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Deery HA, Di Paolo R, Moran C, Egan GF, Jamadar SD. The older adult brain is less modular, more integrated, and less efficient at rest: A systematic review of large-scale resting-state functional brain networks in aging. Psychophysiology 2023; 60:e14159. [PMID: 36106762 PMCID: PMC10909558 DOI: 10.1111/psyp.14159] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 07/22/2022] [Accepted: 07/25/2022] [Indexed: 12/23/2022]
Abstract
The literature on large-scale resting-state functional brain networks across the adult lifespan was systematically reviewed. Studies published between 1986 and July 2021 were retrieved from PubMed. After reviewing 2938 records, 144 studies were included. Results on 11 network measures were summarized and assessed for certainty of the evidence using a modified GRADE method. The evidence provides high certainty that older adults display reduced within-network and increased between-network functional connectivity. Older adults also show lower segregation, modularity, efficiency and hub function, and decreased lateralization and a posterior to anterior shift at rest. Higher-order functional networks reliably showed age differences, whereas primary sensory and motor networks showed more variable results. The inflection point for network changes is often the third or fourth decade of life. Age effects were found with moderate certainty for within- and between-network altered patterns and speed of dynamic connectivity. Research on within-subject bold variability and connectivity using glucose uptake provides low certainty of age differences but warrants further study. Taken together, these age-related changes may contribute to the cognitive decline often seen in older adults.
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Affiliation(s)
- Hamish A. Deery
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
| | - Robert Di Paolo
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
| | - Chris Moran
- Peninsula Clinical School, Central Clinical SchoolMonash UniversityFrankstonVictoriaAustralia
- Department of Geriatric MedicinePeninsula HealthFrankstonVictoriaAustralia
| | - Gary F. Egan
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
- Australian Research Council Centre of Excellence for Integrative Brain FunctionMelbourneVictoriaAustralia
| | - Sharna D. Jamadar
- Turner Institute for Brain and Mental HealthMonash UniversityMelbourneVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
- Australian Research Council Centre of Excellence for Integrative Brain FunctionMelbourneVictoriaAustralia
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2
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Stanford WC, Mucha PJ, Dayan E. A robust core architecture of functional brain networks supports topological resilience and cognitive performance in middle- and old-aged adults. Proc Natl Acad Sci U S A 2022; 119:e2203682119. [PMID: 36282912 PMCID: PMC9636938 DOI: 10.1073/pnas.2203682119] [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: 03/03/2022] [Accepted: 09/21/2022] [Indexed: 11/18/2022] Open
Abstract
Aging is associated with gradual changes in cognition, yet some individuals exhibit protection against age-related cognitive decline. The topological characteristics of brain networks that promote protection against cognitive decline in aging are unknown. Here, we investigated whether the robustness and resilience of brain networks, queried via the delineation of the brain's core network structure, relate to age and cognitive performance in a cross-sectional dataset of healthy middle- and old-aged adults (n = 478, ages 40 to 90 y). First, we decomposed each subject's functional brain network using k-shell decomposition and found that age was negatively associated with robust core network structures. Next, we perturbed these networks, via attack simulations, and found that resilience of core brain network nodes also declined in relationship to age. We then partitioned our dataset into middle- (ages 40 to 65 y, n = 300) and old- (ages 65 to 90 y, n = 178) aged subjects and observed that older individuals had less robust core connectivity and resilience. Following these analyses, we found that episodic memory was positively related to robust connectivity and core resilience, particularly within the default node, limbic, and frontoparietal control networks. Importantly, we found that age-related differences in episodic memory were positively related to core resilience, which indicates a potential role for core resilience in protection against cognitive decline. Together, these findings suggest that robust core connectivity and resilience of brain networks could facilitate high cognitive performance in aging.
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Affiliation(s)
- William C. Stanford
- Biological and Biomedical Sciences Program, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514
| | - Peter J. Mucha
- Department of Mathematics, Dartmouth College, Hanover, NH 03755
| | - Eran Dayan
- Biological and Biomedical Sciences Program, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514
- Department of Radiology, Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514
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3
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Blevins AS, Bassett DS, Scott EK, Vanwalleghem GC. From calcium imaging to graph topology. Netw Neurosci 2022; 6:1125-1147. [PMID: 38800465 PMCID: PMC11117109 DOI: 10.1162/netn_a_00262] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 06/13/2022] [Indexed: 05/29/2024] Open
Abstract
Systems neuroscience is facing an ever-growing mountain of data. Recent advances in protein engineering and microscopy have together led to a paradigm shift in neuroscience; using fluorescence, we can now image the activity of every neuron through the whole brain of behaving animals. Even in larger organisms, the number of neurons that we can record simultaneously is increasing exponentially with time. This increase in the dimensionality of the data is being met with an explosion of computational and mathematical methods, each using disparate terminology, distinct approaches, and diverse mathematical concepts. Here we collect, organize, and explain multiple data analysis techniques that have been, or could be, applied to whole-brain imaging, using larval zebrafish as an example model. We begin with methods such as linear regression that are designed to detect relations between two variables. Next, we progress through network science and applied topological methods, which focus on the patterns of relations among many variables. Finally, we highlight the potential of generative models that could provide testable hypotheses on wiring rules and network progression through time, or disease progression. While we use examples of imaging from larval zebrafish, these approaches are suitable for any population-scale neural network modeling, and indeed, to applications beyond systems neuroscience. Computational approaches from network science and applied topology are not limited to larval zebrafish, or even to systems neuroscience, and we therefore conclude with a discussion of how such methods can be applied to diverse problems across the biological sciences.
