavior (CMBB)Philipps‐University MarburgMarburgGermany,Core Facility Brainimaging, Faculty of MedicineUniversity of MarburgMarburgGermany,Department for Psychiatry and PsychotherapyUniversity Hospital Marburg, Philipps University MarburgMarburgGermany
| | - Peter Mross
- Epilepsy Center Hesse, Department for NeurologyUniversity Hospital Marburg, Philipps University MarburgMarburgGermany
| | - Katja Menzler
- Epilepsy Center Hesse, Department for NeurologyUniversity Hospital Marburg, Philipps University MarburgMarburgGermany,Center for Mind, Brain and Behavior (CMBB)Philipps‐University MarburgMarburgGermany,Core Facility Brainimaging, Faculty of MedicineUniversity of MarburgMarburgGermany
| | - Lena Habermehl
- Epilepsy Center Hesse, Department for NeurologyUniversity Hospital Marburg, Philipps University MarburgMarburgGermany
| | - Susanne Knake
- Epilepsy Center Hesse, Department for NeurologyUniversity Hospital Marburg, Philipps University MarburgMarburgGermany,LOEWE Center for Personalized Translational Epilepsy Research (CePTER)Goethe‐University FrankfurtFrankfurt Am MainGermany,Center for Mind, Brain and Behavior (CMBB)Philipps‐University MarburgMarburgGermany,Core Facility Brainimaging, Faculty of MedicineUniversity of MarburgMarburgGermany
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44
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A Riemannian approach to predicting brain function from the structural connectome. Neuroimage 2022; 257:119299. [PMID: 35636736 DOI: 10.1016/j.neuroimage.2022.119299] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 03/29/2022] [Accepted: 05/09/2022] [Indexed: 11/21/2022] Open
Abstract
Ongoing brain function is largely determined by the underlying wiring of the brain, but the specific rules governing this relationship remain unknown. Emerging literature has suggested that functional interactions between brain regions emerge from the structural connections through mono- as well as polysynaptic mechanisms. Here, we propose a novel approach based on diffusion maps and Riemannian optimization to emulate this dynamic mechanism in the form of random walks on the structural connectome and predict functional interactions as a weighted combination of these random walks. Our proposed approach was evaluated in two different cohorts of healthy adults (Human Connectome Project, HCP; Microstructure-Informed Connectomics, MICs). Our approach outperformed existing approaches and showed that performance plateaus approximately around the third random walk. At macroscale, we found that the largest number of walks was required in nodes of the default mode and frontoparietal networks, underscoring an increasing relevance of polysynaptic communication mechanisms in transmodal cortical networks compared to primary and unimodal systems.
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45
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Seguin C, Mansour L S, Sporns O, Zalesky A, Calamante F. Network communication models narrow the gap between the modular organization of structural and functional brain networks. Neuroimage 2022; 257:119323. [PMID: 35605765 DOI: 10.1016/j.neuroimage.2022.119323] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 04/25/2022] [Accepted: 05/17/2022] [Indexed: 11/28/2022] Open
Abstract
Structural and functional brain networks are modular. Canonical functional systems, such as the default mode network, are well-known modules of the human brain and have been implicated in a large number of cognitive, behavioral and clinical processes. However, modules delineated in structural brain networks inferred from tractography generally do not recapitulate canonical functional systems. Neuroimaging evidence suggests that functional connectivity between regions in the same systems is not always underpinned by anatomical connections. As such, direct structural connectivity alone would be insufficient to characterize the functional modular organization of the brain. Here, we demonstrate that augmenting structural brain networks with models of indirect (polysynaptic) communication unveils a modular network architecture that more closely resembles the brain's established functional systems. We find that diffusion models of polysynaptic connectivity, particularly communicability, narrow the gap between the modular organization of structural and functional brain networks by 20-60%, whereas routing models based on single efficient paths do not improve mesoscopic structure-function correspondence. This suggests that functional modules emerge from the constraints imposed by local network structure that facilitates diffusive neural communication. Our work establishes the importance of modeling polysynaptic communication to understand the structural basis of functional systems.
