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Verma P, Ranasinghe K, Prasad J, Cai C, Xie X, Lerner H, Mizuiri D, Miller B, Rankin K, Vossel K, Cheung SW, Nagarajan SS, Raj A. Impaired long-range excitatory time scale predicts abnormal neural oscillations and cognitive deficits in Alzheimer's disease. Alzheimers Res Ther 2024; 16:62. [PMID: 38504361 PMCID: PMC10953266 DOI: 10.1186/s13195-024-01426-7] [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: 12/07/2023] [Accepted: 03/04/2024] [Indexed: 03/21/2024]
Abstract
BACKGROUND Alzheimer's disease (AD) is the most common form of dementia, progressively impairing cognitive abilities. While neuroimaging studies have revealed functional abnormalities in AD, how these relate to aberrant neuronal circuit mechanisms remains unclear. Using magnetoencephalography imaging we documented abnormal local neural synchrony patterns in patients with AD. To identify global abnormal biophysical mechanisms underlying the spatial and spectral electrophysiological patterns in AD, we estimated the parameters of a biophysical spectral graph model (SGM). METHODS SGM is an analytic neural mass model that describes how long-range fiber projections in the brain mediate the excitatory and inhibitory activity of local neuronal subpopulations. Unlike other coupled neuronal mass models, the SGM is linear, available in closed-form, and parameterized by a small set of biophysical interpretable global parameters. This facilitates their rapid and unambiguous inference which we performed here on a well-characterized clinical population of patients with AD (N = 88, age = 62.73 +/- 8.64 years) and a cohort of age-matched controls (N = 88, age = 65.07 +/- 9.92 years). RESULTS Patients with AD showed significantly elevated long-range excitatory neuronal time scales, local excitatory neuronal time scales and local inhibitory neural synaptic strength. The long-range excitatory time scale had a larger effect size, compared to local excitatory time scale and inhibitory synaptic strength and contributed highest for the accurate classification of patients with AD from controls. Furthermore, increased long-range time scale was associated with greater deficits in global cognition. CONCLUSIONS These results demonstrate that long-range excitatory time scale of neuronal activity, despite being a global measure, is a key determinant in the local spectral signatures and cognition in the human brain, and how it might be a parsimonious factor underlying altered neuronal activity in AD. Our findings provide new insights into mechanistic links between abnormal local spectral signatures and global connectivity measures in AD.
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Affiliation(s)
- Parul Verma
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
| | - Kamalini Ranasinghe
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | | | - Chang Cai
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Xihe Xie
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Hannah Lerner
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Danielle Mizuiri
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Bruce Miller
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Katherine Rankin
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Keith Vossel
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
- Mary S. Easton Center for Alzheimer's Research and Care, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Steven W Cheung
- Department of Otolaryngology-Head and Neck Surgery, University of California San Francisco, San Francisco, CA, USA
- Surgical Services, Veterans Affairs, San Francisco, USA
| | - Srikantan S Nagarajan
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Ashish Raj
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
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Williams N, Ojanperä A, Siebenhühner F, Toselli B, Palva S, Arnulfo G, Kaski S, Palva JM. The influence of inter-regional delays in generating large-scale brain networks of phase synchronization. Neuroimage 2023; 279:120318. [PMID: 37572765 DOI: 10.1016/j.neuroimage.2023.120318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 07/14/2023] [Accepted: 08/10/2023] [Indexed: 08/14/2023] Open
Abstract
Large-scale networks of phase synchronization are considered to regulate the communication between brain regions fundamental to cognitive function, but the mapping to their structural substrates, i.e., the structure-function relationship, remains poorly understood. Biophysical Network Models (BNMs) have demonstrated the influences of local oscillatory activity and inter-regional anatomical connections in generating alpha-band (8-12 Hz) networks of phase synchronization observed with Electroencephalography (EEG) and Magnetoencephalography (MEG). Yet, the influence of inter-regional conduction delays remains unknown. In this study, we compared a BNM with standard "distance-dependent delays", which assumes constant conduction velocity, to BNMs with delays specified by two alternative methods accounting for spatially varying conduction velocities, "isochronous delays" and "mixed delays". We followed the Approximate Bayesian Computation (ABC) workflow, i) specifying neurophysiologically informed prior distributions of BNM parameters, ii) verifying the suitability of the prior distributions with Prior Predictive Checks, iii) fitting each of the three BNMs to alpha-band MEG resting-state data (N = 75) with Bayesian optimization for Likelihood-Free Inference (BOLFI), and iv) choosing between the fitted BNMs with ABC model comparison on a separate MEG dataset (N = 30). Prior Predictive Checks revealed the range of dynamics generated by each of the BNMs to encompass those seen in the MEG data, suggesting the suitability of the prior distributions. Fitting the models to MEG data yielded reliable posterior distributions of the parameters of each of the BNMs. Finally, model comparison revealed the BNM with "distance-dependent delays", as the most probable to describe the generation of alpha-band networks of phase synchronization seen in MEG. These findings suggest that distance-dependent delays might contribute to the neocortical architecture of human alpha-band networks of phase synchronization. Hence, our study illuminates the role of inter-regional delays in generating the large-scale networks of phase synchronization that might subserve the communication between regions vital to cognition.
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Affiliation(s)
- N Williams
- Helsinki Institute of Information Technology, Department of Computer Science, Aalto University, Finland; Department of Neuroscience and Biomedical Engineering, Aalto University, Finland.
