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Rosenblatt M, Tejavibulya L, Sun H, Camp CC, Khaitova M, Adkinson BD, Jiang R, Westwater ML, Noble S, Scheinost D. Power and reproducibility in the external validation of brain-phenotype predictions. Nat Hum Behav 2024:10.1038/s41562-024-01931-7. [PMID: 39085406 DOI: 10.1038/s41562-024-01931-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 06/18/2024] [Indexed: 08/02/2024]
Abstract
Brain-phenotype predictive models seek to identify reproducible and generalizable brain-phenotype associations. External validation, or the evaluation of a model in external datasets, is the gold standard in evaluating the generalizability of models in neuroimaging. Unlike typical studies, external validation involves two sample sizes: the training and the external sample sizes. Thus, traditional power calculations may not be appropriate. Here we ran over 900 million resampling-based simulations in functional and structural connectivity data to investigate the relationship between training sample size, external sample size, phenotype effect size, theoretical power and simulated power. Our analysis included a wide range of datasets: the Healthy Brain Network, the Adolescent Brain Cognitive Development Study, the Human Connectome Project (Development and Young Adult), the Philadelphia Neurodevelopmental Cohort, the Queensland Twin Adolescent Brain Project, and the Chinese Human Connectome Project; and phenotypes: age, body mass index, matrix reasoning, working memory, attention problems, anxiety/depression symptoms and relational processing. High effect size predictions achieved adequate power with training and external sample sizes of a few hundred individuals, whereas low and medium effect size predictions required hundreds to thousands of training and external samples. In addition, most previous external validation studies used sample sizes prone to low power, and theoretical power curves should be adjusted for the training sample size. Furthermore, model performance in internal validation often informed subsequent external validation performance (Pearson's r difference <0.2), particularly for well-harmonized datasets. These results could help decide how to power future external validation studies.
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Affiliation(s)
- Matthew Rosenblatt
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
| | - Link Tejavibulya
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - Huili Sun
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Chris C Camp
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - Milana Khaitova
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Brendan D Adkinson
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - Rongtao Jiang
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Margaret L Westwater
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Stephanie Noble
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Department of Psychology, Northeastern University, Boston, MA, USA
| | - Dustin Scheinost
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Child Study Center, Yale School of Medicine, New Haven, CT, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT, USA
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Blair DS, Miller RL, Calhoun VD. A Dynamic Entropy Approach Reveals Reduced Functional Network Connectivity Trajectory Complexity in Schizophrenia. ENTROPY (BASEL, SWITZERLAND) 2024; 26:545. [PMID: 39056908 PMCID: PMC11275472 DOI: 10.3390/e26070545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 06/07/2024] [Accepted: 06/19/2024] [Indexed: 07/28/2024]
Abstract
Over the past decade and a half, dynamic functional imaging has revealed low-dimensional brain connectivity measures, identified potential common human spatial connectivity states, tracked the transition patterns of these states, and demonstrated meaningful transition alterations in disorders and over the course of development. Recently, researchers have begun to analyze these data from the perspective of dynamic systems and information theory in the hopes of understanding how these dynamics support less easily quantified processes, such as information processing, cortical hierarchy, and consciousness. Little attention has been paid to the effects of psychiatric disease on these measures, however. We begin to rectify this by examining the complexity of subject trajectories in state space through the lens of information theory. Specifically, we identify a basis for the dynamic functional connectivity state space and track subject trajectories through this space over the course of the scan. The dynamic complexity of these trajectories is assessed along each dimension of the proposed basis space. Using these estimates, we demonstrate that schizophrenia patients display substantially simpler trajectories than demographically matched healthy controls and that this drop in complexity concentrates along specific dimensions. We also demonstrate that entropy generation in at least one of these dimensions is linked to cognitive performance. Overall, the results suggest great value in applying dynamic systems theory to problems of neuroimaging and reveal a substantial drop in the complexity of schizophrenia patients' brain function.
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Affiliation(s)
- David Sutherland Blair
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory University, Atlanta, GA 30303, USA (V.D.C.)
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3
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Li Y, Yang H, Gu S. Enhancing neural encoding models for naturalistic perception with a multi-level integration of deep neural networks and cortical networks. Sci Bull (Beijing) 2024; 69:1738-1747. [PMID: 38490889 DOI: 10.1016/j.scib.2024.02.035] [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: 04/01/2023] [Revised: 06/27/2023] [Accepted: 02/23/2024] [Indexed: 03/17/2024]
Abstract
Cognitive neuroscience aims to develop computational models that can accurately predict and explain neural responses to sensory inputs in the cortex. Recent studies attempt to leverage the representation power of deep neural networks (DNNs) to predict the brain response and suggest a correspondence between artificial and biological neural networks in their feature representations. However, typical voxel-wise encoding models tend to rely on specific networks designed for computer vision tasks, leading to suboptimal brain-wide correspondence during cognitive tasks. To address this challenge, this work proposes a novel approach that upgrades voxel-wise encoding models through multi-level integration of features from DNNs and information from brain networks. Our approach combines DNN feature-level ensemble learning and brain atlas-level model integration, resulting in significant improvements in predicting whole-brain neural activity during naturalistic video perception. Furthermore, this multi-level integration framework enables a deeper understanding of the brain's neural representation mechanism, accurately predicting the neural response to complex visual concepts. We demonstrate that neural encoding models can be optimized by leveraging a framework that integrates both data-driven approaches and theoretical insights into the functional structure of the cortical networks.
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Affiliation(s)
- Yuanning Li
- School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai 201210, China.
| | - Huzheng Yang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Shi Gu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen 518110, China.
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4
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Theis N, Bahuguna J, Rubin JE, Cape J, Iyengar S, Prasad KM. Diagnostically distinct resting state fMRI energy distributions: A subject-specific maximum entropy modeling study. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.23.576937. [PMID: 38328170 PMCID: PMC10849576 DOI: 10.1101/2024.01.23.576937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Objective Existing neuroimaging studies of psychotic and mood disorders have reported brain activation differences (first-order properties) and altered pairwise correlation-based functional connectivity (second-order properties). However, both approaches have certain limitations that can be overcome by integrating them in a pairwise maximum entropy model (MEM) that better represents a comprehensive picture of fMRI signal patterns and provides a system-wide summary measure called energy. This study examines the applicability of individual-level MEM for psychiatry and identifies image-derived model coefficients related to model parameters. Method MEMs are fit to resting state fMRI data from each individual with schizophrenia/schizoaffective disorder, bipolar disorder, and major depression (n=132) and demographically matched healthy controls (n=132) from the UK Biobank to different subsets of the default mode network (DMN) regions. Results The model satisfactorily explained observed brain energy state occurrence probabilities across all participants, and model parameters were significantly correlated with image-derived coefficients for all groups. Within clinical groups, averaged energy level distributions were higher in schizophrenia/schizoaffective disorder but lower in bipolar disorder compared to controls for both bilateral and unilateral DMN. Major depression energy distributions were higher compared to controls only in the right hemisphere DMN. Conclusions Diagnostically distinct energy states suggest that probability distributions of temporal changes in synchronously active nodes may underlie each diagnostic entity. Subject-specific MEMs allow for factoring in the individual variations compared to traditional group-level inferences, offering an improved measure of biologically meaningful correlates of brain activity that may have potential clinical utility.
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Affiliation(s)
- Nicholas Theis
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Jyotika Bahuguna
- Department of Neuroscience, Laboratoire de Neurosciences Cognitive et Adaptive, University of Strasbourg, France
| | | | - Joshua Cape
- Department of Statistics, University of Wisconsin-Madison, WI, USA
| | - Satish Iyengar
- Department of Statistics, University of Pittsburgh, PA, USA
| | - Konasale M. Prasad
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, PA, USA
- Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA
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5
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Dagnino PC, Escrichs A, López-González A, Gosseries O, Annen J, Sanz Perl Y, Kringelbach ML, Laureys S, Deco G. Re-awakening the brain: Forcing transitions in disorders of consciousness by external in silico perturbation. PLoS Comput Biol 2024; 20:e1011350. [PMID: 38701063 PMCID: PMC11068192 DOI: 10.1371/journal.pcbi.1011350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 03/31/2024] [Indexed: 05/05/2024] Open
Abstract
A fundamental challenge in neuroscience is accurately defining brain states and predicting how and where to perturb the brain to force a transition. Here, we investigated resting-state fMRI data of patients suffering from disorders of consciousness (DoC) after coma (minimally conscious and unresponsive wakefulness states) and healthy controls. We applied model-free and model-based approaches to help elucidate the underlying brain mechanisms of patients with DoC. The model-free approach allowed us to characterize brain states in DoC and healthy controls as a probabilistic metastable substate (PMS) space. The PMS of each group was defined by a repertoire of unique patterns (i.e., metastable substates) with different probabilities of occurrence. In the model-based approach, we adjusted the PMS of each DoC group to a causal whole-brain model. This allowed us to explore optimal strategies for promoting transitions by applying off-line in silico probing. Furthermore, this approach enabled us to evaluate the impact of local perturbations in terms of their global effects and sensitivity to stimulation, which is a model-based biomarker providing a deeper understanding of the mechanisms underlying DoC. Our results show that transitions were obtained in a synchronous protocol, in which the somatomotor network, thalamus, precuneus and insula were the most sensitive areas to perturbation. This motivates further work to continue understanding brain function and treatments of disorders of consciousness.
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Affiliation(s)
- Paulina Clara Dagnino
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
| | - Anira Escrichs
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
| | - Ane López-González
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
| | - Olivia Gosseries
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium
- Centre du Cerveau 2, University Hospital of Liège, Liège, Belgium
| | - Jitka Annen
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium
- Centre du Cerveau 2, University Hospital of Liège, Liège, Belgium
| | - Yonatan Sanz Perl
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, France
| | - Morten L. Kringelbach
- Centre for Eudaimonia and Human Flourishing, 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
| | - Steven Laureys
- Joint International Research Unit on Consciousness, CERVO Brain Research Centre, University of Laval, Québec, Québec, Canada
| | - 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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6
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Borzou A, Miller SN, Hommel JD, Schwarz JM. Cocaine diminishes functional network robustness and destabilizes the energy landscape of neuronal activity in the medial prefrontal cortex. PNAS NEXUS 2024; 3:pgae092. [PMID: 38476665 PMCID: PMC10929585 DOI: 10.1093/pnasnexus/pgae092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 02/13/2024] [Indexed: 03/14/2024]
Abstract
We present analysis of neuronal activity recordings from a subset of neurons in the medial prefrontal cortex of rats before and after the administration of cocaine. Using an underlying modern Hopfield model as a description for the neuronal network, combined with a machine learning approach, we compute the underlying functional connectivity of the neuronal network. We find that the functional connectivity changes after the administration of cocaine with both functional-excitatory and functional-inhibitory neurons being affected. Using conventional network analysis, we find that the diameter of the graph, or the shortest length between the two most distant nodes, increases with cocaine, suggesting that the neuronal network is less robust. We also find that the betweenness centrality scores for several of the functional-excitatory and functional-inhibitory neurons decrease significantly, while other scores remain essentially unchanged, to also suggest that the neuronal network is less robust. Finally, we study the distribution of neuronal activity and relate it to energy to find that cocaine drives the neuronal network towards destabilization in the energy landscape of neuronal activation. While this destabilization is presumably temporary given one administration of cocaine, perhaps this initial destabilization indicates a transition towards a new stable state with repeated cocaine administration. However, such analyses are useful more generally to understand how neuronal networks respond to perturbations.
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Affiliation(s)
- Ahmad Borzou
- Department of Physics and BioInspired Institute, Syracuse University, Syracuse, NY 13244, USA
- CompuFlair, Houston, TX 77064, USA
| | - Sierra N Miller
- Department of Pharmacology and Toxicology, Center for Addiction Sciences and Therapeutics, University of Texas Medical Branch, Galveston, TX 77555, USA
| | - Jonathan D Hommel
- Department of Pharmacology and Toxicology, Center for Addiction Sciences and Therapeutics, University of Texas Medical Branch, Galveston, TX 77555, USA
| | - J M Schwarz
- Department of Physics and BioInspired Institute, Syracuse University, Syracuse, NY 13244, USA
- Indian Creek Farm, Ithaca, NY 14850, USA
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7
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Vohryzek J, Cabral J, Lord LD, Fernandes HM, Roseman L, Nutt DJ, Carhart-Harris RL, Deco G, Kringelbach ML. Brain dynamics predictive of response to psilocybin for treatment-resistant depression. Brain Commun 2024; 6:fcae049. [PMID: 38515439 PMCID: PMC10957168 DOI: 10.1093/braincomms/fcae049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 10/16/2023] [Accepted: 02/14/2024] [Indexed: 03/23/2024] Open
Abstract
Psilocybin therapy for depression has started to show promise, yet the underlying causal mechanisms are not currently known. Here, we leveraged the differential outcome in responders and non-responders to psilocybin (10 and 25 mg, 7 days apart) therapy for depression-to gain new insights into regions and networks implicated in the restoration of healthy brain dynamics. We used large-scale brain modelling to fit the spatiotemporal brain dynamics at rest in both responders and non-responders before treatment. Dynamic sensitivity analysis of systematic perturbation of these models enabled us to identify specific brain regions implicated in a transition from a depressive brain state to a healthy one. Binarizing the sample into treatment responders (>50% reduction in depressive symptoms) versus non-responders enabled us to identify a subset of regions implicated in this change. Interestingly, these regions correlate with in vivo density maps of serotonin receptors 5-hydroxytryptamine 2a and 5-hydroxytryptamine 1a, which psilocin, the active metabolite of psilocybin, has an appreciable affinity for, and where it acts as a full-to-partial agonist. Serotonergic transmission has long been associated with depression, and our findings provide causal mechanistic evidence for the role of brain regions in the recovery from depression via psilocybin.
