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Gosti G, Milanetti E, Folli V, de Pasquale F, Leonetti M, Corbetta M, Ruocco G, Della Penna S. A recurrent Hopfield network for estimating meso-scale effective connectivity in MEG. Neural Netw 2024; 170:72-93. [PMID: 37977091 DOI: 10.1016/j.neunet.2023.11.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 11/07/2023] [Accepted: 11/09/2023] [Indexed: 11/19/2023]
Abstract
The architecture of communication within the brain, represented by the human connectome, has gained a paramount role in the neuroscience community. Several features of this communication, e.g., the frequency content, spatial topology, and temporal dynamics are currently well established. However, identifying generative models providing the underlying patterns of inhibition/excitation is very challenging. To address this issue, we present a novel generative model to estimate large-scale effective connectivity from MEG. The dynamic evolution of this model is determined by a recurrent Hopfield neural network with asymmetric connections, and thus denoted Recurrent Hopfield Mass Model (RHoMM). Since RHoMM must be applied to binary neurons, it is suitable for analyzing Band Limited Power (BLP) dynamics following a binarization process. We trained RHoMM to predict the MEG dynamics through a gradient descent minimization and we validated it in two steps. First, we showed a significant agreement between the similarity of the effective connectivity patterns and that of the interregional BLP correlation, demonstrating RHoMM's ability to capture individual variability of BLP dynamics. Second, we showed that the simulated BLP correlation connectomes, obtained from RHoMM evolutions of BLP, preserved some important topological features, e.g, the centrality of the real data, assuring the reliability of RHoMM. Compared to other biophysical models, RHoMM is based on recurrent Hopfield neural networks, thus, it has the advantage of being data-driven, less demanding in terms of hyperparameters and scalable to encompass large-scale system interactions. These features are promising for investigating the dynamics of inhibition/excitation at different spatial scales.
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Affiliation(s)
- Giorgio Gosti
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena, 291, 00161, Rome, Italy; Soft and Living Matter Laboratory, Institute of Nanotechnology, Consiglio Nazionale delle Ricerche, Piazzale Aldo Moro, 5, 00185, Rome, Italy; Istituto di Scienze del Patrimonio Culturale, Sede di Roma, Consiglio Nazionale delle Ricerche, CNR-ISPC, Via Salaria km, 34900 Rome, Italy.
| | - Edoardo Milanetti
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena, 291, 00161, Rome, Italy; Department of Physics, Sapienza University of Rome, Piazzale Aldo Moro, 5, 00185, Rome, Italy.
| | - Viola Folli
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena, 291, 00161, Rome, Italy; D-TAILS srl, Via di Torre Rossa, 66, 00165, Rome, Italy.
| | - Francesco de Pasquale
- Faculty of Veterinary Medicine, University of Teramo, 64100 Piano D'Accio, Teramo, Italy.
| | - Marco Leonetti
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena, 291, 00161, Rome, Italy; Soft and Living Matter Laboratory, Institute of Nanotechnology, Consiglio Nazionale delle Ricerche, Piazzale Aldo Moro, 5, 00185, Rome, Italy; D-TAILS srl, Via di Torre Rossa, 66, 00165, Rome, Italy.
| | - Maurizio Corbetta
- Department of Neuroscience, University of Padova, Via Belzoni, 160, 35121, Padova, Italy; Padova Neuroscience Center (PNC), University of Padova, Via Orus, 2/B, 35129, Padova, Italy; Veneto Institute of Molecular Medicine (VIMM), Via Orus, 2, 35129, Padova, Italy.
| | - Giancarlo Ruocco
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena, 291, 00161, Rome, Italy; Department of Physics, Sapienza University of Rome, Piazzale Aldo Moro, 5, 00185, Rome, Italy.
| | - Stefania Della Penna
- Department of Neuroscience, Imaging and Clinical Sciences, and Institute for Advanced Biomedical Technologies, "G. d'Annunzio" University of Chieti-Pescara, Via Luigi Polacchi, 11, 66100 Chieti, Italy.
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2
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Zhao Y, Gao Y, Li M, Anderson AW, Ding Z, Gore JC. Functional Parcellation of Human Brain Using Localized Topo-Connectivity Mapping. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:2670-2680. [PMID: 35442885 PMCID: PMC9844109 DOI: 10.1109/tmi.2022.3168888] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The analysis of connectivity between parcellated regions of cortex provides insights into the functional architecture of the brain at a systems level. However, the derivation of functional structures from voxel-wise analyses at finer scales remains a challenge. We propose a novel method, called localized topo-connectivity mapping with singular-value-decomposition-informed filtering (or filtered LTM), to identify and characterize voxel-wise functional structures in the human brain from resting-state fMRI data. Here we describe its mathematical formulation and provide a proof-of-concept using simulated data that allow an intuitive interpretation of the results of filtered LTM. The algorithm has also been applied to 7T fMRI data acquired as part of the Human Connectome Project to generate group-average LTM images. Generally, most of the functional structures revealed by LTM images agree in the boundaries with anatomical structures identified by T1-weighted images and fractional anisotropy maps derived from diffusion MRI. In addition, the LTM images also reveal subtle functional variations that are not apparent in the anatomical structures. To assess the performance of LTM images, the subcortical region and occipital white matter were separately parcellated. Statistical tests were performed to demonstrate that the synchronies of fMRI signals in LTM-derived functional parcels are significantly larger than those with geometric perturbations. Overall, the filtered LTM approach can serve as a tool to investigate the functional organization of the brain at the scale of individual voxels as measured in fMRI.
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Rabuffo G, Fousek J, Bernard C, Jirsa V. Neuronal Cascades Shape Whole-Brain Functional Dynamics at Rest. eNeuro 2021; 8:ENEURO.0283-21.2021. [PMID: 34583933 PMCID: PMC8555887 DOI: 10.1523/eneuro.0283-21.2021] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 09/11/2021] [Accepted: 09/15/2021] [Indexed: 01/20/2023] Open
Abstract
At rest, mammalian brains display remarkable spatiotemporal complexity, evolving through recurrent functional connectivity (FC) states on a slow timescale of the order of tens of seconds. While the phenomenology of the resting state dynamics is valuable in distinguishing healthy and pathologic brains, little is known about its underlying mechanisms. Here, we identify neuronal cascades as a potential mechanism. Using full-brain network modeling, we show that neuronal populations, coupled via a detailed structural connectome, give rise to large-scale cascades of firing rate fluctuations evolving at the same time scale of resting-state networks (RSNs). The ignition and subsequent propagation of cascades depend on the brain state and connectivity of each region. The largest cascades produce bursts of blood oxygen level-dependent (BOLD) co-fluctuations at pairs of regions across the brain, which shape the simulated RSN dynamics. We experimentally confirm these theoretical predictions. We demonstrate the existence and stability of intermittent epochs of FC comprising BOLD co-activation (CA) bursts in mice and human functional magnetic resonance imaging (fMRI). We then provide evidence for the existence and leading role of the neuronal cascades in humans with simultaneous EEG/fMRI recordings. These results show that neuronal cascades are a major determinant of spontaneous fluctuations in brain dynamics at rest.
