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Renormalization of the brain connectome: Duality of particle and wave
[article]
2020
bioRxiv
pre-print
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Networks in neuroscience determine how brain function unfolds. Perturbations of the network lead to psychiatric disorders and brain disease. Brain networks are characterized by their connectomes, which comprise the totality of all connections, and are commonly described by graph theory. This approach is deeply rooted in a particle view of information processing, based on the quantification of informational bits such as firing rates. Oscillations and brain rhythms demand, however, a wave
doi:10.1101/2020.12.02.408518
fatcat:4izqlgsu7bfz5irgsexvo2z75y
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... ive of information processing based on synchronization. We extend traditional graph theory to a dual particle-wave-perspective, integrate time delays due to finite transmission speeds and derive a renormalization of the connectome. When applied to the data base of the Human Connectome project, we explain the emergence of frequency-specific network cores including the visual and default mode networks. These findings are robust across human subjects (N=100) and are a fundamental network property within the wave picture. The renormalized connectome comprises the particle view in the limit of infinite transmission speeds and opens the applicability of graph theory to a wide range of novel network phenomena, including physiological and pathological brain rhythms.
Controlling seizure propagation in large-scale brain networks
[article]
2018
arXiv
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Information transmission in the human brain is a fundamentally dynamic network process. In partial epilepsy, this process is perturbed and highly synchronous seizures originate in a local network, the so-called epileptogenic zone (EZ), before recruiting other close or distant brain regions. We studied patient-specific brain network models of 15 drug-resistant epilepsy patients with implanted stereotactic electroencephalography (SEEG) electrodes. Each personalized brain model was derived from
arXiv:1804.03588v1
fatcat:dguhzwivw5cqzh6k6yyn7g3eri
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... uctural data of magnetic resonance imaging (MRI) and diffusion tensor weighted imaging (DTI), comprising 88 nodes equipped with region specific neural mass models capable of demonstrating a range of epileptiform discharges. Each patients virtual brain was further personalized through the integration of the clinically hypothesized EZ. Subsequent simulations and connectivity modulations were performed and uncovered a finite repertoire of seizure propagation patterns. Across patients, we found that (i) patient-specific network connectivity is predictive for the subsequent seizure propagation pattern; (ii)seizure propagation is characterized by a systematic sequence of brain states; (iii) propagation can be controlled by an optimal intervention on the connectivity matrix; (iv) the degree of invasiveness can be significantly reduced via the here proposed seizure control as compared to traditional resective surgery. To stop seizures, neurosurgeons typically resect the EZ completely. We showed that stability analysis of the network dynamics using graph theoretical metrics estimates reliably the spatiotemporal properties of seizure propagation. This suggests novel less invasive paradigms of surgical interventions to treat and manage partial epilepsy.
Attention to rhythms: sensory-specific constrained sampling of temporal regularities
[article]
2019
bioRxiv
pre-print
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That attention is a fundamentally rhythmic process has recently received abundant empirical evidence. The essence of temporal attention, however, is to flexibly focus in time. Whether this function is hampered by an underlying rhythmic mechanism is unknown. In six interrelated experiments, we behaviourally quantify the sampling capacities of periodic temporal attention during auditory or visual perception. We reveal the presence of limited attentional capacities, with an optimal sampling rate
doi:10.1101/764563
fatcat:a5mzqxfqtrcn5msdmyngkozwwa
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... ~1.4 Hz in audition and ~0.7 Hz in vision. Investigating the motor contribution to temporal attention, we show that it scales with motor rhythmic precision, maximal at ~1.7 Hz. Critically, the motor modulation is beneficial to auditory but detrimental to visual temporal attention. These results are captured by a computational model of coupled oscillators, that reveals the underlying structural constraints governing the temporal alignment between motor and attention fluctuations.
Natural rhythms of periodic temporal attention
2020
Nature Communications
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That attention is a fundamentally rhythmic process has recently received abundant empirical evidence. The essence of temporal attention, however, is to flexibly focus in time. Whether this function is constrained by an underlying rhythmic neural mechanism is unknown. In six interrelated experiments, we behaviourally quantify the sampling capacities of periodic temporal attention during auditory or visual perception. We reveal the presence of limited attentional capacities, with an optimal
doi:10.1038/s41467-020-14888-8
pmid:32103014
fatcat:gzqpsip4tvc5xin6e2avxqiw5y
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... ng rate of ~1.4 Hz in audition and ~0.7 Hz in vision. Investigating the motor contribution to temporal attention, we show that it scales with motor rhythmic precision, maximal at ~1.7 Hz. Critically, motor modulation is beneficial to auditory but detrimental to visual temporal attention. These results are captured by a computational model of coupled oscillators, that reveals the underlying structural constraints governing the temporal alignment between motor and attention fluctuations.
