1
|
Hassan U, Singhal A. Convolutional neural network framework for EEG-based ADHD diagnosis in children. Health Inf Sci Syst 2024; 12:44. [PMID: 39224441 PMCID: PMC11365922 DOI: 10.1007/s13755-024-00305-7] [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: 04/29/2024] [Accepted: 08/20/2024] [Indexed: 09/04/2024] Open
Abstract
Purpose Attention-deficit hyperactivity disorder (ADHD) stands as a significant psychiatric and neuro-developmental disorder with global prevalence. The prevalence of ADHD among school children in India is estimated to range from 5% to 8%. However, certain studies have reported higher prevalence rates, reaching as high as 11%. Utilizing electroencephalography (EEG) signals for the early detection and classification of ADHD in children is crucial. Methods In this study, we introduce a CNN architecture characterized by its simplicity, comprising solely two convolutional layers. Our approach involves pre-processing EEG signals through a band-pass filter and segmenting them into 5-s frames. Following this, the frames undergo normalization and canonical correlation analysis. Subsequently, the proposed CNN architecture is employed for training and testing purposes. Results Our methodology yields remarkable results, with 100% accuracy, sensitivity, and specificity when utilizing the complete 19-channel EEG signals for diagnosing ADHD in children. However, employing the entire set of EEG channels presents challenges related to the computational complexity. Therefore, we investigate the feasibility of using only frontal brain EEG channels for ADHD detection, which yields an accuracy of 99.08%. Conclusions The proposed method yields high accuracy and is easy to implement, hence, it has the potential for widespread practical deployment to diagnose ADHD.
Collapse
|
2
|
Benozzo D, Baggio G, Baron G, Chiuso A, Zampieri S, Bertoldo A. Analyzing asymmetry in brain hierarchies with a linear state-space model of resting-state fMRI data. Netw Neurosci 2024; 8:965-988. [PMID: 39355437 PMCID: PMC11424037 DOI: 10.1162/netn_a_00381] [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: 01/09/2024] [Accepted: 05/02/2024] [Indexed: 10/03/2024] Open
Abstract
This study challenges the traditional focus on zero-lag statistics in resting-state functional magnetic resonance imaging (rsfMRI) research. Instead, it advocates for considering time-lag interactions to unveil the directionality and asymmetries of the brain hierarchy. Effective connectivity (EC), the state matrix in dynamical causal modeling (DCM), is a commonly used metric for studying dynamical properties and causal interactions within a linear state-space system description. Here, we focused on how time-lag statistics are incorporated within the framework of DCM resulting in an asymmetric EC matrix. Our approach involves decomposing the EC matrix, revealing a steady-state differential cross-covariance matrix that is responsible for modeling information flow and introducing time-irreversibility. Specifically, the system's dynamics, influenced by the off-diagonal part of the differential covariance, exhibit a curl steady-state flow component that breaks detailed balance and diverges the dynamics from equilibrium. Our empirical findings indicate that the EC matrix's outgoing strengths correlate with the flow described by the differential cross covariance, while incoming strengths are primarily driven by zero-lag covariance, emphasizing conditional independence over directionality.
Collapse
Affiliation(s)
- Danilo Benozzo
- Information Engineering Department, University of Padova, Padova, Italy
| | - Giacomo Baggio
- Information Engineering Department, University of Padova, Padova, Italy
| | - Giorgia Baron
- Information Engineering Department, University of Padova, Padova, Italy
| | - Alessandro Chiuso
- Information Engineering Department, University of Padova, Padova, Italy
| | - Sandro Zampieri
- Information Engineering Department, University of Padova, Padova, Italy
| | - Alessandra Bertoldo
- Information Engineering Department, University of Padova, Padova, Italy
- Padova Neuroscience Center, University of Padova, Padova, Italy
| |
Collapse
|
3
|
Yang YL, Liu YX, Wei J, Guo QL, Hao ZP, Xue JY, Liu JY, Guo H, Li Y. Alterations of resting-state network dynamics in Alzheimer's disease based on leading eigenvector dynamics analysis. J Neurophysiol 2024; 132:744-756. [PMID: 39015075 DOI: 10.1152/jn.00027.2024] [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: 01/16/2024] [Revised: 06/10/2024] [Accepted: 07/11/2024] [Indexed: 07/18/2024] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease, and mild cognitive impairment (MCI) is considered a transitional stage between healthy aging and dementia. Early detection of MCI can help slow down the progression of AD. At present, there are few studies exploring the characteristics of abnormal dynamic brain activity in AD. This article uses a method called leading eigenvector dynamics analysis (LEiDA) to study resting-state functional magnetic resonance imaging (rs-fMRI) data of AD, MCI, and cognitively normal (CN) participants. By identifying repetitive states of phase coherence, intergroup differences in brain dynamic activity indicators are examined, and the neurobehavioral scales were used to assess the relationship between abnormal dynamic activities and cognitive function. The results showed that in the indicators of occurrence probability and lifetime, the globally synchronized state of the patient group decreased. The activity state of the limbic regions significantly detected the difference between AD and the other two groups. Compared to CN, AD and MCI have varying degrees of increase in default and visual region activity states. In addition, in the analysis related to the cognitive scales, it was found that individuals with poorer cognitive abilities were less active in the globally synchronized state and more active in limbic region activity state and visual region activity state. Taken together, these findings reveal abnormal dynamic activity of resting-state networks in patients with AD and MCI, provide new insights into the dynamic analysis of brain networks, and contribute to a deeper understanding of abnormal spatial dynamic patterns in AD patients.NEW & NOTEWORTHY Alzheimer's disease (AD) is a neurodegenerative disease, but few studies have explored the characteristics of abnormal dynamic brain activity in AD patients. Here, our report reveals the abnormal dynamic activity of the patients' resting-state network, providing new insights into the dynamic analysis of brain networks and helping to gain a deeper understanding of the abnormal spatial dynamic patterns in AD patients.
Collapse
Affiliation(s)
- Yan-Li Yang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Yu-Xuan Liu
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Jing Wei
- School of Information, Shanxi University of Finance and Economics, Taiyuan, China
| | - Qi-Li Guo
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Zhi-Peng Hao
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Jia-Yue Xue
- School of Information, Shanxi University of Finance and Economics, Taiyuan, China
| | - Jin-Yi Liu
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Hao Guo
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Yao Li
- School of Software, Taiyuan University of Technology, Taiyuan, China
| |
Collapse
|
4
|
Caprioglio E, Berthouze L. Emergence of metastability in frustrated oscillatory networks: the key role of hierarchical modularity. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 4:1436046. [PMID: 39233777 PMCID: PMC11372895 DOI: 10.3389/fnetp.2024.1436046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Accepted: 08/07/2024] [Indexed: 09/06/2024]
Abstract
Oscillatory complex networks in the metastable regime have been used to study the emergence of integrated and segregated activity in the brain, which are hypothesised to be fundamental for cognition. Yet, the parameters and the underlying mechanisms necessary to achieve the metastable regime are hard to identify, often relying on maximising the correlation with empirical functional connectivity dynamics. Here, we propose and show that the brain's hierarchically modular mesoscale structure alone can give rise to robust metastable dynamics and (metastable) chimera states in the presence of phase frustration. We construct unweighted 3-layer hierarchical networks of identical Kuramoto-Sakaguchi oscillators, parameterized by the average degree of the network and a structural parameter determining the ratio of connections between and within blocks in the upper two layers. Together, these parameters affect the characteristic timescales of the system. Away from the critical synchronization point, we detect the emergence of metastable states in the lowest hierarchical layer coexisting with chimera and metastable states in the upper layers. Using the Laplacian renormalization group flow approach, we uncover two distinct pathways towards achieving the metastable regimes detected in these distinct layers. In the upper layers, we show how the symmetry-breaking states depend on the slow eigenmodes of the system. In the lowest layer instead, metastable dynamics can be achieved as the separation of timescales between layers reaches a critical threshold. Our results show an explicit relationship between metastability, chimera states, and the eigenmodes of the system, bridging the gap between harmonic based studies of empirical data and oscillatory models.
Collapse
Affiliation(s)
- Enrico Caprioglio
- Department of Informatics, University of Sussex, Brighton, United Kingdom
| | - Luc Berthouze
- Department of Informatics, University of Sussex, Brighton, United Kingdom
| |
Collapse
|
5
|
Tang D, Zylberberg J, Jia X, Choi H. Stimulus type shapes the topology of cellular functional networks in mouse visual cortex. Nat Commun 2024; 15:5753. [PMID: 38982078 PMCID: PMC11233648 DOI: 10.1038/s41467-024-49704-0] [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: 07/03/2023] [Accepted: 06/13/2024] [Indexed: 07/11/2024] Open
Abstract
On the timescale of sensory processing, neuronal networks have relatively fixed anatomical connectivity, while functional interactions between neurons can vary depending on the ongoing activity of the neurons within the network. We thus hypothesized that different types of stimuli could lead those networks to display stimulus-dependent functional connectivity patterns. To test this hypothesis, we analyzed single-cell resolution electrophysiological data from the Allen Institute, with simultaneous recordings of stimulus-evoked activity from neurons across 6 different regions of mouse visual cortex. Comparing the functional connectivity patterns during different stimulus types, we made several nontrivial observations: (1) while the frequencies of different functional motifs were preserved across stimuli, the identities of the neurons within those motifs changed; (2) the degree to which functional modules are contained within a single brain region increases with stimulus complexity. Altogether, our work reveals unexpected stimulus-dependence to the way groups of neurons interact to process incoming sensory information.
Collapse
Affiliation(s)
- Disheng Tang
- School of Life Sciences, Tsinghua University, Beijing, 100084, PR China.
- Quantitative Biosciences Program, Georgia Institute of Technology, Atlanta, 30332, GA, USA.
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, 100084, PR China.
| | - Joel Zylberberg
- Department of Physics and Astronomy, and Centre for Vision Research, York University, Toronto, ON M3J 1P3, ON, Canada.
- Learning in Machines and Brains Program, CIFAR, Toronto, ON M5G 1M1, ON, Canada.
| | - Xiaoxuan Jia
- School of Life Sciences, Tsinghua University, Beijing, 100084, PR China.
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, 100084, PR China.
- Tsinghua-Peking Center for Life Sciences, Tsinghua University, Beijing, 100084, PR China.
| | - Hannah Choi
- Quantitative Biosciences Program, Georgia Institute of Technology, Atlanta, 30332, GA, USA.
- School of Mathematics, Georgia Institute of Technology, Atlanta, 30332, GA, USA.
| |
Collapse
|
6
|
Dagnino PC, Galadí JA, Càmara E, Deco G, Escrichs A. Inducing a meditative state by artificial perturbations: A mechanistic understanding of brain dynamics underlying meditation. Netw Neurosci 2024; 8:517-540. [PMID: 38952817 PMCID: PMC11168722 DOI: 10.1162/netn_a_00366] [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: 07/27/2023] [Accepted: 01/29/2024] [Indexed: 07/03/2024] Open
Abstract
Contemplative neuroscience has increasingly explored meditation using neuroimaging. However, the brain mechanisms underlying meditation remain elusive. Here, we implemented a mechanistic framework to explore the spatiotemporal dynamics of expert meditators during meditation and rest, and controls during rest. We first applied a model-free approach by defining a probabilistic metastable substate (PMS) space for each condition, consisting of different probabilities of occurrence from a repertoire of dynamic patterns. Moreover, we implemented a model-based approach by adjusting the PMS of each condition to a whole-brain model, which enabled us to explore in silico perturbations to transition from resting-state to meditation and vice versa. Consequently, we assessed the sensitivity of different brain areas regarding their perturbability and their mechanistic local-global effects. Overall, our work reveals distinct whole-brain dynamics in meditation compared to rest, and how transitions can be induced with localized artificial perturbations. It motivates future work regarding meditation as a practice in health and as a potential therapy for brain disorders.
Collapse
Affiliation(s)
- Paulina Clara Dagnino
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Javier A. Galadí
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | | | - Gustavo Deco
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
- Institució Catalana de la Recerca i Estudis Avancats (ICREA), Barcelona, Spain
| | - Anira Escrichs
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| |
Collapse
|
7
|
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.
Collapse
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
| |
Collapse
|
8
|
Fang Z, Sack AT, Leunissen I. The phase of tACS-entrained pre-SMA beta oscillations modulates motor inhibition. Neuroimage 2024; 290:120572. [PMID: 38490584 DOI: 10.1016/j.neuroimage.2024.120572] [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: 09/15/2023] [Revised: 03/06/2024] [Accepted: 03/08/2024] [Indexed: 03/17/2024] Open
Abstract
Inhibitory control has been linked to beta oscillations in the fronto-basal ganglia network. Here we aim to investigate the functional role of the phase of this oscillatory beta rhythm for successful motor inhibition. We applied 20 Hz transcranial alternating current stimulation (tACS) to the pre-supplementary motor area (pre-SMA) while presenting stop signals at 4 (Experiment 1) and 8 (Experiment 2) equidistant phases of the tACS entrained beta oscillations. Participants showed better inhibitory performance when stop signals were presented at the trough of the beta oscillation whereas their inhibitory control performance decreased with stop signals being presented at the oscillatory beta peak. These results are consistent with the communication through coherence theory, in which postsynaptic effects are thought to be greater when an input arrives at an optimal phase within the oscillatory cycle of the target neuronal population. The current study provides mechanistic insights into the neural communication principles underlying successful motor inhibition and may have implications for phase-specific interventions aimed at treating inhibitory control disorders such as PD or OCD.