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Affiliation(s)
- Jakub Vohryzek
- Department of Psychiatry, University of Oxford, Oxford, UK
- Center for Music in the Brain, Aarhus University, Aarhus, Denmark
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Joana Cabral
- Department of Psychiatry, University of Oxford, Oxford, UK
- Center for Music in the Brain, Aarhus University, Aarhus, Denmark
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B’s—PT Government Associate Laboratory, Braga/Guimarães, University of Minho, Portugal
| | - Louis-David Lord
- Department of Psychiatry, University of Oxford, Oxford, UK
- Center for Music in the Brain, Aarhus University, Aarhus, Denmark
| | - Henrique M Fernandes
- Department of Psychiatry, University of Oxford, Oxford, UK
- Center for Music in the Brain, Aarhus University, Aarhus, Denmark
| | - Leor Roseman
- Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, London, UK
| | - David J Nutt
- Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, London, UK
| | - Robin L Carhart-Harris
- Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, London, UK
- Psychedelics Division, Neuroscape, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Spain
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Morten L Kringelbach
- Department of Psychiatry, University of Oxford, Oxford, UK
- Center for Music in the Brain, Aarhus University, Aarhus, Denmark
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
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8
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Xie Y, Brynildsen JK, Windisch K, Blendy JA. Neural Network Connectivity Following Opioid Dependence is Altered by a Common Genetic Variant in the µ-Opioid Receptor, OPRM1 A118G. J Neurosci 2024; 44:e1492232023. [PMID: 38124015 PMCID: PMC10866092 DOI: 10.1523/jneurosci.1492-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 11/24/2023] [Accepted: 11/29/2023] [Indexed: 12/23/2023] Open
Abstract
Opioid use disorder is a chronic, relapsing disease associated with persistent changes in brain plasticity. A common single nucleotide polymorphism (SNP) in the µ-opioid receptor gene, OPRM1 A118G, is associated with altered vulnerability to opioid addiction. Reconfiguration of neuronal connectivity may explain dependence risk in individuals with this SNP. Mice with the equivalent Oprm1 variant, A112G, demonstrate sex-specific alterations in the rewarding properties of morphine and heroin. To determine whether this SNP influences network-level changes in neuronal activity, we compared FOS expression in male and female mice that were opioid-naive or opioid-dependent. Network analyses identified significant differences between the AA and GG Oprm1 genotypes. Based on several graph theory metrics, including small-world analysis and degree centrality, we show that GG females in the opioid-dependent state exhibit distinct patterns of connectivity compared to other groups of the same genotype. Using a network control theory approach, we identified key cortical brain regions that drive the transition between opioid-naive and opioid-dependent brain states; however, these regions are less influential in GG females leading to sixfold higher average minimum energy needed to transition from the acute to the dependent state. In addition, we found that the opioid-dependent brain state is significantly less stable in GG females compared to other groups. Collectively, our findings demonstrate sex- and genotype-specific modifications in local, mesoscale, and global properties of functional brain networks following opioid exposure and provide a framework for identifying genotype differences in specific brain regions that play a role in opioid dependence.
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Affiliation(s)
- Yihan Xie
- Department of Systems Pharmacology and Translational Therapeutics and Perelman School of Medicine, University of Pennsylvania, Philadelphia 19104, Pennsylvania
| | - Julia K Brynildsen
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia 19104, Pennsylvania
| | - Kyle Windisch
- Department of Systems Pharmacology and Translational Therapeutics and Perelman School of Medicine, University of Pennsylvania, Philadelphia 19104, Pennsylvania
| | - Julie A Blendy
- Department of Systems Pharmacology and Translational Therapeutics and Perelman School of Medicine, University of Pennsylvania, Philadelphia 19104, Pennsylvania
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Lord LD, Carletti T, Fernandes H, Turkheimer FE, Expert P. Altered dynamical integration/segregation balance during anesthesia-induced loss of consciousness. FRONTIERS IN NETWORK PHYSIOLOGY 2023; 3:1279646. [PMID: 38116461 PMCID: PMC10728865 DOI: 10.3389/fnetp.2023.1279646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 11/20/2023] [Indexed: 12/21/2023]
Abstract
In recent years, brain imaging studies have begun to shed light on the neural correlates of physiologically-reversible altered states of consciousness such as deep sleep, anesthesia, and psychedelic experiences. The emerging consensus is that normal waking consciousness requires the exploration of a dynamical repertoire enabling both global integration i.e., long-distance interactions between brain regions, and segregation, i.e., local processing in functionally specialized clusters. Altered states of consciousness have notably been characterized by a tipping of the integration/segregation balance away from this equilibrium. Historically, functional MRI (fMRI) has been the modality of choice for such investigations. However, fMRI does not enable characterization of the integration/segregation balance at sub-second temporal resolution. Here, we investigated global brain spatiotemporal patterns in electrocorticography (ECoG) data of a monkey (Macaca fuscata) under either ketamine or propofol general anesthesia. We first studied the effects of these anesthetics from the perspective of band-specific synchronization across the entire ECoG array, treating individual channels as oscillators. We further aimed to determine whether synchrony within spatially localized clusters of oscillators was differently affected by the drugs in comparison to synchronization over spatially distributed subsets of ECoG channels, thereby quantifying changes in integration/segregation balance on physiologically-relevant time scales. The findings reflect global brain dynamics characterized by a loss of long-range integration in multiple frequency bands under both ketamine and propofol anesthesia, most pronounced in the beta (13-30 Hz) and low-gamma bands (30-80 Hz), and with strongly preserved local synchrony in all bands.
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Affiliation(s)
- Louis-David Lord
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- Institut Méditerranéen de Recherches Avancées (IMéRA), Aix-Marseille Université, Marseille, France
| | - Timoteo Carletti
- Institut Méditerranéen de Recherches Avancées (IMéRA), Aix-Marseille Université, Marseille, France
- Department of Mathematics and Namur Institute for Complex Systems (naXys), University of Namur, Namur, Belgium
| | - Henrique Fernandes
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- Institut Méditerranéen de Recherches Avancées (IMéRA), Aix-Marseille Université, Marseille, France
- Centre for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Federico E. Turkheimer
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Paul Expert
- Institut Méditerranéen de Recherches Avancées (IMéRA), Aix-Marseille Université, Marseille, France
- Global Business School for Health, University College London, London, United Kingdom
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10
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Stier AJ, Cardenas-Iniguez C, Kardan O, Moore TM, Meyer FAC, Rosenberg MD, Kaczkurkin AN, Lahey BB, Berman MG. A pattern of cognitive resource disruptions in childhood psychopathology. Netw Neurosci 2023; 7:1153-1180. [PMID: 37781141 PMCID: PMC10473262 DOI: 10.1162/netn_a_00322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 05/01/2023] [Indexed: 10/03/2023] Open
Abstract
The Hurst exponent (H) isolated in fractal analyses of neuroimaging time series is implicated broadly in cognition. Within this literature, H is associated with multiple mental disorders, suggesting that H is transdimensionally associated with psychopathology. Here, we unify these results and demonstrate a pattern of decreased H with increased general psychopathology and attention-deficit/hyperactivity factor scores during a working memory task in 1,839 children. This pattern predicts current and future cognitive performance in children and some psychopathology in 703 adults. This pattern also defines psychological and functional axes associating psychopathology with an imbalance in resource allocation between fronto-parietal and sensorimotor regions, driven by reduced resource allocation to fronto-parietal regions. This suggests the hypothesis that impaired working memory function in psychopathology follows from a reduced cognitive resource pool and a reduction in resources allocated to the task at hand.
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Affiliation(s)
| | | | - Omid Kardan
- Department of Psychology, University of Chicago
| | | | | | - Monica D. Rosenberg
- Department of Psychology, University of Chicago
- The Neuroscience Institute, University of Chicago
| | | | | | - Marc G. Berman
- Department of Psychology, University of Chicago
- The Neuroscience Institute, University of Chicago
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11
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Naik S, Adibpour P, Dubois J, Dehaene-Lambertz G, Battaglia D. Event-related variability is modulated by task and development. Neuroimage 2023; 276:120208. [PMID: 37268095 DOI: 10.1016/j.neuroimage.2023.120208] [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: 03/02/2023] [Revised: 05/11/2023] [Accepted: 05/30/2023] [Indexed: 06/04/2023] Open
Abstract
In carefully designed experimental paradigms, cognitive scientists interpret the mean event-related potentials (ERP) in terms of cognitive operations. However, the huge signal variability from one trial to the next, questions the representability of such mean events. We explored here whether this variability is an unwanted noise, or an informative part of the neural response. We took advantage of the rapid changes in the visual system during human infancy and analyzed the variability of visual responses to central and lateralized faces in 2-to 6-month-old infants compared to adults using high-density electroencephalography (EEG). We observed that neural trajectories of individual trials always remain very far from ERP components, only moderately bending their direction with a substantial temporal jitter across trials. However, single trial trajectories displayed characteristic patterns of acceleration and deceleration when approaching ERP components, as if they were under the active influence of steering forces causing transient attraction and stabilization. These dynamic events could only partly be accounted for by induced microstate transitions or phase reset phenomena. Importantly, these structured modulations of response variability, both between and within trials, had a rich sequential organization, which in infants, was modulated by the task difficulty and age. Our approaches to characterize Event Related Variability (ERV) expand on classic ERP analyses and provide the first evidence for the functional role of ongoing neural variability in human infants.
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Affiliation(s)
- Shruti Naik
- Cognitive Neuroimaging Unit U992, NeuroSpin Center, F-91190 Gif/Yvette, France
| | - Parvaneh Adibpour
- Cognitive Neuroimaging Unit U992, NeuroSpin Center, F-91190 Gif/Yvette, France
| | - Jessica Dubois
- Cognitive Neuroimaging Unit U992, NeuroSpin Center, F-91190 Gif/Yvette, France; Université de Paris, NeuroDiderot, Inserm, F-75019 Paris, France
| | | | - Demian Battaglia
- Institute for System Neuroscience U1106, Aix-Marseille Université, F-13005 Marseille, France; University of Strasbourg Institute for Advanced Studies (USIAS), F-67000 Strasbourg, France.
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12
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Li Y, An S, Zhou T, Su C, Zhang S, Li C, Jiang J, Mu Y, Yao N, Huang ZG. Triple-network analysis of Alzheimer's disease based on the energy landscape. Front Neurosci 2023; 17:1171549. [PMID: 37287802 PMCID: PMC10242117 DOI: 10.3389/fnins.2023.1171549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 04/13/2023] [Indexed: 06/09/2023] Open
Abstract
Introduction Research on the brain activity during resting state has found that brain activation is centered around three networks, including the default mode network (DMN), the salient network (SN), and the central executive network (CEN), and switches between multiple modes. As a common disease in the elderly, Alzheimer's disease (AD) affects the state transitions of functional networks in the resting state. Methods Energy landscape, as a new method, can intuitively and quickly grasp the statistical distribution of system states and information related to state transition mechanisms. Therefore, this study mainly uses the energy landscape method to study the changes of the triple-network brain dynamics in AD patients in the resting state. Results AD brain activity patterns are in an abnormal state, and the dynamics of patients with AD tend to be unstable, with an unusually high flexibility in switching between states. Also , the subjects' dynamic features are correlated with clinical index. Discussion The atypical balance of large-scale brain systems in patients with AD is associated with abnormally active brain dynamics. Our study are helpful for further understanding the intrinsic dynamic characteristics and pathological mechanism of the resting-state brain in AD patients.