Normalizing the brain connectome for communication through synchronization
2022
Network Neuroscience
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The same is true for networks (Petkoski et al., 2018) and not limited to phase oscillators . ...
These could be applied on the spatially decomposed time delays as a first approximation of the spatiotemporal structure of the connectome (Petkoski et al., 2016) . ...
doi:10.1162/netn_a_00231
fatcat:eahbvaai3bbrpep54x3zscjlky
The structured flow on the brain's resting state manifold
[article]
2022
bioRxiv
pre-print
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Spontaneously fluctuating brain activity patterns emerge at rest and relate to brain functional networks involved in task conditions. Despite detailed descriptions of the spatio-temporal brain patterns, our understanding of their generative mechanism is still incomplete. Using a combination of computational modeling and dynamical systems analysis we provide a complete mechanistic description in terms of the constituent entities and the productive relation of their causal activities leading to
doi:10.1101/2022.01.03.474841
fatcat:hdlfzmdoobebhnpocwjzivlw4a
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... e formation of a resting state manifold via the network connectivity. We demonstrate that the symmetry breaking by the connectivity creates a characteristic flow on the manifold, which produces the major empirical data features including spontaneous high amplitude co-activations, neuronal cascades, spectral cortical gradients, multistability and characteristic functional connectivity dynamics. The understanding of the brain's resting state manifold is fundamental for the construction of task-specific flows and manifolds used in theories of brain function such as predictive coding.
Controlling seizure propagation in large-scale brain networks: Supplementary Information
[article]
2018
bioRxiv
pre-print
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Information transmission in the human brain is a fundamentally dynamic network process. In partial epilepsy, this process is perturbed and highly synchronous seizures originate in a local network, the so-called epileptogenic zone (EZ), before recruiting other close or distant brain regions. We studied patient-specific brain network models of 15 drug-resistant epilepsy patients with implanted stereotactic electroencephalography (SEEG) electrodes. Each personalized brain model was derived from
doi:10.1101/505958
fatcat:bjyfjjnoynbdpbzw4gztrei6y4
more »
... uctural data of magnetic resonance imaging (MRI) and diffusion tensor weighted imaging (DTI), comprising 88 nodes equipped with region specific neural mass models capable of demonstrating a range of epileptiform discharges. Each patient's virtual brain was further personalized through the integration of the clinically hypothesized EZ. Subsequent simulations and connectivity modulations were performed and uncovered a finite repertoire of seizure propagation patterns. Across patients, we found that (i) patient-specific network connectivity is predictive for the subsequent seizure propagation pattern; (ii) seizure propagation is characterized by a systematic sequence of brain states; (iii) propagation can be controlled by an optimal intervention on the connectivity matrix; (iv) the degree of invasiveness can be significantly reduced via the proposed seizure control as compared to traditional resective surgery. To stop seizures, neurosurgeons typically resect the EZ completely. We showed that stability analysis of the network dynamics, employing structural and dynamical information, estimates reliably the spatiotemporal properties of seizure propagation. This suggests novel less invasive paradigms of surgical interventions to treat and manage partial epilepsy.
White-matter degradation and dynamical compensation support age-related functional alterations in human brain
[article]
2022
bioRxiv
pre-print
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et al., 2018; Petkoski and Jirsa, 2019) . ...