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Affiliation(s)
- Caio Seguin
- Melbourne Neuropsychiatry Centre, The University of Melbourne and Melbourne Health, Melbourne, VIC, Australia; The University of Sydney, School of Biomedical Engineering, Sydney, NSW, Australia; Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States.
| | - Sina Mansour L
- Melbourne Neuropsychiatry Centre, The University of Melbourne and Melbourne Health, Melbourne, VIC, Australia; Department of Biomedical Engineering, Melbourne School of Engineering, The University of Melbourne, Melbourne, VIC, Australia
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States; Cognitive Science Program, Indiana University, Bloomington, IN, United States; Program in Neuroscience, Indiana University, Bloomington, IN, United States; Network Science Institute, Indiana University, Bloomington, IN, United States
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, The University of Melbourne and Melbourne Health, Melbourne, VIC, Australia; Department of Biomedical Engineering, Melbourne School of Engineering, The University of Melbourne, Melbourne, VIC, Australia
| | - Fernando Calamante
- The University of Sydney, School of Biomedical Engineering, Sydney, NSW, Australia; Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia; Sydney Imaging, The University of Sydney, Sydney, NSW, Australia
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46
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Zeng R, Lv J, Wang H, Zhou L, Barnett M, Calamante F, Wang C. FOD-Net: A deep learning method for fiber orientation distribution angular super resolution. Med Image Anal 2022; 79:102431. [PMID: 35397471 DOI: 10.1016/j.media.2022.102431] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 03/16/2022] [Accepted: 03/21/2022] [Indexed: 10/18/2022]
Abstract
Mapping the human connectome using fiber-tracking permits the study of brain connectivity and yields new insights into neuroscience. However, reliable connectome reconstruction using diffusion magnetic resonance imaging (dMRI) data acquired by widely available clinical protocols remains challenging, thus limiting the connectome/tractography clinical applications. Here we develop fiber orientation distribution (FOD) network (FOD-Net), a deep-learning-based framework for FOD angular super-resolution. Our method enhances the angular resolution of FOD images computed from common clinical-quality dMRI data, to obtain FODs with quality comparable to those produced from advanced research scanners. Super-resolved FOD images enable superior tractography and structural connectome reconstruction from clinical protocols. The method was trained and tested with high-quality data from the Human Connectome Project (HCP) and further validated with a local clinical 3.0T scanner as well as with another public available multicenter-multiscanner dataset. Using this method, we improve the angular resolution of FOD images acquired with typical single-shell low-angular-resolution dMRI data (e.g., 32 directions, b=1000s/mm2) to approximate the quality of FODs derived from time-consuming, multi-shell high-angular-resolution dMRI research protocols. We also demonstrate tractography improvement, removing spurious connections and bridging missing connections. We further demonstrate that connectomes reconstructed by super-resolved FODs achieve comparable results to those obtained with more advanced dMRI acquisition protocols, on both HCP and clinical 3.0T data. Advances in deep-learning approaches used in FOD-Net facilitate the generation of high quality tractography/connectome analysis from existing clinical MRI environments. Our code is freely available at https://github.com/ruizengalways/FOD-Net.
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Affiliation(s)
- Rui Zeng
- School of Biomedical Engineering, The University of Sydney, Sydney 2050, Australia; Brain and Mind Centre, The University of Sydney, Sydney 2050, Australia
| | - Jinglei Lv
- School of Biomedical Engineering, The University of Sydney, Sydney 2050, Australia; Brain and Mind Centre, The University of Sydney, Sydney 2050, Australia
| | - He Wang
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China; Human Phenome Institute, Fudan University, Shanghai, China
| | - Luping Zhou
- School of Computer Science, The University of Sydney, Sydney 2050, Australia
| | - Michael Barnett
- Brain and Mind Centre, The University of Sydney, Sydney 2050, Australia; Sydney Neuroimaging Analysis Centre, Sydney 2050, Australia
| | - Fernando Calamante
- School of Biomedical Engineering, The University of Sydney, Sydney 2050, Australia; Brain and Mind Centre, The University of Sydney, Sydney 2050, Australia; Sydney Imaging, The University of Sydney, Sydney 2050, Australia
| | - Chenyu Wang
- Brain and Mind Centre, The University of Sydney, Sydney 2050, Australia; Sydney Neuroimaging Analysis Centre, Sydney 2050, Australia.