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Affiliation(s)
- Youjun Li
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
- Research Center for Brain-inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Simeng An
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
- Research Center for Brain-inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Tianlin Zhou
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
- Research Center for Brain-inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Chunwang Su
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
- Research Center for Brain-inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Siping Zhang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
- Research Center for Brain-inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Chenxi Li
- Department of Military Medical Psychology, Air Force Medical University, Xi'an, Shaanxi, China
| | - Junjie Jiang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
- Research Center for Brain-inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Yunfeng Mu
- Department of Gynecological Oncology, Shaanxi Provincial Cancer Hospital, Xi'an, China
| | - Nan Yao
- Research Center for Brain-inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
- Department of Applied Physics, Xi'an University of Technology, Xi'an, China
| | - Zi-Gang Huang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
- Research Center for Brain-inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
- The State Key Laboratory of Congnitive Neuroscience and Learning, Beijing Normal University, Beijing, China
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13
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Perez Velazquez JL, Mateos DM, Guevara R. Is the tendency to maximise energy distribution an optimal collective activity for biological purposes? A proposal for a global principle of biological organization. Heliyon 2023; 9:e15005. [PMID: 37095928 PMCID: PMC10121639 DOI: 10.1016/j.heliyon.2023.e15005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 03/06/2023] [Accepted: 03/23/2023] [Indexed: 03/31/2023] Open
Abstract
Our purpose is to address the biological problem of finding foundations of the organization in the collective activity among cell networks in the nervous system, at the meso/macroscale, giving rise to cognition and consciousness. But in doing so, we encounter another problem related to the interpretation of methods to assess the neural interactions and organization of the neurodynamics, because thermodynamic notions, which have precise meaning only under specific conditions, have been widely employed in these studies. The consequence is that apparently contradictory results appear in the literature, but these contradictions diminish upon the considerations of the specific circumstances of each experiment. After clarifying some of these controversial points and surveying some experimental results, we propose that a necessary condition for cognition/consciousness to emerge is to have available enough energy, or cellular activity; and a sufficient condition is the multiplicity of configurations in which cell networks can communicate, resulting in non-uniform energy distribution, the generation and dissipation of energy gradients due to the constant activity. The diversity of sensorimotor processing of higher animals needs a flexible, fluctuating web on neuronal connections, and we review results supporting such multiplicity of configurations among brain regions associated with conscious awareness and healthy brain states. These ideas may reveal possible fundamental principles of brain organization that could be extended to other natural phenomena and how healthy activity may derive to pathological states.
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14
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Li D, Mao M, Zhang X, Hou D, Zhang S, Hao J, Cui X, Niu Y, Xiang J, Wang B. Gender effects on the controllability of hemispheric white matter networks. Cereb Cortex 2023; 33:1643-1658. [PMID: 35483707 DOI: 10.1093/cercor/bhac162] [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: 12/31/2021] [Revised: 04/03/2022] [Accepted: 04/04/2022] [Indexed: 11/13/2022] Open
Abstract
Male and female adults exhibited significant group differences in brain white matter (WM) asymmetry and WM network controllability. However, gender differences in controllability of hemispheric WM networks between males and females remain to be determined. Based on 1 principal atlas and 1 replication atlas, this work characterized the average controllability (AC) and modal controllability (MC) of hemispheric WM network based on 1 principal dataset and 2 replication datasets. All results showed that males had higher AC of left hemispheric networks than females. And significant hemispheric asymmetry was revealed in regional AC and MC. Furthermore, significant gender differences in the AC asymmetry were mainly found in regions lie in the frontoparietal network, and the MC asymmetry was found in regions involving auditory and emotion process. Finally, we found significant associations between regional controllability and cognitive features. Taken together, this work could provide a novel perspective for understanding gender differences in hemispheric WM asymmetry and cognitive function between males and females.
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Affiliation(s)
- Dandan Li
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Min Mao
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Xi Zhang
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Dianni Hou
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Shanshan Zhang
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Jiangping Hao
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Xiaohong Cui
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Yan Niu
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Jie Xiang
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Bin Wang
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
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15
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McGowan AL, Parkes L, He X, Stanoi O, Kang Y, Lomax S, Jovanova M, Mucha PJ, Ochsner KN, Falk EB, Bassett DS, Lydon-Staley DM. Controllability of Structural Brain Networks and the Waxing and Waning of Negative Affect in Daily Life. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2022; 2:432-439. [PMID: 36324655 PMCID: PMC9616346 DOI: 10.1016/j.bpsgos.2021.11.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 11/05/2021] [Accepted: 11/10/2021] [Indexed: 11/20/2022] Open
Abstract
Background The waxing and waning of negative affect in daily life is normative, reflecting an adaptive capacity to respond flexibly to changing circumstances. However, understanding of the brain structure correlates of affective variability in naturalistic settings has been limited. Using network control theory, we examine facets of brain structure that may enable negative affect variability in daily life. Methods We used diffusion-weighted imaging data from 95 young adults (age [in years]: mean = 20.19, SD = 1.80; 56 women) to construct structural connectivity networks that map white matter fiber connections between 200 cortical and 14 subcortical regions. We applied network control theory to these structural networks to estimate the degree to which each brain region's pattern of structural connectivity facilitates the spread of activity to other brain systems. We examined how the average controllability of functional brain systems relates to negative affect variability, computed by taking the standard deviation of negative affect self-reports collected via smartphone-based experience sampling twice per day over 28 days as participants went about their daily lives. Results We found that high average controllability of the cingulo-insular system is associated with increased negative affect variability. We also found that greater negative affect variability is related to the presence of more depressive symptoms, yet average controllability of the cingulo-insular system was not associated with depressive symptoms. Conclusions Our results highlight the role that brain structure plays in affective dynamics as observed in the context of daily life, suggesting that average controllability of the cingulo-insular system promotes normative negative affect variability.
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Affiliation(s)
- Amanda L. McGowan
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Linden Parkes
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Xiaosong He
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania
- Department of Psychology, School of Humanities and Social Sciences, University of Science and Technology of China, Hefei, P.R. China
| | - Ovidia Stanoi
- Department of Psychology, Columbia University, New York, New York
| | - Yoona Kang
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Silicia Lomax
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Mia Jovanova
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Peter J. Mucha
- Department of Mathematics and Applied Physical Sciences, University of North Carolina, Chapel Hill, North Carolina
| | - Kevin N. Ochsner
- Department of Psychology, Columbia University, New York, New York
| | - Emily B. Falk
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania
- Department of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania
- Marketing Department, Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Dani S. Bassett
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania
- Department of Electrical & Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania
- Department of Physics & Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Santa Fe Institute, Santa Fe, New Mexico
| | - David M. Lydon-Staley
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, Pennsylvania
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16
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Blair DS, Soriano-Mas C, Cabral J, Moreira P, Morgado P, Deco G. Complexity changes in functional state dynamics suggest focal connectivity reductions. Front Hum Neurosci 2022; 16:958706. [PMID: 36211126 PMCID: PMC9540393 DOI: 10.3389/fnhum.2022.958706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 08/03/2022] [Indexed: 11/13/2022] Open
Abstract
The past two decades have seen an explosion in the methods and directions of neuroscience research. Along with many others, complexity research has rapidly gained traction as both an independent research field and a valuable subdiscipline in computational neuroscience. In the past decade alone, several studies have suggested that psychiatric disorders affect the spatiotemporal complexity of both global and region-specific brain activity (Liu et al., 2013; Adhikari et al., 2017; Li et al., 2018). However, many of these studies have not accounted for the distributed nature of cognition in either the global or regional complexity estimates, which may lead to erroneous interpretations of both global and region-specific entropy estimates. To alleviate this concern, we propose a novel method for estimating complexity. This method relies upon projecting dynamic functional connectivity into a low-dimensional space which captures the distributed nature of brain activity. Dimension-specific entropy may be estimated within this space, which in turn allows for a rapid estimate of global signal complexity. Testing this method on a recently acquired obsessive-compulsive disorder dataset reveals substantial increases in the complexity of both global and dimension-specific activity versus healthy controls, suggesting that obsessive-compulsive patients may experience increased disorder in cognition. To probe the potential causes of this alteration, we estimate subject-level effective connectivity via a Hopf oscillator-based model dynamic model, the results of which suggest that obsessive-compulsive patients may experience abnormally high connectivity across a broad network in the cortex. These findings are broadly in line with results from previous studies, suggesting that this method is both robust and sensitive to group-level complexity alterations.
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Affiliation(s)
| | - Carles Soriano-Mas
- Psychiatry and Mental Health Group, Neuroscience Program, Institut d’Investigació Biomèdica de Bellvitge, Barcelona, Spain
- Network Center for Biomedical Research on Mental Health, Carlos III Health Institute, Madrid, Spain
- Department of Social Psychology and Quantitative Psychology, Universitat de Barcelona, Barcelona, Spain
| | - Joana Cabral
- Life and Health Sciences Research Institute, School of Medicine, University of Minho, Braga, Portugal
| | - Pedro Moreira
- Life and Health Sciences Research Institute, School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B’s, PT Government Associate Laboratory, Braga, Portugal
- Psychological Neuroscience Lab, CIPsi, School of Psychology, University of Minho, Braga, Portugal
| | - Pedro Morgado
- Life and Health Sciences Research Institute, School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B’s, PT Government Associate Laboratory, Braga, Portugal
- Clinical Academic Center—Braga, Braga, Portugal
| | - Gustavo Deco
- Facultad de Comunicación, Universitat Pompeu Fabra, Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- School of Psychological Sciences, Monash University, Clayton, VIC, Australia
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17
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Fan L, Li C, Huang ZG, Zhao J, Wu X, Liu T, Li Y, Wang J. The longitudinal neural dynamics changes of whole brain connectome during natural recovery from poststroke aphasia. NEUROIMAGE: CLINICAL 2022; 36:103190. [PMID: 36174256 PMCID: PMC9668607 DOI: 10.1016/j.nicl.2022.103190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 07/24/2022] [Accepted: 09/08/2022] [Indexed: 12/14/2022] Open
Abstract
Poststroke aphasia is one of the most dramatic functional deficits that results from direct damage of focal brain regions and dysfunction of large-scale brain networks. The reconstruction of language function depends on the hierarchical whole-brain dynamic reorganization. However, investigations into the longitudinal neural changes of large-scale brain networks for poststroke aphasia remain scarce. Here we characterize large-scale brain dynamics in left-frontal-stroke aphasia through energy landscape analysis. Using fMRI during an auditory comprehension task, we find that aphasia patients suffer serious whole-brain dynamics perturbation in the acute and subacute stages after stroke, in which the brains were restricted into two major activity patterns. Following spontaneous recovery process, the brain flexibility improved in the chronic stage. Critically, we demonstrated that the abnormal neural dynamics are correlated with the aberrant brain network coordination. Taken together, the energy landscape analysis exhibited that the acute poststroke aphasia has a constrained, low dimensional brain dynamics, which were replaced by less constrained and high dimensional dynamics at chronic aphasia. Our study provides a new perspective to profoundly understand the pathological mechanisms of poststroke aphasia.
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Affiliation(s)
- Liming Fan
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, PR China,National Engineering Research Center of Health Care and Medical Devices. Guangzhou, Guangdong 510500, PR China
| | - Chenxi Li
- Department of the Psychology of Military Medicine, Air Force Medical University, Xi’an, Shaanxi 710032, PR China
| | - Zi-gang Huang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, PR China,National Engineering Research Center of Health Care and Medical Devices. Guangzhou, Guangdong 510500, PR China
| | - Jie Zhao
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, PR China,National Engineering Research Center of Health Care and Medical Devices. Guangzhou, Guangdong 510500, PR China
| | - Xiaofeng Wu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, PR China,National Engineering Research Center of Health Care and Medical Devices. Guangzhou, Guangdong 510500, PR China
| | - Tian Liu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, PR China,National Engineering Research Center of Health Care and Medical Devices. Guangzhou, Guangdong 510500, PR China
| | - Youjun Li
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, PR China,National Engineering Research Center of Health Care and Medical Devices. Guangzhou, Guangdong 510500, PR China,Corresponding authors at: The Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Biomedical Engineering, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an 710049, PR China.
| | - Jue Wang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, PR China,National Engineering Research Center of Health Care and Medical Devices. Guangzhou, Guangdong 510500, PR China,The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi’an, Shaanxi 710049, PR China,Corresponding authors at: The Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Biomedical Engineering, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an 710049, PR China.
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18
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Vohryzek J, Cabral J, Vuust P, Deco G, Kringelbach ML. Understanding brain states across spacetime informed by whole-brain modelling. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022; 380:20210247. [PMID: 35599554 PMCID: PMC9125224 DOI: 10.1098/rsta.2021.0247] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 11/23/2021] [Indexed: 05/26/2023]
Abstract
In order to survive in a complex environment, the human brain relies on the ability to flexibly adapt ongoing behaviour according to intrinsic and extrinsic signals. This capability has been linked to specific whole-brain activity patterns whose relative stability (order) allows for consistent functioning, supported by sufficient intrinsic instability needed for optimal adaptability. The emergent, spontaneous balance between order and disorder in brain activity over spacetime underpins distinct brain states. For example, depression is characterized by excessively rigid, highly ordered states, while psychedelics can bring about more disordered, sometimes overly flexible states. Recent developments in systems, computational and theoretical neuroscience have started to make inroads into the characterization of such complex dynamics over space and time. Here, we review recent insights drawn from neuroimaging and whole-brain modelling motivating using mechanistic principles from dynamical system theory to study and characterize brain states. We show how different healthy and altered brain states are associated to characteristic spacetime dynamics which in turn may offer insights that in time can inspire new treatments for rebalancing brain states in disease. This article is part of the theme issue 'Emergent phenomena in complex physical and socio-technical systems: from cells to societies'.