Collapse
Affiliation(s)
- Zhou Fang
- Section Brain Stimulation and Cognition, Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, The Netherlands; Maastricht Brain Imaging Centre (MBIC), Maastricht University, Oxfordlaan 55, 6229EV, Maastricht, The Netherlands
| | - Alexander T Sack
- Section Brain Stimulation and Cognition, Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, The Netherlands; Maastricht Brain Imaging Centre (MBIC), Maastricht University, Oxfordlaan 55, 6229EV, Maastricht, The Netherlands; Centre for Integrative Neuroscience, Faculty of Psychology and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, The Netherlands
| | - Inge Leunissen
- Section Brain Stimulation and Cognition, Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, The Netherlands; Maastricht Brain Imaging Centre (MBIC), Maastricht University, Oxfordlaan 55, 6229EV, Maastricht, The Netherlands.
| |
Collapse
|
9
|
Bapat R, Pathak A, Banerjee A. Metastability indexes global changes in the dynamic working point of the brain following brain stimulation. Front Neurorobot 2024; 18:1336438. [PMID: 38440318 PMCID: PMC10909933 DOI: 10.3389/fnbot.2024.1336438] [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: 11/10/2023] [Accepted: 02/01/2024] [Indexed: 03/06/2024] Open
Abstract
Several studies have shown that coordination among neural ensembles is a key to understand human cognition. A well charted path is to identify coordination states associated with cognitive functions from spectral changes in the oscillations of EEG or MEG. A growing number of studies suggest that the tendency to switch between coordination states, sculpts the dynamic repertoire of the brain and can be indexed by a measure known as metastability. In this article, we characterize perturbations in the metastability of global brain network dynamics following Transcranial Magnetic Stimulation that could quantify the duration for which information processing is altered. Thus allowing researchers to understand the network effects of brain stimulation, standardize stimulation protocols and design experimental tasks. We demonstrate the effect empirically using publicly available datasets and use a digital twin (a whole brain connectome model) to understand the dynamic principles that generate such observations. We observed a significant reduction in metastability, concurrent with an increase in coherence following single-pulse TMS reflecting the existence of a window where neural coordination is altered. The reduction in complexity was validated by an additional measure based on the Lempel-Ziv complexity of microstate labeled EEG data. Interestingly, higher frequencies in the EEG signal showed faster recovery in metastability than lower frequencies. The digital twin shed light on how the phase resetting introduced by the single-pulse TMS in local cortical networks can propagate globally, giving rise to changes in metastability and coherence.
Collapse
Affiliation(s)
| | | | - Arpan Banerjee
- Cognitive Brain Dynamics Lab, National Brain Research Centre, Manesar, Haryana, India
| |
Collapse
|
10
|
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.
Collapse
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
| |
Collapse
|
11
|
Dai R, Huang Z, Larkin TE, Tarnal V, Picton P, Vlisides PE, Janke E, McKinney A, Hudetz AG, Harris RE, Mashour GA. Psychedelic concentrations of nitrous oxide reduce functional differentiation in frontoparietal and somatomotor cortical networks. Commun Biol 2023; 6:1284. [PMID: 38114805 PMCID: PMC10730842 DOI: 10.1038/s42003-023-05678-1] [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: 04/26/2023] [Accepted: 12/05/2023] [Indexed: 12/21/2023] Open
Abstract
Despite the longstanding use of nitrous oxide and descriptions of its psychological effects more than a century ago, there is a paucity of neurobiological investigation of associated psychedelic experiences. We measure the brain's functional geometry (through analysis of cortical gradients) and temporal dynamics (through analysis of co-activation patterns) using human resting-state functional magnetic resonance imaging data acquired before and during administration of 35% nitrous oxide. Both analyses demonstrate that nitrous oxide reduces functional differentiation in frontoparietal and somatomotor networks. Importantly, the subjective psychedelic experience induced by nitrous oxide is inversely correlated with the degree of functional differentiation. Thus, like classical psychedelics acting on serotonin receptors, nitrous oxide flattens the functional geometry of the cortex and disrupts temporal dynamics in association with psychoactive effects.
Collapse
Affiliation(s)
- Rui Dai
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Michigan Psychedelic Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Zirui Huang
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
- Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
- Michigan Psychedelic Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, 48109, USA.
| | - Tony E Larkin
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Chronic Pain and Fatigue Research Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Vijay Tarnal
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Michigan Psychedelic Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Paul Picton
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Michigan Psychedelic Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Phillip E Vlisides
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Michigan Psychedelic Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Ellen Janke
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Michigan Psychedelic Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Amy McKinney
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Anthony G Hudetz
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Michigan Psychedelic Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Richard E Harris
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Michigan Psychedelic Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, 48109, USA
- Chronic Pain and Fatigue Research Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - George A Mashour
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Michigan Psychedelic Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Pharmacology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| |
Collapse
|
12
|
Jajcay N, Hlinka J. Towards a dynamical understanding of microstate analysis of M/EEG data. Neuroimage 2023; 281:120371. [PMID: 37716592 DOI: 10.1016/j.neuroimage.2023.120371] [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: 04/13/2023] [Revised: 09/04/2023] [Accepted: 09/08/2023] [Indexed: 09/18/2023] Open
Abstract
One of the interesting aspects of EEG data is the presence of temporally stable and spatially coherent patterns of activity, known as microstates, which have been linked to various cognitive and clinical phenomena. However, there is still no general agreement on the interpretation of microstate analysis. Various clustering algorithms have been used for microstate computation, and multiple studies suggest that the microstate time series may provide insight into the neural activity of the brain in the resting state. This study addresses two gaps in the literature. Firstly, by applying several state-of-the-art microstate algorithms to a large dataset of EEG recordings, we aim to characterise and describe various microstate algorithms. We demonstrate and discuss why the three "classically" used algorithms ((T)AAHC and modified K-Means) yield virtually the same results, while HMM algorithm generates the most dissimilar results. Secondly, we aim to test the hypothesis that dynamical microstate properties might be, to a large extent, determined by the linear characteristics of the underlying EEG signal, in particular, by the cross-covariance and autocorrelation structure of the EEG data. To this end, we generated a Fourier transform surrogate of the EEG signal to compare microstate properties. Here, we found that these are largely similar, thus hinting that microstate properties depend to a very high degree on the linear covariance and autocorrelation structure of the underlying EEG data. Finally, we treated the EEG data as a vector autoregression process, estimated its parameters, and generated surrogate stationary and linear data from fitted VAR. We observed that such a linear model generates microstates highly comparable to those estimated from real EEG data, supporting the conclusion that a linear EEG model can help with the methodological and clinical interpretation of both static and dynamic human brain microstate properties.
Collapse
Affiliation(s)
- Nikola Jajcay
- Center for Advanced Studies of Brain and Consciousness, National Institute of Mental Health, Klecany, 250 67, Czech Republic; Department of Complex Systems, Institute of Computer Science, Czech Academy of Sciences, Prague, 182 07, Czech Republic.
| | - Jaroslav Hlinka
- Center for Advanced Studies of Brain and Consciousness, National Institute of Mental Health, Klecany, 250 67, Czech Republic; Department of Complex Systems, Institute of Computer Science, Czech Academy of Sciences, Prague, 182 07, Czech Republic.
| |
Collapse
|
13
|
Liu Z, Han F, Wang Q. Task-relevant brain dynamics among cognitive subsystems induced by regional stimulation in a whole-brain computational model. Phys Rev E 2023; 108:044402. [PMID: 37978611 DOI: 10.1103/physreve.108.044402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 09/11/2023] [Indexed: 11/19/2023]
Abstract
Cognition involves the global integration of distributed brain regions that are known to work cohesively as cognitive subsystems during brain functioning. Empirical evidence has suggested that spatiotemporal phase relationships between brain regions, measured as synchronization and metastability, may encode important task-relevant information. However, it remains largely unknown how phase relationships aggregate at the level of cognitive subsystems under different cognitive processing. Here, we probe this question by simulating task-relevant brain dynamics through regional stimulation of a whole-brain dynamical network model operating in the resting-state dynamical regime. The model is constructed with structurally embedded Stuart-Laudon oscillators and then fitted with human resting-state functional magnetic resonance imaging data. Based on this framework, we first demonstrate the plausibility of introducing the cognitive system partition into the modeling analysis framework by showing that the clustering of regions across functional networks is better circumscribed by the predefined partition. At the cognitive subsystem level, we focus on how task-relevant phase dynamics are organized in terms of synchronization and metastability. We found that patterns of cognitive synchronization are more task specific, whereas patterns of cognitive metastability are more consistent across different states, suggesting it may encode a more task-general property during cognitive processing, an inherent property conferred by brain organization. This consistent network architecture in cognitive metastability may be related to the distinct functional responses of realistic cognitive systems. We also provide empirical evidence to partially support our computational results. Our paper may provide insights for the mechanisms underlying task-relevant brain dynamics, and establish a model-based link between brain structure, dynamics, and cognition, a fundamental step for computationally aided brain interventions.
Collapse
Affiliation(s)
- Zilu Liu
- Department of Dynamics and Control, Beihang University, Beijing 100191, China
| | - Fang Han
- College of Information Science and Technology, Donghua University, Shanghai 200051, China
| | - Qingyun Wang
- Department of Dynamics and Control, Beihang University, Beijing 100191, China
| |
Collapse
|
14
|
Idesis S, Allegra M, Vohryzek J, Sanz Perl Y, Faskowitz J, Sporns O, Corbetta M, Deco G. A low dimensional embedding of brain dynamics enhances diagnostic accuracy and behavioral prediction in stroke. Sci Rep 2023; 13:15698. [PMID: 37735201 PMCID: PMC10514061 DOI: 10.1038/s41598-023-42533-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 09/11/2023] [Indexed: 09/23/2023] Open
Abstract
Large-scale brain networks reveal structural connections as well as functional synchronization between distinct regions of the brain. The latter, referred to as functional connectivity (FC), can be derived from neuroimaging techniques such as functional magnetic resonance imaging (fMRI). FC studies have shown that brain networks are severely disrupted by stroke. However, since FC data are usually large and high-dimensional, extracting clinically useful information from this vast amount of data is still a great challenge, and our understanding of the functional consequences of stroke remains limited. Here, we propose a dimensionality reduction approach to simplify the analysis of this complex neural data. By using autoencoders, we find a low-dimensional representation encoding the fMRI data which preserves the typical FC anomalies known to be present in stroke patients. By employing the latent representations emerging from the autoencoders, we enhanced patients' diagnostics and severity classification. Furthermore, we showed how low-dimensional representation increased the accuracy of recovery prediction.
Collapse
Affiliation(s)
- Sebastian Idesis
- Center for Brain and Cognition (CBC), Department of Information Technologies and Communications (DTIC), Pompeu Fabra University, Edifici Mercè Rodoreda, Carrer Trias i Fargas 25-27, 08005, Barcelona, Catalonia, Spain.
| | - Michele Allegra
- Padova Neuroscience Center (PNC), University of Padova, via Orus 2/B, 35129, Padua, Italy
- Department of Physics and Astronomy "G. Galilei", University of Padova, via Marzolo 8, 35131, Padua, Italy
| | - Jakub Vohryzek
- Center for Brain and Cognition (CBC), Department of Information Technologies and Communications (DTIC), Pompeu Fabra University, Edifici Mercè Rodoreda, Carrer Trias i Fargas 25-27, 08005, Barcelona, Catalonia, Spain
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK
| | - Yonatan Sanz Perl
- Center for Brain and Cognition (CBC), Department of Information Technologies and Communications (DTIC), Pompeu Fabra University, Edifici Mercè Rodoreda, Carrer Trias i Fargas 25-27, 08005, Barcelona, Catalonia, Spain
- Universidad de San Andrés, Buenos Aires, Argentina
- National Scientific and Technical Research Council, Buenos Aires, Argentina
- Institut du Cerveau et de la Moelle Épinière, ICM, Paris, France
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, 47405, USA
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, 47405, USA
| | - Maurizio Corbetta
- Padova Neuroscience Center (PNC), University of Padova, via Orus 2/B, 35129, Padua, Italy
- Department of Neuroscience, University of Padova, via Giustiniani 5, 35128, Padua, Italy
- Veneto Institute of Molecular Medicine (VIMM), via Orus 2/B, 35129, Padua, Italy
| | - Gustavo Deco
- Center for Brain and Cognition (CBC), Department of Information Technologies and Communications (DTIC), Pompeu Fabra University, Edifici Mercè Rodoreda, Carrer Trias i Fargas 25-27, 08005, Barcelona, Catalonia, Spain
| |
Collapse
|
15
|
Pope M, Seguin C, Varley TF, Faskowitz J, Sporns O. Co-evolving dynamics and topology in a coupled oscillator model of resting brain function. Neuroimage 2023; 277:120266. [PMID: 37414231 DOI: 10.1016/j.neuroimage.2023.120266] [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: 01/25/2023] [Revised: 05/24/2023] [Accepted: 07/04/2023] [Indexed: 07/08/2023] Open
Abstract
Dynamic models of ongoing BOLD fMRI brain dynamics and models of communication strategies have been two important approaches to understanding how brain network structure constrains function. However, dynamic models have yet to widely incorporate one of the most important insights from communication models: the brain may not use all of its connections in the same way or at the same time. Here we present a variation of a phase delayed Kuramoto coupled oscillator model that dynamically limits communication between nodes on each time step. An active subgraph of the empirically derived anatomical brain network is chosen in accordance with the local dynamic state on every time step, thus coupling dynamics and network structure in a novel way. We analyze this model with respect to its fit to empirical time-averaged functional connectivity, finding that, with the addition of only one parameter, it significantly outperforms standard Kuramoto models with phase delays. We also perform analyses on the novel time series of active edges it produces, demonstrating a slowly evolving topology moving through intermittent episodes of integration and segregation. We hope to demonstrate that the exploration of novel modeling mechanisms and the investigation of dynamics of networks in addition to dynamics on networks may advance our understanding of the relationship between brain structure and function.
