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Martínez-Molina N, Escrichs A, Sanz-Perl Y, Sihvonen AJ, Särkämö T, Kringelbach ML, Deco G. The evolution of whole-brain turbulent dynamics during recovery from traumatic brain injury. Netw Neurosci 2024; 8:158-177. [PMID: 38562284 PMCID: PMC10898780 DOI: 10.1162/netn_a_00346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 10/23/2023] [Indexed: 04/04/2024] Open
Abstract
It has been previously shown that traumatic brain injury (TBI) is associated with reductions in metastability in large-scale networks in resting-state fMRI (rsfMRI). However, little is known about how TBI affects the local level of synchronization and how this evolves during the recovery trajectory. Here, we applied a novel turbulent dynamics framework to investigate whole-brain dynamics using an rsfMRI dataset from a cohort of moderate to severe TBI patients and healthy controls (HCs). We first examined how several measures related to turbulent dynamics differ between HCs and TBI patients at 3, 6, and 12 months post-injury. We found a significant reduction in these empirical measures after TBI, with the largest change at 6 months post-injury. Next, we built a Hopf whole-brain model with coupled oscillators and conducted in silico perturbations to investigate the mechanistic principles underlying the reduced turbulent dynamics found in the empirical data. A simulated attack was used to account for the effect of focal lesions. This revealed a shift to lower coupling parameters in the TBI dataset and, critically, decreased susceptibility and information-encoding capability. These findings confirm the potential of the turbulent framework to characterize longitudinal changes in whole-brain dynamics and in the reactivity to external perturbations after TBI.
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Affiliation(s)
- Noelia Martínez-Molina
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
- Cognitive Brain Research Unit, Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Centre of Excellence in Music, Mind, Body and Brain, University of Helsinki, Helsinki, Finland
| | - Anira Escrichs
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
| | - Yonatan Sanz-Perl
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
| | - Aleksi J. Sihvonen
- Cognitive Brain Research Unit, Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Centre of Excellence in Music, Mind, Body and Brain, University of Helsinki, Helsinki, Finland
- School of Health and Rehabilitation Sciences, Queensland Aphasia Research Centre and UQ Centre for Clinical Research, University of Queensland, Brisbane, Australia
- Department of Neurology, University of Helsinki, and Helsinki University Hospital, Helsinki, Finland
| | - Teppo Särkämö
- Cognitive Brain Research Unit, Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Centre of Excellence in Music, Mind, Body and Brain, University of Helsinki, Helsinki, Finland
| | - Morten L. Kringelbach
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, United Kingdom
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Gustavo Deco
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain
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Filipowicz A, Lalsiamthara J, Aballay A. Dissection of a sensorimotor circuit underlying pathogen aversion in C. elegans. BMC Biol 2022; 20:229. [PMID: 36209082 PMCID: PMC9548130 DOI: 10.1186/s12915-022-01424-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 09/28/2022] [Indexed: 11/10/2022] Open
Abstract
Background Altering animal behavior to reduce pathogen exposure is a key line of defense against pathogen attack. In Caenorhabditis elegans, alterations in intestinal physiology caused by pathogen colonization and sensation of microbial metabolites may lead to activation of pathogen aversive behaviors ranging from aversive reflexes to learned avoidance. However, the neural circuitry between chemosensory neurons that sense pathogenic bacterial cues and the motor neurons responsible for avoidance-associated locomotion remains unknown. Results Using C. elegans, we found that backward locomotion was a component of learned pathogen avoidance, as animals pre-exposed to Pseudomonas aeruginosa or Enterococcus faecalis showed reflexive aversion to drops of the bacteria driven by chemosensory neurons, including the olfactory AWB neurons. This response also involved intestinal distention and, for E. faecalis, required expression of TRPM channels in the intestine and excretory system. Additionally, we uncovered a circuit composed of olfactory neurons, interneurons, and motor neurons that controls the backward locomotion crucial for learned reflexive aversion to pathogenic bacteria, learned avoidance, and the repulsive odor 2-nonanone. Conclusions Using whole-brain simulation and functional assays, we uncovered a novel sensorimotor circuit governing learned reflexive aversion. The discovery of a complete sensorimotor circuit for reflexive aversion demonstrates the utility of using the C. elegans connectome and computational modeling in uncovering new neuronal regulators of behavior. Supplementary Information The online version contains supplementary material available at 10.1186/s12915-022-01424-x.
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Affiliation(s)
- Adam Filipowicz
- Department of Molecular Microbiology & Immunology, Oregon Health & Science University, Portland, OR, 97239, USA.,Neuroscience Graduate Program, Oregon Health & Science University, Portland, OR, 97239, USA
| | - Jonathan Lalsiamthara
- Department of Molecular Microbiology & Immunology, Oregon Health & Science University, Portland, OR, 97239, USA
| | - Alejandro Aballay
- Department of Molecular Microbiology & Immunology, Oregon Health & Science University, Portland, OR, 97239, USA.