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Affiliation(s)
- Ann S. Blevins
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Dani S. Bassett
- Department of Bioengineering, School of Engineering and Applied Science, 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, University of Pennsylvania, Philadelphia, PA, USA
- Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics and Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Santa Fe Institute, Santa Fe, NM, USA
| | - Ethan K. Scott
- Queensland Brain Institute, University of Queensland, Brisbane, Australia
- Department of Anatomy and Physiology, School of Biomedical Sciences, University of Melbourne, Parkville, Australia
| | - Gilles C. Vanwalleghem
- Danish Research Institute of Translational Neuroscience (DANDRITE), Nordic EMBL Partnership for Molecular Medicine, Aarhus University, Aarhus, Denmark
- Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark
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4
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Paraskevopoulos E, Chalas N, Anagnostopoulou A, Bamidis PD. Interaction within and between cortical networks subserving multisensory learning and its reorganization due to musical expertise. Sci Rep 2022; 12:7891. [PMID: 35552516 PMCID: PMC9098427 DOI: 10.1038/s41598-022-12158-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 05/04/2022] [Indexed: 12/04/2022] Open
Abstract
Recent advancements in the field of network science allow us to quantify inter-network information exchange and model the interaction within and between task-defined states of large-scale networks. Here, we modeled the inter- and intra- network interactions related to multisensory statistical learning. To this aim, we implemented a multifeatured statistical learning paradigm and measured evoked magnetoencephalographic responses to estimate task-defined state of functional connectivity based on cortical phase interaction. Each network state represented the whole-brain network processing modality-specific (auditory, visual and audiovisual) statistical learning irregularities embedded within a multisensory stimulation stream. The way by which domain-specific expertise re-organizes the interaction between the networks was investigated by a comparison of musicians and non-musicians. Between the modality-specific network states, the estimated connectivity quantified the characteristics of a supramodal mechanism supporting the identification of statistical irregularities that are compartmentalized and applied in the identification of uni-modal irregularities embedded within multisensory stimuli. Expertise-related re-organization was expressed by an increase of intra- and a decrease of inter-network connectivity, showing increased compartmentalization.
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Affiliation(s)
- Evangelos Paraskevopoulos
- Department of Psychology, University of Cyprus, P.O. Box 20537, CY 1678, Nicosia, Cyprus. .,School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece.
| | - Nikolas Chalas
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany
| | - Alexandra Anagnostopoulou
- School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Panagiotis D Bamidis
- School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
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5
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Wendel CJ, Wilhelm RA, Gable PA. Individual differences in motivation and impulsivity link resting frontal alpha asymmetry and motor beta activation. Biol Psychol 2021; 162:108088. [PMID: 33811974 DOI: 10.1016/j.biopsycho.2021.108088] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 03/27/2021] [Accepted: 03/27/2021] [Indexed: 11/18/2022]
Abstract
Previous research has linked neural correlates with motivational traits and measures of impulsivity. However, few previous studies have investigated whether individual differences in motivation and impulsivity moderate the relationship between these disparate neural activity patterns. In a sample of 118 young adults, we used Electroencephalography (EEG) to examine whether behavioral activation and inhibition systems (BIS/BAS) and impulsivity facets (negative urgency, lack of perseverance), moderate the relationship between beta power and resting frontal alpha asymmetry. Regression analyses revealed a novel relationship between lesser beta power and greater left frontal alpha asymmetry (LFA). Moderation analyses suggest this relationship may strengthen as BIS/BAS levels increase, and trait impulsivity levels decrease from the mean. These results are among the first revealing a relationship between two widely investigated neural activity patterns of motivation and provide some indication individual differences moderate this relationship. The limitations of these findings and need for future research are discussed.
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Affiliation(s)
- Christopher J Wendel
- Department of Psychology, University of Alabama, 505 Hackberry Lane, Tuscaloosa, AL, 35401, United States.
| | - Ricardo A Wilhelm
- Department of Psychology, University of Alabama, 505 Hackberry Lane, Tuscaloosa, AL, 35401, United States
| | - Philip A Gable
- Department of Psychological & Brain Sciences, University of Delaware 105 The Green, Newark, DE, 19716, United States
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6
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Fan L, Zhong Q, Qin J, Li N, Su J, Zeng LL, Hu D, Shen H. Brain parcellation driven by dynamic functional connectivity better capture intrinsic network dynamics. Hum Brain Mapp 2020; 42:1416-1433. [PMID: 33283954 PMCID: PMC7927310 DOI: 10.1002/hbm.25303] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 11/19/2020] [Accepted: 11/19/2020] [Indexed: 01/04/2023] Open
Abstract
Until now, dynamic functional connectivity (dFC) based on functional magnetic resonance imaging is typically estimated on a set of predefined regions of interest (ROIs) derived from an anatomical or static functional atlas which follows an implicit assumption of functional homogeneity within ROIs underlying temporal fluctuation of functional coupling, potentially leading to biases or underestimation of brain network dynamics. Here, we presented a novel computational method based on dynamic functional connectivity degree (dFCD) to derive meaningful brain parcellations that can capture functional homogeneous regions in temporal variance of functional connectivity. Several spatially distributed but functionally meaningful areas that are well consistent with known intrinsic connectivity networks were identified through independent component analysis (ICA) of time‐varying dFCD maps. Furthermore, a systematical comparison with commonly used brain atlases, including the Anatomical Automatic Labeling template, static ICA‐driven parcellation and random parcellation, demonstrated that the ROI‐definition strategy based on the proposed dFC‐driven parcellation could better capture the interindividual variability in dFC and predict observed individual cognitive performance (e.g., fluid intelligence, cognitive flexibility, and sustained attention) based on chronnectome. Together, our findings shed new light on the functional organization of resting brains at the timescale of seconds and emphasized the significance of a dFC‐driven and voxel‐wise functional homogeneous parcellation for network dynamics analyses in neuroscience.
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Affiliation(s)
- Liangwei Fan
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China
| | - Qi Zhong
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China
| | - Jian Qin
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China
| | - Na Li
- Department of Radiology, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Jianpo Su
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China
| | - Ling-Li Zeng
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China
| | - Dewen Hu
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China
| | - Hui Shen
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China
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7
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Huskey R, Turner BO, Weber R. Individual Differences in Brain Responses: New Opportunities for Tailoring Health Communication Campaigns. Front Hum Neurosci 2020; 14:565973. [PMID: 33343317 PMCID: PMC7744697 DOI: 10.3389/fnhum.2020.565973] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Accepted: 11/10/2020] [Indexed: 11/13/2022] Open
Abstract
Prevention neuroscience investigates the brain basis of attitude and behavior change. Over the years, an increasingly structurally and functionally resolved "persuasion network" has emerged. However, current studies have only identified a small handful of neural structures that are commonly recruited during persuasive message processing, and the extent to which these (and other) structures are sensitive to numerous individual difference factors remains largely unknown. In this project we apply a multi-dimensional similarity-based individual differences analysis to explore which individual factors-including characteristics of messages and target audiences-drive patterns of brain activity to be more or less similar across individuals encountering the same anti-drug public service announcements (PSAs). We demonstrate that several ensembles of brain regions show response patterns that are driven by a variety of unique factors. These results are discussed in terms of their implications for neural models of persuasion, prevention neuroscience and message tailoring, and methodological implications for future research.