Collapse
Affiliation(s)
- Maria Pope
- Program in Neuroscience, Indiana University, Bloomington, IN 47405, United States; School of Informatics, Computing & Engineering, Indiana University, Bloomington, IN 47405, United States.
| | - Caio Seguin
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States
| | - Thomas F Varley
- School of Informatics, Computing & Engineering, Indiana University, Bloomington, IN 47405, United States; Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States
| | - Olaf Sporns
- Program in Neuroscience, Indiana University, Bloomington, IN 47405, United States; Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States; Network Science Institute, Indiana University, Bloomington, IN 47405, United States; Cognitive Science Program, Indiana University, Bloomington, IN 47405, United States
| |
Collapse
|
16
|
Luppi AI, Cabral J, Cofre R, Mediano PAM, Rosas FE, Qureshi AY, Kuceyeski A, Tagliazucchi E, Raimondo F, Deco G, Shine JM, Kringelbach ML, Orio P, Ching S, Sanz Perl Y, Diringer MN, Stevens RD, Sitt JD. Computational modelling in disorders of consciousness: Closing the gap towards personalised models for restoring consciousness. Neuroimage 2023; 275:120162. [PMID: 37196986 PMCID: PMC10262065 DOI: 10.1016/j.neuroimage.2023.120162] [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: 01/15/2023] [Revised: 04/16/2023] [Accepted: 05/09/2023] [Indexed: 05/19/2023] Open
Abstract
Disorders of consciousness are complex conditions characterised by persistent loss of responsiveness due to brain injury. They present diagnostic challenges and limited options for treatment, and highlight the urgent need for a more thorough understanding of how human consciousness arises from coordinated neural activity. The increasing availability of multimodal neuroimaging data has given rise to a wide range of clinically- and scientifically-motivated modelling efforts, seeking to improve data-driven stratification of patients, to identify causal mechanisms for patient pathophysiology and loss of consciousness more broadly, and to develop simulations as a means of testing in silico potential treatment avenues to restore consciousness. As a dedicated Working Group of clinicians and neuroscientists of the international Curing Coma Campaign, here we provide our framework and vision to understand the diverse statistical and generative computational modelling approaches that are being employed in this fast-growing field. We identify the gaps that exist between the current state-of-the-art in statistical and biophysical computational modelling in human neuroscience, and the aspirational goal of a mature field of modelling disorders of consciousness; which might drive improved treatments and outcomes in the clinic. Finally, we make several recommendations for how the field as a whole can work together to address these challenges.
Collapse
Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia and Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.
| | - Joana Cabral
- Life and Health Sciences Research Institute, University of Minho, Portugal
| | - Rodrigo Cofre
- CIMFAV-Ingemat, Facultad de Ingeniería, Universidad de Valparaíso, Valparaíso, Chile; Centre National de la Recherche Scientifique (CNRS), Institute of Neuroscience (NeuroPSI), Paris-Saclay University, Gif-sur-Yvette, France
| | - Pedro A M Mediano
- Department of Computing, Imperial College London, London, UK; Department of Psychology, University of Cambridge, Cambridge, UK
| | - Fernando E Rosas
- Department of Informatics, University of Sussex, Brighton, UK; Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, London, UK; Centre for Complexity Science, Imperial College London, London, UK; Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK
| | - Abid Y Qureshi
- University of Kansas Medical Center, Kansas City, MO, USA
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York, USA
| | - Enzo Tagliazucchi
- Departamento de Física (UBA) e Instituto de Fisica de Buenos Aires (CONICET), Buenos Aires, Argentina; Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Federico Raimondo
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Germany; Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Gustavo Deco
- Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain; Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain; Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC, Australia
| | - James M Shine
- Brain and Mind Center, The University of Sydney, Sydney, Australia
| | - Morten L Kringelbach
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK; Department of Psychiatry, University of Oxford, Oxford, UK; Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Patricio Orio
- Centro Interdisciplinario de Neurociencia de Valparaíso and Instituto de Neurociencia, Universidad de Valparaíso, Valparaíso, Chile
| | - ShiNung Ching
- Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Yonatan Sanz Perl
- Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain; Institut du Cerveau et de la Moelle épinière - Paris Brain Institute, ICM, Paris, France; National Scientific and Technical Research Council (CONICET), Godoy Cruz, CABA 2290, Argentina
| | - Michael N Diringer
- Department of Neurology and Neurosurgery, Washington University in St. Louis, St. Louis, MO, USA
| | - Robert D Stevens
- Departments of Anesthesiology and Critical Care Medicine, Neurology, and Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jacobo Diego Sitt
- Institut du Cerveau et de la Moelle épinière - Paris Brain Institute, ICM, Paris, France; Sorbonne Université, Inserm, CNRS, APHP, Hôpital de la Pitié Salpêtrière, Paris, France.
| |
Collapse
|
17
|
Tang D, Zylberberg J, Jia X, Choi H. Stimulus-dependent functional network topology in mouse visual cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.03.547364. [PMID: 37461471 PMCID: PMC10349950 DOI: 10.1101/2023.07.03.547364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/21/2023]
Abstract
Information is processed by networks of neurons in the brain. On the timescale of sensory processing, those neuronal networks have relatively fixed anatomical connectivity, while functional connectivity, which defines the interactions between neurons, can vary depending on the ongoing activity of the neurons within the network. We thus hypothesized that different types of stimuli, which drive different neuronal activities in the network, could lead those networks to display stimulus-dependent functional connectivity patterns. To test this hypothesis, we analyzed electrophysiological data from the Allen Brain Observatory, which utilized Neuropixels probes to simultaneously record stimulus-evoked activity from hundreds of neurons across 6 different regions of mouse visual cortex. The recordings had single-cell resolution and high temporal fidelity, enabling us to determine fine-scale functional connectivity. Comparing the functional connectivity patterns observed when different stimuli were presented to the mice, we made several nontrivial observations. First, while the frequencies of different connectivity motifs (i.e., the patterns of connectivity between triplets of neurons) were preserved across stimuli, the identities of the neurons within those motifs changed. This means that functional connectivity dynamically changes along with the input stimulus, but does so in a way that preserves the motif frequencies. Secondly, we found that the degree to which functional modules are contained within a single brain region (as opposed to being distributed between regions) increases with increasing stimulus complexity. This suggests a mechanism for how the brain could dynamically alter its computations based on its inputs. Altogether, our work reveals unexpected stimulus-dependence to the way groups of neurons interact to process incoming sensory information.
Collapse
Affiliation(s)
- Disheng Tang
- School of Life Sciences, Tsinghua University
- Quantitative Biosciences Program, Georgia Institute of Technology
- IDG/McGovern Institute for Brain Research, Tsinghua University
| | - Joel Zylberberg
- Department of Physics and Astronomy, and Centre for Vision Research, York University
- Learning in Machines and Brains Program, CIFAR
- These authors jointly supervised this work: Joel Zylberberg, Xiaoxuan Jia, Hannah Choi
| | - Xiaoxuan Jia
- School of Life Sciences, Tsinghua University
- IDG/McGovern Institute for Brain Research, Tsinghua University
- Tsinghua–Peking Center for Life Sciences
- Allen Institute for Brain Science
- These authors jointly supervised this work: Joel Zylberberg, Xiaoxuan Jia, Hannah Choi
| | - Hannah Choi
- Quantitative Biosciences Program, Georgia Institute of Technology
- School of Mathematics, Georgia Institute of Technology
- These authors jointly supervised this work: Joel Zylberberg, Xiaoxuan Jia, Hannah Choi
| |
Collapse
|
18
|
Hancock F, Rosas FE, McCutcheon RA, Cabral J, Dipasquale O, Turkheimer FE. Metastability as a candidate neuromechanistic biomarker of schizophrenia pathology. PLoS One 2023; 18:e0282707. [PMID: 36952467 PMCID: PMC10035891 DOI: 10.1371/journal.pone.0282707] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 02/21/2023] [Indexed: 03/25/2023] Open
Abstract
The disconnection hypothesis of schizophrenia proposes that symptoms of the disorder arise as a result of aberrant functional integration between segregated areas of the brain. The concept of metastability characterizes the coexistence of competing tendencies for functional integration and functional segregation in the brain, and is therefore well suited for the study of schizophrenia. In this study, we investigate metastability as a candidate neuromechanistic biomarker of schizophrenia pathology, including a demonstration of reliability and face validity. Group-level discrimination, individual-level classification, pathophysiological relevance, and explanatory power were assessed using two independent case-control studies of schizophrenia, the Human Connectome Project Early Psychosis (HCPEP) study (controls n = 53, non-affective psychosis n = 82) and the Cobre study (controls n = 71, cases n = 59). In this work we extend Leading Eigenvector Dynamic Analysis (LEiDA) to capture specific features of dynamic functional connectivity and then implement a novel approach to estimate metastability. We used non-parametric testing to evaluate group-level differences and a naïve Bayes classifier to discriminate cases from controls. Our results show that our new approach is capable of discriminating cases from controls with elevated effect sizes relative to published literature, reflected in an up to 76% area under the curve (AUC) in out-of-sample classification analyses. Additionally, our new metric showed explanatory power of between 81-92% for measures of integration and segregation. Furthermore, our analyses demonstrated that patients with early psychosis exhibit intermittent disconnectivity of subcortical regions with frontal cortex and cerebellar regions, introducing new insights about the mechanistic bases of these conditions. Overall, these findings demonstrate reliability and face validity of metastability as a candidate neuromechanistic biomarker of schizophrenia pathology.
Collapse
Affiliation(s)
- Fran Hancock
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, De Crespigny Park, London, United Kingdom
| | - Fernando E. Rosas
- Department of Informatics, University of Sussex, Brighton, United Kingdom
- Centre for Psychedelic Research, Department of Brain Science, Imperial College London, London, United Kingdom
- Centre for Complexity Science, Imperial College London, London, United Kingdom
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, United Kingdom
| | - Robert A. McCutcheon
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, De Crespigny Park, London, United Kingdom
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Joana Cabral
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- Life and Health Sciences Research Institute School of Medicine, University of Minho, Braga, Portugal
| | - Ottavia Dipasquale
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, De Crespigny Park, London, United Kingdom
| | - Federico E. Turkheimer
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, De Crespigny Park, London, United Kingdom
| |
Collapse
|
19
|
Wu H, Xie Q, Pan J, Liang Q, Lan Y, Guo Y, Han J, Xie M, Liu Y, Jiang L, Wu X, Li Y, Qin P. Identifying Patients with Cognitive Motor Dissociation Using Resting-state Temporal Stability. Neuroimage 2023; 272:120050. [PMID: 36963740 DOI: 10.1016/j.neuroimage.2023.120050] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 02/04/2023] [Accepted: 03/21/2023] [Indexed: 03/26/2023] Open
Abstract
Using task-dependent neuroimaging techniques, recent studies discovered a fraction of patients with disorders of consciousness (DOC) who had no command-following behaviors but showed a clear sign of awareness as healthy controls, which was defined as cognitive motor dissociation (CMD). However, existing task-dependent approaches might fail when CMD patients have cognitive function (e.g., attention, memory) impairments, in which patients with covert awareness cannot perform a specific task accurately and are thus wrongly considered unconscious, which leads to false-negative findings. Recent studies have suggested that sustaining a stable functional organization over time, i.e., high temporal stability, is crucial for supporting consciousness. Thus, temporal stability could be a powerful tool to detect the patient's cognitive functions (e.g., consciousness), while its alteration in the DOC and its capacity for identifying CMD were unclear. The resting-state fMRI (rs-fMRI) study included 119 participants from three independent research sites. A sliding-window approach was used to investigate global and regional temporal stability, which measured how stable the brain's functional architecture was across time. The temporal stability was compared in the first dataset (36/16 DOC/controls), and then a Support Vector Machine (SVM) classifier was built to discriminate DOC from controls. Furthermore, the generalizability of the SVM classifier was tested in the second independent dataset (35/21 DOC/controls). Finally, the SVM classifier was applied to the third independent dataset, where patients underwent rs-fMRI and brain-computer interface assessment (4/7 CMD/potential non-CMD), to test its performance in identifying CMD. Our results showed that global and regional temporal stability was impaired in DOC patients, especially in regions of the cingulo-opercular task control network, default-mode network, fronto-parietal task control network, and salience network. Using temporal stability as the feature, the SVM model not only showed good performance in the first dataset (accuracy = 90%), but also good generalizability in the second dataset (accuracy = 84%). Most importantly, the SVM model generalized well in identifying CMD in the third dataset (accuracy = 91%). Our preliminary findings suggested that temporal stability could be a potential tool to assist in diagnosing CMD. Furthermore, the temporal stability investigated in this study also contributed to a deeper understanding of the neural mechanism of consciousness.