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Affiliation(s)
- Giovanni Rabuffo
- Aix Marseille University, INSERM, INS, Institut de Neurosciences des Systèmes, 13005 Marseille, France
| | - Jan Fousek
- Aix Marseille University, INSERM, INS, Institut de Neurosciences des Systèmes, 13005 Marseille, France
| | - Christophe Bernard
- Aix Marseille University, INSERM, INS, Institut de Neurosciences des Systèmes, 13005 Marseille, France
| | - Viktor Jirsa
- Aix Marseille University, INSERM, INS, Institut de Neurosciences des Systèmes, 13005 Marseille, France
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4
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Siffredi V, Farouj Y, Tarun A, Anderson V, Wood AG, McIlroy A, Leventer RJ, Spencer-Smith MM, Ville DVD. Large-scale functional network dynamics in human callosal agenesis: Increased subcortical involvement and preserved laterality. Neuroimage 2021; 243:118471. [PMID: 34455063 DOI: 10.1016/j.neuroimage.2021.118471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 07/20/2021] [Accepted: 08/11/2021] [Indexed: 10/20/2022] Open
Abstract
In the human brain, the corpus callosum is the major white-matter commissural tract enabling the transmission of sensory-motor, and higher level cognitive information between homotopic regions of the two cerebral hemispheres. Despite developmental absence (i.e., agenesis) of the corpus callosum (AgCC), functional connectivity is preserved, including interhemispheric connectivity. Subcortical structures have been hypothesised to provide alternative pathways to enable this preservation. To test this hypothesis, we used functional Magnetic Resonance Imaging (fMRI) recordings in children with AgCC and typically developing children, and a time-resolved approach to retrieve temporal characteristics of whole-brain functional networks. We observed an increased engagement of the cerebellum and amygdala/hippocampus networks in children with AgCC compared to typically developing children. There was little evidence that laterality of activation networks was affected in AgCC. Our findings support the hypothesis that subcortical structures play an essential role in the functional reconfiguration of the brain in the absence of a corpus callosum.
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Affiliation(s)
- Vanessa Siffredi
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland; Brain and Mind Research, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia; Division of Development and Growth, Department of Woman, Child and Adolescent, University Hospitals of Geneva, Geneva, Switzerland.
| | - Younes Farouj
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Anjali Tarun
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Vicki Anderson
- Brain and Mind Research, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia; Neuroscience Research, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia; School of Psychological Sciences, University of Melbourne, Melbourne, Australia; Department of Psychology, Royal Children's Hospital, Melbourne, Australia
| | - Amanda G Wood
- Brain and Mind Research, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia; School of Life and Health Sciences & Aston Neuroscience Institute, Aston University, Birmingham, B4 7ET UK; School of Psychology, Faculty of Health, Melbourne Burwood Campus, Deakin University, Geelong, Victoria, Australia
| | - Alissandra McIlroy
- Brain and Mind Research, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia
| | - Richard J Leventer
- Neuroscience Research, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia; Department of Paediatrics, University of Melbourne, Melbourne, Australia; Department of Neurology, Royal Children's Hospital, Melbourne, Australia
| | - Megan M Spencer-Smith
- Brain and Mind Research, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia; Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Dimitri Van De Ville
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
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5
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Siffredi V, Preti MG, Obertino S, Leventer RJ, Wood AG, McIlroy A, Anderson V, Spencer-Smith MM, Van De Ville D. Revisiting brain rewiring and plasticity in children born without corpus callosum. Dev Sci 2021; 24:e13126. [PMID: 34060677 PMCID: PMC8596429 DOI: 10.1111/desc.13126] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Revised: 04/16/2021] [Accepted: 05/05/2021] [Indexed: 02/06/2023]
Abstract
The corpus callosum is the largest white matter pathway connecting homologous structures of the two cerebral hemispheres. Remarkably, children and adults with developmental absence of the corpus callosum (callosal dysgenesis, CD) show typical interhemispheric integration, which is classically impaired in adult split-brain patients, for whom the corpus callosum is surgically severed. Tovar-Moll and colleagues (2014) proposed alternative neural pathways involved in the preservation of interhemispheric transfer. In a sample of six adults with CD, they revealed two homotopic bundles crossing the midline via the anterior and posterior commissures and connecting parietal cortices, and the microstructural properties of these aberrant bundles were associated with functional connectivity of these regions. The aberrant bundles were specific to CD and not visualised in healthy brains. We extended this study in a developmental cohort of 20 children with CD and 29 typically developing controls (TDC). The two anomalous white-matter bundles were visualised using tractography. Associations between structural properties of these bundles and their regional functional connectivity were explored. The proposed atypical bundles were observed in 30% of our CD cohort crossing via the anterior commissure, and in 30% crossing via the posterior commissure (also observed in 6.9% of TDC). However, the structural property measures of these bundles were not associated with parietal functional connectivity, bringing into question their role and implication for interhemispheric functional connectivity in CD. It is possible that very early disruption of embryological callosal development enhances neuroplasticity and facilitates the formation of these proposed alternative neural pathways, but further evidence is needed.
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Affiliation(s)
- Vanessa Siffredi
- Medical Image Processing Lab, Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Lausanne, VD, Switzerland.,Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Geneva, Geneva, Switzerland.,Brain and Mind Research, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia.,Division of Development and Growth, Department of Paediatrics, Faculty of Medicine, University of Geneva, Geneva, Geneva, Switzerland
| | - Maria G Preti
- Medical Image Processing Lab, Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Lausanne, VD, Switzerland.,Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Geneva, Geneva, Switzerland.,CIBM Center for Biomedical Imaging, Switzerland
| | - Silvia Obertino
- Medical Image Processing Lab, Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Lausanne, VD, Switzerland.,Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Geneva, Geneva, Switzerland
| | - Richard J Leventer
- Department of Paediatrics, University of Melbourne, Melbourne, Australia.,Department of Neurology, Royal Children's Hospital, Melbourne, Australia.,Neuroscience Research, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia
| | - Amanda G Wood
- Brain and Mind Research, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia.,School of Life and Health Sciences & Aston Neuroscience Institute, Aston University, Birmingham, UK.,School of Psychology, Faculty of Health, Melbourne Burwood Campus, Deakin University, Geelong, Victoria, Australia
| | - Alissandra McIlroy
- Brain and Mind Research, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia
| | - Vicki Anderson
- Brain and Mind Research, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia.,Neuroscience Research, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia.,School of Psychological Sciences, University of Melbourne, Melbourne, Australia.,Department of Psychology, Royal Children's Hospital, Melbourne, Australia
| | - Megan M Spencer-Smith
- Brain and Mind Research, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia.,Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Dimitri Van De Ville
- Medical Image Processing Lab, Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Lausanne, VD, Switzerland.,Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Geneva, Geneva, Switzerland.,CIBM Center for Biomedical Imaging, Switzerland
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6
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de Pasquale F, Spadone S, Betti V, Corbetta M, Della Penna S. Temporal modes of hub synchronization at rest. Neuroimage 2021; 235:118005. [PMID: 33819608 DOI: 10.1016/j.neuroimage.2021.118005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 03/12/2021] [Accepted: 03/17/2021] [Indexed: 10/21/2022] Open
Abstract
The brain is a dynamic system that generates a broad repertoire of perceptual, motor, and cognitive states by the integration and segregation of different functional domains represented in large-scale brain networks. However, the fundamental mechanisms underlying brain network integration remain elusive. Here, for the first time to our knowledge, we found that in the resting state the brain visits few synchronization modes defined as clusters of temporally aligned functional hubs. These modes alternate over time and their probability of switching leads to specific temporal loops among them. Notably, although each mode involves a small set of nodes, the brain integration seems highly vulnerable to a simulated attack on this temporal synchronization mechanism. In line with the hypothesis that the resting state represents a prior sculpted by the task activity, the observed synchronization modes might be interpreted as a temporal brain template needed to respond to task/environmental demands .