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Affiliation(s)
- Richard Huskey
- Cognitive Communication Science Lab – C Lab, Center for Mind and Brain, Department of Communication, University of California, Davis, Davis, CA, United States
| | - Benjamin O. Turner
- Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore, Singapore
| | - René Weber
- Media Neuroscience Lab, Department of Communication, University of California, Santa Barbara, Santa Barbara, CA, United States
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8
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Arbabyazd LM, Lombardo D, Blin O, Didic M, Battaglia D, Jirsa V. Dynamic Functional Connectivity as a complex random walk: Definitions and the dFCwalk toolbox. MethodsX 2020; 7:101168. [PMID: 33344179 PMCID: PMC7736993 DOI: 10.1016/j.mex.2020.101168] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Accepted: 11/27/2020] [Indexed: 12/30/2022] Open
Abstract
•We have developed a framework to describe the dynamics of Functional Connectivity (dFC) estimated from brain activity time-series as a complex random walk in the space of possible functional networks. This conceptual and methodological framework considers dFC as a smooth reconfiguration process, combining "liquid" and "coordinated" aspects. Unlike other previous approaches, our method does not require the explicit extraction of discrete connectivity states.•In our previous work, we introduced several metrics for the quantitative characterization of the dFC random walk. First, dFC speed analyses extract the distribution of the time-resolved rate of reconfiguration of FC along time. These distributions have a clear peak (typical dFC speed, that can already serve as a biomarker) and fat tails (denoting deviations from Gaussianity that can be detected by suitable scaling analyses of FC network streams). Second, meta-connectivity (MC) analyses identify groups of functional links whose fluctuations co-vary in time and that define veritable dFC modules organized along specific dFC meta-hub controllers (differing from conventional FC modules and hubs). The decomposition of whole-brain dFC by MC allows performing dFC speed analyses separately for each of the detected dFC modules.•We present here blocks and pipelines for dFC random walk analyses that are made easily available through a dedicated MATLABⓇ toolbox (dFCwalk), openly downloadable. Although we applied such analyses mostly to fMRI resting state data, in principle our methods can be extended to any type of neural activity (from Local Field Potentials to EEG, MEG, fNIRS, etc.) or even non-neural time-series.
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Affiliation(s)
- Lucas M. Arbabyazd
- Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, F-13005 Marseille, France
| | - Diego Lombardo
- Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, F-13005 Marseille, France
| | - Olivier Blin
- Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, F-13005 Marseille, France
- AP-HM, Timone, Service de Pharmacologie Clinique et Pharmacovigilance, F-13005 Marseille, France
| | - Mira Didic
- Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, F-13005 Marseille, France
- AP-HM, Timone, Service de Neurologie et Neuropsychologie, F-13005 Marseille, France
| | - Demian Battaglia
- Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, F-13005 Marseille, France
| | - Viktor Jirsa
- Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, F-13005 Marseille, France
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9
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Wikman P, Sahari E, Salmela V, Leminen A, Leminen M, Laine M, Alho K. Breaking down the cocktail party: Attentional modulation of cerebral audiovisual speech processing. Neuroimage 2020; 224:117365. [PMID: 32941985 DOI: 10.1016/j.neuroimage.2020.117365] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 08/19/2020] [Accepted: 09/07/2020] [Indexed: 12/20/2022] Open
Abstract
Recent studies utilizing electrophysiological speech envelope reconstruction have sparked renewed interest in the cocktail party effect by showing that auditory neurons entrain to selectively attended speech. Yet, the neural networks of attention to speech in naturalistic audiovisual settings with multiple sound sources remain poorly understood. We collected functional brain imaging data while participants viewed audiovisual video clips of lifelike dialogues with concurrent distracting speech in the background. Dialogues were presented in a full-factorial design, comprising task (listen to the dialogues vs. ignore them), audiovisual quality and semantic predictability. We used univariate analyses in combination with multivariate pattern analysis (MVPA) to study modulations of brain activity related to attentive processing of audiovisual speech. We found attentive speech processing to cause distinct spatiotemporal modulation profiles in distributed cortical areas including sensory and frontal-control networks. Semantic coherence modulated attention-related activation patterns in the earliest stages of auditory cortical processing, suggesting that the auditory cortex is involved in high-level speech processing. Our results corroborate views that emphasize the dynamic nature of attention, with task-specificity and context as cornerstones of the underlying neuro-cognitive mechanisms.
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Affiliation(s)
- Patrik Wikman
- Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland.
| | - Elisa Sahari
- Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland
| | - Viljami Salmela
- Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland; Advanced Magnetic Imaging Centre, Aalto NeuroImaging, Aalto University, Espoo, Finland
| | - Alina Leminen
- Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland; Department of Digital Humanities, University of Helsinki, Helsinki, Finland
| | - Miika Leminen
- Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland; Department of Phoniatrics, Helsinki University Hospital, Helsinki, Finland
| | - Matti Laine
- Department of Psychology, Åbo Akademi University, Turku, Finland
| | - Kimmo Alho
- Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland; Advanced Magnetic Imaging Centre, Aalto NeuroImaging, Aalto University, Espoo, Finland
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10
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Fan L, Su J, Qin J, Hu D, Shen H. A Deep Network Model on Dynamic Functional Connectivity With Applications to Gender Classification and Intelligence Prediction. Front Neurosci 2020; 14:881. [PMID: 33013292 PMCID: PMC7461846 DOI: 10.3389/fnins.2020.00881] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 07/28/2020] [Indexed: 02/01/2023] Open
Abstract
Increasing evidence has suggested that the dynamic properties of functional brain networks are related to individual behaviors and cognition traits. However, current fMRI-based approaches mostly focus on statistical characteristics of the windowed correlation time course, potentially overlooking subtle time-varying patterns in dynamic functional connectivity (dFC). Here, we proposed the use of an end-to-end deep learning model that combines the convolutional neural network (CNN) and long short-term memory (LSTM) network to capture temporal and spatial features of functional connectivity sequences simultaneously. The results on a large cohort (Human Connectome Project, n = 1,050) demonstrated that our model could achieve a high classification accuracy of about 93% in a gender classification task and prediction accuracies of 0.31 and 0.49 (Pearson's correlation coefficient) in fluid and crystallized intelligence prediction tasks, significantly outperforming previously reported models. Furthermore, we demonstrated that our model could effectively learn spatiotemporal dynamics underlying dFC with high statistical significance based on the null hypothesis estimated using surrogate data. Overall, this study suggests the advantages of a deep learning model in making full use of dynamic information in resting-state functional connectivity, and highlights the potential of time-varying connectivity patterns in improving the prediction of individualized characterization of demographics and cognition traits.