Collapse
Affiliation(s)
- Hang Wu
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education; School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, Guangdong, 510631, China
| | - Qiuyou Xie
- Joint Center for disorders of consciousness, Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, 510220, China; Centre for Hyperbaric Oxygen and Neurorehabilitation, Guangzhou General Hospital of Guangzhou Military Command, Guangzhou, 510010, China
| | - Jiahui Pan
- School of Software, South China Normal University, Foshan, 528225, China; Pazhou Lab, Guangzhou, 510330, China
| | - Qimei Liang
- Joint Center for disorders of consciousness, Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, 510220, China
| | - Yue Lan
- Joint Center for disorders of consciousness, Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, 510220, China
| | - Yequn Guo
- Centre for Hyperbaric Oxygen and Neurorehabilitation, Guangzhou General Hospital of Guangzhou Military Command, Guangzhou, 510010, China
| | - Junrong Han
- Key Laboratory of Brain, Cognition and Education Science, Ministry of Education, China; Institute for Brain Research and Rehabilitation, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
| | - Musi Xie
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education; School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, Guangdong, 510631, China
| | - Yueyao Liu
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education; School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, Guangdong, 510631, China
| | - Liubei Jiang
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education; School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, Guangdong, 510631, China
| | - Xuehai Wu
- Pazhou Lab, Guangzhou, 510330, China; Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 200433, China; Neurosurgical Institute of Fudan University, Shanghai Clinical Medical Center of Neurosurgery, Shanghai Key laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, 200433, China; State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, School of Basic Medical Sciences and Institutes of Brain Science, Fudan University, Shanghai, 200433, China.
| | - Yuanqing Li
- Pazhou Lab, Guangzhou, 510330, China; School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China.
| | - Pengmin Qin
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education; School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, Guangdong, 510631, China; Pazhou Lab, Guangzhou, 510330, China.
| |
Collapse
|
20
|
Kurtin DL, Scott G, Hebron H, Skeldon AC, Violante IR. Task-based differences in brain state dynamics and their relation to cognitive ability. Neuroimage 2023; 271:119945. [PMID: 36870433 DOI: 10.1016/j.neuroimage.2023.119945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Revised: 02/06/2023] [Accepted: 02/14/2023] [Indexed: 03/06/2023] Open
Abstract
Transient patterns of interregional connectivity form and dissipate in response to varying cognitive demands. Yet, it is not clear how different cognitive demands influence brain state dynamics, and whether these dynamics relate to general cognitive ability. Here, using functional magnetic resonance imaging (fMRI) data, we characterised shared, recurrent, global brain states in 187 participants across the working memory, emotion, language, and relation tasks from the Human Connectome Project. Brain states were determined using Leading Eigenvector Dynamics Analysis (LEiDA). In addition to the LEiDA-based metrics of brain state lifetimes and probabilities, we also computed information-theoretic measures of Block Decomposition Method of complexity, Lempel-Ziv complexity and transition entropy. Information theoretic metrics are notable in their ability to compute relationships amongst sequences of states over time, compared to lifetime and probability, which capture the behaviour of each state in isolation. We then related task-based brain state metrics to fluid intelligence. We observed that brain states exhibited stable topology across a range of numbers of clusters (K = 2:15). Most metrics of brain state dynamics, including state lifetime, probability, and all information theoretic metrics, reliably differed between tasks. However, relationships between state dynamic metrics and cognitive abilities varied according to the task, the metric, and the value of K, indicating that there are contextual relationships between task-dependant state dynamics and trait cognitive ability. This study provides evidence that the brain reconfigures across time in response to cognitive demands, and that there are contextual, rather than generalisable, relationships amongst task, state dynamics, and cognitive ability.
Collapse
Affiliation(s)
- Danielle L Kurtin
- NeuroModulation Lab, Department of Psychology, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK; Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK.
| | - Gregory Scott
- UK Dementia Research Institute, Care Research and Technology Centre at Imperial College, London and the University of Surrey, Guildford, UK; Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK
| | - Henry Hebron
- NeuroModulation Lab, Department of Psychology, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK; UK Dementia Research Institute, Care Research and Technology Centre at Imperial College, London and the University of Surrey, Guildford, UK
| | - Anne C Skeldon
- UK Dementia Research Institute, Care Research and Technology Centre at Imperial College, London and the University of Surrey, Guildford, UK; Department of Mathematics, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, UK
| | - Ines R Violante
- NeuroModulation Lab, Department of Psychology, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK.
| |
Collapse
|
21
|
Sastry NC, Roy D, Banerjee A. Stability of sensorimotor network sculpts the dynamic repertoire of resting state over lifespan. Cereb Cortex 2023; 33:1246-1262. [PMID: 35368068 PMCID: PMC9930636 DOI: 10.1093/cercor/bhac133] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 02/08/2022] [Accepted: 03/07/2022] [Indexed: 11/14/2022] Open
Abstract
Temporally stable patterns of neural coordination among distributed brain regions are crucial for survival. Recently, many studies highlight association between healthy aging and modifications in organization of functional brain networks, across various time-scales. Nonetheless, quantitative characterization of temporal stability of functional brain networks across healthy aging remains unexplored. This study introduces a data-driven unsupervised approach to capture high-dimensional dynamic functional connectivity (dFC) via low-dimensional patterns and subsequent estimation of temporal stability using quantitative metrics. Healthy aging related changes in temporal stability of dFC were characterized across resting-state, movie-viewing, and sensorimotor tasks (SMT) on a large (n = 645) healthy aging dataset (18-88 years). Prominent results reveal that (1) whole-brain temporal dynamics of dFC movie-watching task is closer to resting-state than to SMT with an overall trend of highest temporal stability observed during SMT followed by movie-watching and resting-state, invariant across lifespan aging, (2) in both tasks conditions stability of neurocognitive networks in young adults is higher than older adults, and (3) temporal stability of whole brain resting-state follows a U-shaped curve along lifespan-a pattern shared by sensorimotor network stability indicating their deeper relationship. Overall, the results can be applied generally for studying cohorts of neurological disorders using neuroimaging tools.
Collapse
Affiliation(s)
- Nisha Chetana Sastry
- Cognitive Brain Dynamics Laboratory, National Brain Research Centre, NH 8, Manesar, Gurgaon 122052, India
| | - Dipanjan Roy
- School of Artificial Intelligence & Data Science, Centre for Brain Science & Applications, Indian Institute of Technology, Jodhpur NH 62, Surpura Bypass Rd, Karwar, Rajasthan 342030, India
| | - Arpan Banerjee
- Cognitive Brain Dynamics Laboratory, National Brain Research Centre, NH 8, Manesar, Gurgaon 122052, India
| |
Collapse
|
22
|
Siegel DJ, Drulis C. An interpersonal neurobiology perspective on the mind and mental health: personal, public, and planetary well-being. Ann Gen Psychiatry 2023; 22:5. [PMID: 36737822 PMCID: PMC9897608 DOI: 10.1186/s12991-023-00434-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 01/25/2023] [Indexed: 02/05/2023] Open
Abstract
This article outlines an Interpersonal Neurobiology (IPNB) perspective on the fundamental components that comprise mental health and promote well-being. The central aim of this paper is to answer essential but often overlooked questions related to the field of mental health, such as: What is the mind? What is the basis of well-being? What is the self and how does it develop? We will offer scientific support for the IPNB position that the mind is relational and embodied and that integration is the basis of mental health. It will also describe how the self extends beyond the individual, arising from and inextricably connected to the social, cultural and planetary systems in which we exist. IPNB is not a form of therapy; rather, it is a framework that focuses on deepening our understanding of the mind and human development across the lifespan. Drawing from interdisciplinary principles from a range of fields including physics, mathematics, neuroscience, and psychology, we will provide a practical view of the underlying basis of mental suffering and the scientific mechanisms of change to improve mental well-being. These core principles are building blocks of clinical evaluation and treatment that can be applied across multiple theoretical orientations and client populations. The special emphasis in this article is on the issue of psychache as an underlying cause of suicide and its relationship to personal, public and planetary health.
Collapse
|
23
|
Stumme J, Krämer C, Miller T, Schreiber J, Caspers S, Jockwitz C. Interrelating differences in structural and functional connectivity in the older adult's brain. Hum Brain Mapp 2022; 43:5543-5561. [PMID: 35916531 PMCID: PMC9704795 DOI: 10.1002/hbm.26030] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 07/11/2022] [Accepted: 07/15/2022] [Indexed: 01/15/2023] Open
Abstract
In the normal aging process, the functional connectome restructures and shows a shift from more segregated to more integrated brain networks, which manifests itself in highly different cognitive performances in older adults. Underpinnings of this reorganization are not fully understood, but may be related to age-related differences in structural connectivity, the underlying scaffold for information exchange between regions. The structure-function relationship might be a promising factor to understand the neurobiological sources of interindividual cognitive variability, but remain unclear in older adults. Here, we used diffusion weighted and resting-state functional magnetic resonance imaging as well as cognitive performance data of 573 older subjects from the 1000BRAINS cohort (55-85 years, 287 males) and performed a partial least square regression on 400 regional functional and structural connectivity (FC and SC, respectively) estimates comprising seven resting-state networks. Our aim was to identify FC and SC patterns that are, together with cognitive performance, characteristic of the older adults aging process. Results revealed three different aging profiles prevalent in older adults. FC was found to behave differently depending on the severity of age-related SC deteriorations. A functionally highly interconnected system is associated with a structural connectome that shows only minor age-related decreases. Because this connectivity profile was associated with the most severe age-related cognitive decline, a more interconnected FC system in older adults points to a process of dedifferentiation. Thus, functional network integration appears to increase primarily when SC begins to decline, but this does not appear to mitigate the decline in cognitive performance.
Collapse
Affiliation(s)
- Johanna Stumme
- Institute of Neuroscience and Medicine (INM‐1), Research Centre JülichJülichGermany
- Institute for Anatomy I, Medical Faculty & University Hospital DüsseldorfHeinrich Heine University DüsseldorfDüsseldorfGermany
| | - Camilla Krämer
- Institute of Neuroscience and Medicine (INM‐1), Research Centre JülichJülichGermany
- Institute for Anatomy I, Medical Faculty & University Hospital DüsseldorfHeinrich Heine University DüsseldorfDüsseldorfGermany
| | - Tatiana Miller
- Institute of Neuroscience and Medicine (INM‐1), Research Centre JülichJülichGermany
- Institute for Anatomy I, Medical Faculty & University Hospital DüsseldorfHeinrich Heine University DüsseldorfDüsseldorfGermany
| | - Jan Schreiber
- Institute of Neuroscience and Medicine (INM‐1), Research Centre JülichJülichGermany
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM‐1), Research Centre JülichJülichGermany
- Institute for Anatomy I, Medical Faculty & University Hospital DüsseldorfHeinrich Heine University DüsseldorfDüsseldorfGermany
| | - Christiane Jockwitz
- Institute of Neuroscience and Medicine (INM‐1), Research Centre JülichJülichGermany
- Institute for Anatomy I, Medical Faculty & University Hospital DüsseldorfHeinrich Heine University DüsseldorfDüsseldorfGermany
| |
Collapse
|
24
|
Safron A. Integrated world modeling theory expanded: Implications for the future of consciousness. Front Comput Neurosci 2022; 16:642397. [PMID: 36507308 PMCID: PMC9730424 DOI: 10.3389/fncom.2022.642397] [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: 12/16/2020] [Accepted: 10/24/2022] [Indexed: 11/27/2022] Open
Abstract
Integrated world modeling theory (IWMT) is a synthetic theory of consciousness that uses the free energy principle and active inference (FEP-AI) framework to combine insights from integrated information theory (IIT) and global neuronal workspace theory (GNWT). Here, I first review philosophical principles and neural systems contributing to IWMT's integrative perspective. I then go on to describe predictive processing models of brains and their connections to machine learning architectures, with particular emphasis on autoencoders (perceptual and active inference), turbo-codes (establishment of shared latent spaces for multi-modal integration and inferential synergy), and graph neural networks (spatial and somatic modeling and control). Future directions for IIT and GNWT are considered by exploring ways in which modules and workspaces may be evaluated as both complexes of integrated information and arenas for iterated Bayesian model selection. Based on these considerations, I suggest novel ways in which integrated information might be estimated using concepts from probabilistic graphical models, flow networks, and game theory. Mechanistic and computational principles are also considered with respect to the ongoing debate between IIT and GNWT regarding the physical substrates of different kinds of conscious and unconscious phenomena. I further explore how these ideas might relate to the "Bayesian blur problem," or how it is that a seemingly discrete experience can be generated from probabilistic modeling, with some consideration of analogies from quantum mechanics as potentially revealing different varieties of inferential dynamics. I go on to describe potential means of addressing critiques of causal structure theories based on network unfolding, and the seeming absurdity of conscious expander graphs (without cybernetic symbol grounding). Finally, I discuss future directions for work centered on attentional selection and the evolutionary origins of consciousness as facilitated "unlimited associative learning." While not quite solving the Hard problem, this article expands on IWMT as a unifying model of consciousness and the potential future evolution of minds.
Collapse
Affiliation(s)
- Adam Safron
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Center for Psychedelic and Consciousness Research, Baltimore, MD, United States
- Cognitive Science Program, Indiana University, Bloomington, IN, United States
- Institute for Advanced Consciousness Studies (IACS), Santa Monica, CA, United States
| |
Collapse
|
25
|
Coppola P, Allanson J, Naci L, Adapa R, Finoia P, Williams GB, Pickard JD, Owen AM, Menon DK, Stamatakis EA. The complexity of the stream of consciousness. Commun Biol 2022; 5:1173. [PMID: 36329176 PMCID: PMC9633704 DOI: 10.1038/s42003-022-04109-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 10/10/2022] [Indexed: 11/06/2022] Open
Abstract
Typical consciousness can be defined as an individual-specific stream of experiences. Modern consciousness research on dynamic functional connectivity uses clustering techniques to create common bases on which to compare different individuals. We propose an alternative approach by combining modern theories of consciousness and insights arising from phenomenology and dynamical systems theory. This approach enables a representation of an individual's connectivity dynamics in an intrinsically-defined, individual-specific landscape. Given the wealth of evidence relating functional connectivity to experiential states, we assume this landscape is a proxy measure of an individual's stream of consciousness. By investigating the properties of this landscape in individuals in different states of consciousness, we show that consciousness is associated with short term transitions that are less predictable, quicker, but, on average, more constant. We also show that temporally-specific connectivity states are less easily describable by network patterns that are distant in time, suggesting a richer space of possible states. We show that the cortex, cerebellum and subcortex all display consciousness-relevant dynamics and discuss the implication of our results in forming a point of contact between dynamical systems interpretations and phenomenology.
Collapse
Affiliation(s)
- Peter Coppola
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
- Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
| | - Judith Allanson
- Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
- Department of Neurosciences, Cambridge University Hospitals NHS Foundation, Addenbrooke's Hospital, Cambridge, UK
| | - Lorina Naci
- Trinity College Institute of Neuroscience, School of Psychology, Lloyd Building, Trinity College Dublin, Dublin, Ireland
| | - Ram Adapa
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
| | - Paola Finoia
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
- Division of Neurosurgery, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
| | - Guy B Williams
- Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
- Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK
| | - John D Pickard
- Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
- Division of Neurosurgery, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
- Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK
| | - Adrian M Owen
- The Brain and Mind Institute, Western Interdisciplinary Research Building, N6A 5B7 University of Western Ontario, London, ON, Canada
| | - David K Menon
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
- Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK.
- Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK.
| |
Collapse
|
26
|
Rabuffo G, Sorrentino P, Bernard C, Jirsa V. Spontaneous neuronal avalanches as a correlate of access consciousness. Front Psychol 2022; 13:1008407. [PMID: 36337573 PMCID: PMC9634647 DOI: 10.3389/fpsyg.2022.1008407] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Accepted: 10/04/2022] [Indexed: 09/03/2023] Open
Abstract
Decades of research have advanced our understanding of the biophysical mechanisms underlying consciousness. However, an overarching framework bridging between models of consciousness and the large-scale organization of spontaneous brain activity is still missing. Based on the observation that spontaneous brain activity dynamically switches between epochs of segregation and large-scale integration of information, we hypothesize a brain-state dependence of conscious access, whereby the presence of either segregated or integrated states marks distinct modes of information processing. We first review influential works on the neuronal correlates of consciousness, spontaneous resting-state brain activity and dynamical system theory. Then, we propose a test experiment to validate our hypothesis that conscious access occurs in aperiodic cycles, alternating windows where new incoming information is collected but not experienced, to punctuated short-lived integration events, where conscious access to previously collected content occurs. In particular, we suggest that the integration events correspond to neuronal avalanches, which are collective bursts of neuronal activity ubiquitously observed in electrophysiological recordings. If confirmed, the proposed framework would link the physics of spontaneous cortical dynamics, to the concept of ignition within the global neuronal workspace theory, whereby conscious access manifest itself as a burst of neuronal activity.
Collapse
Affiliation(s)
- Giovanni Rabuffo
- Institut de Neurosciences des Systemes, Aix-Marseille University, Marseille, France
| | | | | | | |
Collapse
|
27
|
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.
Collapse
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
| |
Collapse
|
28
|
O'Byrne J, Jerbi K. How critical is brain criticality? Trends Neurosci 2022; 45:820-837. [PMID: 36096888 DOI: 10.1016/j.tins.2022.08.007] [Citation(s) in RCA: 54] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 07/27/2022] [Accepted: 08/10/2022] [Indexed: 10/31/2022]
Abstract
Criticality is the singular state of complex systems poised at the brink of a phase transition between order and randomness. Such systems display remarkable information-processing capabilities, evoking the compelling hypothesis that the brain may itself be critical. This foundational idea is now drawing renewed interest thanks to high-density data and converging cross-disciplinary knowledge. Together, these lines of inquiry have shed light on the intimate link between criticality, computation, and cognition. Here, we review these emerging trends in criticality neuroscience, highlighting new data pertaining to the edge of chaos and near-criticality, and making a case for the distance to criticality as a useful metric for probing cognitive states and mental illness. This unfolding progress in the field contributes to establishing criticality theory as a powerful mechanistic framework for studying emergent function and its efficiency in both biological and artificial neural networks.
Collapse
Affiliation(s)
- Jordan O'Byrne
- Cognitive and Computational Neuroscience Lab, Psychology Department, University of Montreal, Montreal, Quebec, Canada
| | - Karim Jerbi
- Cognitive and Computational Neuroscience Lab, Psychology Department, University of Montreal, Montreal, Quebec, Canada; MILA (Quebec Artificial Intelligence Institute), Montreal, Quebec, Canada; UNIQUE Center (Quebec Neuro-AI Research Center), Montreal, Quebec, Canada.
| |
Collapse
|
29
|
Panda R, Thibaut A, Lopez-Gonzalez A, Escrichs A, Bahri MA, Hillebrand A, Deco G, Laureys S, Gosseries O, Annen J, Tewarie P. Disruption in structural-functional network repertoire and time-resolved subcortical fronto-temporoparietal connectivity in disorders of consciousness. eLife 2022; 11:e77462. [PMID: 35916363 PMCID: PMC9385205 DOI: 10.7554/elife.77462] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 08/01/2022] [Indexed: 11/13/2022] Open
Abstract
Understanding recovery of consciousness and elucidating its underlying mechanism is believed to be crucial in the field of basic neuroscience and medicine. Ideas such as the global neuronal workspace (GNW) and the mesocircuit theory hypothesize that failure of recovery in conscious states coincide with loss of connectivity between subcortical and frontoparietal areas, a loss of the repertoire of functional networks states and metastable brain activation. We adopted a time-resolved functional connectivity framework to explore these ideas and assessed the repertoire of functional network states as a potential marker of consciousness and its potential ability to tell apart patients in the unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS). In addition, the prediction of these functional network states by underlying hidden spatial patterns in the anatomical network, that is so-called eigenmodes, was supplemented as potential markers. By analysing time-resolved functional connectivity from functional MRI data, we demonstrated a reduction of metastability and functional network repertoire in UWS compared to MCS patients. This was expressed in terms of diminished dwell times and loss of nonstationarity in the default mode network and subcortical fronto-temporoparietal network in UWS compared to MCS patients. We further demonstrated that these findings co-occurred with a loss of dynamic interplay between structural eigenmodes and emerging time-resolved functional connectivity in UWS. These results are, amongst others, in support of the GNW theory and the mesocircuit hypothesis, underpinning the role of time-resolved thalamo-cortical connections and metastability in the recovery of consciousness.
Collapse
Affiliation(s)
- Rajanikant Panda
- Coma Science Group, GIGA-Consciousness, University of LiègeLiègeBelgium
- Centre du Cerveau, University Hospital of LiègeLiègeBelgium
| | - Aurore Thibaut
- Coma Science Group, GIGA-Consciousness, University of LiègeLiègeBelgium
- Centre du Cerveau, University Hospital of LiègeLiègeBelgium
| | - Ane Lopez-Gonzalez
- Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu FabraBracelonaSpain
| | - Anira Escrichs
- Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu FabraBracelonaSpain
| | - Mohamed Ali Bahri
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of LiègeLiègeBelgium
| | - Arjan Hillebrand
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Clinical Neurophysiology and MEG Center, Amsterdam NeuroscienceAmsterdamNetherlands
| | - Gustavo Deco
- Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu FabraBracelonaSpain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA)BarcelonaSpain
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- School of Psychological Sciences, Monash UniversityMelbourneAustralia
| | - Steven Laureys
- Coma Science Group, GIGA-Consciousness, University of LiègeLiègeBelgium
- Centre du Cerveau, University Hospital of LiègeLiègeBelgium
- CERVO Research Center, Laval UniversityQuébecCanada
| | - Olivia Gosseries
- Coma Science Group, GIGA-Consciousness, University of LiègeLiègeBelgium
- Centre du Cerveau, University Hospital of LiègeLiègeBelgium
| | - Jitka Annen
- Coma Science Group, GIGA-Consciousness, University of LiègeLiègeBelgium
- Centre du Cerveau, University Hospital of LiègeLiègeBelgium
| | - Prejaas Tewarie
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Clinical Neurophysiology and MEG Center, Amsterdam NeuroscienceAmsterdamNetherlands
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of NottinghamNottinghamUnited Kingdom
| |
Collapse
|
30
|
Talebi N, Motie Nasrabadi A. Investigating the discrimination of linear and nonlinear effective connectivity patterns of EEG signals in children with Attention-Deficit/Hyperactivity Disorder and Typically Developing children. Comput Biol Med 2022; 148:105791. [PMID: 35863245 DOI: 10.1016/j.compbiomed.2022.105791] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 06/06/2022] [Accepted: 06/26/2022] [Indexed: 11/28/2022]
Abstract
BACKGROUND Analysis of effective connectivity among brain regions is an important key to decipher the mechanisms underlying neural disorders such as Attention Deficit Hyperactivity Disorder (ADHD). We previously introduced a new method, called nCREANN (nonlinear Causal Relationship Estimation by Artificial Neural Network), for estimating linear and nonlinear components of effective connectivity, and provided novel findings about effective connectivity of EEG signals of children with autism. Using the nCREANN method in the present study, we assessed effective connectivity patterns of ADHD children based on their EEG signals recorded during a visual attention task, and compared them with the aged-matched Typically Developing (TD) subjects. METHOD In addition to the nCREANN method for estimating linear and nonlinear aspects of effective connectivity, the direct Directed Transfer Function (dDTF) was utilized to extract the spectral information of connectivity patterns. RESULTS The dDTF results did not suggest a specific frequency band for distinguishing between the two groups, and different patterns of effective connectivity were observed in all bands. Both nCREANN and dDTF methods showed decreased connectivity between temporal/frontal and temporal/occipital regions, and increased connection between frontal/parietal regions in ADHDs than TDs. Furthermore, the nCREANN results showed more left-lateralized connections in ADHDs compared to the symmetric bilateral inter-hemispheric interactions in TDs. In addition, by fusion of linear and nonlinear connectivity measures of nCREANN method, we achieved an accuracy of 99% in classification of the two groups. CONCLUSION These findings emphasize the capability of nCREANN method to investigate the brain functioning of neural disorders and its strength in preciously distinguish between healthy and disordered subjects.
Collapse
Affiliation(s)
- Nasibeh Talebi
- Department of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, Iran.
| | - Ali Motie Nasrabadi
- Department of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, Iran.
| |
Collapse
|
31
|
Biophysical mechanism underlying compensatory preservation of neural synchrony over the adult lifespan. Commun Biol 2022; 5:567. [PMID: 35681107 PMCID: PMC9184644 DOI: 10.1038/s42003-022-03489-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 05/12/2022] [Indexed: 11/17/2022] Open
Abstract
We propose that the preservation of functional integration, estimated from measures of neural synchrony, is a key objective of neurocompensatory mechanisms associated with healthy human ageing. To support this proposal, we demonstrate how phase-locking at the peak alpha frequency in Magnetoencephalography recordings remains invariant over the lifespan in a large cohort of human participants, aged 18-88 years. Using empirically derived connection topologies from diffusion tensor imaging data, we create an in-silico model of whole-brain alpha dynamics. We show that enhancing inter-areal coupling can cancel the effect of increased axonal transmission delays associated with age-related degeneration of white matter tracts, albeit at slower network frequencies. By deriving analytical solutions for simplified connection topologies, we further establish the theoretical principles underlying compensatory network re-organization. Our findings suggest that frequency slowing with age- frequently observed in the alpha band in diverse populations- may be viewed as an epiphenomenon of the underlying compensatory mechanism. Analysis of MEG data from healthy participants and whole-brain network modeling suggests that the brain compensates for age-related disruptions in connectivity by slowing down the frequency of neural synchronization.
Collapse
|
32
|
Hancock F, Rosas FE, Mediano PAM, Luppi AI, Cabral J, Dipasquale O, Turkheimer FE. May the 4C's be with you: an overview of complexity-inspired frameworks for analysing resting-state neuroimaging data. J R Soc Interface 2022; 19:20220214. [PMID: 35765805 PMCID: PMC9240685 DOI: 10.1098/rsif.2022.0214] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 06/09/2022] [Indexed: 11/12/2022] Open
Abstract
Competing and complementary models of resting-state brain dynamics contribute to our phenomenological and mechanistic understanding of whole-brain coordination and communication, and provide potential evidence for differential brain functioning associated with normal and pathological behaviour. These neuroscientific theories stem from the perspectives of physics, engineering, mathematics and psychology and create a complicated landscape of domain-specific terminology and meaning, which, when used outside of that domain, may lead to incorrect assumptions and conclusions within the neuroscience community. Here, we review and clarify the key concepts of connectivity, computation, criticality and coherence-the 4C's-and outline a potential role for metastability as a common denominator across these propositions. We analyse and synthesize whole-brain neuroimaging research, examined through functional magnetic imaging, to demonstrate that complexity science offers a principled and integrated approach to describe, and potentially understand, macroscale spontaneous brain functioning.
Collapse
Affiliation(s)
- Fran Hancock
- 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 Science, Imperial College London, London SW7 2DD, UK
- Data Science Institute, Imperial College London, London SW7 2AZ, UK
- Centre for Complexity Science, Imperial College London, London SW7 2AZ, UK
| | - Pedro A. M. Mediano
- Department of Psychology, University of Cambridge, Cambridge CB2 3EB, UK
- Department of Psychology, Queen Mary University of London, London E1 4NS, UK
| | - Andrea I. Luppi
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Leverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, UK
- Alan Turing Institute, London, UK
| | - Joana Cabral
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Ottavia Dipasquale
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Federico E. Turkheimer
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| |
Collapse
|
33
|
Tewarie P, Prasse B, Meier J, Mandke K, Warrington S, Stam CJ, Brookes MJ, Van Mieghem P, Sotiropoulos SN, Hillebrand A. Predicting time-resolved electrophysiological brain networks from structural eigenmodes. Hum Brain Mapp 2022; 43:4475-4491. [PMID: 35642600 PMCID: PMC9435022 DOI: 10.1002/hbm.25967] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 04/25/2022] [Accepted: 05/16/2022] [Indexed: 01/20/2023] Open
Abstract
How temporal modulations in functional interactions are shaped by the underlying anatomical connections remains an open question. Here, we analyse the role of structural eigenmodes, in the formation and dissolution of temporally evolving functional brain networks using resting-state magnetoencephalography and diffusion magnetic resonance imaging data at the individual subject level. Our results show that even at short timescales, phase and amplitude connectivity can partly be expressed by structural eigenmodes, but hardly by direct structural connections. Albeit a stronger relationship was found between structural eigenmodes and time-resolved amplitude connectivity. Time-resolved connectivity for both phase and amplitude was mostly characterised by a stationary process, superimposed with very brief periods that showed deviations from this stationary process. For these brief periods, dynamic network states were extracted that showed different expressions of eigenmodes. Furthermore, the eigenmode expression was related to overall cognitive performance and co-occurred with fluctuations in community structure of functional networks. These results implicate that ongoing time-resolved resting-state networks, even at short timescales, can to some extent be understood in terms of activation and deactivation of structural eigenmodes and that these eigenmodes play a role in the dynamic integration and segregation of information across the cortex, subserving cognitive functions.