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Affiliation(s)
- Jakub Vohryzek
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Psychiatry, University of Oxford, Oxford, UK
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Spain
| | - Joana Cabral
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK
- Life and Health Sciences Research Institute, University of Minho, Braga, Portugal
| | - Peter Vuust
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Spain
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Morten L. Kringelbach
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Psychiatry, University of Oxford, Oxford, UK
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19
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Wang S, Wen H, Qiu S, Xie P, Qiu J, He H. Driving brain state transitions in major depressive disorder through external stimulation. Hum Brain Mapp 2022; 43:5326-5339. [PMID: 35808927 PMCID: PMC9812249 DOI: 10.1002/hbm.26006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 05/27/2022] [Accepted: 06/22/2022] [Indexed: 01/15/2023] Open
Abstract
Major depressive disorder (MDD) as a dysfunction of neural circuits and brain networks has been established in modern neuroimaging sciences. However, the brain state transitions between MDD and health through external stimulation remain unclear, which limits translation to clinical contexts and demonstrable clinical utility. We propose a framework of the large-scale whole-brain network model for MDD linking the underlying anatomical connectivity with functional dynamics obtained from diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI). Then, we further explored the optimal brain regions to promote the transition of brain states between MDD and health through external stimulation of the model. Based on the whole-brain model successfully fitting the brain state space in MDD and the health, we demonstrated that the transition from MDD to health is achieved by the excitatory activation of the limbic system and from health to MDD by the inhibitory stimulation of the reward circuit. Our finding provides novel biophysical evidence for the neural mechanism of MDD and its recovery and allows the discovery of new stimulation targets for MDD recovery.
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Affiliation(s)
- Shengpei Wang
- Research Centre for Brain‐inspired Intelligence and National Laboratory of Pattern Recognition, Institute of AutomationChinese Academy of SciencesBeijingChina
| | - Hongwei Wen
- Key Laboratory of Cognition and Personality (Ministry of Education)ChongqingChina,School of PsychologySouthwest UniversityChongqingChina
| | - Shuang Qiu
- Research Centre for Brain‐inspired Intelligence and National Laboratory of Pattern Recognition, Institute of AutomationChinese Academy of SciencesBeijingChina
| | - Peng Xie
- Institute of NeuroscienceChongqing Medical UniversityChongqingChina,Chongqing Key Laboratory of NeurobiologyChongqingChina,Department of Neurologythe First Affiliated Hospital of Chongqing Medical UniversityChongqingChina
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (Ministry of Education)ChongqingChina,School of PsychologySouthwest UniversityChongqingChina
| | - Huiguang He
- Research Centre for Brain‐inspired Intelligence and National Laboratory of Pattern Recognition, Institute of AutomationChinese Academy of SciencesBeijingChina,University of Chinese Academy of SciencesBeijingChina,Center for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesBeijingChina
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20
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Fortel I, Butler M, Korthauer LE, Zhan L, Ajilore O, Sidiropoulos A, Wu Y, Driscoll I, Schonfeld D, Leow A. Inferring excitation-inhibition dynamics using a maximum entropy model unifying brain structure and function. Netw Neurosci 2022; 6:420-444. [PMID: 35733430 PMCID: PMC9205431 DOI: 10.1162/netn_a_00220] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 12/07/2021] [Indexed: 11/04/2022] Open
Abstract
Neural activity coordinated across different scales from neuronal circuits to large-scale brain networks gives rise to complex cognitive functions. Bridging the gap between micro- and macroscale processes, we present a novel framework based on the maximum entropy model to infer a hybrid resting-state structural connectome, representing functional interactions constrained by structural connectivity. We demonstrate that the structurally informed network outperforms the unconstrained model in simulating brain dynamics, wherein by constraining the inference model with the network structure we may improve the estimation of pairwise BOLD signal interactions. Further, we simulate brain network dynamics using Monte Carlo simulations with the new hybrid connectome to probe connectome-level differences in excitation-inhibition balance between apolipoprotein E (APOE)-ε4 carriers and noncarriers. Our results reveal sex differences among APOE-ε4 carriers in functional dynamics at criticality; specifically, female carriers appear to exhibit a lower tolerance to network disruptions resulting from increased excitatory interactions. In sum, the new multimodal network explored here enables analysis of brain dynamics through the integration of structure and function, providing insight into the complex interactions underlying neural activity such as the balance of excitation and inhibition.
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Affiliation(s)
- Igor Fortel
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA
| | - Mitchell Butler
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA
| | - Laura E. Korthauer
- Department of Psychology, University of Wisconsin–Milwaukee, Milwaukee, WI, USA
- Warren Alpert Medical School, Brown University, Providence, RI, USA
| | - Liang Zhan
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Olusola Ajilore
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
| | | | - Yichao Wu
- Department of Math, Statistics, and Computer Science, University of Illinois at Chicago, Chicago, IL, USA
| | - Ira Driscoll
- Department of Psychology, University of Wisconsin–Milwaukee, Milwaukee, WI, USA
| | - Dan Schonfeld
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA
- Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, IL, USA
| | - Alex Leow
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
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21
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Yuan J, Ji S, Luo L, Lv J, Liu T. Control energy assessment of spatial interactions among
macro‐scale
brain networks. Hum Brain Mapp 2022; 43:2181-2203. [PMID: 35072300 PMCID: PMC8996365 DOI: 10.1002/hbm.25780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 12/04/2021] [Accepted: 01/03/2022] [Indexed: 11/19/2022] Open
Abstract
Many recent studies have revealed that spatial interactions of functional brain networks derived from fMRI data can well model functional connectomes of the human brain. However, it has been rarely explored what the energy consumption characteristics are for such spatial interactions of macro‐scale functional networks, which remains crucial for the understanding of brain organization, behavior, and dynamics. To explore this unanswered question, this article presents a novel framework for quantitative assessment of energy consumptions of macro‐scale functional brain network's spatial interactions via two main effective computational methodologies. First, we designed a novel scheme combining dictionary learning and hierarchical clustering to derive macro‐scale consistent brain network templates that can be used to define a common reference space for brain network interactions and energy assessments. Second, the control energy consumption for driving the brain networks during their spatial interactions is computed from the viewpoint of the linear network control theory. Especially, the energetically favorable brain networks were identified and their energy characteristics were comprehensively analyzed. Experimental results on the Human Connectome Project (HCP) task‐based fMRI (tfMRI) data showed that the proposed methods can reveal meaningful, diverse energy consumption patterns of macro‐scale network interactions. In particular, those networks present remarkable differences in energy consumption. The energetically least favorable brain networks are stable and consistent across HCP tasks such as motor, language, social, and working memory tasks. In general, our framework provides a new perspective to characterize human brain functional connectomes by quantitative assessment for the energy consumption of spatial interactions of macro‐scale brain networks.
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Affiliation(s)
- Jing Yuan
- College of Artificial Intelligence Nankai University Tianjin China
| | - Senquan Ji
- College of Artificial Intelligence Nankai University Tianjin China
| | - Liao Luo
- College of Artificial Intelligence Nankai University Tianjin China
| | - Jinglei Lv
- School of Biomedical Engineering The University of Sydney Sydney New South Wales Australia
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Laboratory, Department of Computer Science and Bioimaging Research Center The University of Georgia Athens Georgia USA
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22
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Zamani A, Carhart-Harris R, Christoff K. Prefrontal contributions to the stability and variability of thought and conscious experience. Neuropsychopharmacology 2022; 47:329-348. [PMID: 34545195 PMCID: PMC8616944 DOI: 10.1038/s41386-021-01147-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 08/02/2021] [Accepted: 08/03/2021] [Indexed: 02/08/2023]
Abstract
The human prefrontal cortex is a structurally and functionally heterogenous brain region, including multiple subregions that have been linked to different large-scale brain networks. It contributes to a broad range of mental phenomena, from goal-directed thought and executive functions to mind-wandering and psychedelic experience. Here we review what is known about the functions of different prefrontal subregions and their affiliations with large-scale brain networks to examine how they may differentially contribute to the diversity of mental phenomena associated with prefrontal function. An important dimension that distinguishes across different kinds of conscious experience is the stability or variability of mental states across time. This dimension is a central feature of two recently introduced theoretical frameworks-the dynamic framework of thought (DFT) and the relaxed beliefs under psychedelics (REBUS) model-that treat neurocognitive dynamics as central to understanding and distinguishing between different mental phenomena. Here, we bring these two frameworks together to provide a synthesis of how prefrontal subregions may differentially contribute to the stability and variability of thought and conscious experience. We close by considering future directions for this work.
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Affiliation(s)
- Andre Zamani
- Department of Psychology, University of British Columbia, 2136 West Mall, Vancouver, BC, Canada.
| | - Robin Carhart-Harris
- Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, London, UK
| | - Kalina Christoff
- Department of Psychology, University of British Columbia, 2136 West Mall, Vancouver, BC, Canada
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23
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Yufik Y, Malhotra R. Situational Understanding in the Human and the Machine. Front Syst Neurosci 2021; 15:786252. [PMID: 35002643 PMCID: PMC8733725 DOI: 10.3389/fnsys.2021.786252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 11/24/2021] [Indexed: 11/13/2022] Open
Abstract
The Air Force research programs envision developing AI technologies that will ensure battlespace dominance, by radical increases in the speed of battlespace understanding and decision-making. In the last half century, advances in AI have been concentrated in the area of machine learning. Recent experimental findings and insights in systems neuroscience, the biophysics of cognition, and other disciplines provide converging results that set the stage for technologies of machine understanding and machine-augmented Situational Understanding. This paper will review some of the key ideas and results in the literature, and outline new suggestions. We define situational understanding and the distinctions between understanding and awareness, consider examples of how understanding-or lack of it-manifest in performance, and review hypotheses concerning the underlying neuronal mechanisms. Suggestions for further R&D are motivated by these hypotheses and are centered on the notions of Active Inference and Virtual Associative Networks.
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Affiliation(s)
- Yan Yufik
- Virtual Structures Research, Inc., Potomac, MD, United States
| | - Raj Malhotra
- United States Air Force Sensor Directorate, Dayton, OH, United States
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24
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Jeong SO, Kang J, Pae C, Eo J, Park SM, Son J, Park HJ. Empirical Bayes estimation of pairwise maximum entropy model for nonlinear brain state dynamics. Neuroimage 2021; 244:118618. [PMID: 34571159 DOI: 10.1016/j.neuroimage.2021.118618] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 09/22/2021] [Accepted: 09/24/2021] [Indexed: 11/16/2022] Open
Abstract
The pairwise maximum entropy model (pMEM) has recently gained widespread attention to exploring the nonlinear characteristics of brain state dynamics observed in resting-state functional magnetic resonance imaging (rsfMRI). Despite its unique advantageous features, the practical application of pMEM for individuals is limited as it requires a much larger sample than conventional rsfMRI scans. Thus, this study proposes an empirical Bayes estimation of individual pMEM using the variational expectation-maximization algorithm (VEM-MEM). The performance of the VEM-MEM is evaluated for several simulation setups with various sample sizes and network sizes. Unlike conventional maximum likelihood estimation procedures, the VEM-MEM can reliably estimate the individual model parameters, even with small samples, by effectively incorporating the group information as the prior. As a test case, the individual rsfMRI of children with attention deficit hyperactivity disorder (ADHD) is analyzed compared to that of typically developed children using the default mode network, executive control network, and salient network, obtained from the Healthy Brain Network database. We found that the nonlinear dynamic properties uniquely established on the pMEM differ for each group. Furthermore, pMEM parameters are more sensitive to group differences and are better associated with the behavior scores of ADHD compared to the Pearson correlation-based functional connectivity. The simulation and experimental results suggest that the proposed method can reliably estimate the individual pMEM and characterize the dynamic properties of individuals by utilizing empirical information of the group brain state dynamics.