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Affiliation(s)
| | | | | | | | - Hui Shen
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China
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11
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Battaglia D, Boudou T, Hansen ECA, Lombardo D, Chettouf S, Daffertshofer A, McIntosh AR, Zimmermann J, Ritter P, Jirsa V. Dynamic Functional Connectivity between order and randomness and its evolution across the human adult lifespan. Neuroimage 2020; 222:117156. [PMID: 32698027 DOI: 10.1016/j.neuroimage.2020.117156] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Revised: 05/25/2020] [Accepted: 07/07/2020] [Indexed: 12/14/2022] Open
Abstract
Functional Connectivity (FC) during resting-state or task conditions is not static but inherently dynamic. Yet, there is no consensus on whether fluctuations in FC may resemble isolated transitions between discrete FC states rather than continuous changes. This quarrel hampers advancing the study of dynamic FC. This is unfortunate as the structure of fluctuations in FC can certainly provide more information about developmental changes, aging, and progression of pathologies. We merge the two perspectives and consider dynamic FC as an ongoing network reconfiguration, including a stochastic exploration of the space of possible steady FC states. The statistical properties of this random walk deviate both from a purely "order-driven" dynamics, in which the mean FC is preserved, and from a purely "randomness-driven" scenario, in which fluctuations of FC remain uncorrelated over time. Instead, dynamic FC has a complex structure endowed with long-range sequential correlations that give rise to transient slowing and acceleration epochs in the continuous flow of reconfiguration. Our analysis for fMRI data in healthy elderly revealed that dynamic FC tends to slow down and becomes less complex as well as more random with increasing age. These effects appear to be strongly associated with age-related changes in behavioural and cognitive performance.
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Affiliation(s)
- Demian Battaglia
- Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, F-13005, Marseille, France.
| | - Thomas Boudou
- Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, F-13005, Marseille, France; ENSTA ParisTech, F-91762, Palaiseau, France.
| | - Enrique C A Hansen
- Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, F-13005, Marseille, France; Institut de biologie de l'Ecole normale supérieure (IBENS), École Normale Supérieure, CNRS, INSERM, PSL Université Paris, F-75005, Paris, France.
| | - Diego Lombardo
- Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, F-13005, Marseille, France.
| | - Sabrina Chettouf
- Brain Simulation Section, Department of Neurology, Charité Universitätsmedizin and Berlin Institute of Health, D-10117, Berlin, Germany; Bernstein Center for Computational Neuroscience, D-10117, Berlin, Germany; Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, 1081 BT, Amsterdam, the Netherlands.
| | - Andreas Daffertshofer
- Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, 1081 BT, Amsterdam, the Netherlands.
| | - Anthony R McIntosh
- Rotman Research Institute, Baycrest Centre, Toronto, Ontario, M6A 2E1, Canada.
| | - Joelle Zimmermann
- Brain Simulation Section, Department of Neurology, Charité Universitätsmedizin and Berlin Institute of Health, D-10117, Berlin, Germany; Rotman Research Institute, Baycrest Centre, Toronto, Ontario, M6A 2E1, Canada.
| | - Petra Ritter
- Brain Simulation Section, Department of Neurology, Charité Universitätsmedizin and Berlin Institute of Health, D-10117, Berlin, Germany; Bernstein Center for Computational Neuroscience, D-10117, Berlin, Germany.
| | - Viktor Jirsa
- Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, F-13005, Marseille, France.
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12
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Necka EA, Lee IS, Kucyi A, Cheng JC, Yu Q, Atlas LY. Applications of dynamic functional connectivity to pain and its modulation. Pain Rep 2019; 4:e752. [PMID: 31579848 PMCID: PMC6728009 DOI: 10.1097/pr9.0000000000000752] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2019] [Revised: 03/21/2019] [Accepted: 04/07/2019] [Indexed: 12/30/2022] Open
Abstract
Since early work attempting to characterize the brain's role in pain, it has been clear that pain is not generated by a specific brain region, but rather by coordinated activity across a network of brain regions, the "neuromatrix." The advent of noninvasive whole-brain neuroimaging, including functional magnetic resonance imaging, has provided insight on coordinated activity in the pain neuromatrix and how correlations in activity between regions, referred to as "functional connectivity," contribute to pain and its modulation. Initial functional connectivity investigations assumed interregion connectivity remained stable over time, and measured variability across individuals. However, new dynamic functional connectivity (dFC) methods allow researchers to measure how connectivity changes over time within individuals, permitting insights on the dynamic reorganization of the pain neuromatrix in humans. We review how dFC methods have been applied to pain, and insights afforded on how brain connectivity varies across time, either spontaneously or as a function of psychological states, cognitive demands, or the external environment. Specifically, we review psychophysiological interaction, dynamic causal modeling, state-based dynamic community structure, and sliding-window analyses and their use in human functional neuroimaging of acute pain, chronic pain, and pain modulation. We also discuss promising uses of dFC analyses for the investigation of chronic pain conditions and predicting pain treatment efficacy and the relationship between state- and trait-based pain measures. Throughout this review, we provide information regarding the advantages and shortcomings of each approach, and highlight potential future applications of these methodologies for better understanding the brain processes associated with pain.
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Affiliation(s)
- Elizabeth A. Necka
- Division of Intramural Research, National Center for Complementary and Integrative Health, National Institutes of Health, Bethesda, MD, USA
| | - In-Seon Lee
- Division of Intramural Research, National Center for Complementary and Integrative Health, National Institutes of Health, Bethesda, MD, USA
| | - Aaron Kucyi
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA
| | - Joshua C. Cheng
- School of Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Qingbao Yu
- Division of Intramural Research, National Center for Complementary and Integrative Health, National Institutes of Health, Bethesda, MD, USA
| | - Lauren Y. Atlas
- Division of Intramural Research, National Center for Complementary and Integrative Health, National Institutes of Health, Bethesda, MD, USA
- Division of Intramural Research, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, USA
- Division of Intramural Research, National Insitute of Mental Health, Bethesda, MD, USA
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13
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Zhai J, Li K. Predicting Brain Age Based on Spatial and Temporal Features of Human Brain Functional Networks. Front Hum Neurosci 2019; 13:62. [PMID: 30863296 PMCID: PMC6399206 DOI: 10.3389/fnhum.2019.00062] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Accepted: 02/05/2019] [Indexed: 12/01/2022] Open
Abstract
The organization of human brain networks can be measured by capturing correlated brain activity with functional MRI data. There have been a variety of studies showing that human functional connectivities undergo an age-related change over development. In the present study, we employed resting-state functional MRI data to construct functional network models. Principal component analysis was performed on the FC matrices across all the subjects to explore meaningful components especially correlated with age. Coefficients across the components, edge features after a newly proposed feature reduction method as well as temporal features based on fALFF, were extracted as predictor variables and three different regression models were learned to make prediction of brain age. We observed that individual's functional network architecture was shaped by intrinsic component, age-related component and other components and the predictive models extracted sufficient information to provide comparatively accurate predictions of brain age.