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Affiliation(s)
- F de Pasquale
- Faculty of Veterinary Medicine, University of Teramo, Teramo, Italy.
| | - S Spadone
- Department of Neuroscience, Imaging and Clinical Sciences, and Institute for Advanced Biomedical Technologies, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy
| | - V Betti
- Department of Psychology, Sapienza University of Rome, 00185, Rome, Italy; IRCCS Fondazione Santa Lucia, 00142, Rome, Italy
| | - M Corbetta
- Department of Neuroscience and Padova Neuroscience Center (PNC), University of Padua, Padua, Italy; Venetian Institute of Molecular Medicine (VIMM), Padua, Italy; Department of Neurology, Radiology, and Neuroscience, Washington University St. Louis
| | - S Della Penna
- Department of Neuroscience, Imaging and Clinical Sciences, and Institute for Advanced Biomedical Technologies, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy
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7
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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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Lu L, Li H, Mills JA, Schroeder H, Mossman SA, Varney ST, Cecil KM, Huang X, Gong Q, Levine A, DelBello MP, Sweeny JA, Strawn JR. Greater Dynamic and Lower Static Functional Brain Connectivity Prospectively Predict Placebo Response in Pediatric Generalized Anxiety Disorder. J Child Adolesc Psychopharmacol 2020; 30:606-616. [PMID: 32721213 PMCID: PMC7864114 DOI: 10.1089/cap.2020.0024] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Objectives: Placebo response is one of the most significant barriers to detecting treatment effects in pediatric (and adult) clinical trials focusing on affective and anxiety disorders. We sought to identify neurofunctional predictors of placebo response in adolescents with generalized anxiety disorder (GAD) by examining dynamic and static functional brain connectivity. Methods: Before randomization to blinded placebo, adolescents, aged 12-17 years, with GAD (N = 25) underwent resting state functional magnetic resonance imaging. Whole brain voxelwise correlation analyses were used to determine the relationship between change in anxiety symptoms from baseline to week 8 and seed-based dynamic and static functional connectivity maps of regions in the salience and ventral attention networks (amygdala, dorsal anterior cingulate cortex [dACC], and ventrolateral prefrontal cortex [VLPFC]). Results: Greater dynamic functional connectivity variability in amygdala, dACC, VLPFC, and regions within salience, default mode, and frontoparietal networks was associated with greater placebo response. Lower static functional connectivity between amygdala and dorsolateral prefrontal cortex, amygdala and medial prefrontal cortex, dACC and posterior cingulate cortex and greater static functional connectivity between VLPFC and inferior parietal lobule were associated with greater placebo response. Conclusion: Placebo response is associated with a distinct dynamic and static connectivity fingerprint characterized by "variable" dynamic but "weak" static connectivity in the salience, default mode, frontoparietal, and ventral attention networks. These data provide granular evidence of how circuit-based biotypes mechanistically relate to placebo response. Finding biosignatures that predict placebo response is critically important in clinical psychopharmacology and to improve our ability to detect medication-placebo differences in clinical trials.
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Affiliation(s)
- Lu Lu
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China.,Department of Psychiatry, College of Medicine, University of Cincinnati, Cincinnati, Ohio, USA
| | - Hailong Li
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
| | - Jeffrey A. Mills
- Department of Economics, Lindner College of Business, University of Cincinnati, Cincinnati, Ohio, USA
| | - Heidi Schroeder
- Department of Psychiatry, College of Medicine, University of Cincinnati, Cincinnati, Ohio, USA
| | - Sarah A. Mossman
- Department of Psychiatry, College of Medicine, University of Cincinnati, Cincinnati, Ohio, USA
| | - Sara T. Varney
- Department of Psychiatry, College of Medicine, University of Cincinnati, Cincinnati, Ohio, USA
| | - Kim M. Cecil
- Department of Radiology, College of Medicine, University of Cincinnati, Cincinnati, Ohio, USA,Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Xiaoqi Huang
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China.,Psychoradiology Research Unit of Chinese Academy of Medical Sciences, West China Hospital of Sichuan University, Chengdu, China
| | - Qiyong Gong
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China.,Psychoradiology Research Unit of Chinese Academy of Medical Sciences, West China Hospital of Sichuan University, Chengdu, China.,Address correspondence to: Qiyong Gong, MD, PhD, Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, No. 37 Guo Xue Xiang, Chengdu, Sichuan 610041, China
| | - Amir Levine
- Department of Psychiatry, Columbia University and New York State Psychiatric Institute, New York City, New York, USA
| | - Melissa P. DelBello
- Department of Psychiatry, College of Medicine, University of Cincinnati, Cincinnati, Ohio, USA
| | - John A. Sweeny
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China.,Department of Psychiatry, College of Medicine, University of Cincinnati, Cincinnati, Ohio, USA
| | - Jeffrey R. Strawn
- Department of Psychiatry, College of Medicine, University of Cincinnati, Cincinnati, Ohio, USA
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9
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Siffredi V, Preti MG, Kebets V, Obertino S, Leventer RJ, McIlroy A, Wood AG, Anderson V, Spencer-Smith MM, Van De Ville D. Structural Neuroplastic Responses Preserve Functional Connectivity and Neurobehavioural Outcomes in Children Born Without Corpus Callosum. Cereb Cortex 2020; 31:1227-1239. [DOI: 10.1093/cercor/bhaa289] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Revised: 09/02/2020] [Accepted: 09/03/2020] [Indexed: 12/17/2022] Open
Abstract
Abstract
The corpus callosum is the largest white matter pathway in the brain connecting the two hemispheres. In the context of developmental absence (agenesis) of the corpus callosum (AgCC), a proposed candidate for neuroplastic response is strengthening of intrahemispheric pathways. To test this hypothesis, we assessed structural and functional connectivity in a uniquely large cohort of children with AgCC (n = 20) compared with typically developing controls (TDC, n = 29), and then examined associations with neurobehavioral outcomes using a multivariate data-driven approach (partial least squares correlation, PLSC). For structural connectivity, children with AgCC showed a significant increase in intrahemispheric connectivity in addition to a significant decrease in interhemispheric connectivity compared with TDC, in line with the aforementioned hypothesis. In contrast, for functional connectivity, children with AgCC and TDC showed a similar pattern of intrahemispheric and interhemispheric connectivity. In conclusion, we observed structural strengthening of intrahemispheric pathways in children born without corpus callosum, which seems to allow for functional connectivity comparable to a typically developing brain, and were relevant to explain neurobehavioral outcomes in this population. This neuroplasticity might be relevant to other disorders of axonal guidance, and developmental disorders in which corpus callosum alteration is observed.
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Affiliation(s)
- Vanessa Siffredi
- Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Geneva, Geneva 1202, Switzerland
- Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Geneva, Geneva 1206, Switzerland
- Brain and Mind Research, Clinical Sciences, Murdoch Children’s Research Institute, Melbourne, Victoria 3052, Australia
- Division of Development and Growth, Department of Paediatrics, Faculty of Medicine, University of Geneva, Geneva, Geneva 1206, Switzerland
| | - Maria G Preti
- Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Geneva, Geneva 1202, Switzerland
- Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Geneva, Geneva 1206, Switzerland
| | - Valeria Kebets
- Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Geneva, Geneva 1206, Switzerland
- Department of Electrical and Computer Engineering, Clinical Imaging Research Centre, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore 117583, Singapore
| | - Silvia Obertino
- Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Geneva, Geneva 1202, Switzerland
- Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Geneva, Geneva 1206, Switzerland
| | - Richard J Leventer
- Department of Paediatrics, University of Melbourne, Melbourne, Victoria 3010, Australia
- Department of Neurology, Royal Children’s Hospital, Melbourne, Victoria 3052, Australia
- Neuroscience Research, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Victoria 3052, Australia
| | - Alissandra McIlroy
- Brain and Mind Research, Clinical Sciences, Murdoch Children’s Research Institute, Melbourne, Victoria 3052, Australia
| | - Amanda G Wood
- Brain and Mind Research, Clinical Sciences, Murdoch Children’s Research Institute, Melbourne, Victoria 3052, Australia
- School of Life and Health Sciences & Aston Neuroscience Institute, Aston University, Birmingham B4 7ET, UK
- School of Psychology, Faculty of Health, Melbourne Burwood Campus, Deakin University, Geelong, Victoria 3217, Australia
| | - Vicki Anderson
- Brain and Mind Research, Clinical Sciences, Murdoch Children’s Research Institute, Melbourne, Victoria 3052, Australia
- School of Psychological Sciences, University of Melbourne, Melbourne, Victoria 3010, Australia
- Neuroscience Research, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Victoria 3052, Australia
- Department of Psychology, Royal Children’s Hospital, Melbourne, Victoria 3052, Australia
| | - Megan M Spencer-Smith
- Brain and Mind Research, Clinical Sciences, Murdoch Children’s Research Institute, Melbourne, Victoria 3052, Australia
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria 3800, Australia
| | - Dimitri Van De Ville
- Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Geneva, Geneva 1202, Switzerland
- Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Geneva, Geneva 1206, Switzerland
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10
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Iannotti GR, Preti MG, Grouiller F, Carboni M, De Stefano P, Pittau F, Momjian S, Carmichael D, Centeno M, Seeck M, Korff CM, Schaller K, De Ville DV, Vulliemoz S. Modulation of epileptic networks by transient interictal epileptic activity: A dynamic approach to simultaneous EEG-fMRI. NEUROIMAGE-CLINICAL 2020; 28:102467. [PMID: 33395963 PMCID: PMC7645285 DOI: 10.1016/j.nicl.2020.102467] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 09/15/2020] [Accepted: 10/09/2020] [Indexed: 12/27/2022]
Abstract
EEG-fMRI has been instrumental in characterizing brain networks in epilepsy. Its value is documented in the pre-surgical assessment of drug-resistant epilepsy. The delineation of brain areas to resect is fundamental for the post-surgical outcome. Standard EEG-fMRI in epilepsy assesses static functional connectivity of the network. EEG-fMRI dynamic connectivity identifies transitory features of specific connections. We integrate dynamic fMRI connectivity and dynamic patterns of simultaneous scalp EEG. This allows to better characterize the spatiotemporal aspects of epileptic networks. This may help in more efficiently target the surgical intervention.