| | - A Ojanperä
- Department of Computer Science, Aalto University, Finland
| | - F Siebenhühner
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Finland; BioMag laboratory, HUS Medical Imaging Center, Helsinki, Finland
| | - B Toselli
- Department of Informatics, Bioengineering, Robotics & Systems Engineering, University of Genoa, Italy
| | - S Palva
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Finland; Centre for Cognitive Neuroimaging, School of Neuroscience & Psychology, University of Glasgow, United Kingdom
| | - G Arnulfo
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Finland; Department of Informatics, Bioengineering, Robotics & Systems Engineering, University of Genoa, Italy
| | - S Kaski
- Helsinki Institute of Information Technology, Department of Computer Science, Aalto University, Finland; Department of Computer Science, Aalto University, Finland; Department of Computer Science, University of Manchester, United Kingdom
| | - J M Palva
- Department of Neuroscience and Biomedical Engineering, Aalto University, Finland; Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Finland; Centre for Cognitive Neuroimaging, School of Neuroscience & Psychology, University of Glasgow, United Kingdom
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Cabrera-Álvarez J, Sánchez-Claros J, Carrasco-Gómez M, del Cerro-León A, Gómez-Ariza CJ, Maestú F, Mirasso CR, Susi G. Understanding the effects of cortical gyrification in tACS: insights from experiments and computational models. Front Neurosci 2023; 17:1223950. [PMID: 37655010 PMCID: PMC10467425 DOI: 10.3389/fnins.2023.1223950] [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: 05/16/2023] [Accepted: 07/25/2023] [Indexed: 09/02/2023] Open
Abstract
The alpha rhythm is often associated with relaxed wakefulness or idling and is altered by various factors. Abnormalities in the alpha rhythm have been linked to several neurological and psychiatric disorders, including Alzheimer's disease. Transcranial alternating current stimulation (tACS) has been proposed as a potential tool to restore a disrupted alpha rhythm in the brain by stimulating at the individual alpha frequency (IAF), although some research has produced contradictory results. In this study, we applied an IAF-tACS protocol over parieto-occipital areas to a sample of healthy subjects and measured its effects over the power spectra. Additionally, we used computational models to get a deeper understanding of the results observed in the experiment. Both experimental and numerical results showed an increase in alpha power of 8.02% with respect to the sham condition in a widespread set of regions in the cortex, excluding some expected parietal regions. This result could be partially explained by taking into account the orientation of the electric field with respect to the columnar structures of the cortex, showing that the gyrification in parietal regions could generate effects in opposite directions (hyper-/depolarization) at the same time in specific brain regions. Additionally, we used a network model of spiking neuronal populations to explore the effects that these opposite polarities could have on neural activity, and we found that the best predictor of alpha power was the average of the normal components of the electric field. To sum up, our study sheds light on the mechanisms underlying tACS brain activity modulation, using both empirical and computational approaches. Non-invasive brain stimulation techniques hold promise for treating brain disorders, but further research is needed to fully understand and control their effects on brain dynamics and cognition. Our findings contribute to this growing body of research and provide a foundation for future studies aimed at optimizing the use of non-invasive brain stimulation in clinical settings.
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Affiliation(s)
- Jesús Cabrera-Álvarez
- Centre for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain
- Department of Experimental Psychology, Complutense University of Madrid, Madrid, Spain
| | - Jaime Sánchez-Claros
- Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC, UIB-CSIC), Campus UIB, Palma de Mallorca, Spain
| | - Martín Carrasco-Gómez
- Centre for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain
| | - Alberto del Cerro-León
- Centre for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain
- Department of Experimental Psychology, Complutense University of Madrid, Madrid, Spain
| | | | - Fernando Maestú
- Centre for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain
- Department of Experimental Psychology, Complutense University of Madrid, Madrid, Spain
| | - Claudio R. Mirasso
- Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC, UIB-CSIC), Campus UIB, Palma de Mallorca, Spain
| | - Gianluca Susi
- Centre for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain
- Department of Structure of Matter, Thermal Physics and Electronics, School of Physics, Complutense University of Madrid, Madrid, Spain
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Portoles O, Qin Y, Hadida J, Woolrich M, Cao M, van Vugt M. Modulations of local synchrony over time lead to resting-state functional connectivity in a parsimonious large-scale brain model. PLoS One 2022; 17:e0275819. [PMID: 36288273 PMCID: PMC9604991 DOI: 10.1371/journal.pone.0275819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Accepted: 09/24/2022] [Indexed: 11/30/2022] Open
Abstract
Biophysical models of large-scale brain activity are a fundamental tool for understanding the mechanisms underlying the patterns observed with neuroimaging. These models combine a macroscopic description of the within- and between-ensemble dynamics of neurons within a single architecture. A challenge for these models is accounting for modulations of within-ensemble synchrony over time. Such modulations in local synchrony are fundamental for modeling behavioral tasks and resting-state activity. Another challenge comes from the difficulty in parametrizing large scale brain models which hinders researching principles related with between-ensembles differences. Here we derive a parsimonious large scale brain model that can describe fluctuations of local synchrony. Crucially, we do not reduce within-ensemble dynamics to macroscopic variables first, instead we consider within and between-ensemble interactions similarly while preserving their physiological differences. The dynamics of within-ensemble synchrony can be tuned with a parameter which manipulates local connectivity strength. We simulated resting-state static and time-resolved functional connectivity of alpha band envelopes in models with identical and dissimilar local connectivities. We show that functional connectivity emerges when there are high fluctuations of local and global synchrony simultaneously (i.e. metastable dynamics). We also show that for most ensembles, leaning towards local asynchrony or synchrony correlates with the functional connectivity with other ensembles, with the exception of some regions belonging to the default-mode network.
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Affiliation(s)
- Oscar Portoles
- Faculty of Science and Engineering, University of Groningen, Groningen, The Netherlands
- * E-mail:
| | - Yuzhen Qin
- Department of Mechanical Engineering, University of California, Riverside, California, United States of America
| | - Jonathan Hadida
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Mark Woolrich
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Ming Cao
- Faculty of Science and Engineering, University of Groningen, Groningen, The Netherlands
| | - Marieke van Vugt
- Faculty of Science and Engineering, University of Groningen, Groningen, The Netherlands
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5
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Edgar JC, Berman JI, Liu S, Chen YH, Huang M, Brodkin ES, Roberts TPL, Bloy L. Two mechanisms facilitate regional independence between brain regions based on an examination of alpha-band activity in healthy control adult males. Int J Psychophysiol 2022; 178:51-59. [PMID: 35718287 PMCID: PMC10155819 DOI: 10.1016/j.ijpsycho.2022.06.007] [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: 10/14/2021] [Revised: 04/26/2022] [Accepted: 06/10/2022] [Indexed: 11/29/2022]
Abstract
BACKGROUND At rest, 8 to 12 Hz alpha rhythms are the dominant rhythm in the brain, with a common peak alpha frequency (PAF = the frequency at which alpha generators show maximum power) observed across brain regions. Although a common PAF across brain regions should result in high between-region connectivity, especially connectivity measures assessing the phase-similarity between alpha generators, high inter-regional alpha connectivity has not been observed. The present study was conducted as an initial step toward identifying mechanisms that allow brain regions to maintain functional independence in the presence of a common PAF. METHODS MEG data were obtained from 16 healthy control male adults (mean age = 24 years; range 21 to 30 years). A task requiring participants to alternate between a 10 s eyes-closed condition and a 5 s eyes-open condition was used to drive parietal-occipital alpha generators, with the 10 s eyes-closed condition eliciting large-amplitude alpha activity and thus providing alpha measures with good signal-to-noise ratio for source localization. Alpha source-space measures were obtained using Vector-based Spatial-Temporal Analysis using L1-minimum-norm. In each participant, the four strongest parietal-occipital alpha generators were identified. Connectivity between sources was assessed via a measure of phase-based connectivity called inter-site phase clustering (ISPC). RESULTS Intra-class correlations (ICC) showed very high similarity in the average PAF (=computed using all eyes-closed data) between the four alpha sources (ICC single measure = 0.88, p < 0.001). Despite a common average PAF, across participants, significant ISPC was often observed no more than that expected by chance. Examination of the alpha time course data indicated that low ISPC was often due to instantaneous changes in alpha phase (phase slips). ISPC analyses removing data with phase slips indicated that low ISPC was also due to slight continuous changes in the alpha frequency, with frequency drift more likely in non-significant than significant ISPC trials. CONCLUSIONS The present exploratory effort suggested two processes underlying the lack of observed inter-regional alpha phase coherence that may help maintain regional functional independence even in the presence of a common PAF. In particular, although the alpha generators were observed to oscillate at the same rate on average, across time each alpha generator oscillated a little slower or faster, and about every one and a half seconds an alpha generator abruptly lost the beat. Because of this, functional independence among alpha generators (and thus brain regions) was the rule rather than the exception. Studies replicating these processes that allow brain regions to maintain functional independence, using different source localization methods and in different conditions (e.g., a true resting state), are warranted. IMPACT STATEMENT Using source localization to measure parietal-occipital alpha generator activity, two properties that limit between-region alpha functional connectivity are proposed. In particular, a model of alpha generator activity is offered where via transient phase slips occurring approximately every 1.5 s, as well as slight non-stationarity in the alpha frequency, brain regions retain a common alpha frequency while also maintaining regional identity and presumably functionality. Findings also suggest novel markers for use in studies examining changes in alpha activity across maturation as well as in studies examining alpha activity in patient populations where alpha abnormalities have been reported.