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Affiliation(s)
- Jian Zhai
- School of Mathematical Science, Zhejiang University, Hangzhou, China
| | - Ke Li
- School of Mathematical Science, Zhejiang University, Hangzhou, China
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14
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From ideas to action: The prefrontal–premotor connections that shape motor behavior. HANDBOOK OF CLINICAL NEUROLOGY 2019; 163:237-255. [DOI: 10.1016/b978-0-12-804281-6.00013-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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15
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Resting state connectivity in neocortical epilepsy: The epilepsy network as a patient-specific biomarker. Clin Neurophysiol 2018; 130:280-288. [PMID: 30605890 DOI: 10.1016/j.clinph.2018.11.016] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2018] [Revised: 09/04/2018] [Accepted: 11/03/2018] [Indexed: 02/01/2023]
Abstract
OBJECTIVE Localization related epilepsy (LRE) is increasingly accepted as a network disorder. To better understand the network specific characteristics of LRE, we defined individual epilepsy networks and compared them across patients. METHODS The epilepsy network was defined in the slow cortical potential frequency band in 10 patients using intracranial EEG data obtained during interictal periods. Cortical regions were included in the epilepsy network if their connectivity pattern was similar to the connectivity pattern of the seizure onset electrode contact. Patients were subdivided into frontal, temporal, and posterior quadrant cohorts according to the anatomic location of seizure onset. Jaccard similarity was calculated within each cohort to assess for similarity of the epilepsy network between patients within each cohort. RESULTS All patients exhibited an epilepsy network in the slow cortical potential frequency band. The topographic distribution of this correlated network activity was found to be unique at the single subject level. CONCLUSIONS The epilepsy network was unique at the single patient level, even between patients with similar seizure onset locations. SIGNIFICANCE We demonstrated that the epilepsy network is patient-specific. This is in keeping with our current understanding of brain networks and identifies the patient-specific epilepsy network as a possible biomarker in LRE.
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16
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Khambhati AN, Sizemore AE, Betzel RF, Bassett DS. Modeling and interpreting mesoscale network dynamics. Neuroimage 2018; 180:337-349. [PMID: 28645844 PMCID: PMC5738302 DOI: 10.1016/j.neuroimage.2017.06.029] [Citation(s) in RCA: 63] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2017] [Revised: 06/12/2017] [Accepted: 06/14/2017] [Indexed: 11/28/2022] Open
Abstract
Recent advances in brain imaging techniques, measurement approaches, and storage capacities have provided an unprecedented supply of high temporal resolution neural data. These data present a remarkable opportunity to gain a mechanistic understanding not just of circuit structure, but also of circuit dynamics, and its role in cognition and disease. Such understanding necessitates a description of the raw observations, and a delineation of computational models and mathematical theories that accurately capture fundamental principles behind the observations. Here we review recent advances in a range of modeling approaches that embrace the temporally-evolving interconnected structure of the brain and summarize that structure in a dynamic graph. We describe recent efforts to model dynamic patterns of connectivity, dynamic patterns of activity, and patterns of activity atop connectivity. In the context of these models, we review important considerations in statistical testing, including parametric and non-parametric approaches. Finally, we offer thoughts on careful and accurate interpretation of dynamic graph architecture, and outline important future directions for method development.
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Affiliation(s)
- Ankit N Khambhati
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Neuroengineering and Therapeautics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ann E Sizemore
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Richard F Betzel
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Neuroengineering and Therapeautics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
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17
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Sizemore AE, Bassett DS. Dynamic graph metrics: Tutorial, toolbox, and tale. Neuroimage 2018; 180:417-427. [PMID: 28698107 PMCID: PMC5758445 DOI: 10.1016/j.neuroimage.2017.06.081] [Citation(s) in RCA: 76] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2017] [Revised: 05/24/2017] [Accepted: 06/29/2017] [Indexed: 11/23/2022] Open
Abstract
The central nervous system is composed of many individual units - from cells to areas - that are connected with one another in a complex pattern of functional interactions that supports perception, action, and cognition. One natural and parsimonious representation of such a system is a graph in which nodes (units) are connected by edges (interactions). While applicable across spatiotemporal scales, species, and cohorts, the traditional graph approach is unable to address the complexity of time-varying connectivity patterns that may be critically important for an understanding of emotional and cognitive state, task-switching, adaptation and development, or aging and disease progression. Here we survey a set of tools from applied mathematics that offer measures to characterize dynamic graphs. Along with this survey, we offer suggestions for visualization and a publicly-available MATLAB toolbox to facilitate the application of these metrics to existing or yet-to-be acquired neuroimaging data. We illustrate the toolbox by applying it to a previously published data set of time-varying functional graphs, but note that the tools can also be applied to time-varying structural graphs or to other sorts of relational data entirely. Our aim is to provide the neuroimaging community with a useful set of tools, and an intuition regarding how to use them, for addressing emerging questions that hinge on accurate and creative analyses of dynamic graphs.
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Affiliation(s)
- Ann E Sizemore
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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18
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Abstract
The central nervous system is composed of many individual units - from cells to areas - that are connected with one another in a complex pattern of functional interactions that supports perception, action, and cognition. One natural and parsimonious representation of such a system is a graph in which nodes (units) are connected by edges (interactions). While applicable across spatiotemporal scales, species, and cohorts, the traditional graph approach is unable to address the complexity of time-varying connectivity patterns that may be critically important for an understanding of emotional and cognitive state, task-switching, adaptation and development, or aging and disease progression. Here we survey a set of tools from applied mathematics that offer measures to characterize dynamic graphs. Along with this survey, we offer suggestions for visualization and a publicly-available MATLAB toolbox to facilitate the application of these metrics to existing or yet-to-be acquired neuroimaging data. We illustrate the toolbox by applying it to a previously published data set of time-varying functional graphs, but note that the tools can also be applied to time-varying structural graphs or to other sorts of relational data entirely. Our aim is to provide the neuroimaging community with a useful set of tools, and an intuition regarding how to use them, for addressing emerging questions that hinge on accurate and creative analyses of dynamic graphs.