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Affiliation(s)
- Seok-Oh Jeong
- Department of Statistics, Hankuk University of Foreign Studies, Yong-In, Republic of Korea
| | - Jiyoung Kang
- Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea; Department of Nuclear Medicine, Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Chongwon Pae
- Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea; Department of Nuclear Medicine, Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jinseok Eo
- Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea; Department of Nuclear Medicine, Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea; Graduate School of Medical Science, Yonsei University College of Medicine, Brain Korea 21 Project, Seoul, Republic of Korea
| | - Sung Min Park
- Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea
| | - Junho Son
- Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea; Department of Nuclear Medicine, Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea; Graduate School of Medical Science, Yonsei University College of Medicine, Brain Korea 21 Project, Seoul, Republic of Korea
| | - Hae-Jeong Park
- Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea; Department of Nuclear Medicine, Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea; Graduate School of Medical Science, Yonsei University College of Medicine, Brain Korea 21 Project, Seoul, Republic of Korea; Department of Cognitive Science, Yonsei University, Seoul, Republic of Korea.
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25
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Watanabe T. Causal roles of prefrontal cortex during spontaneous perceptual switching are determined by brain state dynamics. eLife 2021; 10:69079. [PMID: 34713803 PMCID: PMC8631941 DOI: 10.7554/elife.69079] [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: 04/03/2021] [Accepted: 10/28/2021] [Indexed: 12/23/2022] Open
Abstract
The prefrontal cortex (PFC) is thought to orchestrate cognitive dynamics. However, in tests of bistable visual perception, no direct evidence supporting such presumable causal roles of the PFC has been reported except for a recent work. Here, using a novel brain-state-dependent neural stimulation system, we identified causal effects on percept dynamics in three PFC activities—right frontal eye fields, dorsolateral PFC (DLPFC), and inferior frontal cortex (IFC). The causality is behaviourally detectable only when we track brain state dynamics and modulate the PFC activity in brain-state-/state-history-dependent manners. The behavioural effects are underpinned by transient neural changes in the brain state dynamics, and such neural effects are quantitatively explainable by structural transformations of the hypothetical energy landscapes. Moreover, these findings indicate distinct functions of the three PFC areas: in particular, the DLPFC enhances the integration of two PFC-active brain states, whereas IFC promotes the functional segregation between them. This work resolves the controversy over the PFC roles in spontaneous perceptual switching and underlines brain state dynamics in fine investigations of brain-behaviour causality. A cube that seems to shift its spatial arrangement as you keep looking; the elegant silhouette of a pirouetting dancer, which starts to spin in the opposite direction the more you stare at it; an illustration that shows two profiles – or is it a vase? These optical illusions are examples of bistable visual perception. Beyond their entertaining aspect, they provide a way for scientists to explore the dynamics of human consciousness, and the neural regions involved in this process. Some studies show that bistable visual perception is associated with the activation of the prefrontal cortex, a brain area involved in complex cognitive processes. However, it is unclear whether this region is required for the illusions to emerge. Some research has showed that even if sections of the prefrontal cortex are temporally deactivated, participants can still experience the illusions. Instead, Takamitsu Watanabe proposes that bistable visual perception is a process tied to dynamic brain states – that is, that distinct regions of the prefontal cortex are required for this fluctuating visual awareness, depending on the state of the whole brain. Such causal link cannot be observed if brain activity is not tracked closely. To investigate this, the brain states of 65 participants were recorded as individuals were experiencing the optical illusions; the activity of their various brain regions could therefore be mapped, and then areas of the prefrontal cortex could precisely be inhibited at the right time using transcranial magnetic stimulation. This revealed that, indeed, prefrontal cortex regions were necessary for bistable visual perception, but not in a simple way. Instead, which ones were required and when depended on activity dynamics taking place in the whole brain. Overall, these results indicate that monitoring brain states is necessary to better understand – and ultimately, control – the neural pathways underlying perception and behaviour.
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Affiliation(s)
- Takamitsu Watanabe
- International Research Centre for Neurointelligence, The University of Tokyo Institutes for Advanced Study, Tokyo, Japan.,RIKEN Centre for Brain Science, Saitama, Japan
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26
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Scangos KW, Khambhati AN, Daly PM, Owen LW, Manning JR, Ambrose JB, Austin E, Dawes HE, Krystal AD, Chang EF. Distributed Subnetworks of Depression Defined by Direct Intracranial Neurophysiology. Front Hum Neurosci 2021; 15:746499. [PMID: 34744662 PMCID: PMC8566975 DOI: 10.3389/fnhum.2021.746499] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 09/02/2021] [Indexed: 12/30/2022] Open
Abstract
Major depressive disorder is a common and disabling disorder with high rates of treatment resistance. Evidence suggests it is characterized by distributed network dysfunction that may be variable across patients, challenging the identification of quantitative biological substrates. We carried out this study to determine whether application of a novel computational approach to a large sample of high spatiotemporal resolution direct neural recordings in humans could unlock the functional organization and coordinated activity patterns of depression networks. This group level analysis of depression networks from heterogenous intracranial recordings was possible due to application of a correlational model-based method for inferring whole-brain neural activity. We then applied a network framework to discover brain dynamics across this model that could classify depression. We found a highly distributed pattern of neural activity and connectivity across cortical and subcortical structures that was present in the majority of depressed subjects. Furthermore, we found that this depression signature consisted of two subnetworks across individuals. The first was characterized by left temporal lobe hypoconnectivity and pathological beta activity. The second was characterized by a hypoactive, but hyperconnected left frontal cortex. These findings have applications toward personalization of therapy.
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Affiliation(s)
- Katherine Wilson Scangos
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Ankit N. Khambhati
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Patrick M. Daly
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Lucy W. Owen
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States
| | - Jeremy R. Manning
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States
| | - Josiah B. Ambrose
- Kaiser Permanente Redwood City Medical Center, Redwood City, CA, United States
| | - Everett Austin
- Kaiser Permanente Redwood City Medical Center, Redwood City, CA, United States
| | - Heather E. Dawes
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Andrew D. Krystal
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Edward F. Chang
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
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27
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Functional harmonics reveal multi-dimensional basis functions underlying cortical organization. Cell Rep 2021; 36:109554. [PMID: 34433059 PMCID: PMC8411120 DOI: 10.1016/j.celrep.2021.109554] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 04/06/2021] [Accepted: 07/27/2021] [Indexed: 11/27/2022] Open
Abstract
The human brain consists of specialized areas that flexibly interact to form a multitude of functional networks. Complementary to this notion of modular organization, brain function has been shown to vary along a smooth continuum across the whole cortex. We demonstrate a mathematical framework that accounts for both of these perspectives: harmonic modes. We calculate the harmonic modes of the brain's functional connectivity graph, called "functional harmonics," revealing a multi-dimensional, frequency-ordered set of basis functions. Functional harmonics link characteristics of cortical organization across several spatial scales, capturing aspects of intra-areal organizational features (retinotopy, somatotopy), delineating brain areas, and explaining macroscopic functional networks as well as global cortical gradients. Furthermore, we show how the activity patterns elicited by seven different tasks are reconstructed from a very small subset of functional harmonics. Our results suggest that the principle of harmonicity, ubiquitous in nature, also underlies functional cortical organization in the human brain.
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28
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A Multivariate Randomized Controlled Experiment about the Effects of Mindfulness Priming on EEG Neurofeedback Self-Regulation Serious Games. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11167725] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Neurofeedback training (NFT) is a technique often proposed to train brain activity SR with promising results. However, some criticism has been raised due to the lack of evaluation, reliability, and validation of its learning effects. The current work evaluates the hypothesis that SR learning may be improved by priming the subject before NFT with guided mindfulness meditation (MM). The proposed framework was tested in a two-way parallel-group randomized controlled intervention with a single session alpha NFT, in a simplistic serious game design. Sixty-two healthy naïve subjects, aged between 18 and 43 years, were divided into MM priming and no-priming groups. Although both the EG and CG successfully attained the up-regulation of alpha rhythms (F(1,59) = 20.67, p < 0.001, ηp2 = 0.26), the EG showed a significantly enhanced ability (t(29) = 4.38, p < 0.001) to control brain activity, compared to the CG (t(29) = 1.18, p > 0.1). Furthermore, EG superior performance on NFT seems to be explained by the subject’s lack of awareness at pre-intervention, less vigour at post-intervention, increased task engagement, and a relaxed non-judgemental attitude towards the NFT tasks. This study is a preliminary validation of the proposed assisted priming framework, advancing some implicit and explicit metrics about its efficacy on NFT performance, and a promising tool for improving naïve “users” self-regulation ability.
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29
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Klepl D, He F, Wu M, Marco MD, Blackburn DJ, Sarrigiannis PG. Characterising Alzheimer's Disease with EEG-based Energy Landscape Analysis. IEEE J Biomed Health Inform 2021; 26:992-1000. [PMID: 34406951 DOI: 10.1109/jbhi.2021.3105397] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Alzheimer's disease (AD) is one of the most common neurodegenerative diseases, with around 50 million patients worldwide. Accessible and non-invasive methods of diagnosing and characterising AD are therefore urgently required. Electroencephalography (EEG) fulfils these criteria and is often used when studying AD. Several features derived from EEG were shown to predict AD with high accuracy, e.g. signal complexity and synchronisation. However, the dynamics of how the brain transitions between stable states have not been properly studied in the case of AD and EEG. Energy landscape analysis is a method that can be used to quantify these dynamics. This work presents the first application of this method to both AD and EEG. Energy landscape assigns energy value to each possible state, i.e. pattern of activations across brain regions. The energy is inversely proportional to the probability of occurrence. By studying the features of energy landscapes of 20 AD patients and 20 age-matched healthy counterparts (HC), significant differences are found. The dynamics of AD patients' EEG are shown to be more constrained - with more local minima, less variation in basin size, and smaller basins. We show that energy landscapes can predict AD with high accuracy, performing significantly better than baseline models. Moreover, these findings are replicated in a separate dataset including 9 AD and 10 HC above 70 years old.
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30
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Kringelbach ML, Deco G. Brain States and Transitions: Insights from Computational Neuroscience. Cell Rep 2021; 32:108128. [PMID: 32905760 DOI: 10.1016/j.celrep.2020.108128] [Citation(s) in RCA: 94] [Impact Index Per Article: 31.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 07/22/2020] [Accepted: 08/19/2020] [Indexed: 11/25/2022] Open
Abstract
Within the field of computational neuroscience there are great expectations of finding new ways to rebalance the complex dynamic system of the human brain through controlled pharmacological or electromagnetic perturbation. Yet many obstacles remain between the ability to accurately predict how and where best to perturb to force a transition from one brain state to another. The foremost challenge is a commonly agreed definition of a given brain state. Recent progress in computational neuroscience has made it possible to robustly define brain states and force transitions between them. Here, we review the state of the art and propose a framework for determining the functional hierarchical organization describing any given brain state. We describe the latest advances in creating sophisticated whole-brain computational models with interacting neuronal and neurotransmitter systems that can be studied fully in silico to predict and design novel pharmacological and electromagnetic interventions to rebalance them in disease.
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Affiliation(s)
- Morten L Kringelbach
- Department of Psychiatry, University of Oxford, Oxford, UK; Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK.
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain; Institució Catalana de la Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona 08010, Spain; Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany; School of Psychological Sciences, Monash University, Melbourne, Clayton, VIC 3800, Australia.
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31
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Kang J, Jeong S, Pae C, Park H. Bayesian estimation of maximum entropy model for individualized energy landscape analysis of brain state dynamics. Hum Brain Mapp 2021; 42:3411-3428. [PMID: 33934421 PMCID: PMC8249903 DOI: 10.1002/hbm.25442] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 03/25/2021] [Accepted: 03/29/2021] [Indexed: 11/24/2022] Open
Abstract
The pairwise maximum entropy model (MEM) for resting state functional MRI (rsfMRI) has been used to generate energy landscape of brain states and to explore nonlinear brain state dynamics. Researches using MEM, however, has mostly been restricted to fixed‐effect group‐level analyses, using concatenated time series across individuals, due to the need for large samples in the parameter estimation of MEM. To mitigate the small sample problem in analyzing energy landscapes for individuals, we propose a Bayesian estimation of individual MEM using variational Bayes approximation (BMEM). We evaluated the performances of BMEM with respect to sample sizes and prior information using simulation. BMEM showed advantages over conventional maximum likelihood estimation in reliably estimating model parameters for individuals with small sample data, particularly utilizing the empirical priors derived from group data. We then analyzed individual rsfMRI of the Human Connectome Project to show the usefulness of MEM in differentiating individuals and in exploring neural correlates for human behavior. MEM and its energy landscape properties showed high subject specificity comparable to that of functional connectivity. Canonical correlation analysis identified canonical variables for MEM highly associated with cognitive scores. Inter‐individual variations of cognitive scores were also reflected in energy landscape properties such as energies, occupation times, and basin sizes at local minima. We conclude that BMEM provides an efficient method to characterize dynamic properties of individuals using energy landscape analysis of individual brain states.