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De Filippi E, Escrichs A, Càmara E, Garrido C, Marins T, Sánchez-Fibla M, Gilson M, Deco G. Meditation-induced effects on whole-brain structural and effective connectivity. Brain Struct Funct 2022. [PMID: 35524072 DOI: 10.1007/s00429-022-02496-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 04/04/2022] [Indexed: 12/26/2022]
Abstract
In the past decades, there has been a growing scientific interest in characterizing neural correlates of meditation training. Nonetheless, the mechanisms underlying meditation remain elusive. In the present work, we investigated meditation-related changes in functional dynamics and structural connectivity (SC). For this purpose, we scanned experienced meditators and control (naive) subjects using magnetic resonance imaging (MRI) to acquire structural and functional data during two conditions, resting-state and meditation (focused attention on breathing). In this way, we aimed to characterize and distinguish both short-term and long-term modifications in the brain’s structure and function. First, to analyze the fMRI data, we calculated whole-brain effective connectivity (EC) estimates, relying on a dynamical network model to replicate BOLD signals’ spatio-temporal structure, akin to functional connectivity (FC) with lagged correlations. We compared the estimated EC, FC, and SC links as features to train classifiers to predict behavioral conditions and group identity. Then, we performed a network-based analysis of anatomical connectivity. We demonstrated through a machine-learning approach that EC features were more informative than FC and SC solely. We showed that the most informative EC links that discriminated between meditators and controls involved several large-scale networks mainly within the left hemisphere. Moreover, we found that differences in the functional domain were reflected to a smaller extent in changes at the anatomical level as well. The network-based analysis of anatomical pathways revealed strengthened connectivity for meditators compared to controls between four areas in the left hemisphere belonging to the somatomotor, dorsal attention, subcortical and visual networks. Overall, the results of our whole-brain model-based approach revealed a mechanism underlying meditation by providing causal relationships at the structure-function level.
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Iravani B, Arshamian A, Fransson P, Kaboodvand N. Whole-brain modelling of resting state fMRI differentiates ADHD subtypes and facilitates stratified neuro-stimulation therapy. Neuroimage 2021; 231:117844. [PMID: 33577937 DOI: 10.1016/j.neuroimage.2021.117844] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 01/27/2021] [Accepted: 02/04/2021] [Indexed: 01/13/2023] Open
Abstract
Recent advances in non-linear computational and dynamical modelling have opened up the possibility to parametrize dynamic neural mechanisms that drive complex behavior. Importantly, building models of neuronal processes is of key importance to fully understand disorders of the brain as it may provide a quantitative platform that is capable of binding multiple neurophysiological processes to phenotype profiles. In this study, we apply a newly developed adaptive frequency-based model of whole-brain oscillations to resting-state fMRI data acquired from healthy controls and a cohort of attention deficit hyperactivity disorder (ADHD) subjects. As expected, we found that healthy control subjects differed from ADHD in terms of attractor dynamics. However, we also found a marked dichotomy in neural dynamics within the ADHD cohort. Next, we classified the ADHD group according to the level of distance of each individual's empirical network from the two model-based simulated networks. Critically, the model was mirrored in the empirical behavior data with the two ADHD subgroups displaying distinct behavioral phenotypes related to emotional instability (i.e., depression and hypomanic personality traits). Finally, we investigated the applicability and feasibility of our whole-brain model in a therapeutic setting by conducting in silico excitatory stimulations to parsimoniously mimic clinical neuro-stimulation paradigms in ADHD. We tested the effect of stimulating any individual brain region on the key network measures derived from the simulated brain network and its contribution in rectifying the brain dynamics to that of the healthy brain, separately for each ADHD subgroup. This showed that this was indeed possible for both subgroups. However, the current effect sizes were small suggesting that the stimulation protocol needs to be tailored at the individual level. These findings demonstrate the potential of this new modelling framework to unveil hidden neurophysiological profiles and establish tailored clinical interventions.
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Affiliation(s)
- Behzad Iravani
- Department of Clinical Neuroscience, Karolinska Institutet, 17177 Stockholm, Sweden.
| | - Artin Arshamian
- Department of Clinical Neuroscience, Karolinska Institutet, 17177 Stockholm, Sweden
| | - Peter Fransson
- Department of Clinical Neuroscience, Karolinska Institutet, 17177 Stockholm, Sweden
| | - Neda Kaboodvand
- Department of Clinical Neuroscience, Karolinska Institutet, 17177 Stockholm, Sweden
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Chen X, Zhang C, Li Y, Huang P, Lv Q, Yu W, Chen S, Sun B, Wang Z. Functional Connectivity-Based Modelling Simulates Subject-Specific Network Spreading Effects of Focal Brain Stimulation. Neurosci Bull 2018; 34:921-938. [PMID: 30043099 PMCID: PMC6246850 DOI: 10.1007/s12264-018-0256-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2018] [Accepted: 05/16/2018] [Indexed: 12/23/2022] Open
Abstract
Neurostimulation remarkably alleviates the symptoms in a variety of brain disorders by modulating the brain-wide network. However, how brain-wide effects on the direct and indirect pathways evoked by focal neurostimulation elicit therapeutic effects in an individual patient is unknown. Understanding this remains crucial for advancing neural circuit-based guidance to optimize candidate patient screening, pre-surgical target selection, and post-surgical parameter tuning. To address this issue, we propose a functional brain connectome-based modeling approach that simulates the spreading effects of stimulating different brain regions and quantifies the rectification of abnormal network topology in silico. We validated these analyses by pinpointing nuclei in the basal ganglia circuits as top-ranked targets for 43 local patients with Parkinson’s disease and 90 patients from a public database. Individual connectome-based analysis demonstrated that the globus pallidus was the best choice for 21.1% and the subthalamic nucleus for 19.5% of patients. Down-regulation of functional connectivity (up to 12%) at these prioritized targets optimally maximized the therapeutic effects. Notably, the priority rank of the subthalamic nucleus significantly correlated with motor symptom severity (Unified Parkinson’s Disease Rating Scale III) in the local cohort. These findings underscore the potential of neural network modeling for advancing personalized brain stimulation therapy, and warrant future experimental investigation to validate its clinical utility.
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Affiliation(s)
- Xiaoyu Chen
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Chencheng Zhang
- Department of Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yuxin Li
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.,Department of Radiology, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Pei Huang
- Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Qian Lv
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Wenwen Yu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Shengdi Chen
- Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Bomin Sun
- University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Zheng Wang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
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