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Liang Q, Ma J, Chen X, Lin Q, Shu N, Dai Z, Lin Y. A Hybrid Routing Pattern in Human Brain Structural Network Revealed By Evolutionary Computation. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:1895-1909. [PMID: 38194401 DOI: 10.1109/tmi.2024.3351907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
Abstract
The human brain functional connectivity network (FCN) is constrained and shaped by the communication processes in the structural connectivity network (SCN). The underlying communication mechanism thus becomes a critical issue for understanding the formation and organization of the FCN. A number of communication models supported by different routing strategies have been proposed, with shortest path (SP), random diffusion (DIF), and spatial navigation (NAV) as the most typical, respectively requiring network global knowledge, local knowledge, and both for path seeking. Yet these models all assumed every brain region to use one routing strategy uniformly, ignoring convergent evidence that supports the regional heterogeneity in both terms of biological substrates and functional roles. In this regard, the current study developed a hybrid communication model that allowed each brain region to choose a routing strategy from SP, DIF, and NAV independently. A genetic algorithm was designed to uncover the underlying region-wise hybrid routing strategy (namely HYB). The HYB was found to outperform the three typical routing strategies in predicting FCN and facilitating robust communication. Analyses on HYB further revealed that brain regions in lower-order functional modules inclined to route signals using global knowledge, while those in higher-order functional modules preferred DIF that requires only local knowledge. Compared to regions that used global knowledge for routing, regions using DIF had denser structural connections, participated in more functional modules, but played a less dominant role within modules. Together, our findings further evidenced that hybrid routing underpins efficient SCN communication and locally heterogeneous structure-function coupling.
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Lyu D, Pappas I, Menon DK, Stamatakis EA. A Precuneal Causal Loop Mediates External and Internal Information Integration in the Human Brain. J Neurosci 2021; 41:9944-9956. [PMID: 34675087 PMCID: PMC8638689 DOI: 10.1523/jneurosci.0647-21.2021] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Revised: 08/29/2021] [Accepted: 09/14/2021] [Indexed: 11/21/2022] Open
Abstract
Human brains interpret external stimuli based on internal representations. One untested hypothesis is that the default-mode network (DMN), widely considered responsible for internally oriented cognition, can decode external information. Here, we posit that the unique structural and functional fingerprint of the precuneus (PCu) supports a prominent role for the posterior part of the DMN in this process. By analyzing the imaging data of 100 participants performing two attention-demanding tasks, we found that the PCu is functionally divided into dorsal and ventral subdivisions. We then conducted a comprehensive examination of their connectivity profiles and found that at rest, both the ventral PCu (vPCu) and dorsal PCu (dPCu) are mainly connected with the DMN but also are differentially connected with internally oriented networks (IoN) and externally oriented networks (EoN). During tasks, the double associations between the v/dPCu and the IoN/EoN are correlated with task performance and can switch depending on cognitive demand. Furthermore, dynamic causal modeling (DCM) revealed that the strength and direction of the effective connectivity (EC) between v/dPCu is modulated by task difficulty in a manner potentially dictated by the balance of internal versus external cognitive demands. Our study provides evidence that the posterior medial part of the DMN may drive interactions between large-scale networks, potentially allowing access to stored representations for moment-to-moment interpretation of an ever-changing environment.SIGNIFICANCE STATEMENT The default-mode network (DMN) is widely known for its association with internalized thinking processes, e.g., spontaneous thoughts, which is the most interesting but least understood component in human consciousness. The precuneus (PCu), a posteromedial DMN hub, is thought to play a role in this, but a mechanistic explanation has not yet been established. In this study we found that the associations between ventral PCu (vPCu)/dorsal PCu (dPCu) subdivisions and internally oriented network (IoN)/externally oriented network (EoN) are flexibly modulated by cognitive demand and correlate with task performance. We further propose that the recurrent causal connectivity between the ventral and dorsal PCu supports conscious processing by constantly interpreting external information based on an internal model, meanwhile updating the internal model with the incoming information.