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47
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Zhang F, Daducci A, He Y, Schiavi S, Seguin C, Smith RE, Yeh CH, Zhao T, O'Donnell LJ. Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: A review. Neuroimage 2022; 249:118870. [PMID: 34979249 PMCID: PMC9257891 DOI: 10.1016/j.neuroimage.2021.118870] [Citation(s) in RCA: 127] [Impact Index Per Article: 42.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 12/03/2021] [Accepted: 12/31/2021] [Indexed: 12/13/2022] Open
Abstract
Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo reconstruction of the brain's white matter connections at macro scale. It provides an important tool for quantitative mapping of the brain's structural connectivity using measures of connectivity or tissue microstructure. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain's structural connectivity in health and disease. We focus on two types of quantitative analyses of tractography, including: 1) tract-specific analysis that refers to research that is typically hypothesis-driven and studies particular anatomical fiber tracts, and 2) connectome-based analysis that refers to research that is more data-driven and generally studies the structural connectivity of the entire brain. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain's white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the "best" methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications.
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Affiliation(s)
- Fan Zhang
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
| | | | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Chinese Institute for Brain Research, Beijing, China
| | - Simona Schiavi
- Department of Computer Science, University of Verona, Verona, Italy
| | - Caio Seguin
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Australia; The University of Sydney, School of Biomedical Engineering, Sydney, Australia
| | - Robert E Smith
- The Florey Institute of Neuroscience and Mental Health, Melbourne, Australia; Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Australia
| | - Chun-Hung Yeh
- Institute for Radiological Research, Chang Gung University, Taoyuan, Taiwan; Department of Psychiatry, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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48
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Cha J, Spielberg JM, Hu B, Altinay M, Anand A. Differences in network properties of the structural connectome in bipolar and unipolar depression. Psychiatry Res Neuroimaging 2022; 321:111442. [PMID: 35152051 PMCID: PMC10577577 DOI: 10.1016/j.pscychresns.2022.111442] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 01/03/2022] [Accepted: 01/10/2022] [Indexed: 10/19/2022]
Abstract
BACKGROUND Differentiation between Bipolar Disorder Depression (BDD) and Unipolar Major Depressive Disorder (MDD) is critical to clinical practice. This study investigated machine learning classification of BDD and MDD using graph properties of Diffusion-weighted Imaging (DWI)-based structural connectome. METHODS This study included a large number of medication-free (N =229) subjects: 60 BDD, 95 MDD, and 74 Healthy Control (HC) subjects. DWI probabilistic tractography was performed to create Fractional Anisotropy (FA) and Total Streamline (TS)-based structural connectivity matrices. Global and nodal graph properties were computed from these matrices and tested for group differences. Next, using identified graph properties, machine learning classification (MLC) between BDD, MDD, MDD with risk factors for developing BD (MDD+), and MDD without risk factors for developing BD (MDD-) was conducted. RESULTS Communicability Efficiency of the left superior frontal gyrus (SFG) was significantly higher in BDD vs. MDD. In particular, Communicability Efficiency using TS-based connectivity in the left SFG as well as FA-based connectivity in the right middle anterior cingulate area was higher in the BDD vs. MDD- group. There were no significant differences in graph properties between BDD and MDD+. Direct comparison between MDD+ and MDD- showed differences in Eigenvector Centrality (TS-based connectivity) of the left middle frontal sulcus. Acceptable Area Under Curve (AUC) for classification were seen between the BDD and MDD- groups, and between the MDD+ and MDD- groups, using the differing graph properties. CONCLUSION Graph properties of DWI-based connectivity can discriminate between BDD and MDD subjects without risk factors for BD.
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Affiliation(s)
- Jungwon Cha
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, USA; Center for Behavioral Health, Cleveland Clinic, USA
| | | | - Bo Hu
- Center for Quantitative Health Sciences, Cleveland Clinic, USA
| | | | - Amit Anand
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, USA; Center for Behavioral Health, Cleveland Clinic, USA
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49
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Khalifa K, Islam F, Gamboa JP, Wilkenfeld DA, Kostić D. Integrating Philosophy of Understanding With the Cognitive Sciences. Front Syst Neurosci 2022; 16:764708. [PMID: 35359623 PMCID: PMC8960449 DOI: 10.3389/fnsys.2022.764708] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 02/10/2022] [Indexed: 11/25/2022] Open
Abstract
We provide two programmatic frameworks for integrating philosophical research on understanding with complementary work in computer science, psychology, and neuroscience. First, philosophical theories of understanding have consequences about how agents should reason if they are to understand that can then be evaluated empirically by their concordance with findings in scientific studies of reasoning. Second, these studies use a multitude of explanations, and a philosophical theory of understanding is well suited to integrating these explanations in illuminating ways.