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Affiliation(s)
| | | | - Song Liu
- The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Yu-Han Chen
- The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Mingxiong Huang
- The University of California San Diego, Department of Radiology, San Diego, CA, USA; San Diego VA Healthcare System, Department of Radiology, San Diego, CA, USA
| | - Edward S Brodkin
- Department of Psychiatry, Center for Neurobiology and Behavior, Perelman School of Medicine at the University of Pennsylvania, Translational Research Laboratory, Philadelphia, PA, USA
| | | | - Luke Bloy
- The Children's Hospital of Philadelphia, Philadelphia, PA, USA
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6
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Pathak A, Roy D, Banerjee A. Whole-Brain Network Models: From Physics to Bedside. Front Comput Neurosci 2022; 16:866517. [PMID: 35694610 PMCID: PMC9180729 DOI: 10.3389/fncom.2022.866517] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 05/02/2022] [Indexed: 11/13/2022] Open
Abstract
Computational neuroscience has come a long way from its humble origins in the pioneering work of Hodgkin and Huxley. Contemporary computational models of the brain span multiple spatiotemporal scales, from single neuronal compartments to models of social cognition. Each spatial scale comes with its own unique set of promises and challenges. Here, we review models of large-scale neural communication facilitated by white matter tracts, also known as whole-brain models (WBMs). Whole-brain approaches employ inputs from neuroimaging data and insights from graph theory and non-linear systems theory to model brain-wide dynamics. Over the years, WBM models have shown promise in providing predictive insights into various facets of neuropathologies such as Alzheimer's disease, Schizophrenia, Epilepsy, Traumatic brain injury, while also offering mechanistic insights into large-scale cortical communication. First, we briefly trace the history of WBMs, leading up to the state-of-the-art. We discuss various methodological considerations for implementing a whole-brain modeling pipeline, such as choice of node dynamics, model fitting and appropriate parcellations. We then demonstrate the applicability of WBMs toward understanding various neuropathologies. We conclude by discussing ways of augmenting the biological and clinical validity of whole-brain models.
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Affiliation(s)
| | - Dipanjan Roy
- Centre for Brain Science and Applications, School of Artificial Intelligence and Data Science, Indian Institute of Technology, Jodhpur, India
| | - Arpan Banerjee
- National Brain Research Centre, Gurgaon, India
- *Correspondence: Arpan Banerjee
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7
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Verma P, Nagarajan S, Raj A. Spectral graph theory of brain oscillations - revisited and improved. Neuroimage 2022; 249:118919. [PMID: 35051584 PMCID: PMC9506601 DOI: 10.1016/j.neuroimage.2022.118919] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 12/11/2021] [Accepted: 01/16/2022] [Indexed: 11/30/2022] Open
Abstract
Mathematical modeling of the relationship between the functional activity and the structural wiring of the brain has largely been undertaken using non-linear and biophysically detailed mathematical models with regionally varying parameters. While this approach provides us a rich repertoire of multistable dynamics that can be displayed by the brain, it is computationally demanding. Moreover, although neuronal dynamics at the microscopic level are nonlinear and chaotic, it is unclear if such detailed nonlinear models are required to capture the emergent meso-(regional population ensemble) and macro-scale (whole brain) behavior, which is largely deterministic and reproducible across individuals. Indeed, recent modeling effort based on spectral graph theory has shown that an analytical model without regionally varying parameters and without multistable dynamics can capture the empirical magnetoencephalography frequency spectra and the spatial patterns of the alpha and beta frequency bands accurately. In this work, we demonstrate an improved hierarchical, linearized, and analytic spectral graph theory-based model that can capture the frequency spectra obtained from magnetoencephalography recordings of resting healthy subjects. We reformulated the spectral graph theory model in line with classical neural mass models, therefore providing more biologically interpretable parameters, especially at the local scale. We demonstrated that this model performs better than the original model when comparing the spectral correlation of modeled frequency spectra and that obtained from the magnetoencephalography recordings. This model also performs equally well in predicting the spatial patterns of the empirical alpha and beta frequency bands.
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Affiliation(s)
- Parul Verma
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 400 Parnassus Avenue, San Francisco, CA, 94117, USA.
| | - Srikantan Nagarajan
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 400 Parnassus Avenue, San Francisco, CA, 94117, USA.
| | - Ashish Raj
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 400 Parnassus Avenue, San Francisco, CA, 94117, USA.
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FNS allows efficient event-driven spiking neural network simulations based on a neuron model supporting spike latency. Sci Rep 2021; 11:12160. [PMID: 34108523 PMCID: PMC8190312 DOI: 10.1038/s41598-021-91513-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Accepted: 05/24/2021] [Indexed: 02/05/2023] Open
Abstract
Neural modelling tools are increasingly employed to describe, explain, and predict the human brain's behavior. Among them, spiking neural networks (SNNs) make possible the simulation of neural activity at the level of single neurons, but their use is often threatened by the resources needed in terms of processing capabilities and memory. Emerging applications where a low energy burden is required (e.g. implanted neuroprostheses) motivate the exploration of new strategies able to capture the relevant principles of neuronal dynamics in reduced and efficient models. The recent Leaky Integrate-and-Fire with Latency (LIFL) spiking neuron model shows some realistic neuronal features and efficiency at the same time, a combination of characteristics that may result appealing for SNN-based brain modelling. In this paper we introduce FNS, the first LIFL-based SNN framework, which combines spiking/synaptic modelling with the event-driven approach, allowing us to define heterogeneous neuron groups and multi-scale connectivity, with delayed connections and plastic synapses. FNS allows multi-thread, precise simulations, integrating a novel parallelization strategy and a mechanism of periodic dumping. We evaluate the performance of FNS in terms of simulation time and used memory, and compare it with those obtained with neuronal models having a similar neurocomputational profile, implemented in NEST, showing that FNS performs better in both scenarios. FNS can be advantageously used to explore the interaction within and between populations of spiking neurons, even for long time-scales and with a limited hardware configuration.