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Affiliation(s)
- Dian Lyu
- University Division of Anaesthesia, University of Cambridge, Addenbrooke's Hospital, Cambridge CB2 0SP, United Kingdom
- Department of Clinical Neuroscience, University of Cambridge, Addenbrooke's Hospital, Cambridge CB2 0SP, United Kingdom
| | - Ioannis Pappas
- University Division of Anaesthesia, University of Cambridge, Addenbrooke's Hospital, Cambridge CB2 0SP, United Kingdom
- Helen Wills Neuroscience Institute, University of California, Berkeley, California 94720
| | - David K Menon
- University Division of Anaesthesia, University of Cambridge, Addenbrooke's Hospital, Cambridge CB2 0SP, United Kingdom
- Wolfson Brain Imaging Centre, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
| | - Emmanuel A Stamatakis
- University Division of Anaesthesia, University of Cambridge, Addenbrooke's Hospital, Cambridge CB2 0SP, United Kingdom
- Department of Clinical Neuroscience, University of Cambridge, Addenbrooke's Hospital, Cambridge CB2 0SP, United Kingdom
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Neural optimization: Understanding trade-offs with Pareto theory. Curr Opin Neurobiol 2021; 71:84-91. [PMID: 34688051 DOI: 10.1016/j.conb.2021.08.008] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 08/26/2021] [Indexed: 11/21/2022]
Abstract
Nervous systems, like any organismal structure, have been shaped by evolutionary processes to increase fitness. The resulting neural 'bauplan' has to account for multiple objectives simultaneously, including computational function, as well as additional factors such as robustness to environmental changes and energetic limitations. Oftentimes these objectives compete, and quantification of the relative impact of individual optimization targets is non-trivial. Pareto optimality offers a theoretical framework to decipher objectives and trade-offs between them. We, therefore, highlight Pareto theory as a useful tool for the analysis of neurobiological systems from biophysically detailed cells to large-scale network structures and behavior. The Pareto approach can help to assess optimality, identify relevant objectives and their respective impact, and formulate testable hypotheses.
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Abstract
PURPOSE OF REVIEW The prevalence of new public datasets of brain-wide and single-cell transcriptome data has created new opportunities to link neuroimaging findings with genetic data. The aim of this study is to present the different methodological approaches that have been used to combine this data. RECENT FINDINGS Drawing from various sources of open access data, several studies have been able to correlate neuroimaging maps with spatial distribution of brain expression. These efforts have enabled researchers to identify functional annotations of related genes, identify specific cell types related to brain phenotypes, study the expression of genes across life span and highlight the importance of selected brain genes in disease genetic networks. SUMMARY New transcriptome datasets and methodological approaches complement current neuroimaging work and will be crucial to improve our understanding of the biological mechanism that underlies many neurological conditions.
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Affiliation(s)
- Ibai Diez
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston MA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA
| | - Jorge Sepulcre
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston MA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA
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Luppi AI, Stamatakis EA. Combining network topology and information theory to construct representative brain networks. Netw Neurosci 2021; 5:96-124. [PMID: 33688608 PMCID: PMC7935031 DOI: 10.1162/netn_a_00170] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 09/23/2020] [Indexed: 01/21/2023] Open
Abstract
Network neuroscience employs graph theory to investigate the human brain as a complex network, and derive generalizable insights about the brain's network properties. However, graph-theoretical results obtained from network construction pipelines that produce idiosyncratic networks may not generalize when alternative pipelines are employed. This issue is especially pressing because a wide variety of network construction pipelines have been employed in the human network neuroscience literature, making comparisons between studies problematic. Here, we investigate how to produce networks that are maximally representative of the broader set of brain networks obtained from the same neuroimaging data. We do so by minimizing an information-theoretic measure of divergence between network topologies, known as the portrait divergence. Based on functional and diffusion MRI data from the Human Connectome Project, we consider anatomical, functional, and multimodal parcellations at three different scales, and 48 distinct ways of defining network edges. We show that the highest representativeness can be obtained by using parcellations in the order of 200 regions and filtering functional networks based on efficiency-cost optimization-though suitable alternatives are also highlighted. Overall, we identify specific node definition and thresholding procedures that neuroscientists can follow in order to derive representative networks from their human neuroimaging data.