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Affiliation(s)
- Jiyoung Kang
- Center for Systems and Translational Brain ScienceInstitute of Human Complexity and Systems Science, Yonsei UniversitySeoulSouth Korea
- Department of Nuclear Medicine, PsychiatryYonsei University College of MedicineSeoulSouth Korea
| | - Seok‐Oh Jeong
- Department of StatisticsHankuk University of Foreign StudiesYong‐In, SeoulSouth Korea
| | - Chongwon Pae
- Center for Systems and Translational Brain ScienceInstitute of Human Complexity and Systems Science, Yonsei UniversitySeoulSouth Korea
- Department of Nuclear Medicine, PsychiatryYonsei University College of MedicineSeoulSouth Korea
| | - Hae‐Jeong Park
- Center for Systems and Translational Brain ScienceInstitute of Human Complexity and Systems Science, Yonsei UniversitySeoulSouth Korea
- Department of Nuclear Medicine, PsychiatryYonsei University College of MedicineSeoulSouth Korea
- Graduate School of Medical Science, Brain Korea 21 ProjectYonsei University College of MedicineSeoulSouth Korea
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32
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Park HJ, Kang J. A Computational Framework for Controlling the Self-Restorative Brain Based on the Free Energy and Degeneracy Principles. Front Comput Neurosci 2021; 15:590019. [PMID: 33935674 PMCID: PMC8079648 DOI: 10.3389/fncom.2021.590019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 03/18/2021] [Indexed: 11/13/2022] Open
Abstract
The brain is a non-linear dynamical system with a self-restoration process, which protects itself from external damage but is often a bottleneck for clinical treatment. To treat the brain to induce the desired functionality, formulation of a self-restoration process is necessary for optimal brain control. This study proposes a computational model for the brain's self-restoration process following the free-energy and degeneracy principles. Based on this model, a computational framework for brain control is established. We posited that the pre-treatment brain circuit has long been configured in response to the environmental (the other neural populations') demands on the circuit. Since the demands persist even after treatment, the treated circuit's response to the demand may gradually approximate the pre-treatment functionality. In this framework, an energy landscape of regional activities, estimated from resting-state endogenous activities by a pairwise maximum entropy model, is used to represent the pre-treatment functionality. The approximation of the pre-treatment functionality occurs via reconfiguration of interactions among neural populations within the treated circuit. To establish the current framework's construct validity, we conducted various simulations. The simulations suggested that brain control should include the self-restoration process, without which the treatment was not optimal. We also presented simulations for optimizing repetitive treatments and optimal timing of the treatment. These results suggest a plausibility of the current framework in controlling the non-linear dynamical brain with a self-restoration process.
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Affiliation(s)
- Hae-Jeong Park
- Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, South Korea.,Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, South Korea.,Department of Psychiatry, Yonsei University College of Medicine, Seoul, South Korea.,Brain Korea 21 Project, Graduate School of Medical Science, Yonsei University College of Medicine, Seoul, South Korea.,Department of Cognitive Science, Yonsei University, Seoul, South Korea
| | - Jiyoung Kang
- Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, South Korea.,Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, South Korea
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33
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Friston KJ, Fagerholm ED, Zarghami TS, Parr T, Hipólito I, Magrou L, Razi A. Parcels and particles: Markov blankets in the brain. Netw Neurosci 2021; 5:211-251. [PMID: 33688613 PMCID: PMC7935044 DOI: 10.1162/netn_a_00175] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2020] [Accepted: 11/24/2020] [Indexed: 11/04/2022] Open
Abstract
At the inception of human brain mapping, two principles of functional anatomy underwrote most conceptions-and analyses-of distributed brain responses: namely, functional segregation and integration. There are currently two main approaches to characterizing functional integration. The first is a mechanistic modeling of connectomics in terms of directed effective connectivity that mediates neuronal message passing and dynamics on neuronal circuits. The second phenomenological approach usually characterizes undirected functional connectivity (i.e., measurable correlations), in terms of intrinsic brain networks, self-organized criticality, dynamical instability, and so on. This paper describes a treatment of effective connectivity that speaks to the emergence of intrinsic brain networks and critical dynamics. It is predicated on the notion of Markov blankets that play a fundamental role in the self-organization of far from equilibrium systems. Using the apparatus of the renormalization group, we show that much of the phenomenology found in network neuroscience is an emergent property of a particular partition of neuronal states, over progressively coarser scales. As such, it offers a way of linking dynamics on directed graphs to the phenomenology of intrinsic brain networks.
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Affiliation(s)
- Karl J. Friston
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
| | - Erik D. Fagerholm
- Department of Neuroimaging, King’s College London, London, United Kingdom
| | - Tahereh S. Zarghami
- Bio-Electric Department, School of Electrical and Computer Engineering, University of Tehran, Amirabad, Tehran, Iran
| | - Thomas Parr
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
| | - Inês Hipólito
- Berlin School of Mind and Brain, and Institut für Philosophie, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Loïc Magrou
- Univ Lyon, Université Claude Bernard Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, Bron, France
| | - Adeel Razi
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
- Turner Institute for Brain and Mental Health, Monash University, Clayton, Australia
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34
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Ashourvan A, Shah P, Pines A, Gu S, Lynn CW, Bassett DS, Davis KA, Litt B. Pairwise maximum entropy model explains the role of white matter structure in shaping emergent co-activation states. Commun Biol 2021; 4:210. [PMID: 33594239 PMCID: PMC7887247 DOI: 10.1038/s42003-021-01700-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Accepted: 01/06/2021] [Indexed: 01/30/2023] Open
Abstract
A major challenge in neuroscience is determining a quantitative relationship between the brain's white matter structural connectivity and emergent activity. We seek to uncover the intrinsic relationship among brain regions fundamental to their functional activity by constructing a pairwise maximum entropy model (MEM) of the inter-ictal activation patterns of five patients with medically refractory epilepsy over an average of ~14 hours of band-passed intracranial EEG (iEEG) recordings per patient. We find that the pairwise MEM accurately predicts iEEG electrodes' activation patterns' probability and their pairwise correlations. We demonstrate that the estimated pairwise MEM's interaction weights predict structural connectivity and its strength over several frequencies significantly beyond what is expected based solely on sampled regions' distance in most patients. Together, the pairwise MEM offers a framework for explaining iEEG functional connectivity and provides insight into how the brain's structural connectome gives rise to large-scale activation patterns by promoting co-activation between connected structures.
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Affiliation(s)
- Arian Ashourvan
- grid.25879.310000 0004 1936 8972Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA USA ,grid.25879.310000 0004 1936 8972Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA USA
| | - Preya Shah
- grid.25879.310000 0004 1936 8972Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA USA ,grid.25879.310000 0004 1936 8972Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA USA
| | - Adam Pines
- grid.25879.310000 0004 1936 8972Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA USA
| | - Shi Gu
- grid.54549.390000 0004 0369 4060Department of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Christopher W. Lynn
- grid.25879.310000 0004 1936 8972Department of Physics & Astronomy, School of Arts & Sciences, University of Pennsylvania, Philadelphia, PA USA
| | - Danielle S. Bassett
- grid.25879.310000 0004 1936 8972Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA USA ,grid.25879.310000 0004 1936 8972Department of Physics & Astronomy, School of Arts & Sciences, University of Pennsylvania, Philadelphia, PA USA ,grid.25879.310000 0004 1936 8972Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA USA ,grid.25879.310000 0004 1936 8972Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA ,grid.411115.10000 0004 0435 0884Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA USA
| | - Kathryn A. Davis
- grid.25879.310000 0004 1936 8972Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA USA ,grid.25879.310000 0004 1936 8972Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA USA
| | - Brian Litt
- grid.25879.310000 0004 1936 8972Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA USA ,grid.25879.310000 0004 1936 8972Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA USA ,grid.411115.10000 0004 0435 0884Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA USA
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35
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Turkheimer FE, Rosas FE, Dipasquale O, Martins D, Fagerholm ED, Expert P, Váša F, Lord LD, Leech R. A Complex Systems Perspective on Neuroimaging Studies of Behavior and Its Disorders. Neuroscientist 2021; 28:382-399. [PMID: 33593120 PMCID: PMC9344570 DOI: 10.1177/1073858421994784] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The study of complex systems deals with emergent behavior that arises as
a result of nonlinear spatiotemporal interactions between a large
number of components both within the system, as well as between the
system and its environment. There is a strong case to be made that
neural systems as well as their emergent behavior and disorders can be
studied within the framework of complexity science. In particular, the
field of neuroimaging has begun to apply both theoretical and
experimental procedures originating in complexity science—usually in
parallel with traditional methodologies. Here, we illustrate the basic
properties that characterize complex systems and evaluate how they
relate to what we have learned about brain structure and function from
neuroimaging experiments. We then argue in favor of adopting a complex
systems-based methodology in the study of neuroimaging, alongside
appropriate experimental paradigms, and with minimal influences from
noncomplex system approaches. Our exposition includes a review of the
fundamental mathematical concepts, combined with practical examples
and a compilation of results from the literature.
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Affiliation(s)
- Federico E Turkheimer
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Fernando E Rosas
- Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, London, UK.,Data Science Institute, Imperial College London, London, UK.,Centre for Complexity Science, Imperial College London, London, UK
| | - Ottavia Dipasquale
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Daniel Martins
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Erik D Fagerholm
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Paul Expert
- Global Digital Health Unit, School of Public Health, Imperial College London, London, UK
| | - František Váša
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | | | - Robert Leech
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
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36
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Saberi M, Khosrowabadi R, Khatibi A, Misic B, Jafari G. Topological impact of negative links on the stability of resting-state brain network. Sci Rep 2021; 11:2176. [PMID: 33500525 PMCID: PMC7838299 DOI: 10.1038/s41598-021-81767-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Accepted: 01/12/2021] [Indexed: 11/08/2022] Open
Abstract
Stability is a physical attribute that stands opposite the change. However, it is still unclear how the arrangement of links called topology affects network stability. In this study, we tackled this issue in the resting-state brain network using structural balance. Structural balance theory employs the quality of triadic associations between signed links to determine the network stability. In this study, we showed that negative links of the resting-state network make hubs to reduce balance-energy and push the network into a more stable state compared to null-networks with trivial topologies. In this regard, we created a global measure entitled 'tendency to make hub' to assess the hubness of the network. Besides, we revealed nodal degrees of negative links have an exponential distribution that confirms the existence of negative hubs. Our findings indicate that the arrangement of negative links plays an important role in the balance (stability) of the resting-state brain network.
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Affiliation(s)
- Majid Saberi
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, G.C., Evin Sq., Tehran, 19839-63113, Iran
| | - Reza Khosrowabadi
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, G.C., Evin Sq., Tehran, 19839-63113, Iran.
| | - Ali Khatibi
- Centre of Precision Rehabilitation for Spinal Pain (CPR Spine), School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, Birmingham, UK
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Gholamreza Jafari
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, G.C., Evin Sq., Tehran, 19839-63113, Iran
- Physics Department, Shahid Beheshti University, G.C., Tehran, 1983969411, Iran
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37
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Regonia PR, Takamura M, Nakano T, Ichikawa N, Fermin A, Okada G, Okamoto Y, Yamawaki S, Ikeda K, Yoshimoto J. Modeling Heterogeneous Brain Dynamics of Depression and Melancholia Using Energy Landscape Analysis. Front Psychiatry 2021; 12:780997. [PMID: 34899435 PMCID: PMC8656401 DOI: 10.3389/fpsyt.2021.780997] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 11/01/2021] [Indexed: 11/13/2022] Open
Abstract
Our current understanding of melancholic depression is shaped by its position in the depression spectrum. The lack of consensus on how it should be treated-whether as a subtype of depression, or as a distinct disorder altogethe-interferes with the recovery of suffering patients. In this study, we analyzed brain state energy landscape models of melancholic depression, in contrast to healthy and non-melancholic energy landscapes. Our analyses showed significant group differences on basin energy, basin frequency, and transition dynamics in several functional brain networks such as basal ganglia, dorsal default mode, and left executive control networks. Furthermore, we found evidences suggesting the connection between energy landscape characteristics (basin characteristics) and depressive symptom scores (BDI-II and SHAPS). These results indicate that melancholic depression is distinguishable from its non-melancholic counterpart, not only in terms of depression severity, but also in brain dynamics.