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Affiliation(s)
- Ann E Sizemore
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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19
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Garcia JO, Ashourvan A, Muldoon SF, Vettel JM, Bassett DS. Applications of community detection techniques to brain graphs: Algorithmic considerations and implications for neural function. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2018; 106:846-867. [PMID: 30559531 PMCID: PMC6294140 DOI: 10.1109/jproc.2017.2786710] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
The human brain can be represented as a graph in which neural units such as cells or small volumes of tissue are heterogeneously connected to one another through structural or functional links. Brain graphs are parsimonious representations of neural systems that have begun to offer fundamental insights into healthy human cognition, as well as its alteration in disease. A critical open question in network neuroscience lies in how neural units cluster into densely interconnected groups that can provide the coordinated activity that is characteristic of perception, action, and adaptive behaviors. Tools that have proven particularly useful for addressing this question are community detection approaches, which can identify communities or modules: groups of neural units that are densely interconnected with other units in their own group but sparsely interconnected with units in other groups. In this paper, we describe a common community detection algorithm known as modularity maximization, and we detail its applications to brain graphs constructed from neuroimaging data. We pay particular attention to important algorithmic considerations, especially in recent extensions of these techniques to graphs that evolve in time. After recounting a few fundamental insights that these techniques have provided into brain function, we highlight potential avenues of methodological advancements for future studies seeking to better characterize the patterns of coordinated activity in the brain that accompany human behavior. This tutorial provides a naive reader with an introduction to theoretical considerations pertinent to the generation of brain graphs, an understanding of modularity maximization for community detection, a resource of statistical measures that can be used to characterize community structure, and an appreciation of the usefulness of these approaches in uncovering behaviorally-relevant network dynamics in neuroimaging data.
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Affiliation(s)
- Javier O Garcia
- U.S. Army Research Laboratory, Aberdeen Proving Ground, MD 21005 USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104 USA
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104 USA
- Department of Mathematics and CDSE Program, University at Buffalo, Buffalo, NY 14260 USA
- Department of Psychological & Brain Sciences, University of California, Santa Barbara, CA, 93106 USA
- Department of Electrical & Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Arian Ashourvan
- U.S. Army Research Laboratory, Aberdeen Proving Ground, MD 21005 USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104 USA
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104 USA
- Department of Mathematics and CDSE Program, University at Buffalo, Buffalo, NY 14260 USA
- Department of Psychological & Brain Sciences, University of California, Santa Barbara, CA, 93106 USA
- Department of Electrical & Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Sarah F Muldoon
- U.S. Army Research Laboratory, Aberdeen Proving Ground, MD 21005 USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104 USA
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104 USA
- Department of Mathematics and CDSE Program, University at Buffalo, Buffalo, NY 14260 USA
- Department of Psychological & Brain Sciences, University of California, Santa Barbara, CA, 93106 USA
- Department of Electrical & Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Jean M Vettel
- U.S. Army Research Laboratory, Aberdeen Proving Ground, MD 21005 USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104 USA
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104 USA
- Department of Mathematics and CDSE Program, University at Buffalo, Buffalo, NY 14260 USA
- Department of Psychological & Brain Sciences, University of California, Santa Barbara, CA, 93106 USA
- Department of Electrical & Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Danielle S Bassett
- U.S. Army Research Laboratory, Aberdeen Proving Ground, MD 21005 USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104 USA
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104 USA
- Department of Mathematics and CDSE Program, University at Buffalo, Buffalo, NY 14260 USA
- Department of Psychological & Brain Sciences, University of California, Santa Barbara, CA, 93106 USA
- Department of Electrical & Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104 USA
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20
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Yu Q, Du Y, Chen J, Sui J, Adali T, Pearlson G, Calhoun VD. Application of Graph Theory to Assess Static and Dynamic Brain Connectivity: Approaches for Building Brain Graphs. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2018; 106:886-906. [PMID: 30364630 PMCID: PMC6197492 DOI: 10.1109/jproc.2018.2825200] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Human brain connectivity is complex. Graph theory based analysis has become a powerful and popular approach for analyzing brain imaging data, largely because of its potential to quantitatively illuminate the networks, the static architecture in structure and function, the organization of dynamic behavior over time, and disease related brain changes. The first step in creating brain graphs is to define the nodes and edges connecting them. We review a number of approaches for defining brain nodes including fixed versus data-driven nodes. Expanding the narrow view of most studies which focus on static and/or single modality brain connectivity, we also survey advanced approaches and their performances in building dynamic and multi-modal brain graphs. We show results from both simulated and real data from healthy controls and patients with mental illnesse. We outline the advantages and challenges of these various techniques. By summarizing and inspecting recent studies which analyzed brain imaging data based on graph theory, this article provides a guide for developing new powerful tools to explore complex brain networks.
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Affiliation(s)
- Qingbao Yu
- Mind Research Network, Albuquerque NM 87106 USA
| | - Yuhui Du
- Mind Research Network, Albuquerque NM 87106 USA. And also with School of Computer & Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Jiayu Chen
- Mind Research Network, Albuquerque NM 87106 USA
| | - Jing Sui
- University of Chinese Academy of Sciences, Beijing 100049 China. And also with CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Science (CAS), University of CAS, Beijing 100190 China
| | - Tulay Adali
- Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
| | - Godfrey Pearlson
- Olin Neuropsychiatry Research Center, Hartford, CT 06106, USA. And also with Departments of Psychiatry and Neurobiology, Yale University, New Haven, CT 06520, USA
| | - Vince D Calhoun
- Mind Research Network, Albuquerque NM 87106 USA. And also with Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA
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21
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Schlesinger KJ, Turner BO, Grafton ST, Miller MB, Carlson JM. Improving resolution of dynamic communities in human brain networks through targeted node removal. PLoS One 2017; 12:e0187715. [PMID: 29261662 PMCID: PMC5737970 DOI: 10.1371/journal.pone.0187715] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2017] [Accepted: 10/24/2017] [Indexed: 01/01/2023] Open
Abstract
Current approaches to dynamic community detection in complex networks can fail to identify multi-scale community structure, or to resolve key features of community dynamics. We propose a targeted node removal technique to improve the resolution of community detection. Using synthetic oscillator networks with well-defined “ground truth” communities, we quantify the community detection performance of a common modularity maximization algorithm. We show that the performance of the algorithm on communities of a given size deteriorates when these communities are embedded in multi-scale networks with communities of different sizes, compared to the performance in a single-scale network. We demonstrate that targeted node removal during community detection improves performance on multi-scale networks, particularly when removing the most functionally cohesive nodes. Applying this approach to network neuroscience, we compare dynamic functional brain networks derived from fMRI data taken during both repetitive single-task and varied multi-task experiments. After the removal of regions in visual cortex, the most coherent functional brain area during the tasks, community detection is better able to resolve known functional brain systems into communities. In addition, node removal enables the algorithm to distinguish clear differences in brain network dynamics between these experiments, revealing task-switching behavior that was not identified with the visual regions present in the network. These results indicate that targeted node removal can improve spatial and temporal resolution in community detection, and they demonstrate a promising approach for comparison of network dynamics between neuroscientific data sets with different resolution parameters.