Epileptic networks, defined as brain regions involved in epileptic brain activity, have been mapped by functional connectivity in simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) recordings. This technique allows to define brain hemodynamic changes, measured by the Blood Oxygen Level Dependent (BOLD) signal, associated to the interictal epileptic discharges (IED), which together with ictal events constitute a signature of epileptic disease. Given the highly time-varying nature of epileptic activity, a dynamic functional connectivity (dFC) analysis of EEG-fMRI data appears particularly suitable, having the potential to identify transitory features of specific connections in epileptic networks. In the present study, we propose a novel method, defined dFC-EEG, that integrates dFC assessed by fMRI with the information recorded by simultaneous scalp EEG, in order to identify the connections characterised by a dynamic profile correlated with the occurrence of IED, forming the dynamic epileptic subnetwork. Ten patients with drug-resistant focal epilepsy were included, with different aetiology and showing a widespread (or multilobar) BOLD activation, defined as involving at least two distinct clusters, located in two different lobes and/or extended to the hemisphere contralateral to the epileptic focus. The epileptic focus was defined from the IED-related BOLD map. Regions involved in the occurrence of interictal epileptic activity; i.e., forming the epileptic network, were identified by a general linear model considering the timecourse of the fMRI-defined focus as main regressor. dFC between these regions was assessed with a sliding-window approach. dFC timecourses were then correlated with the sliding-window variance of the IED signal (VarIED), to identify connections whose dynamics related to the epileptic activity; i.e., the dynamic epileptic subnetwork. As expected, given the very different clinical picture of each individual, the extent of this subnetwork was highly variable across patients, but was but was reduced of at least 30% with respect to the initially identified epileptic network in 9/10 patients. The connections of the dynamic subnetwork were most commonly close to the epileptic focus, as reflected by the laterality index of the subnetwork connections, reported higher than the one within the original epileptic network. Moreover, the correlation between dFC timecourses and VarIED was predominantly positive, suggesting a strengthening of the dynamic subnetwork associated to the occurrence of IED. The integration of dFC and scalp IED offers a more specific description of the epileptic network, identifying connections strongly influenced by IED. These findings could be relevant in the pre-surgical evaluation for the resection or disconnection of the epileptogenic zone and help in reaching a better post-surgical outcome. This would be particularly important for patients characterised by a widespread pathological brain activity which challenges the surgical intervention.
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Affiliation(s)
- G R Iannotti
- EEG and Epilepsy, Clinical Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Switzerland; Functional Brain Mapping Lab, Department of Fundamental Neurosciences, University of Geneva, Switzerland; Neurosurgery, Clinical Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Switzerland.
| | - M G Preti
- Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - F Grouiller
- Swiss Center for Affective Sciences, University of Geneva, Switzerland; Laboratory of Behavioral Neurology and Imaging of Cognition, Department of Fundamental Neurosciences, University of Geneva, Switzerland
| | - M Carboni
- EEG and Epilepsy, Clinical Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Switzerland; Functional Brain Mapping Lab, Department of Fundamental Neurosciences, University of Geneva, Switzerland
| | - P De Stefano
- EEG and Epilepsy, Clinical Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Switzerland
| | - F Pittau
- EEG and Epilepsy, Clinical Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Switzerland; Epilepsy Unit, Institution de Lavigny, Switzerland
| | - S Momjian
- Neurosurgery, Clinical Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Switzerland
| | - D Carmichael
- Biomedical Engineering Department, Kings College London, United Kingdom; Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, Queen Square, London, United Kingdom
| | - M Centeno
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, Queen Square, London, United Kingdom; Epilepsy Unit, Neurology Department, Clinica Universidad de Pamplona, Navarra, Spain
| | - M Seeck
- EEG and Epilepsy, Clinical Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Switzerland
| | - C M Korff
- Pediatric Neurology Unit, University Hospitals of Geneva, Geneva, Switzerland
| | - K Schaller
- Neurosurgery, Clinical Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Switzerland
| | - D Van De Ville
- Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - S Vulliemoz
- EEG and Epilepsy, Clinical Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Switzerland
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11
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Harrison SJ, Bijsterbosch JD, Segerdahl AR, Fitzgibbon SP, Farahibozorg SR, Duff EP, Smith SM, Woolrich MW. Modelling subject variability in the spatial and temporal characteristics of functional modes. Neuroimage 2020; 222:117226. [PMID: 32771617 PMCID: PMC7779373 DOI: 10.1016/j.neuroimage.2020.117226] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Revised: 06/26/2020] [Accepted: 07/30/2020] [Indexed: 11/19/2022] Open
Abstract
Recent work has highlighted the scale and ubiquity of subject variability in observations from functional MRI data (fMRI). Furthermore, it is highly likely that errors in the estimation of either the spatial presentation of, or the coupling between, functional regions can confound cross-subject analyses, making accurate and unbiased representations of functional data essential for interpreting any downstream analyses. Here, we extend the framework of probabilistic functional modes (PFMs) (Harrison et al., 2015) to capture cross-subject variability not only in the mode spatial maps, but also in the functional coupling between modes and in mode amplitudes. A new implementation of the inference now also allows for the analysis of modern, large-scale data sets, and the combined inference and analysis package, PROFUMO, is available from git.fmrib.ox.ac.uk/samh/profumo. A new implementation of the inference now also allows for the analysis of modern, large-scale data sets. Using simulated data, resting-state data from 1000 subjects collected as part of the Human Connectome Project (Van Essen et al., 2013), and an analysis of 14 subjects in a variety of continuous task-states (Kieliba et al., 2019), we demonstrate how PFMs are able to capture, within a single model, a rich description of how the spatio-temporal structure of resting-state fMRI activity varies across subjects. We also compare the new PFM model to the well established independent component analysis with dual regression (ICA-DR) pipeline. This reveals that, under PFM assumptions, much more of the (behaviorally relevant) cross-subject variability in fMRI activity should be attributed to the variability in spatial maps, and that, after accounting for this, functional coupling between modes primarily reflects current cognitive state. This has fundamental implications for the interpretation of cross-sectional studies of functional connectivity that do not capture cross-subject variability to the same extent as PFMs.