Collapse
Affiliation(s)
- Prejaas Tewarie
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Bastian Prasse
- Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands
| | - Jil Meier
- Department of Neurology, Brain Simulation Section, Charité-Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Kanad Mandke
- Centre for Neuroscience in Education, Department of Psychology, University of Cambridge, Cambridge, UK
| | - Shaun Warrington
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Matthew J Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Piet Van Mieghem
- Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands
| | - Stamatios N Sotiropoulos
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK.,Wellcome Centre for Integrative Neuroimaging (WIN-FMRIB), University of Oxford, Oxford, UK.,NIHR Biomedical Research Centre, University of Nottingham, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| |
Collapse
|
34
|
Müller V. Neural Synchrony and Network Dynamics in Social Interaction: A Hyper-Brain Cell Assembly Hypothesis. Front Hum Neurosci 2022; 16:848026. [PMID: 35572007 PMCID: PMC9101304 DOI: 10.3389/fnhum.2022.848026] [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: 01/03/2022] [Accepted: 03/25/2022] [Indexed: 11/13/2022] Open
Abstract
Mounting neurophysiological evidence suggests that interpersonal interaction relies on continual communication between cell assemblies within interacting brains and continual adjustments of these neuronal dynamic states between the brains. In this Hypothesis and Theory article, a Hyper-Brain Cell Assembly Hypothesis is suggested on the basis of a conceptual review of neural synchrony and network dynamics and their roles in emerging cell assemblies within the interacting brains. The proposed hypothesis states that such cell assemblies can emerge not only within, but also between the interacting brains. More precisely, the hyper-brain cell assembly encompasses and integrates oscillatory activity within and between brains, and represents a common hyper-brain unit, which has a certain relation to social behavior and interaction. Hyper-brain modules or communities, comprising nodes across two or several brains, are considered as one of the possible representations of the hypothesized hyper-brain cell assemblies, which can also have a multidimensional or multilayer structure. It is concluded that the neuronal dynamics during interpersonal interaction is brain-wide, i.e., it is based on common neuronal activity of several brains or, more generally, of the coupled physiological systems including brains.
Collapse
Affiliation(s)
- Viktor Müller
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| |
Collapse
|
35
|
Ristič D, Gosak M. Interlayer Connectivity Affects the Coherence Resonance and Population Activity Patterns in Two-Layered Networks of Excitatory and Inhibitory Neurons. Front Comput Neurosci 2022; 16:885720. [PMID: 35521427 PMCID: PMC9062746 DOI: 10.3389/fncom.2022.885720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 03/24/2022] [Indexed: 11/13/2022] Open
Abstract
The firing patterns of neuronal populations often exhibit emergent collective oscillations, which can display substantial regularity even though the dynamics of individual elements is very stochastic. One of the many phenomena that is often studied in this context is coherence resonance, where additional noise leads to improved regularity of spiking activity in neurons. In this work, we investigate how the coherence resonance phenomenon manifests itself in populations of excitatory and inhibitory neurons. In our simulations, we use the coupled FitzHugh-Nagumo oscillators in the excitable regime and in the presence of neuronal noise. Formally, our model is based on the concept of a two-layered network, where one layer contains inhibitory neurons, the other excitatory neurons, and the interlayer connections represent heterotypic interactions. The neuronal activity is simulated in realistic coupling schemes in which neurons within each layer are connected with undirected connections, whereas neurons of different types are connected with directed interlayer connections. In this setting, we investigate how different neurophysiological determinants affect the coherence resonance. Specifically, we focus on the proportion of inhibitory neurons, the proportion of excitatory interlayer axons, and the architecture of interlayer connections between inhibitory and excitatory neurons. Our results reveal that the regularity of simulated neural activity can be increased by a stronger damping of the excitatory layer. This can be accomplished with a higher proportion of inhibitory neurons, a higher fraction of inhibitory interlayer axons, a stronger coupling between inhibitory axons, or by a heterogeneous configuration of interlayer connections. Our approach of modeling multilayered neuronal networks in combination with stochastic dynamics offers a novel perspective on how the neural architecture can affect neural information processing and provide possible applications in designing networks of artificial neural circuits to optimize their function via noise-induced phenomena.
Collapse
Affiliation(s)
- David Ristič
- Faculty of Natural Sciences and Mathematics, University of Maribor, Maribor, Slovenia
| | - Marko Gosak
- Faculty of Natural Sciences and Mathematics, University of Maribor, Maribor, Slovenia
- Faculty of Medicine, University of Maribor, Maribor, Slovenia
| |
Collapse
|
36
|
Winkler M, Dumont G, Schöll E, Gutkin B. Phase response approaches to neural activity models with distributed delay. BIOLOGICAL CYBERNETICS 2022; 116:191-203. [PMID: 34853889 DOI: 10.1007/s00422-021-00910-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 10/31/2021] [Indexed: 06/13/2023]
Abstract
In weakly coupled neural oscillator networks describing brain dynamics, the coupling delay is often distributed. We present a theoretical framework to calculate the phase response curve of distributed-delay induced limit cycles with infinite-dimensional phase space. Extending previous works, in which non-delayed or discrete-delay systems were investigated, we develop analytical results for phase response curves of oscillatory systems with distributed delay using Gaussian and log-normal delay distributions. We determine the scalar product and normalization condition for the linearized adjoint of the system required for the calculation of the phase response curve. As a paradigmatic example, we apply our technique to the Wilson-Cowan oscillator model of excitatory and inhibitory neuronal populations under the two delay distributions. We calculate and compare the phase response curves for the Gaussian and log-normal delay distributions. The phase response curves obtained from our adjoint calculations match those compiled by the direct perturbation method, thereby proving that the theory of weakly coupled oscillators can be applied successfully for distributed-delay-induced limit cycles. We further use the obtained phase response curves to derive phase interaction functions and determine the possible phase locked states of multiple inter-coupled populations to illuminate different synchronization scenarios. In numerical simulations, we show that the coupling delay distribution can impact the stability of the synchronization between inter-coupled gamma-oscillatory networks.
Collapse
Affiliation(s)
- Marius Winkler
- Group for Neural Theory, LNC INSERM U960, DEC, Ecole Normale Supérieure PSL* University, 24 rue Lhomond, 75005, Paris, France
- Institut für Theoretische Physik, Technische Universität Berlin, Hardenbergstrasse 36, 10623, Berlin, Germany
| | - Grégory Dumont
- Group for Neural Theory, LNC INSERM U960, DEC, Ecole Normale Supérieure PSL* University, 24 rue Lhomond, 75005, Paris, France
| | - Eckehard Schöll
- Institut für Theoretische Physik, Technische Universität Berlin, Hardenbergstrasse 36, 10623, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Humboldt-Universität, Philippstraße 13, 10115, Berlin, Germany
- Potsdam Institute for Climate Impact Research, Telegrafenberg A 31, 14473, Potsdam, Germany
| | - Boris Gutkin
- Group for Neural Theory, LNC INSERM U960, DEC, Ecole Normale Supérieure PSL* University, 24 rue Lhomond, 75005, Paris, France.
- Center for Cognition and Decision Making, Institue for Cognitive Neuroscience, NRU Higher School of Economics, Krivokolenniy sidewalk 3, 101000, Moscow, Russia.
| |
Collapse
|
37
|
Zhang R, Tam SKTS, Wong NML, Wu J, Tao J, Chen L, Lin K, Lee TMC. Aberrant functional metastability and structural connectivity are associated with rumination in individuals with major depressive disorder. Neuroimage Clin 2022; 33:102916. [PMID: 34923200 PMCID: PMC8693354 DOI: 10.1016/j.nicl.2021.102916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 11/30/2021] [Accepted: 12/13/2021] [Indexed: 11/23/2022]
Abstract
Higher synchrony and lower metastability in adults with higher levels of rumination. Prefrontal white matter integrity deficits are also associated with rumination. Most prominent aberrations were found in the genu of the corpus callosum. Structural connectivity as the basis between dynamic connectivity and rumination. New outlook on altered structural integrity and metastability subserving rumination.
Rumination is a repetitive and compulsive thinking focusing on oneself, and the nature and consequences of distress. It is a core characteristic in psychiatric disorders characterized by affective dysregulation, and emerging evidence suggests that rumination is associated with aberrant dynamic functional connectivity and structural connectivity. However, the underlying neural mechanisms remain poorly understood. Here, we adopted a multimodal approach and tested the hypothesis that white matter connectivity forms the basis of the implications of temporal dynamics of functional connectivity in the rumination trait. Fifty-three depressed and ruminative individuals and a control group of 47 age- and gender-matched individuals with low levels of rumination underwent resting-state fMRI and diffusion tensor imaging. We found that lower global metastability and higher global synchrony of the dynamic functional connectivity were associated with higher levels of rumination. Specifically, the altered global synchrony and global metastability mediated the association between white matter integrity of the genu of the corpus callosum to rumination. Hence, our findings offered the first line of evidence for the intricate role of (sub)optimal transition of functional brain states in the connection of structural brain connectivity in ruminative thinking.
Collapse
Affiliation(s)
- Ruibin Zhang
- Laboratory of Cognitive Control and Brain Healthy, Department of Psychology, School of Public Health, Southern Medical University, Department of Psychiatry, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Sammi-Kenzie T S Tam
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China; Laboratory of Neuropsychology and Human Neuroscience, The University of Hong Kong, Hong Kong, China
| | - Nichol M L Wong
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China; Laboratory of Neuropsychology and Human Neuroscience, The University of Hong Kong, Hong Kong, China.
| | - Jingsong Wu
- Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Jing Tao
- Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Lidian Chen
- Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Kangguang Lin
- Department of Affective Disorders, Guangzhou Huiai Hospital, The Affiliated Brain Hospital of Guangzhou Medical University, China.
| | - Tatia M C Lee
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China; Laboratory of Neuropsychology and Human Neuroscience, The University of Hong Kong, Hong Kong, China; Institute of Clinical Neuropsychology, The University of Hong Kong, Hong Kong, China.
| |
Collapse
|
38
|
Bauer LG, Hirsch F, Jones C, Hollander M, Grohs P, Anand A, Plant C, Wohlschläger A. Quantification of Kuramoto Coupling Between Intrinsic Brain Networks Applied to fMRI Data in Major Depressive Disorder. Front Comput Neurosci 2022; 16:729556. [PMID: 35311219 PMCID: PMC8929174 DOI: 10.3389/fncom.2022.729556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 01/28/2022] [Indexed: 11/13/2022] Open
Abstract
Organized patterns of system-wide neural activity adapt fluently within the brain to adjust behavioral performance to environmental demands. In major depressive disorder (MD), markedly different co-activation patterns across the brain emerge from a rather similar structural substrate. Despite the application of advanced methods to describe the functional architecture, e.g., between intrinsic brain networks (IBNs), the underlying mechanisms mediating these differences remain elusive. Here we propose a novel complementary approach for quantifying the functional relations between IBNs based on the Kuramoto model. We directly estimate the Kuramoto coupling parameters (K) from IBN time courses derived from empirical fMRI data in 24 MD patients and 24 healthy controls. We find a large pattern with a significant number of Ks depending on the disease severity score Hamilton D, as assessed by permutation testing. We successfully reproduced the dependency in an independent test data set of 44 MD patients and 37 healthy controls. Comparing the results to functional connectivity from partial correlations (FC), to phase synchrony (PS) as well as to first order auto-regressive measures (AR) between the same IBNs did not show similar correlations. In subsequent validation experiments with artificial data we find that a ground truth of parametric dependencies on artificial regressors can be recovered. The results indicate that the calculation of Ks can be a useful addition to standard methods of quantifying the brain's functional architecture.
Collapse
Affiliation(s)
- Lena G. Bauer
- Research Network Data Science, University of Vienna, Vienna, Austria
| | - Fabian Hirsch
- Departement of Neuroradiology, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
- TUMNIC, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Corey Jones
- Departement of Neuroradiology, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
- TUMNIC, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Matthew Hollander
- Departement of Neuroradiology, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
- TUMNIC, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Philipp Grohs
- Research Network Data Science, University of Vienna, Vienna, Austria
- Faculty of Mathematics, University of Vienna, Vienna, Austria
| | - Amit Anand
- Center for Behavioral Health, Cleveland Clinic, Cleveland, OH, United States
| | - Claudia Plant
- Research Network Data Science, University of Vienna, Vienna, Austria
- Faculty of Computer Science, University of Vienna, Vienna, Austria
| | - Afra Wohlschläger
- Departement of Neuroradiology, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
- TUMNIC, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| |
Collapse
|
39
|
Schirner M, Kong X, Yeo BTT, Deco G, Ritter P. Dynamic primitives of brain network interaction Special Issue "Advances in Mapping the Connectome". Neuroimage 2022; 250:118928. [PMID: 35101596 DOI: 10.1016/j.neuroimage.2022.118928] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 12/03/2021] [Accepted: 01/20/2022] [Indexed: 01/04/2023] Open
Abstract
What dynamic processes underly functional brain networks? Functional connectivity (FC) and functional connectivity dynamics (FCD) are used to represent the patterns and dynamics of functional brain networks. FC(D) is related to the synchrony of brain activity: when brain areas oscillate in a coordinated manner this yields a high correlation between their signal time series. To explain the processes underlying FC(D) we review how synchronized oscillations emerge from coupled neural populations in brain network models (BNMs). From detailed spiking networks to more abstract population models, there is strong support for the idea that the brain operates near critical instabilities that give rise to multistable or metastable dynamics that in turn lead to the intermittently synchronized slow oscillations underlying FC(D). We explore further consequences from these fundamental mechanisms and how they fit with reality. We conclude by highlighting the need for integrative brain models that connect separate mechanisms across levels of description and spatiotemporal scales and link them with cognitive function.