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Affiliation(s)
- Kimberly J. Schlesinger
- Department of Physics, University of California Santa Barbara, Santa Barbara, California, United States of America
- * E-mail:
| | - Benjamin O. Turner
- Department of Psychological and Brain Sciences, University of California Santa Barbara, Santa Barbara, California, United States of America
| | - Scott T. Grafton
- Department of Psychological and Brain Sciences, University of California Santa Barbara, Santa Barbara, California, United States of America
| | - Michael B. Miller
- Department of Psychological and Brain Sciences, University of California Santa Barbara, Santa Barbara, California, United States of America
| | - Jean M. Carlson
- Department of Physics, University of California Santa Barbara, Santa Barbara, California, United States of America
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22
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Liu J, Liao X, Xia M, He Y. Chronnectome fingerprinting: Identifying individuals and predicting higher cognitive functions using dynamic brain connectivity patterns. Hum Brain Mapp 2017; 39:902-915. [PMID: 29143409 DOI: 10.1002/hbm.23890] [Citation(s) in RCA: 135] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Revised: 11/03/2017] [Accepted: 11/07/2017] [Indexed: 12/23/2022] Open
Abstract
The human brain is a large, interacting dynamic network, and its architecture of coupling among brain regions varies across time (termed the "chronnectome"). However, very little is known about whether and how the dynamic properties of the chronnectome can characterize individual uniqueness, such as identifying individuals as a "fingerprint" of the brain. Here, we employed multiband resting-state functional magnetic resonance imaging data from the Human Connectome Project (N = 105) and a sliding time-window dynamic network analysis approach to systematically examine individual time-varying properties of the chronnectome. We revealed stable and remarkable individual variability in three dynamic characteristics of brain connectivity (i.e., strength, stability, and variability), which was mainly distributed in three higher order cognitive systems (i.e., default mode, dorsal attention, and fronto-parietal) and in two primary systems (i.e., visual and sensorimotor). Intriguingly, the spatial patterns of these dynamic characteristics of brain connectivity could successfully identify individuals with high accuracy and could further significantly predict individual higher cognitive performance (e.g., fluid intelligence and executive function), which was primarily contributed by the higher order cognitive systems. Together, our findings highlight that the chronnectome captures inherent functional dynamics of individual brain networks and provides implications for individualized characterization of health and disease.
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Affiliation(s)
- Jin Liu
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Xuhong Liao
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Mingrui Xia
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Yong He
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
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23
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Khambhati AN, Mattar MG, Wymbs NF, Grafton ST, Bassett DS. Beyond modularity: Fine-scale mechanisms and rules for brain network reconfiguration. Neuroimage 2017; 166:385-399. [PMID: 29138087 DOI: 10.1016/j.neuroimage.2017.11.015] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2017] [Revised: 10/18/2017] [Accepted: 11/07/2017] [Indexed: 11/15/2022] Open
Abstract
The human brain is in constant flux, as distinct areas engage in transient communication to support basic behaviors as well as complex cognition. The collection of interactions between cortical and subcortical areas forms a functional brain network whose topology evolves with time. Despite the nontrivial dynamics that are germane to this networked system, experimental evidence demonstrates that functional interactions organize into putative brain systems that facilitate different facets of cognitive computation. We hypothesize that such dynamic functional networks are organized around a set of rules that constrain their spatial architecture - which brain regions may functionally interact - and their temporal architecture - how these interactions fluctuate over time. To objectively uncover these organizing principles, we apply an unsupervised machine learning approach called non-negative matrix factorization to time-evolving, resting state functional networks in 20 healthy subjects. This machine learning approach automatically groups temporally co-varying functional interactions into subgraphs that represent putative topological modes of dynamic functional architecture. We find that subgraphs are stratified based on both the underlying modular organization and the topographical distance of their strongest interactions: while many subgraphs are largely contained within modules, others span between modules and are expressed differently over time. The relationship between dynamic subgraphs and modular architecture is further highlighted by the ability of time-varying subgraph expression to explain inter-individual differences in module reorganization. Collectively, these results point to the critical role that subgraphs play in constraining the topography and topology of functional brain networks. More broadly, this machine learning approach opens a new door for understanding the architecture of dynamic functional networks during both task and rest states, and for probing alterations of that architecture in disease.
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Affiliation(s)
- Ankit N Khambhati
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Marcelo G Mattar
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Nicholas F Wymbs
- Department of Physical Medicine and Rehabilitation, Johns Hopkins Medical Institution, Baltimore, MD 21205, USA
| | - Scott T Grafton
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA 93106, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104 USA.
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24
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Abstract
The ability to map brain networks in living individuals is fundamental in efforts to chart the relation between human behavior, health and disease. Advances in network neuroscience may benefit from developing new frameworks for mapping brain connectomes. We present a framework to encode structural brain connectomes and diffusion-weighted magnetic resonance (dMRI) data using multidimensional arrays. The framework integrates the relation between connectome nodes, edges, white matter fascicles and diffusion data. We demonstrate the utility of the framework for in vivo white matter mapping and anatomical computing by evaluating 1,490 connectomes, thirteen tractography methods, and three data sets. The framework dramatically reduces storage requirements for connectome evaluation methods, with up to 40x compression factors. Evaluation of multiple, diverse datasets demonstrates the importance of spatial resolution in dMRI. We measured large increases in connectome resolution as function of data spatial resolution (up to 52%). Moreover, we demonstrate that the framework allows performing anatomical manipulations on white matter tracts for statistical inference and to study the white matter geometrical organization. Finally, we provide open-source software implementing the method and data to reproduce the results.