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Affiliation(s)
- Samuel J Harrison
- FMRIB, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK; OHBA, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK; Translational Neuromodeling Unit, University of Zurich & ETH Zurich, Zurich, Switzerland.
| | - Janine D Bijsterbosch
- FMRIB, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK; Department of Radiology, Washington University Medical School, Saint Louis, USA
| | - Andrew R Segerdahl
- FMRIB, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Sean P Fitzgibbon
- FMRIB, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | | | - Eugene P Duff
- FMRIB, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK; Department of Paediatrics, University of Oxford, Oxford, UK
| | - Stephen M Smith
- FMRIB, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Mark W Woolrich
- FMRIB, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK; OHBA, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
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12
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MacDowell CJ, Buschman TJ. Low-Dimensional Spatiotemporal Dynamics Underlie Cortex-wide Neural Activity. Curr Biol 2020; 30:2665-2680.e8. [PMID: 32470366 DOI: 10.1016/j.cub.2020.04.090] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Revised: 04/07/2020] [Accepted: 04/30/2020] [Indexed: 01/04/2023]
Abstract
Cognition arises from the dynamic flow of neural activity through the brain. To capture these dynamics, we used mesoscale calcium imaging to record neural activity across the dorsal cortex of awake mice. We found that the large majority of variance in cortex-wide activity (∼75%) could be explained by a limited set of ∼14 "motifs" of neural activity. Each motif captured a unique spatiotemporal pattern of neural activity across the cortex. These motifs generalized across animals and were seen in multiple behavioral environments. Motif expression differed across behavioral states, and specific motifs were engaged by sensory processing, suggesting the motifs reflect core cortical computations. Together, our results show that cortex-wide neural activity is highly dynamic but that these dynamics are restricted to a low-dimensional set of motifs, potentially allowing for efficient control of behavior.
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Affiliation(s)
- Camden J MacDowell
- Princeton Neuroscience Institute, Princeton University, Washington Rd., Princeton, NJ 08540, USA; Department of Molecular Biology, Princeton University, Washington Rd., Princeton, NJ 08540, USA; Rutgers Robert Wood Johnson Medical School, 125 Paterson St., New Brunswick, NJ 08901, USA.
| | - Timothy J Buschman
- Princeton Neuroscience Institute, Princeton University, Washington Rd., Princeton, NJ 08540, USA; Department of Psychology, Princeton University, Washington Rd., Princeton, NJ 08540, USA.
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13
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Premi E, Gazzina S, Diano M, Girelli A, Calhoun VD, Iraji A, Gong Q, Li K, Cauda F, Gasparotti R, Padovani A, Borroni B, Magoni M. Enhanced dynamic functional connectivity (whole-brain chronnectome) in chess experts. Sci Rep 2020; 10:7051. [PMID: 32341444 PMCID: PMC7184623 DOI: 10.1038/s41598-020-63984-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Accepted: 04/08/2020] [Indexed: 02/05/2023] Open
Abstract
Multidisciplinary approaches have demonstrated that the brain is potentially modulated by the long-term acquisition and practice of specific skills. Chess playing can be considered a paradigm for shaping brain function, with complex interactions among brain networks possibly enhancing cognitive processing. Dynamic network analysis based on resting-state magnetic resonance imaging (rs-fMRI) can be useful to explore the effect of chess playing on whole-brain fluidity/dynamism (the chronnectome). Dynamic connectivity parameters of 18 professional chess players and 20 beginner chess players were evaluated applying spatial independent component analysis (sICA), sliding-time window correlation, and meta-state approaches to rs-fMRI data. Four indexes of meta-state dynamic fluidity were studied: i) the number of distinct meta-states a subject pass through, ii) the number of switches from one meta-state to another, iii) the span of the realized meta-states (the largest distance between two meta-states that subjects occupied), and iv) the total distance travelled in the state space. Professional chess players exhibited an increased dynamic fluidity, expressed as a higher number of occupied meta-states (meta-state numbers, 75.8 ± 7.9 vs 68.8 ± 12.0, p = 0.043 FDR-corrected) and changes from one meta-state to another (meta-state changes, 77.1 ± 7.3 vs 71.2 ± 11.0, p = 0.043 FDR-corrected) than beginner chess players. Furthermore, professional chess players exhibited an increased dynamic range, with increased traveling between successive meta-states (meta-state total distance, 131.7 ± 17.8 vs 108.7 ± 19.7, p = 0.0004 FDR-corrected). Chess playing may induce changes in brain activity through the modulation of the chronnectome. Future studies are warranted to evaluate if these potential effects lead to enhanced cognitive processing and if “gaming” might be used as a treatment in clinical practice.
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Affiliation(s)
- Enrico Premi
- Stroke Unit, Azienda Socio Sanitaria Territoriale Spedali Civili, Spedali Civili Hospital, Brescia, Italy.
| | - Stefano Gazzina
- Neurophysiology Unit, Azienda Socio Sanitaria Territoriale Spedali Civili, Spedali Civili Hospital, Brescia, Italy
| | - Matteo Diano
- Department of Psychology, University of Turin, Turin, Italy
| | | | - Vince D Calhoun
- Tri-institutional center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
| | - Armin Iraji
- Tri-institutional center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
| | - Qiyong Gong
- Huaxi MR Research Center, Section of Neuroradiology, Department of Radiology, West China Hospital of Sichuan University, Sichuan, China
| | - Kaiming Li
- Huaxi MR Research Center, Section of Neuroradiology, Department of Radiology, West China Hospital of Sichuan University, Sichuan, China
| | - Franco Cauda
- GCS fMRI, Koelliker Hospital and University of Turin, Turin, Italy.,Focus Lab, Department of Psychology, University of Turin, Turin, Italy
| | - Roberto Gasparotti
- Neuroradiology Unit, Department of Medical-Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy
| | - Alessandro Padovani
- Centre for Neurodegenerative Disorders, Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Barbara Borroni
- Centre for Neurodegenerative Disorders, Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Mauro Magoni
- Stroke Unit, Azienda Socio Sanitaria Territoriale Spedali Civili, Spedali Civili Hospital, Brescia, Italy
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14
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Towards a Universal Taxonomy of Macro-scale Functional Human Brain Networks. Brain Topogr 2019; 32:926-942. [PMID: 31707621 DOI: 10.1007/s10548-019-00744-6] [Citation(s) in RCA: 308] [Impact Index Per Article: 61.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Accepted: 11/02/2019] [Indexed: 12/25/2022]
Abstract
The past decade has witnessed a proliferation of studies aimed at characterizing the human connectome. These projects map the brain regions comprising large-scale systems underlying cognition using non-invasive neuroimaging approaches and advanced analytic techniques adopted from network science. While the idea that the human brain is composed of multiple macro-scale functional networks has been gaining traction in cognitive neuroscience, the field has yet to reach consensus on several key issues regarding terminology. What constitutes a functional brain network? Are there "core" functional networks, and if so, what are their spatial topographies? What naming conventions, if universally adopted, will provide the most utility and facilitate communication amongst researchers? Can a taxonomy of functional brain networks be delineated? Here we survey the current landscape to identify six common macro-scale brain network naming schemes and conventions utilized in the literature, highlighting inconsistencies and points of confusion where appropriate. As a minimum recommendation upon which to build, we propose that a scheme incorporating anatomical terminology should provide the foundation for a taxonomy of functional brain networks. A logical starting point in this endeavor might delineate systems that we refer to here as "occipital", "pericentral", "dorsal frontoparietal", "lateral frontoparietal", "midcingulo-insular", and "medial frontoparietal" networks. We posit that as the field of network neuroscience matures, it will become increasingly imperative to arrive at a taxonomy such as that proposed here, that can be consistently referenced across research groups.
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15
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Kunert-Graf JM, Eschenburg KM, Galas DJ, Kutz JN, Rane SD, Brunton BW. Extracting Reproducible Time-Resolved Resting State Networks Using Dynamic Mode Decomposition. Front Comput Neurosci 2019; 13:75. [PMID: 31736734 PMCID: PMC6834549 DOI: 10.3389/fncom.2019.00075] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Accepted: 10/11/2019] [Indexed: 12/19/2022] Open
Abstract
Resting state networks (RSNs) extracted from functional magnetic resonance imaging (fMRI) scans are believed to reflect the intrinsic organization and network structure of brain regions. Most traditional methods for computing RSNs typically assume these functional networks are static throughout the duration of a scan lasting 5-15 min. However, they are known to vary on timescales ranging from seconds to years; in addition, the dynamic properties of RSNs are affected in a wide variety of neurological disorders. Recently, there has been a proliferation of methods for characterizing RSN dynamics, yet it remains a challenge to extract reproducible time-resolved networks. In this paper, we develop a novel method based on dynamic mode decomposition (DMD) to extract networks from short windows of noisy, high-dimensional fMRI data, allowing RSNs from single scans to be resolved robustly at a temporal resolution of seconds. After validating the method on a synthetic dataset, we analyze data from 120 individuals from the Human Connectome Project and show that unsupervised clustering of DMD modes discovers RSNs at both the group (gDMD) and the single subject (sDMD) levels. The gDMD modes closely resemble canonical RSNs. Compared to established methods, sDMD modes capture individualized RSN structure that both better resembles the population RSN and better captures subject-level variation. We further leverage this time-resolved sDMD analysis to infer occupancy and transitions among RSNs with high reproducibility. This automated DMD-based method is a powerful tool to characterize spatial and temporal structures of RSNs in individual subjects.