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Affiliation(s)
- Paul Rossener Regonia
- Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan.,Department of Computer Science, College of Engineering, University of the Philippines Diliman, Quezon City, Philippines
| | - Masahiro Takamura
- Center for Brain, Mind and KANSEI Research Sciences, Hiroshima University, Hiroshima, Japan.,Department of Neurology, Faculty of Medicine, Shimane University, Izumo, Japan
| | - Takashi Nakano
- Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan.,School of Medicine, Fujita Health University, Toyoake, Japan
| | - Naho Ichikawa
- Center for Brain, Mind and KANSEI Research Sciences, Hiroshima University, Hiroshima, Japan
| | - Alan Fermin
- Center for Brain, Mind and KANSEI Research Sciences, Hiroshima University, Hiroshima, Japan
| | - Go Okada
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
| | - Yasumasa Okamoto
- Center for Brain, Mind and KANSEI Research Sciences, Hiroshima University, Hiroshima, Japan.,Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
| | - Shigeto Yamawaki
- Center for Brain, Mind and KANSEI Research Sciences, Hiroshima University, Hiroshima, Japan
| | - Kazushi Ikeda
- Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan
| | - Junichiro Yoshimoto
- Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan
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38
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Büttiker P, Weissenberger S, Stefano GB, Kream RM, Ptacek R. SARS-CoV-2, Trait Anxiety, and the Microbiome. Front Psychiatry 2021; 12:720082. [PMID: 34566721 PMCID: PMC8455943 DOI: 10.3389/fpsyt.2021.720082] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 08/16/2021] [Indexed: 12/21/2022] Open
Abstract
During the COVID-19 pandemic, research on the relationships between the virus and its human host has become fundamental to understand this pathology and its effects. Attaining this profound understanding is critical for the effective containment and treatment of infections caused by the virus. In this review, we present some possible mechanisms by which psychopathological symptoms emerge following viral infections of the central nervous system (CNS). These proposed mechanisms are based on microbial communication and the induced priming of microglial antibody activation within the CNS through Toll-like receptor signaling. In this process, chronic microglial activation causes increased glutamate release in virally-altered, high-density neuronal structures, thereby modulating cognitive networks and information integration processes. This modulation, in turn, we suggest, affects the accuracy of sensory integration and connectivity of major control networks, such as the default mode network. The chronic activation of immunological responses and neurochemical shifts toward an elevated glutamate/gamma-aminobutyric acid ratio lead to negative reinforcement learning and suboptimal organismic functioning, for example, maintaining the body in an anxious state, which can later become internalized as trait anxiety. Therefore, we hypothesize that the homeostatic relationship between host, microbiome, and virome, would be decisive in determining the efficiency of subsequent immunological responses, disease susceptibility, and long-term psychopathological effects of diseases that impact the CNS, such as the COVID-19.
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Affiliation(s)
- Pascal Büttiker
- First Faculty of Medicine, Center for Cognitive and Molecular Neuroscience, Charles University in Prague, Prague, Czechia
| | - Simon Weissenberger
- First Faculty of Medicine, Center for Cognitive and Molecular Neuroscience, Charles University in Prague, Prague, Czechia.,Department of Psychology, University of New York in Prague, Prague, Czechia
| | - George B Stefano
- First Faculty of Medicine, Center for Cognitive and Molecular Neuroscience, Charles University in Prague, Prague, Czechia
| | - Richard M Kream
- First Faculty of Medicine, Center for Cognitive and Molecular Neuroscience, Charles University in Prague, Prague, Czechia
| | - Radek Ptacek
- First Faculty of Medicine, Center for Cognitive and Molecular Neuroscience, Charles University in Prague, Prague, Czechia
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39
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Dutta A, Karanth SS, Bhattacharya M, Liput M, Augustyniak J, Cheung M, Stachowiak EK, Stachowiak MK. A proof of concept 'phase zero' study of neurodevelopment using brain organoid models with Vis/near-infrared spectroscopy and electrophysiology. Sci Rep 2020; 10:20987. [PMID: 33268815 PMCID: PMC7710726 DOI: 10.1038/s41598-020-77929-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Accepted: 11/18/2020] [Indexed: 12/14/2022] Open
Abstract
Homeostatic control of neuronal excitability by modulation of synaptic inhibition (I) and excitation (E) of the principal neurons is important during brain maturation. The fundamental features of in-utero brain development, including local synaptic E-I ratio and bioenergetics, can be modeled by cerebral organoids (CO) that have exhibited highly regular nested oscillatory network events. Therefore, we evaluated a 'Phase Zero' clinical study platform combining broadband Vis/near-infrared(NIR) spectroscopy and electrophysiology with studying E-I ratio based on the spectral exponent of local field potentials and bioenergetics based on the activity of mitochondrial Cytochrome-C Oxidase (CCO). We found a significant effect of the age of the healthy controls iPSC CO from 23 days to 3 months on the CCO activity (chi-square (2, N = 10) = 20, p = 4.5400e-05), and spectral exponent between 30-50 Hz (chi-square (2, N = 16) = 13.88, p = 0.001). Also, a significant effect of drugs, choline (CHO), idebenone (IDB), R-alpha-lipoic acid plus acetyl-L-carnitine (LCLA), was found on the CCO activity (chi-square (3, N = 10) = 25.44, p = 1.2492e-05), spectral exponent between 1 and 20 Hz (chi-square (3, N = 16) = 43.5, p = 1.9273e-09) and 30-50 Hz (chi-square (3, N = 16) = 23.47, p = 3.2148e-05) in 34 days old CO from schizophrenia (SCZ) patients iPSC. We present the feasibility of a multimodal approach, combining electrophysiology and broadband Vis-NIR spectroscopy, to monitor neurodevelopment in brain organoid models that can complement traditional drug design approaches to test clinically meaningful hypotheses.
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Affiliation(s)
- Anirban Dutta
- Department of Biomedical Engineering, University at Buffalo, Buffalo, 14260, USA.
- Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, 14260, USA.
| | | | | | - Michal Liput
- Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, 14260, USA
- Department of Stem Cells Bioengineering, Mossakowski Medical Research Centre, Polish Academy of Sciences, Warsaw, Poland
| | - Justyna Augustyniak
- Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, 14260, USA
- Department of Neurochemistry, Mossakowski Medical Research Centre, Polish Academy of Sciences, Warsaw, Poland
| | - Mancheung Cheung
- Department of Biomedical Engineering, University at Buffalo, Buffalo, 14260, USA
| | - Ewa K Stachowiak
- Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, 14260, USA
| | - Michal K Stachowiak
- Department of Biomedical Engineering, University at Buffalo, Buffalo, 14260, USA.
- Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, 14260, USA.
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40
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Topological analysis reveals state transitions in human gut and marine bacterial communities. NPJ Biofilms Microbiomes 2020; 6:41. [PMID: 33057043 PMCID: PMC7560872 DOI: 10.1038/s41522-020-00145-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Accepted: 08/28/2020] [Indexed: 12/16/2022] Open
Abstract
Microbiome dynamics influence the health and functioning of human physiology and the environment and are driven in part by interactions between large numbers of microbial taxa, making large-scale prediction and modeling a challenge. Here, using topological data analysis, we identify states and dynamical features relevant to macroscopic processes. We show that gut disease processes and marine geochemical events are associated with transitions between community states, defined as topological features of the data density. We find a reproducible two-state succession during recovery from cholera in the gut microbiomes of multiple patients, evidence of dynamic stability in the gut microbiome of a healthy human after experiencing diarrhea during travel, and periodic state transitions in a marine Prochlorococcus community driven by water column cycling. Our approach bridges small-scale fluctuations in microbiome composition and large-scale changes in phenotype without details of underlying mechanisms, and provides an assessment of microbiome stability and its relation to human and environmental health.
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41
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Sajid N, Parr T, Hope TM, Price CJ, Friston KJ. Degeneracy and Redundancy in Active Inference. Cereb Cortex 2020; 30:5750-5766. [PMID: 32488244 PMCID: PMC7899066 DOI: 10.1093/cercor/bhaa148] [Citation(s) in RCA: 21] [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: 02/20/2020] [Revised: 05/11/2020] [Accepted: 05/11/2020] [Indexed: 12/16/2022] Open
Abstract
The notions of degeneracy and redundancy are important constructs in many areas, ranging from genomics through to network science. Degeneracy finds a powerful role in neuroscience, explaining key aspects of distributed processing and structure-function relationships in the brain. For example, degeneracy accounts for the superadditive effect of lesions on functional deficits in terms of a "many-to-one" structure-function mapping. In this paper, we offer a principled account of degeneracy and redundancy, when function is operationalized in terms of active inference, namely, a formulation of perception and action as belief updating under generative models of the world. In brief, "degeneracy" is quantified by the "entropy" of posterior beliefs about the causes of sensations, while "redundancy" is the "complexity" cost incurred by forming those beliefs. From this perspective, degeneracy and redundancy are complementary: Active inference tries to minimize redundancy while maintaining degeneracy. This formulation is substantiated using statistical and mathematical notions of degenerate mappings and statistical efficiency. We then illustrate changes in degeneracy and redundancy during the learning of a word repetition task. Finally, we characterize the effects of lesions-to intrinsic and extrinsic connections-using in silico disconnections. These numerical analyses highlight the fundamental difference between degeneracy and redundancy-and how they score distinct imperatives for perceptual inference and structure learning that are relevant to synthetic and biological intelligence.
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Affiliation(s)
- Noor Sajid
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, WC1N 3AR, UK
| | - Thomas Parr
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, WC1N 3AR, UK
| | - Thomas M Hope
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, WC1N 3AR, UK
| | - Cathy J Price
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, WC1N 3AR, UK
| | - Karl J Friston
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, WC1N 3AR, UK
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42
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Garcia JO, Ashourvan A, Thurman SM, Srinivasan R, Bassett DS, Vettel JM. Reconfigurations within resonating communities of brain regions following TMS reveal different scales of processing. Netw Neurosci 2020; 4:611-636. [PMID: 32885118 PMCID: PMC7462427 DOI: 10.1162/netn_a_00139] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Accepted: 03/23/2020] [Indexed: 11/23/2022] Open
Abstract
An overarching goal of neuroscience research is to understand how heterogeneous neuronal ensembles cohere into networks of coordinated activity to support cognition. To investigate how local activity harmonizes with global signals, we measured electroencephalography (EEG) while single pulses of transcranial magnetic stimulation (TMS) perturbed occipital and parietal cortices. We estimate the rapid network reconfigurations in dynamic network communities within specific frequency bands of the EEG, and characterize two distinct features of network reconfiguration, flexibility and allegiance, among spatially distributed neural sources following TMS. Using distance from the stimulation site to infer local and global effects, we find that alpha activity (8–12 Hz) reflects concurrent local and global effects on network dynamics. Pairwise allegiance of brain regions to communities on average increased near the stimulation site, whereas TMS-induced changes to flexibility were generally invariant to distance and stimulation site. In contrast, communities within the beta (13–20 Hz) band demonstrated a high level of spatial specificity, particularly within a cluster comprising paracentral areas. Together, these results suggest that focal magnetic neurostimulation to distinct cortical sites can help identify both local and global effects on brain network dynamics, and highlight fundamental differences in the manifestation of network reconfigurations within alpha and beta frequency bands. TMS may be used to probe the causal link between local regional activity and global brain dynamics. Using simultaneous TMS-EEG and dynamic community detection, we introduce what we call “resonating communities” or frequency band-specific clusters in the brain, as a way to index local and global processing. These resonating communities within the alpha and beta bands display both global (or integrating) behavior and local specificity, highlighting fundamental differences in the manifestation of network reconfigurations.
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Affiliation(s)
- Javier O Garcia
- U.S. Army CCDC Army Research Laboratory, Aberdeen Proving Ground, MD, USA
| | - Arian Ashourvan
- U.S. Army CCDC Army Research Laboratory, Aberdeen Proving Ground, MD, USA
| | - Steven M Thurman
- U.S. Army CCDC Army Research Laboratory, Aberdeen Proving Ground, MD, USA
| | - Ramesh Srinivasan
- Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Jean M Vettel
- U.S. Army CCDC Army Research Laboratory, Aberdeen Proving Ground, MD, USA
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43
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Connectome spectral analysis to track EEG task dynamics on a subsecond scale. Neuroimage 2020; 221:117137. [PMID: 32652217 DOI: 10.1016/j.neuroimage.2020.117137] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 06/10/2020] [Accepted: 07/02/2020] [Indexed: 12/11/2022] Open
Abstract
We present an approach for tracking fast spatiotemporal cortical dynamics in which we combine white matter connectivity data with source-projected electroencephalographic (EEG) data. We employ the mathematical framework of graph signal processing in order to derive the Fourier modes of the brain structural connectivity graph, or "network harmonics". These network harmonics are naturally ordered by smoothness. Smoothness in this context can be understood as the amount of variation along the cortex, leading to a multi-scale representation of brain connectivity. We demonstrate that network harmonics provide a sparse representation of the EEG signal, where, at certain times, the smoothest 15 network harmonics capture 90% of the signal power. This suggests that network harmonics are functionally meaningful, which we demonstrate by using them as a basis for the functional EEG data recorded from a face detection task. There, only 13 network harmonics are sufficient to track the large-scale cortical activity during the processing of the stimuli with a 50 ms resolution, reproducing well-known activity in the fusiform face area as well as revealing co-activation patterns in somatosensory/motor and frontal cortices that an unconstrained ROI-by-ROI analysis fails to capture. The proposed approach is simple and fast, provides a means of integration of multimodal datasets, and is tied to a theoretical framework in mathematics and physics. Thus, network harmonics point towards promising research directions both theoretically - for example in exploring the relationship between structure and function in the brain - and practically - for example for network tracking in different tasks and groups of individuals, such as patients.