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Affiliation(s)
- Cesar F Caiafa
- Department of Psychological and, Brain Sciences Indiana University Bloomington, IN, 47405, USA
- Instituto Argentino de Radioastronomía (IAR), CONICET CCT, La Plata Villa Elisa, 1894, Argentina
- Facultad de Ingeniería - Departamento de Computación, UBA Buenos Aires, C1063ACV, Argentina
| | - Franco Pestilli
- Department of Psychological and, Brain Sciences Indiana University Bloomington, IN, 47405, USA.
- Department of Intelligent Systems, Engineering Indiana University Bloomington, IN, 47405, USA.
- Department of Computer Science, Indiana University Bloomington, IN, 47405, USA.
- Program in Neuroscience Indiana University Bloomington, IN, 47405, USA.
- Program in Cognitive Science Indiana University Bloomington, IN, 47405, USA.
- School of Optometry Indiana University Bloomington, IN, 47405, USA.
- Indiana Network Science Institute Indiana University Bloomington, IN, 47405, USA.
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25
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Gu S, Yang M, Medaglia JD, Gur RC, Gur RE, Satterthwaite TD, Bassett DS. Functional hypergraph uncovers novel covariant structures over neurodevelopment. Hum Brain Mapp 2017; 38:3823-3835. [PMID: 28493536 PMCID: PMC6323637 DOI: 10.1002/hbm.23631] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2016] [Revised: 04/02/2017] [Accepted: 04/20/2017] [Indexed: 02/01/2023] Open
Abstract
Brain development during adolescence is marked by substantial changes in brain structure and function, leading to a stable network topology in adulthood. However, most prior work has examined the data through the lens of brain areas connected to one another in large-scale functional networks. Here, we apply a recently developed hypergraph approach that treats network connections (edges) rather than brain regions as the unit of interest, allowing us to describe functional network topology from a fundamentally different perspective. Capitalizing on a sample of 780 youth imaged as part of the Philadelphia Neurodevelopmental Cohort, this hypergraph representation of resting-state functional MRI data reveals three distinct classes of subnetworks (hyperedges): clusters, bridges, and stars, which respectively represent homogeneously connected, bipartite, and focal architectures. Cluster hyperedges show a strong resemblance to previously-described functional modules of the brain including somatomotor, visual, default mode, and salience systems. In contrast, star hyperedges represent highly localized subnetworks centered on a small set of regions, and are distributed across the entire cortex. Finally, bridge hyperedges link clusters and stars in a core-periphery organization. Notably, developmental changes within hyperedges are ordered in a similar core-periphery fashion, with the greatest developmental effects occurring in networked hyperedges within the functional core. Taken together, these results reveal a novel decomposition of the network organization of human brain, and further provide a new perspective on the role of local structures that emerge across neurodevelopment. Hum Brain Mapp 38:3823-3835, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Shi Gu
- Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvania
- Department of Electrical and Systems EngineeringUniversity of PennsylvaniaPhiladelphiaPennsylvania
- Department of BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvania
| | - Muzhi Yang
- Applied Mathematics and Computational Science Graduate GroupUniversity of PennsylvaniaPhiladelphiaPennsylvania
- Department of BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvania
| | | | - Ruben C. Gur
- Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvania
| | - Raquel E. Gur
- Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvania
| | | | - Danielle S. Bassett
- Department of Electrical and Systems EngineeringUniversity of PennsylvaniaPhiladelphiaPennsylvania
- Department of BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvania
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26
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Ciric R, Nomi JS, Uddin LQ, Satpute AB. Contextual connectivity: A framework for understanding the intrinsic dynamic architecture of large-scale functional brain networks. Sci Rep 2017; 7:6537. [PMID: 28747717 PMCID: PMC5529582 DOI: 10.1038/s41598-017-06866-w] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2016] [Accepted: 06/20/2017] [Indexed: 11/09/2022] Open
Abstract
Investigations of the human brain's connectomic architecture have produced two alternative models: one describes the brain's spatial structure in terms of static localized networks, and the other describes the brain's temporal structure in terms of dynamic whole-brain states. Here, we used tools from connectivity dynamics to develop a synthesis that bridges these models. Using resting fMRI data, we investigated the assumptions undergirding current models of the human connectome. Consistent with state-based models, our results suggest that static localized networks are superordinate approximations of underlying dynamic states. Furthermore, each of these localized, dynamic connectivity states is associated with global changes in the whole-brain functional connectome. By nesting localized dynamic connectivity states within their whole-brain contexts, we demonstrate the relative temporal independence of brain networks. Our assay for functional autonomy of coordinated neural systems is broadly applicable, and our findings provide evidence of structure in temporal state dynamics that complements the well-described static spatial organization of the brain.
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Affiliation(s)
- Rastko Ciric
- Dept. of Neuroscience, Pomona College, Claremont, CA, USA.
| | - Jason S Nomi
- Dept. of Psychology, University of Miami, Coral Gables, FL, USA
| | - Lucina Q Uddin
- Dept. of Psychology, University of Miami, Coral Gables, FL, USA
| | - Ajay B Satpute
- Dept. of Neuroscience, Pomona College, Claremont, CA, USA.
- Dept. of Psychology, Pomona College, Claremont, CA, USA.
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27
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Metastability in Senescence. Trends Cogn Sci 2017; 21:509-521. [DOI: 10.1016/j.tics.2017.04.007] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2016] [Revised: 04/05/2017] [Accepted: 04/13/2017] [Indexed: 02/01/2023]
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28
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Thompson WH, Brantefors P, Fransson P. From static to temporal network theory: Applications to functional brain connectivity. Netw Neurosci 2017; 1:69-99. [PMID: 29911669 PMCID: PMC5988396 DOI: 10.1162/netn_a_00011] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2016] [Accepted: 03/29/2017] [Indexed: 11/25/2022] Open
Abstract
Network neuroscience has become an established paradigm to tackle questions related to the functional and structural connectome of the brain. Recently, interest has been growing in examining the temporal dynamics of the brain's network activity. Although different approaches to capturing fluctuations in brain connectivity have been proposed, there have been few attempts to quantify these fluctuations using temporal network theory. This theory is an extension of network theory that has been successfully applied to the modeling of dynamic processes in economics, social sciences, and engineering article but it has not been adopted to a great extent within network neuroscience. The objective of this article is twofold: (i) to present a detailed description of the central tenets of temporal network theory and describe its measures, and; (ii) to apply these measures to a resting-state fMRI dataset to illustrate their utility. Furthermore, we discuss the interpretation of temporal network theory in the context of the dynamic functional brain connectome. All the temporal network measures and plotting functions described in this article are freely available as the Python package Teneto.
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Affiliation(s)
| | - Per Brantefors
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Peter Fransson
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
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