Collapse
Affiliation(s)
- Michael Schirner
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Department of Neurology with Experimental Neurology, Charitéplatz 1, 10117 Berlin, Germany; Bernstein Focus State Dependencies of Learning & Bernstein Center for Computational Neuroscience, Berlin, Germany; Einstein Center for Neuroscience Berlin, Charitéplatz 1, 10117 Berlin, Germany; Einstein Center Digital Future, Wilhelmstraße 67, 10117 Berlin, Germany.
| | - Xiaolu Kong
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore; Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore; Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, 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, Barcelona, Spain; Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; School of Psychological Sciences, Turner Institute for Brain and Mental Health, Monash University, Melbourne, Clayton, Australia
| | - Petra Ritter
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Department of Neurology with Experimental Neurology, Charitéplatz 1, 10117 Berlin, Germany; Bernstein Focus State Dependencies of Learning & Bernstein Center for Computational Neuroscience, Berlin, Germany; Einstein Center for Neuroscience Berlin, Charitéplatz 1, 10117 Berlin, Germany; Einstein Center Digital Future, Wilhelmstraße 67, 10117 Berlin, Germany.
| |
Collapse
|
40
|
Abstract
We offer an account of mental health and well-being using the predictive processing framework (PPF). According to this framework, the difference between mental health and psychopathology can be located in the goodness of the predictive model as a regulator of action. What is crucial for avoiding the rigid patterns of thinking, feeling and acting associated with psychopathology is the regulation of action based on the valence of affective states. In PPF, valence is modelled as error dynamics—the change in prediction errors over time . Our aim in this paper is to show how error dynamics can account for both momentary happiness and longer term well-being. What will emerge is a new neurocomputational framework for making sense of human flourishing.
Collapse
Affiliation(s)
- Mark Miller
- Center for Consciousness and Contemplative StudiesMonash University, Melbourne, Australia
| | - Erik Rietveld
- ILLC/Department of Philosophy, University of Amsterdam, Amsterdamhe Netherlands Department of PhilosophyUniversity of Twente, Enschede, the Netherlands
| | - Julian Kiverstein
- ILLC/Department of Philosophy, University of Amsterdam, Amsterdamhe Netherlands Department of PhilosophyUniversity of Twente, Enschede, the Netherlands
| |
Collapse
|
41
|
Robinson PA. Discrete spectral eigenmode-resonance network of brain dynamics and connectivity. Phys Rev E 2021; 104:034411. [PMID: 34654199 DOI: 10.1103/physreve.104.034411] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 09/02/2021] [Indexed: 12/27/2022]
Abstract
The problem of finding a compact natural representation of brain dynamics and connectivity is addressed using an expansion in terms of physical spatial eigenmodes and their frequency resonances. It is demonstrated that this discrete expansion via the system transfer function enables linear and nonlinear dynamics to be analyzed in compact form in terms of natural dynamic "atoms," each of which is a frequency resonance of an eigenmode. Because these modal resonances are determined by the system dynamics, not the investigator, they are privileged over widely used phenomenological patterns, and obviate the need for artificial discretizations and thresholding in coordinate space. It is shown that modal resonances participate as nodes of a discrete spectral network, are noninteracting in the linear regime, but are linked nonlinearly by wave-wave coalescence and decay processes. The modal resonance formulation is shown to be capable of speeding numerical calculations of strongly nonlinear interactions. Recent work in brain dynamics, especially based on neural field theory (NFT) approaches, allows eigenmodes and their resonances to be estimated from data without assuming a specific brain model. This means that dynamic equations can be inferred using system identification methods from control theory, rather than being assumed, and resonances can be interpreted as control-systems data filters. The results link brain activity and connectivity with control-systems functions such as prediction and attention via gain control and can also be linked to specific NFT predictions if desired, thereby providing a convenient bridge between physiologically based theories and experiment. Amplitudes of modes and resonances can also be tracked to provide a more direct and temporally localized representation of the dynamics than correlations and covariances, which are widely used in the field. By synthesizing many different lines of research, this work provides a way to link quantitative electrophysiological and imaging measurements, connectivity, brain dynamics, and function. This underlines the need to move between coordinate and spectral representations as required. Moreover, standard theoretical-physics approaches and mathematical methods can be used in place of ad hoc statistical measures such as those based on graph theory of artificially discretized and decimated networks, which are highly prone to selection effects and artifacts.
Collapse
Affiliation(s)
- P A Robinson
- School of Physics, University of Sydney, New South Wales 2006, Australia and Center for Integrative Brain Function, University of Sydney, New South Wales 2006, Australia
| |
Collapse
|
42
|
Cakan C, Jajcay N, Obermayer K. neurolib: A Simulation Framework for Whole-Brain Neural Mass Modeling. Cognit Comput 2021. [DOI: 10.1007/s12559-021-09931-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Abstractneurolib is a computational framework for whole-brain modeling written in Python. It provides a set of neural mass models that represent the average activity of a brain region on a mesoscopic scale. In a whole-brain network model, brain regions are connected with each other based on biologically informed structural connectivity, i.e., the connectome of the brain. neurolib can load structural and functional datasets, set up a whole-brain model, manage its parameters, simulate it, and organize its outputs for later analysis. The activity of each brain region can be converted into a simulated BOLD signal in order to calibrate the model against empirical data from functional magnetic resonance imaging (fMRI). Extensive model analysis is made possible using a parameter exploration module, which allows one to characterize a model’s behavior as a function of changing parameters. An optimization module is provided for fitting models to multimodal empirical data using evolutionary algorithms. neurolib is designed to be extendable and allows for easy implementation of custom neural mass models, offering a versatile platform for computational neuroscientists for prototyping models, managing large numerical experiments, studying the structure–function relationship of brain networks, and for performing in-silico optimization of whole-brain models.
Collapse
|
43
|
Heggli OA, Konvalinka I, Kringelbach ML, Vuust P. A metastable attractor model of self-other integration (MEAMSO) in rhythmic synchronization. Philos Trans R Soc Lond B Biol Sci 2021; 376:20200332. [PMID: 34420393 DOI: 10.1098/rstb.2020.0332] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Human interaction is often accompanied by synchronized bodily rhythms. Such synchronization may emerge spontaneously as when a crowd's applause turns into a steady beat, be encouraged as in nursery rhymes, or be intentional as in the case of playing music together. The latter has been extensively studied using joint finger-tapping paradigms as a simplified version of rhythmic interpersonal synchronization. A key finding is that synchronization in such cases is multifaceted, with synchronized behaviour resting upon different synchronization strategies such as mutual adaptation, leading-following and leading-leading. However, there are multiple open questions regarding the mechanism behind these strategies and how they develop dynamically over time. Here, we propose a metastable attractor model of self-other integration (MEAMSO). This model conceptualizes dyadic rhythmic interpersonal synchronization as a process of integrating and segregating signals of self and other. Perceived sounds are continuously evaluated as either being attributed to self-produced or other-produced actions. The model entails a metastable system with two particular attractor states: one where an individual maintains two separate predictive models for self- and other-produced actions, and the other where these two predictive models integrate into one. The MEAMSO explains the three known synchronization strategies and makes testable predictions about the dynamics of interpersonal synchronization both in behaviour and the brain. This article is part of the theme issue 'Synchrony and rhythm interaction: from the brain to behavioural ecology'.
Collapse
Affiliation(s)
- Ole Adrian Heggli
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University and the Royal Academy of Music Aarhus/Aalborg, Aarhus, Denmark
| | - Ivana Konvalinka
- SINe Lab, Section for Cognitive Systems, DTU Compute, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Morten L Kringelbach
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University and the Royal Academy of Music Aarhus/Aalborg, Aarhus, Denmark.,Centre for Eudaimonia and Human Flourishing, Department of Psychiatry, University of Oxford, Oxford, UK
| | - Peter Vuust
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University and the Royal Academy of Music Aarhus/Aalborg, Aarhus, Denmark
| |
Collapse
|
44
|
López-González A, Panda R, Ponce-Alvarez A, Zamora-López G, Escrichs A, Martial C, Thibaut A, Gosseries O, Kringelbach ML, Annen J, Laureys S, Deco G. Loss of consciousness reduces the stability of brain hubs and the heterogeneity of brain dynamics. Commun Biol 2021; 4:1037. [PMID: 34489535 PMCID: PMC8421429 DOI: 10.1038/s42003-021-02537-9] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 08/11/2021] [Indexed: 01/07/2023] Open
Abstract
Low-level states of consciousness are characterized by disruptions of brain activity that sustain arousal and awareness. Yet, how structural, dynamical, local and network brain properties interplay in the different levels of consciousness is unknown. Here, we study fMRI brain dynamics from patients that suffered brain injuries leading to a disorder of consciousness and from healthy subjects undergoing propofol-induced sedation. We show that pathological and pharmacological low-level states of consciousness display less recurrent, less connected and more segregated synchronization patterns than conscious state. We use whole-brain models built upon healthy and injured structural connectivity to interpret these dynamical effects. We found that low-level states of consciousness were associated with reduced network interactions, together with more homogeneous and more structurally constrained local dynamics. Notably, these changes lead the structural hub regions to lose their stability during low-level states of consciousness, thus attenuating the differences between hubs and non-hubs brain dynamics. López-González et al study the fMRI brain dynamics and their underlying mechanism from patients that suffered brain injuries leading to a disorder of consciousness as well as from healthy subjects undergoing propofol-induced sedation. They show that pathological and pharmacological low-level states of consciousness display disrupted synchronization patterns, higher constraint to the anatomy and a loss of heterogeneity and stability in the structural hubs compared to conscious states.
Collapse
Affiliation(s)
- Ane López-González
- Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain.
| | - Rajanikant Panda
- GIGA-Consciousness, Coma Science Group, University of Liège, Liège, Belgium.,Centre du Cerveau2, University Hospital of Liège, Liège, Belgium
| | - Adrián Ponce-Alvarez
- Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
| | - Gorka Zamora-López
- Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
| | - Anira Escrichs
- Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
| | - Charlotte Martial
- GIGA-Consciousness, Coma Science Group, University of Liège, Liège, Belgium.,Centre du Cerveau2, University Hospital of Liège, Liège, Belgium
| | - Aurore Thibaut
- GIGA-Consciousness, Coma Science Group, University of Liège, Liège, Belgium.,Centre du Cerveau2, University Hospital of Liège, Liège, Belgium
| | - Olivia Gosseries
- GIGA-Consciousness, Coma Science Group, University of Liège, Liège, Belgium.,Centre du Cerveau2, University Hospital of Liège, Liège, Belgium
| | - Morten L Kringelbach
- Department of Psychiatry, University of Oxford, Oxford, UK.,Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus C, Denmark.,Life and Health Sciences Research Institute, School of Medicine, University of Minho, Braga, Portugal
| | - Jitka Annen
- GIGA-Consciousness, Coma Science Group, University of Liège, Liège, Belgium.,Centre du Cerveau2, University Hospital of Liège, Liège, Belgium
| | - Steven Laureys
- GIGA-Consciousness, Coma Science Group, University of Liège, Liège, Belgium.,Centre du Cerveau2, University Hospital of Liège, Liège, Belgium
| | - Gustavo Deco
- Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain.,Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| |
Collapse
|
45
|
Rué-Queralt J, Stevner A, Tagliazucchi E, Laufs H, Kringelbach ML, Deco G, Atasoy S. Decoding brain states on the intrinsic manifold of human brain dynamics across wakefulness and sleep. Commun Biol 2021; 4:854. [PMID: 34244598 PMCID: PMC8270946 DOI: 10.1038/s42003-021-02369-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 06/18/2021] [Indexed: 02/06/2023] Open
Abstract
Current state-of-the-art functional magnetic resonance imaging (fMRI) offers remarkable imaging quality and resolution, yet, the intrinsic dimensionality of brain dynamics in different states (wakefulness, light and deep sleep) remains unknown. Here we present a method to reveal the low dimensional intrinsic manifold underlying human brain dynamics, which is invariant of the high dimensional spatio-temporal representation of the neuroimaging technology. By applying this intrinsic manifold framework to fMRI data acquired in wakefulness and sleep, we reveal the nonlinear differences between wakefulness and three different sleep stages, and successfully decode these different brain states with a mean accuracy across participants of 96%. Remarkably, a further group analysis shows that the intrinsic manifolds of all participants share a common topology. Overall, our results reveal the intrinsic manifold underlying the spatiotemporal dynamics of brain activity and demonstrate how this manifold enables the decoding of different brain states such as wakefulness and various sleep stages.