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Ziaeemehr A, Valizadeh A. Frequency-Resolved Functional Connectivity: Role of Delay and the Strength of Connections. Front Neural Circuits 2021; 15:608655. [PMID: 33841105 PMCID: PMC8024621 DOI: 10.3389/fncir.2021.608655] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 02/26/2021] [Indexed: 12/04/2022] Open
Abstract
The brain functional network extracted from the BOLD signals reveals the correlated activity of the different brain regions, which is hypothesized to underlie the integration of the information across functionally specialized areas. Functional networks are not static and change over time and in different brain states, enabling the nervous system to engage and disengage different local areas in specific tasks on demand. Due to the low temporal resolution, however, BOLD signals do not allow the exploration of spectral properties of the brain dynamics over different frequency bands which are known to be important in cognitive processes. Recent studies using imaging tools with a high temporal resolution has made it possible to explore the correlation between the regions at multiple frequency bands. These studies introduce the frequency as a new dimension over which the functional networks change, enabling brain networks to transmit multiplex of information at any time. In this computational study, we explore the functional connectivity at different frequency ranges and highlight the role of the distance between the nodes in their correlation. We run the generalized Kuramoto model with delayed interactions on top of the brain's connectome and show that how the transmission delay and the strength of the connections, affect the correlation between the pair of nodes over different frequency bands.
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Affiliation(s)
- Abolfazl Ziaeemehr
- Department of Physics, Institute of Advanced Studies in Basic Sciences, Zanjan, Iran
| | - Alireza Valizadeh
- Department of Physics, Institute of Advanced Studies in Basic Sciences, Zanjan, Iran
- School of Biological Sciences, Institute for Research in Fundamental Sciences, Tehran, Iran
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10
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Ziaeemehr A, Zarei M, Valizadeh A, Mirasso CR. Frequency-dependent organization of the brain’s functional network through delayed-interactions. Neural Netw 2020; 132:155-165. [DOI: 10.1016/j.neunet.2020.08.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 07/19/2020] [Accepted: 08/06/2020] [Indexed: 01/29/2023]
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Raj A, Cai C, Xie X, Palacios E, Owen J, Mukherjee P, Nagarajan S. Spectral graph theory of brain oscillations. Hum Brain Mapp 2020; 41:2980-2998. [PMID: 32202027 PMCID: PMC7336150 DOI: 10.1002/hbm.24991] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Revised: 02/13/2020] [Accepted: 02/16/2020] [Indexed: 11/10/2022] Open
Abstract
The relationship between the brain's structural wiring and the functional patterns of neural activity is of fundamental interest in computational neuroscience. We examine a hierarchical, linear graph spectral model of brain activity at mesoscopic and macroscopic scales. The model formulation yields an elegant closed-form solution for the structure-function problem, specified by the graph spectrum of the structural connectome's Laplacian, with simple, universal rules of dynamics specified by a minimal set of global parameters. The resulting parsimonious and analytical solution stands in contrast to complex numerical simulations of high dimensional coupled nonlinear neural field models. This spectral graph model accurately predicts spatial and spectral features of neural oscillatory activity across the brain and was successful in simultaneously reproducing empirically observed spatial and spectral patterns of alpha-band (8-12 Hz) and beta-band (15-30 Hz) activity estimated from source localized magnetoencephalography (MEG). This spectral graph model demonstrates that certain brain oscillations are emergent properties of the graph structure of the structural connectome and provides important insights towards understanding the fundamental relationship between network topology and macroscopic whole-brain dynamics. .
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Affiliation(s)
- Ashish Raj
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCalifornia
- Department of Bioengineering and Therapeutic SciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Chang Cai
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCalifornia
| | - Xihe Xie
- Department of Neuroscience, Weill Cornell Graduate School of Medical SciencesWeill Cornell MedicineNew YorkNew YorkUSA
| | - Eva Palacios
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCalifornia
| | - Julia Owen
- Department of RadiologyUniversity of WashingtonSeattleWashingtonUSA
| | - Pratik Mukherjee
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCalifornia
- Department of Bioengineering and Therapeutic SciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Srikantan Nagarajan
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCalifornia
- Department of Bioengineering and Therapeutic SciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
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12
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Endo H, Hiroe N, Yamashita O. Evaluation of Resting Spatio-Temporal Dynamics of a Neural Mass Model Using Resting fMRI Connectivity and EEG Microstates. Front Comput Neurosci 2020; 13:91. [PMID: 32009922 PMCID: PMC6978716 DOI: 10.3389/fncom.2019.00091] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Accepted: 12/20/2019] [Indexed: 12/11/2022] Open
Abstract
Resting-state brain activities have been extensively investigated to understand the macro-scale network architecture of the human brain using non-invasive imaging methods such as fMRI, EEG, and MEG. Previous studies revealed a mechanistic origin of resting-state networks (RSNs) using the connectome dynamics modeling approach, where the neural mass dynamics model constrained by the structural connectivity is simulated to replicate the resting-state networks measured with fMRI and/or fast synchronization transitions with EEG/MEG. However, there is still little understanding of the relationship between the slow fluctuations measured with fMRI and the fast synchronization transitions with EEG/MEG. In this study, as a first step toward evaluating experimental evidence of resting state activity at two different time scales but in a unified way, we investigate connectome dynamics models that simultaneously explain resting-state functional connectivity (rsFC) and EEG microstates. Here, we introduce empirical rsFC and microstates as evaluation criteria of simulated neuronal dynamics obtained by the Larter-Breakspear model in one cortical region connected with those in other cortical regions based on structural connectivity. We optimized the global coupling strength and the local gain parameter (variance of the excitatory and inhibitory threshold) of the simulated neuronal dynamics by fitting both rsFC and microstate spatial patterns to those of experimental ones. As a result, we found that simulated neuronal dynamics in a narrow optimal parameter range simultaneously reproduced empirical rsFC and microstates. Two parameter groups had different inter-regional interdependence. One type of dynamics was synchronized across the whole brain region, and the other type was synchronized between brain regions with strong structural connectivity. In other words, both fast synchronization transitions and slow BOLD fluctuation changed based on structural connectivity in the two parameter groups. Empirical microstates were similar to simulated microstates in the two parameter groups. Thus, fast synchronization transitions correlated with slow BOLD fluctuation based on structural connectivity yielded characteristics of microstates. Our results demonstrate that a bottom-up approach, which extends the single neuronal dynamics model based on empirical observations into a neural mass dynamics model and integrates structural connectivity, effectively reveals both macroscopic fast, and slow resting-state network dynamics.