Moreover, the results hold even if space-time structure is spatially decomposed (Petkoski et al., 2016 (Petkoski et al., , 2018;; Petkoski and Jirsa, 2019) , such that only lumped intra and interhemispheric ...
doi:10.1101/2021.12.30.474565
fatcat:vckiaszi2zewpplcnxlvrp4ndi
Controlling seizure propagation in large-scale brain networks
2019
PLoS Computational Biology
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Writing -review & editing: Spase Petkoski, Maxime Guye, Fabrice Bartolomei, Viktor Jirsa. ...
doi:10.1371/journal.pcbi.1006805
fatcat:iqw4dp4sdfcenizcyrknoepuqy
Effects of multimodal distribution of delays in brain network dynamics
2015
BMC Neuroscience
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© 2015 Petkoski et al. ...
doi:10.1186/1471-2202-16-s1-p109
pmcid:PMC4697638
fatcat:aq4hc2t25rfknpojtldwuturmm
Editorial: Synchronization, Swarming and Emergent Behaviors in Complex Networks and Neuroscience
2022
Frontiers in Computational Neuroscience
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Dynamical mechanisms of interictal resting-state functional connectivity in epilepsy
2020
Journal of Neuroscience
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., 2008; Petkoski et al., 2016 Petkoski et al., , 2018 Petkoski and Jirsa, 2019) , here being on the phenomenological level, we assumed their impact to be encompassed in the neural masses and we neglected ...
doi:10.1523/jneurosci.0905-19.2020
pmid:32513827
pmcid:PMC7363471
fatcat:iywx3kptdrbvngjvdmk7cmypm4
Extension of the Kuramoto model to encompass time variability in neuronal synchronization and brain dynamics
2011
BMC Neuroscience
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The Kuramoto model (KM) is extended to incorporate at a basic level one of the most fundamental properties of living systemstheir inherent time-variability. In building the model, we encompass earlier generalizations of the KM that included time-varying parameters in a purely physical way [1,2] together with a model introduced to describe changes in neuronal synchronization during anaesthesia [3] , as one of the many experimentally confirmed phenomena [4, 5] which this model should address. We
doi:10.1186/1471-2202-12-s1-p313
pmcid:PMC3240427
fatcat:fryjgepkwjcqrldxlvn54htpvy
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... hus allow for the time-variabilities of both the oscillator natural frequencies and of the inter-oscillator couplings. The latter can be considered as describing in an intuitive way the non-autonomous character of the individual oscillators, each of which is subject to the influence of its neighbors. The couplings have been found to provide a convenient basis for modeling the depth of anaesthesia [3] . Non-autonomous natural frequencies in an ensemble of oscillators, on the other hand, have already been investigated and interpreted as attributable to external forcing [6] . Our numerical simulations have confirmed some interesting, and, at first sight counter-intuitive, dynamics of the model for this case, and have also revealed certain limitations of this approach. Hence, we further examine the other aspects of the frequencies' time-variability. In addition, we apply the Sakaguchi extension (see [3] and the references therein) of the original KM and investigate its influence on the system's synchronization. Furthermore, we propose the use of a bounded distribution for the natural frequencies of the oscillators. A truncated Lorentzian distribution appears to be a good choice in that it allows the Kuramoto transition to be solved analytically: the resultant expression for the mean field amplitude matches perfectly the results obtained numerically. The work to be presented helps to describe time-varying neural synchronization as an inherent phenomenon of brain dynamics. It accounts for the experimental results reported earlier [4] and it extends and complements a previous attempt [3] at explanation. References 1. Rougemont J, Felix N: Collective synchronization in populations of globally coupled phase oscillators with drifting frequencies. Phys. Rev. E 2006, 73:011104. 2. Taylor D, Ott E, Restrepo JG: Spontaneous synchronization of coupled oscillator systems with frequency adaptation. Phys. Rev. E 2010, 81:046214. 3. Sheeba JH, Stefanovska A, McClintock PVE: Neuronal synchrony during anesthesia: A thalamocortical model. Biophys. J 95:2722-2727. 4. Musizza B, Stefanovska A, McClintock PVE, Palus M, Petrovcic J, Ribaric S, Bajrovic FF: Interactions between cardiac, respiratory and EEG-δ oscillations in rats during anaesthesia. J. Physiol 2007, 580:315326, Bahraminasab A, Ghasemi F, Stefanovska A, McClintock PVE, Friedrich R: Physics of brain dynamics: Fokker-Planck analysis reveals changes in EEG δ-θ interactions in anaesthesia, New Journal of Physics 2009, 11: 103051. 5. Rudrauf D, et al: Frequency flows and the time-frequency dynamics of multivariate phase synchronization in brain signals,.