Collapse
Affiliation(s)
- Joan Rué-Queralt
- grid.5612.00000 0001 2172 2676Center of Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
| | - Angus Stevner
- grid.4991.50000 0004 1936 8948Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK ,grid.7048.b0000 0001 1956 2722Center for Music in the Brain, Aarhus University, Aarhus, Denmark
| | - Enzo Tagliazucchi
- grid.7345.50000 0001 0056 1981Instituto de Física de Buenos Aires and Physics Deparment (University of Buenos Aires), Buenos Aires, Argentina
| | - Helmut Laufs
- grid.7839.50000 0004 1936 9721Department of Neurology and Brain Imaging Center, Goethe University, Frankfurt am Main, Germany ,grid.9764.c0000 0001 2153 9986Department of Neurology, University Hospital Schleswig-Holstein, Christian-Albrechts-University, Kiel, Germany
| | - Morten L. Kringelbach
- grid.4991.50000 0004 1936 8948Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK ,grid.7048.b0000 0001 1956 2722Center for Music in the Brain, Aarhus University, Aarhus, Denmark
| | - Gustavo Deco
- grid.5612.00000 0001 2172 2676Center of Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain ,grid.425902.80000 0000 9601 989XInstitució Catalana de Recerca i Estudis Avancats (ICREA), Barcelona, Spain ,grid.419524.f0000 0001 0041 5028Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany ,grid.1002.30000 0004 1936 7857School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Selen Atasoy
- grid.4991.50000 0004 1936 8948Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK ,grid.7048.b0000 0001 1956 2722Center for Music in the Brain, Aarhus University, Aarhus, Denmark
| |
Collapse
|
46
|
Safron A. The Radically Embodied Conscious Cybernetic Bayesian Brain: From Free Energy to Free Will and Back Again. ENTROPY (BASEL, SWITZERLAND) 2021; 23:783. [PMID: 34202965 PMCID: PMC8234656 DOI: 10.3390/e23060783] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 05/12/2021] [Accepted: 05/27/2021] [Indexed: 11/24/2022]
Abstract
Drawing from both enactivist and cognitivist perspectives on mind, I propose that explaining teleological phenomena may require reappraising both "Cartesian theaters" and mental homunculi in terms of embodied self-models (ESMs), understood as body maps with agentic properties, functioning as predictive-memory systems and cybernetic controllers. Quasi-homuncular ESMs are suggested to constitute a major organizing principle for neural architectures due to their initial and ongoing significance for solutions to inference problems in cognitive (and affective) development. Embodied experiences provide foundational lessons in learning curriculums in which agents explore increasingly challenging problem spaces, so answering an unresolved question in Bayesian cognitive science: what are biologically plausible mechanisms for equipping learners with sufficiently powerful inductive biases to adequately constrain inference spaces? Drawing on models from neurophysiology, psychology, and developmental robotics, I describe how embodiment provides fundamental sources of empirical priors (as reliably learnable posterior expectations). If ESMs play this kind of foundational role in cognitive development, then bidirectional linkages will be found between all sensory modalities and frontal-parietal control hierarchies, so infusing all senses with somatic-motoric properties, thereby structuring all perception by relevant affordances, so solving frame problems for embodied agents. Drawing upon the Free Energy Principle and Active Inference framework, I describe a particular mechanism for intentional action selection via consciously imagined (and explicitly represented) goal realization, where contrasts between desired and present states influence ongoing policy selection via predictive coding mechanisms and backward-chained imaginings (as self-realizing predictions). This embodied developmental legacy suggests a mechanism by which imaginings can be intentionally shaped by (internalized) partially-expressed motor acts, so providing means of agentic control for attention, working memory, imagination, and behavior. I further describe the nature(s) of mental causation and self-control, and also provide an account of readiness potentials in Libet paradigms wherein conscious intentions shape causal streams leading to enaction. Finally, I provide neurophenomenological handlings of prototypical qualia including pleasure, pain, and desire in terms of self-annihilating free energy gradients via quasi-synesthetic interoceptive active inference. In brief, this manuscript is intended to illustrate how radically embodied minds may create foundations for intelligence (as capacity for learning and inference), consciousness (as somatically-grounded self-world modeling), and will (as deployment of predictive models for enacting valued goals).
Collapse
Affiliation(s)
- Adam Safron
- Center for Psychedelic and Consciousness Research, Johns Hopkins University School of Medicine, Baltimore, MD 21218, USA;
- Kinsey Institute, Indiana University, Bloomington, IN 47405, USA
- Cognitive Science Program, Indiana University, Bloomington, IN 47405, USA
| |
Collapse
|
47
|
Jung K, Eickhoff SB, Popovych OV. Tractography density affects whole-brain structural architecture and resting-state dynamical modeling. Neuroimage 2021; 237:118176. [PMID: 34000399 DOI: 10.1016/j.neuroimage.2021.118176] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 05/09/2021] [Accepted: 05/13/2021] [Indexed: 11/24/2022] Open
Abstract
Dynamical modeling of the resting-state brain dynamics essentially relies on the empirical neuroimaging data utilized for the model derivation and validation. There is however still no standardized data processing for magnetic resonance imaging pipelines and the structural and functional connectomes involved in the models. In this study, we thus address how the parameters of diffusion-weighted data processing for structural connectivity (SC) can influence the validation results of the whole-brain mathematical models informed by SC. For this, we introduce a set of simulation conditions including the varying number of total streamlines of the whole-brain tractography (WBT) used for extraction of SC, cortical parcellations based on functional and anatomical brain properties and distinct model fitting modalities. The main objective of this study is to explore how the quality of the model validation can vary across the considered simulation conditions. We observed that the graph-theoretical network properties of structural connectome can be affected by varying tractography density and strongly relate to the model performance. We also found that the optimal number of the total streamlines of WBT can vary for different brain atlases. Consequently, we suggest a way how to improve the model performance based on the network properties and the optimal parameter configurations from multiple WBT conditions. Furthermore, the population of subjects can be stratified into subgroups with divergent behaviors induced by the varying WBT density such that different recommendations can be made with respect to the data processing for individual subjects and brain parcellations.
Collapse
Affiliation(s)
- Kyesam Jung
- Institute of Neuroscience and Medicine, Brain and Behavior (INM-7), Research Center Jülich, Germany; Institute for Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Germany.
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain and Behavior (INM-7), Research Center Jülich, Germany; Institute for Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Germany.
| | - Oleksandr V Popovych
- Institute of Neuroscience and Medicine, Brain and Behavior (INM-7), Research Center Jülich, Germany; Institute for Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Germany.
| |
Collapse
|
48
|
Synchronous Brain Dynamics Establish Brief States of Communality in Distant Neuronal Populations. eNeuro 2021; 8:ENEURO.0005-21.2021. [PMID: 33875454 PMCID: PMC8116110 DOI: 10.1523/eneuro.0005-21.2021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 02/22/2021] [Accepted: 03/11/2021] [Indexed: 11/21/2022] Open
Abstract
Intrinsic brain dynamics co-fluctuate between distant regions in an organized manner during rest, establishing large-scale functional networks. We investigate these brain dynamics on a millisecond time scale by focusing on electroencephalographic (EEG) source analyses. While synchrony is thought of as a neuronal mechanism grouping distant neuronal populations into assemblies, the relevance of simultaneous zero-lag synchronization between brain areas in humans remains largely unexplored. This negligence is because of the confound of volume conduction, leading inherently to temporal dependencies of source estimates derived from scalp EEG [and magnetoencephalography (MEG)], referred to as spatial leakage. Here, we focus on the analyses of simultaneous, i.e., quasi zero-lag related, synchronization that cannot be explained by spatial leakage phenomenon. In eighteen subjects during rest with eyes closed, we provide evidence that first, simultaneous synchronization is present between distant brain areas and second, that this long-range synchronization is occurring in brief epochs, i.e., 54-80 ms. Simultaneous synchronization might signify the functional convergence of remote neuronal populations. Given the simultaneity of distant regions, these synchronization patterns might relate to the representation and maintenance, rather than processing of information. This long-range synchronization is briefly stable, not persistently, indicating flexible spatial reconfiguration pertaining to the establishment of particular, re-occurring states. Taken together, we suggest that the balance between temporal stability and spatial flexibility of long-range, simultaneous synchronization patterns is characteristic of the dynamic coordination of large-scale functional brain networks. As such, quasi zero-phase related EEG source fluctuations are physiologically meaningful if spatial leakage is considered appropriately.
Collapse
|
49
|
Baspinar E, Schülen L, Olmi S, Zakharova A. Coherence resonance in neuronal populations: Mean-field versus network model. Phys Rev E 2021; 103:032308. [PMID: 33862689 DOI: 10.1103/physreve.103.032308] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Accepted: 02/22/2021] [Indexed: 01/17/2023]
Abstract
The counterintuitive phenomenon of coherence resonance describes a nonmonotonic behavior of the regularity of noise-induced oscillations in the excitable regime, leading to an optimal response in terms of regularity of the excited oscillations for an intermediate noise intensity. We study this phenomenon in populations of FitzHugh-Nagumo (FHN) neurons with different coupling architectures. For networks of FHN systems in an excitable regime, coherence resonance has been previously analyzed numerically. Here we focus on an analytical approach studying the mean-field limits of the globally and locally coupled populations. The mean-field limit refers to an averaged behavior of a complex network as the number of elements goes to infinity. We apply the mean-field approach to the globally coupled FHN network. Further, we derive a mean-field limit approximating the locally coupled FHN network with low noise intensities. We study the effects of the coupling strength and noise intensity on coherence resonance for both the network and the mean-field models. We compare the results of the mean-field and network frameworks and find good agreement in the globally coupled case, where the correspondence between the two approaches is sufficiently good to capture the emergence of coherence resonance, as well as of anticoherence resonance.
Collapse
Affiliation(s)
- Emre Baspinar
- Inria Sophia Antipolis Méditerranée Research Centre, 2004 Route des Lucioles, 06902 Valbonne, France
| | - Leonhard Schülen
- Institut für Theoretische Physik, Technische Universität Berlin, Hardenbergstraße 36, 10623 Berlin, Germany
| | - Simona Olmi
- Inria Sophia Antipolis Méditerranée Research Centre, 2004 Route des Lucioles, 06902 Valbonne, France.,CNR - Consiglio Nazionale delle Ricerche - Istituto dei Sistemi complessi, 50019, Sesto Fiorentino, Italy.,Joint Senior Authorship
| | - Anna Zakharova
- Institut für Theoretische Physik, Technische Universität Berlin, Hardenbergstraße 36, 10623 Berlin, Germany.,Joint Senior Authorship
| |
Collapse
|
50
|
Escrichs A, Biarnes C, Garre-Olmo J, Fernández-Real JM, Ramos R, Pamplona R, Brugada R, Serena J, Ramió-Torrentà L, Coll-De-Tuero G, Gallart L, Barretina J, Vilanova JC, Mayneris-Perxachs J, Essig M, Figley CR, Pedraza S, Puig J, Deco G. Whole-Brain Dynamics in Aging: Disruptions in Functional Connectivity and the Role of the Rich Club. Cereb Cortex 2021; 31:2466-2481. [PMID: 33350451 DOI: 10.1093/cercor/bhaa367] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Revised: 10/29/2020] [Accepted: 10/29/2020] [Indexed: 12/19/2022] Open
Abstract
Normal aging causes disruptions in the brain that can lead to cognitive decline. Resting-state functional magnetic resonance imaging studies have found significant age-related alterations in functional connectivity across various networks. Nevertheless, most of the studies have focused mainly on static functional connectivity. Studying the dynamics of resting-state brain activity across the whole-brain functional network can provide a better characterization of age-related changes. Here, we employed two data-driven whole-brain approaches based on the phase synchronization of blood-oxygen-level-dependent signals to analyze resting-state fMRI data from 620 subjects divided into two groups (middle-age group (n = 310); age range, 50-64 years versus older group (n = 310); age range, 65-91 years). Applying the intrinsic-ignition framework to assess the effect of spontaneous local activation events on local-global integration, we found that the older group showed higher intrinsic ignition across the whole-brain functional network, but lower metastability. Using Leading Eigenvector Dynamics Analysis, we found that the older group showed reduced ability to access a metastable substate that closely overlaps with the so-called rich club. These findings suggest that functional whole-brain dynamics are altered in aging, probably due to a deficiency in a metastable substate that is key for efficient global communication in the brain.
Collapse
Affiliation(s)
- Anira Escrichs
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
| | - Carles Biarnes
- Department of Radiology (IDI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain
| | - Josep Garre-Olmo
- Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain.,Institut d'Assistència Sanitària, Salt (Girona), Spain
| | - José Manuel Fernández-Real
- Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain.,Department of Diabetes, Endocrinology and Nutrition, IDIBGI, Hospital Universitari de Girona Dr Josep Trueta, and CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Girona, Spain
| | - Rafel Ramos
- Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain.,Vascular Health Research Group of Girona (ISV-Girona), Institut Universitari d'Investigació en Atenció Primària Jordi Gol (IDIAP Jordi Gol), Girona, Spain.,Primary Care Services, Catalan Institute of Health (ICS), Girona, Spain
| | - Reinald Pamplona
- Department of Experimental Medicine, Faculty of Medicine, University of Lleida-IRBLleida, Lleida, Spain
| | - Ramon Brugada
- Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain.,Cardiovascular Genetics Center, IDIBGI, CIBER-CV, Girona, Spain
| | - Joaquin Serena
- Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain.,Department of Neurology, Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain
| | - Lluís Ramió-Torrentà
- Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain.,Department of Neurology, Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain
| | - Gabriel Coll-De-Tuero
- Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain.,Vascular Health Research Group of Girona (ISV-Girona), Institut Universitari d'Investigació en Atenció Primària Jordi Gol (IDIAP Jordi Gol), Girona, Spain.,CIBER of Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Luís Gallart
- Biobanc, Girona Biomedical Research Institute (IDIBGI), Girona, Spain
| | - Jordi Barretina
- Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain
| | - Joan C Vilanova
- Department of Radiology (IDI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain
| | - Jordi Mayneris-Perxachs
- Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Department of Diabetes, Endocrinology and Nutrition, IDIBGI, Hospital Universitari de Girona Dr Josep Trueta, and CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Girona, Spain
| | - Marco Essig
- Department of Radiology, University of Manitoba, Winnipeg, Canada
| | - Chase R Figley
- Department of Radiology, University of Manitoba, Winnipeg, Canada
| | - Salvador Pedraza
- Department of Radiology (IDI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain
| | - Josep Puig
- Department of Radiology (IDI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain
| | - Gustavo Deco
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain.,Institucio Catalana de la Recerca i Estudis Avancats (ICREA), Barcelona, Catalonia, Spain.,Department of Neuropsychology, Max Planck Institute for human Cognitive and Brain Sciences, Leipzig, Germany.,Turner Institute for Brain and Mental Health, Monash University, Melbourne, Victoria, Australia
| |
Collapse
|