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Affiliation(s)
- Hidenori Endo
- Graduate School of Information Science, Nara Institute of Science and Technology, Nara, Japan.,ATR Neural Information Analysis Laboratories, Kyoto, Japan
| | - Nobuo Hiroe
- ATR Neural Information Analysis Laboratories, Kyoto, Japan
| | - Okito Yamashita
- ATR Neural Information Analysis Laboratories, Kyoto, Japan.,Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan
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13
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Saenger VM, Ponce-Alvarez A, Adhikari M, Hagmann P, Deco G, Corbetta M. Linking Entropy at Rest with the Underlying Structural Connectivity in the Healthy and Lesioned Brain. Cereb Cortex 2019; 28:2948-2958. [PMID: 28981635 DOI: 10.1093/cercor/bhx176] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2016] [Indexed: 01/06/2023] Open
Abstract
The brain is a network that mediates information processing through a wide range of states. The extent of state diversity is a reflection of the entropy of the network. Here we measured the entropy of brain regions (nodes) in empirical and modeled functional networks reconstructed from resting state fMRI to address the connection of entropy at rest with the underlying structure measured through diffusion spectrum imaging. Using 18 empirical and 18 modeled stroke networks, we also investigated the effect that focal lesions have on node entropy and information diffusion. Overall, positive correlations between node entropy and structure were observed, especially between node entropy and node strength in both empirical and modeled data. Although lesions were restricted to one hemisphere in all stroke patients, entropy reduction was not only present in nodes from the damaged hemisphere, but also in nodes from the contralesioned hemisphere, an effect replicated in modeled stroke networks. Globally, information diffusion was also affected in empirical and modeled strokes compared with healthy controls. This is the first study showing that artificial lesions affect local and global network aspects in very similar ways compared with empirical strokes, shedding new light into the functional nature of stroke.
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Affiliation(s)
- Victor M Saenger
- Department of Information and Communication Technologies, Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain
| | - Adrián Ponce-Alvarez
- Department of Information and Communication Technologies, Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain
| | - Mohit Adhikari
- Department of Information and Communication Technologies, Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain
| | - Patric Hagmann
- Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Gustavo Deco
- Department of Information and Communication Technologies, Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain.,Instituci Catalana de la Recerca i Estudis Avanats (ICREA), Universitat Pompeu Fabra, Barcelona, Spain.,Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,School of Psychological Sciences, Monash University, Melbourne, Clayton VIC, Australia
| | - Maurizio Corbetta
- Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
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14
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Abstract
Integrated information theory (IIT) describes consciousness as information integrated across highly differentiated but irreducible constituent parts in a system. However, in a complex dynamic system such as the brain, the optimal conditions for large integrated information systems have not been elucidated. In this study, we hypothesized that network criticality, a balanced state between a large variation in functional network configuration and a large constraint on structural network configuration, may be the basis of the emergence of a large Φ¯, a surrogate of integrated information. We also hypothesized that as consciousness diminishes, the brain loses network criticality and Φ¯ decreases. We tested these hypotheses with a large-scale brain network model and high-density electroencephalography (EEG) acquired during various levels of human consciousness under general anesthesia. In the modeling study, maximal criticality coincided with maximal Φ¯. The EEG study demonstrated an explicit relationship between Φ¯, criticality, and level of consciousness. The conscious resting state showed the largest Φ¯ and criticality, whereas the balance between variation and constraint in the brain network broke down as the response rate dwindled. The results suggest network criticality as a necessary condition of a large Φ¯ in the human brain.
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15
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Nobukawa S, Nishimura H, Yamanishi T. Temporal-specific complexity of spiking patterns in spontaneous activity induced by a dual complex network structure. Sci Rep 2019; 9:12749. [PMID: 31484990 PMCID: PMC6726653 DOI: 10.1038/s41598-019-49286-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Accepted: 08/22/2019] [Indexed: 11/08/2022] Open
Abstract
Temporal fluctuation of neural activity in the brain has an important function in optimal information processing. Spontaneous activity is a source of such fluctuation. The distribution of excitatory postsynaptic potentials (EPSPs) between cortical pyramidal neurons can follow a log-normal distribution. Recent studies have shown that networks connected by weak synapses exhibit characteristics of a random network, whereas networks connected by strong synapses have small-world characteristics of small path lengths and large cluster coefficients. To investigate the relationship between temporal complexity spontaneous activity and structural network duality in synaptic connections, we executed a simulation study using the leaky integrate-and-fire spiking neural network with log-normal synaptic weight distribution for the EPSPs and duality of synaptic connectivity, depending on synaptic weight. We conducted multiscale entropy analysis of the temporal spiking activity. Our simulation demonstrated that, when strong synaptic connections approach a small-world network, specific spiking patterns arise during irregular spatio-temporal spiking activity, and the complexity at the large temporal scale (i.e., slow frequency) is enhanced. Moreover, we confirmed through a surrogate data analysis that slow temporal dynamics reflect a deterministic process in the spiking neural networks. This modelling approach may improve the understanding of the spatio-temporal complex neural activity in the brain.
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Affiliation(s)
- Sou Nobukawa
- Department of Computer Science, Chiba Institute of Technology, 2-17-1 Tsudanuma, Narashino, Chiba, 275-0016, Japan.
| | - Haruhiko Nishimura
- Graduate School of Applied Informatics, University of Hyogo, 7-1-28 Chuo-ku, Kobe, Hyogo, 650-8588, Japan
| | - Teruya Yamanishi
- AI & IoT Center, Department of Management and Information Sciences, Fukui University of Technology, 3-6-1 Gakuen, Fukui, 910-8505, Japan
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16
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Donnelly-Kehoe P, Saenger VM, Lisofsky N, Kühn S, Kringelbach ML, Schwarzbach J, Lindenberger U, Deco G. Reliable local dynamics in the brain across sessions are revealed by whole-brain modeling of resting state activity. Hum Brain Mapp 2019; 40:2967-2980. [PMID: 30882961 DOI: 10.1002/hbm.24572] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2018] [Revised: 02/05/2019] [Accepted: 03/04/2019] [Indexed: 01/10/2023] Open
Abstract
Resting state fMRI is a tool for studying the functional organization of the human brain. Ongoing brain activity at "rest" is highly dynamic, but procedures such as correlation or independent component analysis treat functional connectivity (FC) as if, theoretically, it is stationary and therefore the fluctuations observed in FC are thought as noise. Consequently, FC is not usually used as a single-subject level marker and it is limited to group studies. Here we develop an imaging-based technique capable of reliably portraying information of local dynamics at a single-subject level by using a whole-brain model of ongoing dynamics that estimates a local parameter, which reflects if each brain region presents stable, asynchronous or transitory oscillations. Using 50 longitudinal resting-state sessions of one single subject and single resting-state sessions from a group of 50 participants we demonstrate that brain dynamics can be quantified consistently with respect to group dynamics using a scanning time of 20 min. We show that brain hubs are closer to a transition point between synchronous and asynchronous oscillatory dynamics and that dynamics in frontal areas have larger heterogeneity in its values compared to other lobules. Nevertheless, frontal regions and hubs showed higher consistency within the same subject while the inter-session variability found in primary visual and motor areas was only as high as the one found across subjects. The framework presented here can be used to study functional brain dynamics at group and, more importantly, at individual level, opening new avenues for possible clinical applications.