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Affiliation(s)
| | | | - David J. Galas
- Pacific Northwest Research Institute, Seattle, WA, United States
| | - J. Nathan Kutz
- Department of Applied Math, University of Washington, Seattle, WA, United States
| | - Swati D. Rane
- Department of Radiology, University of Washington, Seattle, WA, United States
| | - Bingni W. Brunton
- Department of Biology, University of Washington, Seattle, WA, United States
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16
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Valsasina P, Hidalgo de la Cruz M, Filippi M, Rocca MA. Characterizing Rapid Fluctuations of Resting State Functional Connectivity in Demyelinating, Neurodegenerative, and Psychiatric Conditions: From Static to Time-Varying Analysis. Front Neurosci 2019; 13:618. [PMID: 31354402 PMCID: PMC6636554 DOI: 10.3389/fnins.2019.00618] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Accepted: 05/29/2019] [Indexed: 01/27/2023] Open
Abstract
Functional magnetic resonance imaging (fMRI) at resting state (RS) has been widely used to characterize the main brain networks. Functional connectivity (FC) has been mostly assessed assuming that FC is static across the whole fMRI examination. However, FC is highly variable at a very fast time-scale, as demonstrated by neurophysiological techniques. Time-varying functional connectivity (TVC) is a novel approach that allows capturing reoccurring patterns of interaction among functional brain networks. Aim of this review is to provide a description of the methods currently used to assess TVC on RS fMRI data, and to summarize the main results of studies applying TVC in healthy controls and patients with multiple sclerosis (MS). An overview of the main results obtained in neurodegenerative and psychiatric conditions is also provided. The most popular TVC approach is based on the so-called “sliding windows,” in which the RS fMRI acquisition is divided in small temporal segments (windows). A window of fixed length is shifted over RS fMRI time courses, and data within each window are used to calculate FC and its variability over time. Sliding windows can be combined with clustering techniques to identify recurring FC states or used to assess global TVC properties of large-scale functional networks or specific brain regions. TVC studies have used heterogeneous methodologies so far. Despite this, similar results have been obtained across investigations. In healthy subjects, the default-mode network (DMN) exhibited the highest degree of connectivity dynamism. In MS patients, abnormal global TVC properties and TVC strengths were found mainly in sensorimotor, DMN and salience networks, and were associated with more severe structural MRI damage and with more severe physical and cognitive disability. Conversely, abnormal TVC measures of the temporal network were correlated with better cognitive performances and less severe fatigue. In patients with neurodegenerative and psychiatric conditions, TVC abnormalities of the DMN, attention and executive networks were associated to more severe clinical manifestations. TVC helps to provide novel insights into fundamental properties of functional networks, and improves the understanding of brain reorganization mechanisms. Future technical advances might help to clarify TVC association with disease prognosis and response to treatment.
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Affiliation(s)
- Paola Valsasina
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Milagros Hidalgo de la Cruz
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Vita-Salute San Raffaele University, Milan, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Vita-Salute San Raffaele University, Milan, Italy.,Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Maria A Rocca
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
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17
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Wink AM. Eigenvector Centrality Dynamics From Resting-State fMRI: Gender and Age Differences in Healthy Subjects. Front Neurosci 2019; 13:648. [PMID: 31316335 PMCID: PMC6609310 DOI: 10.3389/fnins.2019.00648] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Accepted: 06/06/2019] [Indexed: 01/20/2023] Open
Abstract
With the increasing use of functional brain network properties as markers of brain disorders, efficient visualization and evaluation methods have become essential. Eigenvector centrality mapping (ECM) of functional MRI (fMRI) data enables the representation of per-node graph theoretical measures as brain maps. This paper studies the use of centrality dynamics for measuring group differences in imaging studies. Imaging data were used from a publicly available imaging study, which included resting fMRI data. After warping the images to a standard space and masking cortical regions, ECM were computed in a sliding window. The dual regression method was used to identify dynamic centrality differences inside well-known resting-state networks between gender and age groups. Gender-related differences were found in the medial and lateral visual, motor, default mode, and executive control RSN, where male subjects had more consistent centrality variations within the network. Age-related differences between the youngest and oldest subjects, based on a median split, were found in the medial visual, executive control and left frontoparietal networks, where younger subjects had more consistent centrality variations within the network. Our findings show that centrality dynamics can be used to identify between-group functional brain network centrality differences, and that age and gender distributions studies need to be taken into account in functional imaging studies.
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Affiliation(s)
- Alle Meije Wink
- Radiology and Nuclear Medicine, Amsterdam University Medical Center, Amsterdam, Netherlands
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18
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Eijlers AJC, Wink AM, Meijer KA, Douw L, Geurts JJG, Schoonheim MM. Reduced Network Dynamics on Functional MRI Signals Cognitive Impairment in Multiple Sclerosis. Radiology 2019; 292:449-457. [PMID: 31237498 DOI: 10.1148/radiol.2019182623] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Background Previous studies have demonstrated extensive functional network disturbances in patients with multiple sclerosis (MS), showing a less efficient brain network. Recent studies indicate that the dynamic properties of the brain network show a strong correlation with cognitive function. Purpose To investigate network dynamics on functional MRI in cognitively impaired patients with MS. Materials and Methods In secondary analysis of prospectively acquired data, with imaging performed between 2008 and 2012, differences in regional functional network dynamics (ie, eigenvector centrality dynamics) between cognitively impaired and cognitively preserved participants with MS were investigated. Functional network dynamics were computed on images from functional MRI (3 T) by using a sliding-window approach. Cognitively impaired and preserved groups were compared by using a clusterwise permutation-based method. Results The study included 96 healthy control subjects and 332 participants with MS (including 226 women and 106 men; median age, 48.1 years ± 11.0). Among the 332 participants with MS, 87 were cognitively impaired and 180 had preserved cognitive function; mildly impaired patients (n = 65) were excluded. The cognitively impaired group included a higher proportion of men compared with the cognitively preserved group (35 of 87 [40%] vs 48 of 180 [27%], respectively; P = .02) and had a higher mean age (51.1 years vs 46.3 years, respectively; P < .01). The clusterwise permutation-based comparison at P less than .05 showed reduced centrality dynamics in default-mode, frontoparietal, and visual network regions on functional MRI in cognitively impaired participants versus cognitively preserved participants. A subsequent correlation and hierarchical clustering analysis revealed that the default-mode and visual networks normally demonstrate negatively correlated fluctuations in functional importance (r = -0.23 in healthy control subjects), with an almost complete loss of this negative correlation in cognitively impaired participants compared with cognitively preserved participants (r = -0.04 vs r = -0.14; corrected P = .02). Conclusion As shown on functional MRI, cognitively impaired patients with multiple sclerosis not only demonstrate reduced dynamics in default-mode, frontoparietal, and visual networks, but also show a loss of interplay between default-mode and visual networks. © RSNA, 2019 Online supplemental material is available for this article. See also the article by Eijlers et al and the editorial by Zivadinov and Dwyer in this issue.