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Affiliation(s)
- Kareem Khalifa
- Department of Philosophy, Middlebury College, Middlebury, VT, United States
| | - Farhan Islam
- Independent Researcher, Madison, WI, United States
| | - J. P. Gamboa
- Department of History and Philosophy of Science, University of Pittsburgh, Pittsburgh, PA, United States
| | - Daniel A. Wilkenfeld
- Department of Acute and Tertiary Care, University of Pittsburgh School of Nursing, Pittsburgh, PA, United States
| | - Daniel Kostić
- Institute for Science in Society (ISiS), Radboud University, Nijmegen, Netherlands
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50
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Zhou D, Lynn CW, Cui Z, Ciric R, Baum GL, Moore TM, Roalf DR, Detre JA, Gur RC, Gur RE, Satterthwaite TD, Bassett DS. Efficient coding in the economics of human brain connectomics. Netw Neurosci 2022; 6:234-274. [PMID: 36605887 PMCID: PMC9810280 DOI: 10.1162/netn_a_00223] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 12/08/2021] [Indexed: 01/07/2023] Open
Abstract
In systems neuroscience, most models posit that brain regions communicate information under constraints of efficiency. Yet, evidence for efficient communication in structural brain networks characterized by hierarchical organization and highly connected hubs remains sparse. The principle of efficient coding proposes that the brain transmits maximal information in a metabolically economical or compressed form to improve future behavior. To determine how structural connectivity supports efficient coding, we develop a theory specifying minimum rates of message transmission between brain regions to achieve an expected fidelity, and we test five predictions from the theory based on random walk communication dynamics. In doing so, we introduce the metric of compression efficiency, which quantifies the trade-off between lossy compression and transmission fidelity in structural networks. In a large sample of youth (n = 1,042; age 8-23 years), we analyze structural networks derived from diffusion-weighted imaging and metabolic expenditure operationalized using cerebral blood flow. We show that structural networks strike compression efficiency trade-offs consistent with theoretical predictions. We find that compression efficiency prioritizes fidelity with development, heightens when metabolic resources and myelination guide communication, explains advantages of hierarchical organization, links higher input fidelity to disproportionate areal expansion, and shows that hubs integrate information by lossy compression. Lastly, compression efficiency is predictive of behavior-beyond the conventional network efficiency metric-for cognitive domains including executive function, memory, complex reasoning, and social cognition. Our findings elucidate how macroscale connectivity supports efficient coding and serve to foreground communication processes that utilize random walk dynamics constrained by network connectivity.
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Affiliation(s)
- Dale Zhou
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Christopher W. Lynn
- Initiative for the Theoretical Sciences, Graduate Center, City University of New York, New York, NY, USA,Joseph Henry Laboratories of Physics, Princeton University, Princeton, NJ, USA
| | - Zaixu Cui
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Rastko Ciric
- Department of Bioengineering, Schools of Engineering and Medicine, Stanford University, Stanford, CA, USA
| | - Graham L. Baum
- Department of Psychology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Tyler M. Moore
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA,Penn-Children’s Hospital of Philadelphia Lifespan Brain Institute, Philadelphia, PA, USA
| | - David R. Roalf
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - John A. Detre
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ruben C. Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA,Penn-Children’s Hospital of Philadelphia Lifespan Brain Institute, Philadelphia, PA, USA
| | - Raquel E. Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA,Penn-Children’s Hospital of Philadelphia Lifespan Brain Institute, Philadelphia, PA, USA
| | - Theodore D. Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA,Penn-Children’s Hospital of Philadelphia Lifespan Brain Institute, Philadelphia, PA, USA
| | - Dani S. Bassett
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA,Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA,Department of Physics & Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA,Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA,Department of Electrical & Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA,Santa Fe Institute, Santa Fe, NM, USA,* Corresponding Author:
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