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Affiliation(s)
- Patricio Donnelly-Kehoe
- Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas (CIFASIS), National Scientific and Technical Research Council (CONICET), Rosario, Argentina.,Laboratory for System Dynamics and Signal Processing, Universidad Nacional de Rosario, Rosario, Argentina.,Laboratory of Neuroimaging and Neuroscience (LANEN), INECO Foundation Rosario, Rosario, Argentina
| | - Victor M Saenger
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Nina Lisofsky
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany.,Department of Psychiatry and Psychotherapy, University Clinic Hamburg-Eppendorf, Germany
| | - Simone Kühn
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany.,Department of Psychiatry and Psychotherapy, University Clinic Hamburg-Eppendorf, Germany
| | - Morten L Kringelbach
- Department of Psychiatry, University of Oxford, Oxford, UK.,Center for Music in the Brain (MIB), Dept. of Clinical Medicine, Aarhus University, Denmark
| | - Jens Schwarzbach
- Department of Psychiatry and Psychotherapy, University of Regensburg, Germany
| | - Ulman Lindenberger
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany.,Max Planck University College London, Centre for Computational Psychiatry and Ageing Research, London, UK
| | - 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), Universitat Pompeu Fabra, Barcelona, Spain
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17
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O'Neill GC, Tewarie P, Vidaurre D, Liuzzi L, Woolrich MW, Brookes MJ. Dynamics of large-scale electrophysiological networks: A technical review. Neuroimage 2018; 180:559-576. [PMID: 28988134 DOI: 10.1016/j.neuroimage.2017.10.003] [Citation(s) in RCA: 110] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2017] [Revised: 09/23/2017] [Accepted: 10/02/2017] [Indexed: 12/12/2022] Open
Abstract
For several years it has been argued that neural synchronisation is crucial for cognition. The idea that synchronised temporal patterns between different neural groups carries information above and beyond the isolated activity of these groups has inspired a shift in focus in the field of functional neuroimaging. Specifically, investigation into the activation elicited within certain regions by some stimulus or task has, in part, given way to analysis of patterns of co-activation or functional connectivity between distal regions. Recently, the functional connectivity community has been looking beyond the assumptions of stationarity that earlier work was based on, and has introduced methods to incorporate temporal dynamics into the analysis of connectivity. In particular, non-invasive electrophysiological data (magnetoencephalography/electroencephalography (MEG/EEG)), which provides direct measurement of whole-brain activity and rich temporal information, offers an exceptional window into such (potentially fast) brain dynamics. In this review, we discuss challenges, solutions, and a collection of analysis tools that have been developed in recent years to facilitate the investigation of dynamic functional connectivity using these imaging modalities. Further, we discuss the applications of these approaches in the study of cognition and neuropsychiatric disorders. Finally, we review some existing developments that, by using realistic computational models, pursue a deeper understanding of the underlying causes of non-stationary connectivity.
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Affiliation(s)
- George C O'Neill
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom
| | - Prejaas Tewarie
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom
| | - Diego Vidaurre
- Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Lucrezia Liuzzi
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom
| | - Mark W Woolrich
- Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Matthew J Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom.
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18
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Williams NJ, Daly I, Nasuto SJ. Markov Model-Based Method to Analyse Time-Varying Networks in EEG Task-Related Data. Front Comput Neurosci 2018; 12:76. [PMID: 30297993 PMCID: PMC6160873 DOI: 10.3389/fncom.2018.00076] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Accepted: 08/20/2018] [Indexed: 12/27/2022] Open
Abstract
The dynamic nature of functional brain networks is being increasingly recognized in cognitive neuroscience, and methods to analyse such time-varying networks in EEG/MEG data are required. In this work, we propose a pipeline to characterize time-varying networks in single-subject EEG task-related data and further, evaluate its validity on both simulated and experimental datasets. Pre-processing is done to remove channel-wise and trial-wise differences in activity. Functional networks are estimated from short non-overlapping time windows within each “trial,” using a sparse-MVAR (Multi-Variate Auto-Regressive) model. Functional “states” are then identified by partitioning the entire space of functional networks into a small number of groups/symbols via k-means clustering.The multi-trial sequence of symbols is then described by a Markov Model (MM). We show validity of this pipeline on realistic electrode-level simulated EEG data, by demonstrating its ability to discriminate “trials” from two experimental conditions in a range of scenarios. We then apply it to experimental data from two individuals using a Brain-Computer Interface (BCI) via a P300 oddball task. Using just the Markov Model parameters, we obtain statistically significant discrimination between target and non-target trials. The functional networks characterizing each ‘state’ were also highly similar between the two individuals. This work marks the first application of the Markov Model framework to infer time-varying networks from EEG/MEG data. Due to the pre-processing, results from the pipeline are orthogonal to those from conventional ERP averaging or a typical EEG microstate analysis. The results provide powerful proof-of-concept for a Markov model-based approach to analyzing the data, paving the way for its use to track rapid changes in interaction patterns as a task is being performed. MATLAB code for the entire pipeline has been made available.
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Affiliation(s)
- Nitin J Williams
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Ian Daly
- Brain-Computer Interfaces and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester, United Kingdom
| | - Slawomir J Nasuto
- Biomedical Sciences and Biomedical Engineering Division, School of Biological Sciences, University of Reading, Reading, United Kingdom
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19
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Abeysuriya RG, Hadida J, Sotiropoulos SN, Jbabdi S, Becker R, Hunt BAE, Brookes MJ, Woolrich MW. A biophysical model of dynamic balancing of excitation and inhibition in fast oscillatory large-scale networks. PLoS Comput Biol 2018; 14:e1006007. [PMID: 29474352 PMCID: PMC5841816 DOI: 10.1371/journal.pcbi.1006007] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Revised: 03/07/2018] [Accepted: 01/28/2018] [Indexed: 01/03/2023] Open
Abstract
Over long timescales, neuronal dynamics can be robust to quite large perturbations, such as changes in white matter connectivity and grey matter structure through processes including learning, aging, development and certain disease processes. One possible explanation is that robust dynamics are facilitated by homeostatic mechanisms that can dynamically rebalance brain networks. In this study, we simulate a cortical brain network using the Wilson-Cowan neural mass model with conduction delays and noise, and use inhibitory synaptic plasticity (ISP) to dynamically achieve a spatially local balance between excitation and inhibition. Using MEG data from 55 subjects we find that ISP enables us to simultaneously achieve high correlation with multiple measures of functional connectivity, including amplitude envelope correlation and phase locking. Further, we find that ISP successfully achieves local E/I balance, and can consistently predict the functional connectivity computed from real MEG data, for a much wider range of model parameters than is possible with a model without ISP.