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44
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Qiu L, Nan W. Brain Network Constancy and Participant Recognition: an Integrated Approach to Big Data and Complex Network Analysis. Front Psychol 2020; 11:1003. [PMID: 32581918 PMCID: PMC7283910 DOI: 10.3389/fpsyg.2020.01003] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2019] [Accepted: 04/22/2020] [Indexed: 12/29/2022] Open
Abstract
With the development of big data sharing and data standardization, electroencephalogram (EEG) data are increasingly used in the exploration of human cognitive behavior. Most of the existing studies focus on the changes of human brain network topology (the number of connections, degree distribution, clustering coefficient phantom) in various cognitive behaviors. However, there has been little exploration into the steady state of multi-cognitive behaviors and the recognition of multi-participant brain networks. To solve these two problems, we used EEG data of 99 healthy participants from the PhysioBank to study multi-cognitive behaviors. Specifically, we calculated the symbolic transfer entropy (STE) between 64 electrode sequences of EEG data and constructed the brain networks of various cognitive behaviors of each participant using the directed minimum spanning tree (DMST) algorithm. We then investigated the eigenvalue spectrum of the STE matrix of each individual's cognitive behavior. The results also showed that the spectrum distributions of different cognitive states of the same participant remained relatively stable, but those of the same cognitive state of different participants varied considerably, verifying the relative stability and uniqueness of the human brain network similar to a human's fingerprint. Based on these features, we used the spectral distribution set of 99 participants of various cognitive states as the original data set and developed a spectral distribution set scoring (SDSS) method to identify the brain network participants. It was found that most labels (69.35%) of the test participant with the highest score were identical to the labeled participant. This study provided further evidence for the existence of human brain fingerprints, and furnished a new approach for dynamic identification of brain fingerprints.
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Affiliation(s)
- Lu Qiu
- School of Finance and Business, Shanghai Normal University, Shanghai, China.,Department of Finance, East China University of Science and Technology, Shanghai, China
| | - Wenya Nan
- Department of Psychology, College of Education, Shanghai Normal University, Shanghai, China
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45
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Dynamic representations in networked neural systems. Nat Neurosci 2020; 23:908-917. [PMID: 32541963 DOI: 10.1038/s41593-020-0653-3] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Accepted: 05/12/2020] [Indexed: 11/08/2022]
Abstract
A group of neurons can generate patterns of activity that represent information about stimuli; subsequently, the group can transform and transmit activity patterns across synapses to spatially distributed areas. Recent studies in neuroscience have begun to independently address the two components of information processing: the representation of stimuli in neural activity and the transmission of information in networks that model neural interactions. Yet only recently are studies seeking to link these two types of approaches. Here we briefly review the two separate bodies of literature; we then review the recent strides made to address this gap. We continue with a discussion of how patterns of activity evolve from one representation to another, forming dynamic representations that unfold on the underlying network. Our goal is to offer a holistic framework for understanding and describing neural information representation and transmission while revealing exciting frontiers for future research.
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Krzemiński D, Masuda N, Hamandi K, Singh KD, Routley B, Zhang J. Energy landscape of resting magnetoencephalography reveals fronto-parietal network impairments in epilepsy. Netw Neurosci 2020; 4:374-396. [PMID: 32537532 PMCID: PMC7286306 DOI: 10.1162/netn_a_00125] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Accepted: 01/07/2020] [Indexed: 12/13/2022] Open
Abstract
Juvenile myoclonic epilepsy (JME) is a form of idiopathic generalized epilepsy. It is yet unclear to what extent JME leads to abnormal network activation patterns. Here, we characterized statistical regularities in magnetoencephalograph (MEG) resting-state networks and their differences between JME patients and controls by combining a pairwise maximum entropy model (pMEM) and novel energy landscape analyses for MEG. First, we fitted the pMEM to the MEG oscillatory power in the front-oparietal network (FPN) and other resting-state networks, which provided a good estimation of the occurrence probability of network states. Then, we used energy values derived from the pMEM to depict an energy landscape, with a higher energy state corresponding to a lower occurrence probability. JME patients showed fewer local energy minima than controls and had elevated energy values for the FPN within the theta, beta, and gamma bands. Furthermore, simulations of the fitted pMEM showed that the proportion of time the FPN was occupied within the basins of energy minima was shortened in JME patients. These network alterations were highlighted by significant classification of individual participants employing energy values as multivariate features. Our findings suggested that JME patients had altered multistability in selective functional networks and frequency bands in the fronto-parietal cortices.
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Affiliation(s)
- Dominik Krzemiński
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, United Kingdom
| | - Naoki Masuda
- Department of Engineering Mathematics, University of Bristol, United Kingdom
| | - Khalid Hamandi
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, United Kingdom
| | - Krish D Singh
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, United Kingdom
| | - Bethany Routley
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, United Kingdom
| | - Jiaxiang Zhang
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, United Kingdom
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Miller RL, Pearlson G, Calhoun VD. Whole brain polarity regime dynamics are significantly disrupted in schizophrenia and correlate strongly with network connectivity measures. PLoS One 2019; 14:e0224744. [PMID: 31825974 PMCID: PMC6905532 DOI: 10.1371/journal.pone.0224744] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 10/21/2019] [Indexed: 11/18/2022] Open
Abstract
From a large clinical blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) study, we report several interrelated findings involving transient supra-network brainwide states characterized by a saturation phenomenon we are referring to as "polarization." These are whole-brain states in which the voxelwise-normalized BOLD (vn-BOLD) activation of a large proportion of voxels is simultaneously either very high or very low. The presence of such states during a resting-state fMRI (rs-fMRI) scan is significantly anti-correlated with diagnosed schizophrenia, significantly anti-correlated with connectivity between subcortical networks and auditory, visual and sensorimotor networks and also significantly anti-correlated with contemporaneous occupancy of transient functional network connectivity states featuring broad disconnectivity or strong inhibitory connections between the default mode and other networks. Conversely, the presence of highly polarized vn-BOLD states is significantly correlated with connectivity strength between auditory, visual and sensorimotor networks and with contemporaneous occupancy of transient whole-brain patterns of strongly modularized network connectivity and diffuse hyperconnectivity. Despite their consistency with well-documented effects of schizophrenia on static and time-varying functional network connectivity, the observed relationships between polarization and network connectivity are with very few exceptions unmediated by schizophrenia diagnosis. Many differences observed between patients and controls are echoed within the patient population itself in the effect patterns of positive symptomology (e.g. hallucinations, delusions, grandiosity). Our findings highlight a particular whole-brain spatiotemporal BOLD activation phenomenon that differs markedly between healthy subjects and schizophrenia patients, one that also strongly informs time-resolved network connectivity patterns that are associated with this serious clinical disorder.
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Affiliation(s)
- Robyn L. Miller
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State Univsersity, Georgia Institute of Technology, Emory University, Atlanta, GA, United States of America
- Georgia State University, Atlanta, GA, United States of America
- * E-mail:
| | - Godfrey Pearlson
- Yale University School of Medicine, New Haven, CT, United States of America
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State Univsersity, Georgia Institute of Technology, Emory University, Atlanta, GA, United States of America
- Georgia State University, Atlanta, GA, United States of America
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Bahrami M, Lyday RG, Casanova R, Burdette JH, Simpson SL, Laurienti PJ. Using Low-Dimensional Manifolds to Map Relationships Between Dynamic Brain Networks. Front Hum Neurosci 2019; 13:430. [PMID: 31920590 PMCID: PMC6914694 DOI: 10.3389/fnhum.2019.00430] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Accepted: 11/21/2019] [Indexed: 01/12/2023] Open
Abstract
As the field of dynamic brain networks continues to expand, new methods are needed to allow for optimal handling and understanding of this explosion in data. We propose here a novel approach that embeds dynamic brain networks onto a two-dimensional (2D) manifold based on similarities and differences in network organization. Each brain network is represented as a single point on the low dimensional manifold with networks of similar topology being located in close proximity. The rich spatio-temporal information has great potential for visualization, analysis, and interpretation of dynamic brain networks. The fact that each network is represented by a single point makes it possible to switch between the low-dimensional space and the full connectivity of any given brain network. Thus, networks in a specific region of the low-dimensional space can be examined to identify network features, such as the location of brain network hubs or the interconnectivity between brain circuits. In this proof-of-concept manuscript, we show that these low dimensional manifolds contain meaningful information, as they were able to successfully discriminate between cognitive tasks and study populations. This work provides evidence that embedding dynamic brain networks onto low dimensional manifolds has the potential to help us better visualize and understand dynamic brain networks with the hope of gaining a deeper understanding of normal and abnormal brain dynamics.
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Affiliation(s)
- Mohsen Bahrami
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, United States.,Department of Biomedical Engineering, Virginia Tech-Wake Forest School of Biomedical Engineering and Sciences, Winston-Salem, NC, United States
| | - Robert G Lyday
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, United States.,Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Ramon Casanova
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Jonathan H Burdette
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, United States.,Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Sean L Simpson
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, United States.,Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Paul J Laurienti
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, United States.,Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, United States
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Kang J, Pae C, Park HJ. Graph-theoretical analysis for energy landscape reveals the organization of state transitions in the resting-state human cerebral cortex. PLoS One 2019; 14:e0222161. [PMID: 31498822 PMCID: PMC6733463 DOI: 10.1371/journal.pone.0222161] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Accepted: 08/22/2019] [Indexed: 11/19/2022] Open
Abstract
The resting-state brain is often considered a nonlinear dynamic system transitioning among multiple coexisting stable states. Despite the increasing number of studies on the multistability of the brain system, the processes of state transitions have rarely been systematically explored. Thus, we investigated the state transition processes of the human cerebral cortex system at rest by introducing a graph-theoretical analysis of the state transition network. The energy landscape analysis of brain state occurrences, estimated using the pairwise maximum entropy model for resting-state fMRI data, identified multiple local minima, some of which mediate multi-step transitions toward the global minimum. The state transition among local minima is clustered into two groups according to state transition rates and most inter-group state transitions were mediated by a hub transition state. The distance to the hub transition state determined the path length of the inter-group transition. The cortical system appeared to have redundancy in inter-group transitions when the hub transition state was removed. Such a hub-like organization of transition processes disappeared when the connectivity of the cortical system was altered from the resting-state configuration. In the state transition, the default mode network acts as a transition hub, while coactivation of the prefrontal cortex and default mode network is captured as the global minimum. In summary, the resting-state cerebral cortex has a well-organized architecture of state transitions among stable states, when evaluated by a graph-theoretical analysis of the nonlinear state transition network of the brain.
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Affiliation(s)
- Jiyoung Kang
- Center for Systems and Translational Brain Sciences, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea
- Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Chongwon Pae
- Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Hae-Jeong Park
- Center for Systems and Translational Brain Sciences, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea
- Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
- BK21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Republic of Korea
- Department of Cognitive Science, Yonsei University, Seoul, Republic of Korea
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Awakening: Predicting external stimulation to force transitions between different brain states. Proc Natl Acad Sci U S A 2019; 116:18088-18097. [PMID: 31427539 PMCID: PMC6731634 DOI: 10.1073/pnas.1905534116] [Citation(s) in RCA: 116] [Impact Index Per Article: 23.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
We describe a quantitative and robust definition of a brain state as an ensemble of “metastable substates,” each with a probabilistic stability and occurrence frequency. Fitting this to a generative whole-brain model provides an innovative avenue for predicting where simulated brain stimulation can force transitions between different brain states. We provide proof-of-concept by systematically applying this model framework to neuroimaging data of the human sleep cycle and show where to stimulate to awaken the human sleeping brain and vice versa. These results suggest an avenue for using causal whole-brain models to discover in silico where to force a transition between brain states, which may potentially support recovery in disease. A fundamental problem in systems neuroscience is how to force a transition from one brain state to another by external driven stimulation in, for example, wakefulness, sleep, coma, or neuropsychiatric diseases. This requires a quantitative and robust definition of a brain state, which has so far proven elusive. Here, we provide such a definition, which, together with whole-brain modeling, permits the systematic study in silico of how simulated brain stimulation can force transitions between different brain states in humans. Specifically, we use a unique neuroimaging dataset of human sleep to systematically investigate where to stimulate the brain to force an awakening of the human sleeping brain and vice versa. We show where this is possible using a definition of a brain state as an ensemble of “metastable substates,” each with a probabilistic stability and occurrence frequency fitted by a generative whole-brain model, fine-tuned on the basis of the effective connectivity. Given the biophysical limitations of direct electrical stimulation (DES) of microcircuits, this opens exciting possibilities for discovering stimulation targets and selecting connectivity patterns that can ensure propagation of DES-induced neural excitation, potentially making it possible to create awakenings from complex cases of brain injury.
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