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Affiliation(s)
- Andrea I Luppi
- Division of Anesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Emmanuel A Stamatakis
- Division of Anesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
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Seguin C, Tian Y, Zalesky A. Network communication models improve the behavioral and functional predictive utility of the human structural connectome. Netw Neurosci 2020; 4:980-1006. [PMID: 33195945 PMCID: PMC7655041 DOI: 10.1162/netn_a_00161] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 08/03/2020] [Indexed: 12/11/2022] Open
Abstract
The connectome provides the structural substrate facilitating communication between brain regions. We aimed to establish whether accounting for polysynaptic communication in structural connectomes would improve prediction of interindividual variation in behavior as well as increase structure-function coupling strength. Connectomes were mapped for 889 healthy adults participating in the Human Connectome Project. To account for polysynaptic signaling, connectomes were transformed into communication matrices for each of 15 different network communication models. Communication matrices were (a) used to perform predictions of five data-driven behavioral dimensions and (b) correlated to resting-state functional connectivity (FC). While FC was the most accurate predictor of behavior, communication models, in particular communicability and navigation, improved the performance of structural connectomes. Communication also strengthened structure-function coupling, with the navigation and shortest paths models leading to 35-65% increases in association strength with FC. We combined behavioral and functional results into a single ranking that provides insight into which communication models may more faithfully recapitulate underlying neural signaling patterns. Comparing results across multiple connectome mapping pipelines suggested that modeling polysynaptic communication is particularly beneficial in sparse high-resolution connectomes. We conclude that network communication models can augment the functional and behavioral predictive utility of the human structural connectome.
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Affiliation(s)
- Caio Seguin
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Australia
| | - Ye Tian
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Australia
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Australia
- Department of Biomedical Engineering, Melbourne School of Engineering, University of Melbourne, Melbourne, Australia
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Pappas I, Craig MM, Menon DK, Stamatakis EA. Structural optimality and neurogenetic expression mediate functional dynamics in the human brain. Hum Brain Mapp 2020; 41:2229-2243. [PMID: 32027077 PMCID: PMC7267953 DOI: 10.1002/hbm.24942] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Revised: 01/24/2020] [Accepted: 01/27/2020] [Indexed: 12/17/2022] Open
Abstract
The human brain exhibits a rich functional repertoire in terms of complex functional connectivity patterns during rest and tasks. However, how this is developed upon a fixed structural anatomy remains poorly understood. Here we investigated the hypothesis that resting state functional connectivity and the manner in which it changes during tasks related to a set of underlying structural connections that promote optimal communication in the brain. We used a game‐theoretic model to identify such optimal connections in the structural connectome of 50 healthy individuals and subsequently used the optimal structural connections to predict resting‐state functional connectivity with high accuracy. In contrast, we found that nonoptimal connections accurately predicted functional connectivity during a working memory task. We further found that this balance between optimal and nonoptimal connections between brain regions was associated with a specific gene expression linked to neurotransmission. This multimodal evidence shows for the first time that structure–function relationships in the human brain are related to how brain networks navigate information along different white matter connections as well as the brain's underlying genetic profile.
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Affiliation(s)
- Ioannis Pappas
- Division of Anaesthesia, Department of Medicine, School of Clinical Medicine, University of Cambridge, Cambridge, UK.,Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Cambridge, UK.,Helen Wills Neuroscience Institute, University of California - Berkeley, Berkeley, CA
| | - Michael M Craig
- Division of Anaesthesia, Department of Medicine, School of Clinical Medicine, University of Cambridge, Cambridge, UK.,Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - David K Menon
- Division of Anaesthesia, Department of Medicine, School of Clinical Medicine, University of Cambridge, Cambridge, UK.,Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, Department of Medicine, School of Clinical Medicine, University of Cambridge, Cambridge, UK.,Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Cambridge, UK
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