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Affiliation(s)
- Romesh G. Abeysuriya
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, United Kingdom
| | - Jonathan Hadida
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, United Kingdom
- Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, Wellcome Centre for Integrative Neuroimaging, University of Oxford, United Kingdom
| | - Stamatios N. Sotiropoulos
- Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, Wellcome Centre for Integrative Neuroimaging, University of Oxford, United Kingdom
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, United Kingdom
- National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre, Queens Medical Centre, Nottingham
| | - Saad Jbabdi
- Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, Wellcome Centre for Integrative Neuroimaging, University of Oxford, United Kingdom
| | - Robert Becker
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, United Kingdom
| | - Benjamin A. E. Hunt
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, United Kingdom
- Department of Diagnostic Imaging, Neurosciences & Mental Health, Research Institute, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Matthew J. Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, United Kingdom
| | - Mark W. Woolrich
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, United Kingdom
- Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, Wellcome Centre for Integrative Neuroimaging, University of Oxford, United Kingdom
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20
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Demirtaş M, Deco G. Computational Models of Dysconnectivity in Large-Scale Resting-State Networks. COMPUTATIONAL PSYCHIATRY 2018. [DOI: 10.1016/b978-0-12-809825-7.00004-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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21
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Williams N, Henson RN. Recent advances in functional neuroimaging analysis for cognitive neuroscience. Brain Neurosci Adv 2018; 2:2398212817752727. [PMID: 32285010 PMCID: PMC7119434 DOI: 10.1177/2398212817752727] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2017] [Accepted: 12/10/2017] [Indexed: 11/17/2022] Open
Abstract
Functional magnetic resonance imaging and electro-/magneto-encephalography are some of the main neuroimaging technologies used by cognitive neuroscientists to study how the brain works. However, the methods for analysing the rich spatial and temporal data they provide are constantly evolving, and these new methods in turn allow new scientific questions to be asked about the brain. In this brief review, we highlight a handful of recent analysis developments that promise to further advance our knowledge about the working of the brain. These include (1) multivariate approaches to decoding the content of brain activity, (2) time-varying approaches to characterising states of brain connectivity, (3) neurobiological modelling of neuroimaging data, and (4) standardisation and big data initiatives.
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Affiliation(s)
- Nitin Williams
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Richard N. Henson
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
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22
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Functional connectivity dynamically evolves on multiple time-scales over a static structural connectome: Models and mechanisms. Neuroimage 2017; 160:84-96. [DOI: 10.1016/j.neuroimage.2017.03.045] [Citation(s) in RCA: 218] [Impact Index Per Article: 31.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Revised: 01/27/2017] [Accepted: 03/20/2017] [Indexed: 11/20/2022] Open
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23
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Besson P, Carrière N, Bandt SK, Tommasi M, Leclerc X, Derambure P, Lopes R, Tyvaert L. Whole-Brain High-Resolution Structural Connectome: Inter-Subject Validation and Application to the Anatomical Segmentation of the Striatum. Brain Topogr 2017; 30:291-302. [PMID: 28176164 DOI: 10.1007/s10548-017-0548-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2016] [Accepted: 01/18/2017] [Indexed: 01/30/2023]
Abstract
The present study describes extraction of high-resolution structural connectome (HRSC) in 99 healthy subjects, acquired and made available by the Human Connectome Project. Single subject connectomes were then registered to the common surface space to allow assessment of inter-individual reproducibility of this novel technique using a leave-one-out approach. The anatomic relevance of the surface-based connectome was examined via a clustering algorithm, which identified anatomic subdivisions within the striatum. The connectivity of these striatal subdivisions were then mapped on the cortical and other subcortical surfaces. Findings demonstrate that HRSC analysis is robust across individuals and accurately models the actual underlying brain networks related to the striatum. This suggests that this method has the potential to model and characterize the healthy whole-brain structural network at high anatomic resolution.
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Affiliation(s)
- Pierre Besson
- Aix Marseille Université, CNRS, CRMBM, 7339, Marseille, France. .,AP-HM, CHU Timone, Pôle d'Imagerie, CEMEREM, 264 rue Saint-Pierre, Marseille, 13385, France.
| | - Nicolas Carrière
- U1171, INSERM, Université de Lille, Lille, France.,Neurology and Movement disorders Department, Lille University Hospital, Lille, France
| | - S Kathleen Bandt
- Aix Marseille Université, CNRS, CRMBM, 7339, Marseille, France.,AP-HM, CHU Timone, Pôle d'Imagerie, CEMEREM, 264 rue Saint-Pierre, Marseille, 13385, France
| | - Marc Tommasi
- Université de Lille, CRIStAL UMR9189, INRIA, Magnet Team, Lille, France
| | - Xavier Leclerc
- Clinical Imaging Core Facility, INSERM U1171, Lille University Hospital, Lille, France
| | - Philippe Derambure
- U1171, INSERM, Université de Lille, Lille, France.,Department of Clinical Neurophysiology, Lille University Hospital, Lille, France
| | - Renaud Lopes
- Clinical Imaging Core Facility, INSERM U1171, Lille University Hospital, Lille, France
| | - Louise Tyvaert
- Department of Neurology, Nancy University Hospital, Nancy, France.,CRAN, UMR CNRS 7039, University of Lorraine, Nancy, France
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24
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Tewarie P, Hillebrand A, van Dijk BW, Stam CJ, O'Neill GC, Van Mieghem P, Meier JM, Woolrich MW, Morris PG, Brookes MJ. Integrating cross-frequency and within band functional networks in resting-state MEG: A multi-layer network approach. Neuroimage 2016; 142:324-336. [PMID: 27498371 DOI: 10.1016/j.neuroimage.2016.07.057] [Citation(s) in RCA: 69] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Revised: 06/17/2016] [Accepted: 07/27/2016] [Indexed: 10/21/2022] Open
Abstract
Neuronal oscillations exist across a broad frequency spectrum, and are thought to provide a mechanism of interaction between spatially separated brain regions. Since ongoing mental activity necessitates the simultaneous formation of multiple networks, it seems likely that the brain employs interactions within multiple frequency bands, as well as cross-frequency coupling, to support such networks. Here, we propose a multi-layer network framework that elucidates this pan-spectral picture of network interactions. Our network consists of multiple layers (frequency-band specific networks) that influence each other via inter-layer (cross-frequency) coupling. Applying this model to MEG resting-state data and using envelope correlations as connectivity metric, we demonstrate strong dependency between within layer structure and inter-layer coupling, indicating that networks obtained in different frequency bands do not act as independent entities. More specifically, our results suggest that frequency band specific networks are characterised by a common structure seen across all layers, superimposed by layer specific connectivity, and inter-layer coupling is most strongly associated with this common mode. Finally, using a biophysical model, we demonstrate that there are two regimes of multi-layer network behaviour; one in which different layers are independent and a second in which they operate highly dependent. Results suggest that the healthy human brain operates at the transition point between these regimes, allowing for integration and segregation between layers. Overall, our observations show that a complete picture of global brain network connectivity requires integration of connectivity patterns across the full frequency spectrum.