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Affiliation(s)
- Anand J C Eijlers
- From the Departments of Anatomy and Neurosciences (A.J.C.E., K.A.M., L.D., J.J.G.G., M.M.S.) and Radiology and Nuclear Medicine (A.M.W.), MS Center Amsterdam, Amsterdam UMC, Locatie VUmc, Amsterdam Neuroscience, De Boelelaan 1117, PO Box 7057, 1007 MB, Amsterdam, the Netherlands
| | - Alle Meije Wink
- From the Departments of Anatomy and Neurosciences (A.J.C.E., K.A.M., L.D., J.J.G.G., M.M.S.) and Radiology and Nuclear Medicine (A.M.W.), MS Center Amsterdam, Amsterdam UMC, Locatie VUmc, Amsterdam Neuroscience, De Boelelaan 1117, PO Box 7057, 1007 MB, Amsterdam, the Netherlands
| | - Kim A Meijer
- From the Departments of Anatomy and Neurosciences (A.J.C.E., K.A.M., L.D., J.J.G.G., M.M.S.) and Radiology and Nuclear Medicine (A.M.W.), MS Center Amsterdam, Amsterdam UMC, Locatie VUmc, Amsterdam Neuroscience, De Boelelaan 1117, PO Box 7057, 1007 MB, Amsterdam, the Netherlands
| | - Linda Douw
- From the Departments of Anatomy and Neurosciences (A.J.C.E., K.A.M., L.D., J.J.G.G., M.M.S.) and Radiology and Nuclear Medicine (A.M.W.), MS Center Amsterdam, Amsterdam UMC, Locatie VUmc, Amsterdam Neuroscience, De Boelelaan 1117, PO Box 7057, 1007 MB, Amsterdam, the Netherlands
| | - Jeroen J G Geurts
- From the Departments of Anatomy and Neurosciences (A.J.C.E., K.A.M., L.D., J.J.G.G., M.M.S.) and Radiology and Nuclear Medicine (A.M.W.), MS Center Amsterdam, Amsterdam UMC, Locatie VUmc, Amsterdam Neuroscience, De Boelelaan 1117, PO Box 7057, 1007 MB, Amsterdam, the Netherlands
| | - Menno M Schoonheim
- From the Departments of Anatomy and Neurosciences (A.J.C.E., K.A.M., L.D., J.J.G.G., M.M.S.) and Radiology and Nuclear Medicine (A.M.W.), MS Center Amsterdam, Amsterdam UMC, Locatie VUmc, Amsterdam Neuroscience, De Boelelaan 1117, PO Box 7057, 1007 MB, Amsterdam, the Netherlands
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19
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Menon SS, Krishnamurthy K. A Comparison of Static and Dynamic Functional Connectivities for Identifying Subjects and Biological Sex Using Intrinsic Individual Brain Connectivity. Sci Rep 2019; 9:5729. [PMID: 30952913 PMCID: PMC6450922 DOI: 10.1038/s41598-019-42090-4] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2018] [Accepted: 03/22/2019] [Indexed: 11/09/2022] Open
Abstract
Functional magnetic resonance imaging has revealed correlated activities in brain regions even in the absence of a task. Initial studies assumed this resting-state functional connectivity (FC) to be stationary in nature, but recent studies have modeled these activities as a dynamic network. Dynamic spatiotemporal models better model the brain activities, but are computationally more involved. A comparison of static and dynamic FCs was made to quantitatively study their efficacies in identifying intrinsic individual connectivity patterns using data from the Human Connectome Project. Results show that the intrinsic individual brain connectivity pattern can be used as a 'fingerprint' to distinguish among and identify subjects and is more accurately captured with partial correlation and assuming static FC. It was also seen that the intrinsic individual brain connectivity patterns were invariant over a few months. Additionally, biological sex identification was successfully performed using the intrinsic individual connectivity patterns, and group averages of male and female FC matrices. Edge consistency, edge variability and differential power measures were used to identify the major resting-state networks involved in identifying subjects and their sex.
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Affiliation(s)
- Sreevalsan S Menon
- Missouri University of Science and Technology, Department of Mechanical and Aerospace Engineering, Rolla, MO, 65409, USA
| | - K Krishnamurthy
- Missouri University of Science and Technology, Department of Mechanical and Aerospace Engineering, Rolla, MO, 65409, USA.
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20
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Iraji A, Deramus TP, Lewis N, Yaesoubi M, Stephen JM, Erhardt E, Belger A, Ford JM, McEwen S, Mathalon DH, Mueller BA, Pearlson GD, Potkin SG, Preda A, Turner JA, Vaidya JG, van Erp TGM, Calhoun VD. The spatial chronnectome reveals a dynamic interplay between functional segregation and integration. Hum Brain Mapp 2019; 40:3058-3077. [PMID: 30884018 DOI: 10.1002/hbm.24580] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Revised: 02/26/2019] [Accepted: 03/07/2019] [Indexed: 12/21/2022] Open
Abstract
The brain is highly dynamic, reorganizing its activity at different interacting spatial and temporal scales, including variation within and between brain networks. The chronnectome is a model of the brain in which nodal activity and connectivity patterns change in fundamental and recurring ways over time. Most literature assumes fixed spatial nodes/networks, ignoring the possibility that spatial nodes/networks may vary in time. Here, we introduce an approach to calculate a spatially fluid chronnectome (called the spatial chronnectome for clarity), which focuses on the variations of networks coupling at the voxel level, and identify a novel set of spatially dynamic features. Results reveal transient spatially fluid interactions between intra- and internetwork relationships in which brain networks transiently merge and separate, emphasizing dynamic segregation and integration. Brain networks also exhibit distinct spatial patterns with unique temporal characteristics, potentially explaining a broad spectrum of inconsistencies in previous studies that assumed static networks. Moreover, we show anticorrelative connections to brain networks are transient as opposed to constant across the entire scan. Preliminary assessments using a multi-site dataset reveal the ability of the approach to obtain new information and nuanced alterations that remain undetected during static analysis. Patients with schizophrenia (SZ) display transient decreases in voxel-wise network coupling within visual and auditory networks, and higher intradomain coupling variability. In summary, the spatial chronnectome represents a new direction of research enabling the study of functional networks which are transient at the voxel level, and the identification of mechanisms for within- and between-subject spatial variability.
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Affiliation(s)
- Armin Iraji
- The Mind Research Network, Albuquerque, New Mexico
| | | | - Noah Lewis
- The Mind Research Network, Albuquerque, New Mexico
| | | | | | - Erik Erhardt
- Department of Mathematics and Statistics, University of New Mexico, Albuquerque, New Mexico
| | - Aysneil Belger
- Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina
| | - Judith M Ford
- Department of Psychiatry, University of California San Francisco, San Francisco, California.,Psychiatry Service, San Francisco VA Medical Center, San Francisco, California
| | - Sarah McEwen
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California
| | - Daniel H Mathalon
- Department of Psychiatry, University of California San Francisco, San Francisco, California.,Psychiatry Service, San Francisco VA Medical Center, San Francisco, California
| | - Bryon A Mueller
- Department of Psychiatry, University of Minnesota, Minneapolis, Minnesota
| | - Godfrey D Pearlson
- Department of Psychiatry, Yale University, School of Medicine, New Haven, Connecticut
| | - Steven G Potkin
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California
| | - Jessica A Turner
- Department of Psychology, Georgia State University, Atlanta, Georgia
| | - Jatin G Vaidya
- Department of Psychiatry, University of Iowa, Iowa City, Iowa
| | - Theo G M van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, New Mexico.,Department of Psychiatry, Yale University, School of Medicine, New Haven, Connecticut.,Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico
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21
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Zoller DM, Bolton TAW, Karahanoglu FI, Eliez S, Schaer M, Van De Ville D. Robust Recovery of Temporal Overlap Between Network Activity Using Transient-Informed Spatio-Temporal Regression. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:291-302. [PMID: 30188815 DOI: 10.1109/tmi.2018.2863944] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Functional magnetic resonance imaging is a non-invasive tomographic imaging modality that has provided insights into system-level brain function. New analysis methods are emerging to study the dynamic behavior of brain activity. The innovation-driven co-activation pattern (iCAP) approach is one such approach that relies on the detection of timepoints with a significant transient activity to subsequently retrieve spatially and temporally overlapping large-scale brain networks. To recover temporal profiles of the iCAPs for further time-resolved analysis, spatial patterns are fitted back to the activity-inducing signals. In this crucial step, spatial dependences can hinder the recovery of temporal overlapping activity. To overcome this effect, we propose a novel back-projection method that optimally fits activity-inducing signals given a set of transient timepoints and spatial maps of iCAPs, thus taking into account both spatial and temporal constraints. Validation on simulated data shows that transient-based constraints improve the quality of fitted time courses. Further evaluation on experimental data demonstrates that overfitting and underfitting are prevented by the use of optimized spatio-temporal constraints. Spatial and temporal properties of resulting iCAPs support that brain activity is characterized by the recurrent co-activation and co-deactivation of spatially overlapping large-scale brain networks. This new approach opens new avenues to explore the brain's dynamic core.