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Affiliation(s)
- Prejaas Tewarie
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom.
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology, MEG Center, VU University Medical Centre, Amsterdam, The Netherlands
| | - Bob W van Dijk
- Department of Clinical Neurophysiology, MEG Center, VU University Medical Centre, Amsterdam, The Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology, MEG Center, VU University Medical Centre, Amsterdam, The Netherlands
| | - George C O'Neill
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom
| | - Piet Van Mieghem
- Delft University of Technology, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft, The Netherlands
| | - Jil M Meier
- Delft University of Technology, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft, The Netherlands
| | - Mark W Woolrich
- Oxford Centre for Human Brain Activity (OHBA), University of Oxford, Oxford, United Kingdom; Centre for the Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Oxford, United Kingdom
| | - Peter G Morris
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom
| | - Matthew J Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom
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25
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Schellenberger Costa M, Weigenand A, Ngo HVV, Marshall L, Born J, Martinetz T, Claussen JC. A Thalamocortical Neural Mass Model of the EEG during NREM Sleep and Its Response to Auditory Stimulation. PLoS Comput Biol 2016; 12:e1005022. [PMID: 27584827 PMCID: PMC5008627 DOI: 10.1371/journal.pcbi.1005022] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2015] [Accepted: 06/17/2016] [Indexed: 11/18/2022] Open
Abstract
Few models exist that accurately reproduce the complex rhythms of the thalamocortical system that are apparent in measured scalp EEG and at the same time, are suitable for large-scale simulations of brain activity. Here, we present a neural mass model of the thalamocortical system during natural non-REM sleep, which is able to generate fast sleep spindles (12–15 Hz), slow oscillations (<1 Hz) and K-complexes, as well as their distinct temporal relations, and response to auditory stimuli. We show that with the inclusion of detailed calcium currents, the thalamic neural mass model is able to generate different firing modes, and validate the model with EEG-data from a recent sleep study in humans, where closed-loop auditory stimulation was applied. The model output relates directly to the EEG, which makes it a useful basis to develop new stimulation protocols. Sleep plays a pivotal role for the consolidation of memory. While REM sleep had originally been the focus of research due to its similarity with wakefulness, more recent studies suggest that different sleep stages are responsible for the consolidation of different types of memory. To better understand the changes in neuronal dynamics between the different sleep stages, neural mass models are a valuable tool, as they relate directly to the large-scale dynamics measured by an EEG. Here, we present a model of the sleeping thalamocortical system. We first show that the isolated thalamic submodule is able to generate different oscillatory behavior found in vivo. Furthermore, the full thalamocortical model reproduces the EEG of sleep stages N2 and N3 and preserves the temporal relationship between cortical K-complexes/slow oscillations and thalamic sleep spindles. A comparison with event related potential data from a recent sleep study in humans demonstrates its possible application in predicting the outcome of external stimulation on EEG rhythms. Our study shows, that a neural mass model incorporating few key mechanisms is sufficient to reproduce the complex brain dynamics observed during sleep.
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Affiliation(s)
- Michael Schellenberger Costa
- Institute for Neuro- and Bioinformatics, University of Lübeck, Lübeck, Germany
- Institute for Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
- * E-mail:
| | - Arne Weigenand
- Institute for Neuro- and Bioinformatics, University of Lübeck, Lübeck, Germany
- Graduate School for Computing in Medicine and Life Science, University of Lübeck, Lübeck, Germany
| | - Hong-Viet V. Ngo
- Institute for Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Lisa Marshall
- Graduate School for Computing in Medicine and Life Science, University of Lübeck, Lübeck, Germany
- Institute of Experimental and Clinical Pharmacology and Toxicology, University of Lübeck, Lübeck, Germany
| | - Jan Born
- Institute for Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
- Center for Integrative Neuroscience, University of Tübingen, Tübingen, Germany
| | - Thomas Martinetz
- Institute for Neuro- and Bioinformatics, University of Lübeck, Lübeck, Germany
- Graduate School for Computing in Medicine and Life Science, University of Lübeck, Lübeck, Germany
| | - Jens Christian Claussen
- Institute for Neuro- and Bioinformatics, University of Lübeck, Lübeck, Germany
- Computational Systems Biology, Jacobs University Bremen, Bremen, Germany
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Stephan K, Iglesias S, Heinzle J, Diaconescu A. Translational Perspectives for Computational Neuroimaging. Neuron 2015; 87:716-32. [DOI: 10.1016/j.neuron.2015.07.008] [Citation(s) in RCA: 132] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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Abstract
Rhythmic activity plays a central role in neural computations and brain functions ranging from homeostasis to attention, as well as in neurological and neuropsychiatric disorders. Despite this pervasiveness, little is known about the mechanisms whereby the frequency and power of oscillatory activity are modulated, and how they reflect the inputs received by neurons. Numerous studies have reported input-dependent fluctuations in peak frequency and power (as well as couplings across these features). However, it remains unresolved what mediates these spectral shifts among neural populations. Extending previous findings regarding stochastic nonlinear systems and experimental observations, we provide analytical insights regarding oscillatory responses of neural populations to stimulation from either endogenous or exogenous origins. Using a deceptively simple yet sparse and randomly connected network of neurons, we show how spiking inputs can reliably modulate the peak frequency and power expressed by synchronous neural populations without any changes in circuitry. Our results reveal that a generic, non-nonlinear and input-induced mechanism can robustly mediate these spectral fluctuations, and thus provide a framework in which inputs to the neurons bidirectionally regulate both the frequency and power expressed by synchronous populations. Theoretical and computational analysis of the ensuing spectral fluctuations was found to reflect the underlying dynamics of the input stimuli driving the neurons. Our results provide insights regarding a generic mechanism supporting spectral transitions observed across cortical networks and spanning multiple frequency bands.
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