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22
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Iraji A, Fu Z, Damaraju E, DeRamus TP, Lewis N, Bustillo JR, Lenroot RK, Belger A, Ford JM, McEwen S, Mathalon DH, Mueller BA, Pearlson GD, Potkin SG, Preda A, Turner JA, Vaidya JG, van Erp TGM, Calhoun VD. Spatial dynamics within and between brain functional domains: A hierarchical approach to study time-varying brain function. Hum Brain Mapp 2018; 40:1969-1986. [PMID: 30588687 DOI: 10.1002/hbm.24505] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Revised: 12/12/2018] [Accepted: 12/19/2018] [Indexed: 12/16/2022] Open
Abstract
The analysis of time-varying activity and connectivity patterns (i.e., the chronnectome) using resting-state magnetic resonance imaging has become an important part of ongoing neuroscience discussions. The majority of previous work has focused on variations of temporal coupling among fixed spatial nodes or transition of the dominant activity/connectivity pattern over time. Here, we introduce an approach to capture spatial dynamics within functional domains (FDs), as well as temporal dynamics within and between FDs. The approach models the brain as a hierarchical functional architecture with different levels of granularity, where lower levels have higher functional homogeneity and less dynamic behavior and higher levels have less homogeneity and more dynamic behavior. First, a high-order spatial independent component analysis is used to approximate functional units. A functional unit is a pattern of regions with very similar functional activity over time. Next, functional units are used to construct FDs. Finally, functional modules (FMs) are calculated from FDs, providing an overall view of brain dynamics. Results highlight the spatial fluidity within FDs, including a broad spectrum of changes in regional associations, from strong coupling to complete decoupling. Moreover, FMs capture the dynamic interplay between FDs. Patients with schizophrenia show transient reductions in functional activity and state connectivity across several FDs, particularly the subcortical domain. Activity and connectivity differences convey unique information in many cases (e.g., the default mode) highlighting their complementarity information. The proposed hierarchical model to capture FD spatiotemporal variations provides new insight into the macroscale chronnectome and identifies changes hidden from existing approaches.
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Affiliation(s)
- Armin Iraji
- The Mind Research Network, Albuquerque, New Mexico
| | - Zening Fu
- The Mind Research Network, Albuquerque, New Mexico
| | | | | | - Noah Lewis
- The Mind Research Network, Albuquerque, New Mexico
| | - Juan R Bustillo
- Department of Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, New Mexico
| | - Rhoshel K Lenroot
- Department of Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, New Mexico
| | - Aysneil Belger
- Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina
| | - Judith M Ford
- Department of Psychiatry, University of California San Francisco, San Francisco, California.,Psychiatry Service, San Francisco VA Medical Center, San Francisco, California
| | - Sarah McEwen
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California
| | - Daniel H Mathalon
- Department of Psychiatry, University of California San Francisco, San Francisco, California.,Psychiatry Service, San Francisco VA Medical Center, San Francisco, California
| | - Bryon A Mueller
- Department of Psychiatry, University of Minnesota, Minneapolis, Minnesota
| | - Godfrey D Pearlson
- Departments of Psychiatry and Neurobiology, Yale University, School of Medicine, New Haven, Connecticut
| | - Steven G Potkin
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California
| | - Jessica A Turner
- Department of Psychology, Georgia State University, Atlanta, Georgia
| | - Jatin G Vaidya
- Department of Psychiatry, University of Iowa, Iowa, Iowa
| | - Theo G M van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, New Mexico.,Departments of Psychiatry and Neurobiology, Yale University, School of Medicine, New Haven, Connecticut.,Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico
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23
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Wang C, Ng B, Garbi R. Multimodal Brain Parcellation Based on Functional and Anatomical Connectivity. Brain Connect 2018; 8:604-617. [PMID: 30499336 DOI: 10.1089/brain.2017.0576] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Brain parcellation is often a prerequisite for network analysis due to the statistical challenges, computational burdens, and interpretation difficulties arising from the high dimensionality of neuroimaging data. Predominant approaches are largely unimodal with functional magnetic resonance imaging (fMRI) being the primary modality used. These approaches thus neglect other brain attributes that relate to brain organization. In this paper, we propose an approach for integrating fMRI and diffusion MRI (dMRI) data. Our approach introduces a nonlinear mapping between the connectivity values of two modalities, and adaptively balances their weighting based on their voxel-wise test-retest reliability. An efficient region level extension that additionally incorporates structural information on gyri and sulci is further presented. To validate, we compare multimodal parcellations with unimodal parcellations and existing atlases on the Human Connectome Project data. We show that multimodal parcellations achieve higher reproducibility, comparable/higher functional homogeneity, and comparable/higher leftout data likelihood. The boundaries of multimodal parcels are observed to align to those based on cyto-architecture, and subnetworks extracted from multimodal parcels matched well with established brain systems. Our results thus show that multimodal information improves brain parcellation.
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Affiliation(s)
- Chendi Wang
- University of British Columbia, Electrical and Computer Engineering , ICICS x421-2366 Main Mall , Vancouver, British Columbia, Canada , V6T 1Z4 ;
| | - Bernard Ng
- University of British Columbia, Department of Statistics , Vancouver, British Columbia, Canada ;
| | - Rafeef Garbi
- University of British Columbia, Electrical and Computer Engineering, Vancouver, British Columbia, Canada ;
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24
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Ryyppö E, Glerean E, Brattico E, Saramäki J, Korhonen O. Regions of Interest as nodes of dynamic functional brain networks. Netw Neurosci 2018; 2:513-535. [PMID: 30294707 PMCID: PMC6147715 DOI: 10.1162/netn_a_00047] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Accepted: 02/06/2018] [Indexed: 11/04/2022] Open
Abstract
The properties of functional brain networks strongly depend on how their nodes are chosen. Commonly, nodes are defined by Regions of Interest (ROIs), predetermined groupings of fMRI measurement voxels. Earlier, we demonstrated that the functional homogeneity of ROIs, captured by their spatial consistency, varies widely across ROIs in commonly used brain atlases. Here, we ask how ROIs behave as nodes of dynamic brain networks. To this end, we use two measures: spatiotemporal consistency measures changes in spatial consistency across time and network turnover quantifies the changes in the local network structure around an ROI. We find that spatial consistency varies non-uniformly in space and time, which is reflected in the variation of spatiotemporal consistency across ROIs. Furthermore, we see time-dependent changes in the network neighborhoods of the ROIs, reflected in high network turnover. Network turnover is nonuniformly distributed across ROIs: ROIs with high spatiotemporal consistency have low network turnover. Finally, we reveal that there is rich voxel-level correlation structure inside ROIs. Because the internal structure and the connectivity of ROIs vary in time, the common approach of using static node definitions may be surprisingly inaccurate. Therefore, network neuroscience would greatly benefit from node definition strategies tailored for dynamical networks.
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Affiliation(s)
- Elisa Ryyppö
- Department of Computer Science, School of Science, Aalto University, Espoo, Finland
| | - Enrico Glerean
- Turku PET Centre, University of Turku, Turku, Finland
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland
| | - Elvira Brattico
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, and The Royal Academy of Music Aarhus/Aalborg, Denmark
| | - Jari Saramäki
- Department of Computer Science, School of Science, Aalto University, Espoo, Finland
| | - Onerva Korhonen
- Department of Computer Science, School of Science, Aalto University, Espoo, Finland
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland
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