On the topochronic map of the human brain dynamics
[article]
2021
bioRxiv
pre-print
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Large-scale brain activity evolves dynamically over time across multiple time-scales. The structural connectome imposes a spatial network constraint since two structurally connected brain regions are more likely to coordinate their activity. It also imposes a temporal network constraint by virtue of time delays via signal transmission, which has modulatory effects on oscillatory signals. Specifically, the lengths of the structural bundles, their widths, myelination, and the topology of the
doi:10.1101/2021.07.01.447872
fatcat:c4ox4upodvgrtcflicykl5zv2u
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... tural connectome influence the timing of the interactions across the brain network. Together, they define a space-time structure (topochronic map) spanned by connection strengths (space) and signal transmission delays (time), which together establish the deterministic scaffold underlying the evolution of brain dynamics. We introduce a novel in vivo approach for directly measuring functional delays across the whole brain using magneto/electroencephalography and integrating them with the structural connectome derived from magnetic resonance imaging. We developed a map of the functional delays characterizing the connections across the human brain and a map of the corresponding functional velocities. This yields a topochronic map of the human brain dynamics. The functional delays are tightly regulated, with a trend showing, as expected, that larger structural bundles had faster velocities, with the delays varying much less than expected if they only depended upon distance. Then, we estimated the delays from magnetoencephalography (MEG) data in a cohort of multiple sclerosis patients, who have damaged myelin sheaths, and demonstrated that patients showed greater delays across the whole network than a matched control group. Furthermore, within each patient, individual lesioned connections were slowed down more than unaffected ones. Our technique provides a practical approach for estimating functional transmission delays in vivo at the single-subject and single-fiber level and, thus, opens the possibility for novel diagnostic and curative interventions as well as providing empirical, subject-specific constraints to tailor brain models.
Phase-lags in large scale brain synchronization: Methodological considerations and in-silico analysis
2018
PLoS Computational Biology
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Architecture of phase relationships among neural oscillations is central for their functional significance but has remained theoretically poorly understood. We use phenomenological model of delay-coupled oscillators with increasing degree of topological complexity to identify underlying principles by which the spatio-temporal structure of the brain governs the phase lags between oscillatory activity at distant regions. Phase relations and their regions of stability are derived and numerically
doi:10.1371/journal.pcbi.1006160
pmid:29990339
pmcid:PMC6039010
fatcat:omjrfpqabbbgtmeubszdveqihi
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... nfirmed for two oscillators and for networks with randomly distributed or clustered bimodal delays, as a first approximation for the brain structural connectivity. Besides in-phase, clustered delays can induce anti-phase synchronization for certain frequencies, while the sign of the lags is determined by the natural frequencies and by the inhomogeneous network interactions. For in-phase synchronization faster oscillators always phase lead, while stronger connected nodes lag behind the weaker during frequency depression, which consistently arises for in-silico results. If nodes are in antiphase regime, then a distance π is added to the in-phase trends. The statistics of the phases is calculated from the phase locking values (PLV), as in many empirical studies, and we scrutinize the method's impact. The choice of surrogates do not affects the mean of the observed phase lags, but higher significance levels that are generated by some surrogates, cause decreased variance and might fail to detect the generally weaker coherence of the interhemispheric links. These links are also affected by the non-stationary and intermittent synchronization, which causes multimodal phase lags that can be misleading if averaged. Taken together, the results describe quantitatively the impact of the spatio-temporal connectivity of the brain to the synchronization patterns between brain regions, and to uncover mechanisms through which the spatio-temporal structure of the brain renders phases to be distributed around 0 and π. Trial registration: South African Clinical Trials Register: http://www.sanctr.gov.za/ SAClinicalbrnbspTrials/tabid/169/Default.aspx, then link to respiratory tract then link to tuberculosis, pulmonary; and TASK Applied Sciences Clinical Trials, AP-TB-201-16 (ALO-PEXX): https://task.org.za/clinical-trials/. PLOS Computational Biology | https://doi. Author summary Functional connectivity, and in particular, phase coupling between distant brain regions may be fundamental in regulating neuronal processing and communication. However, phase relationships between the nodes of the brain and how they are confined by its spatio-temporal structure, have been mostly overlooked. We use a model of oscillatory dynamics superimposed on the space-time structure defined by the connectome, and we analyze the possible regimes of synchronization. Limitations of data analysis are also considered and we show that the choice of the significance threshold for coherence does not essentially impact the statistics of the observed phase lags, although it is crucial for the right detection of statistically significant coherence. Analytical insights are obtained for networks with heterogeneous time-delays, based on the empirical data from the connectome, and these are confirmed by numerical simulations, which show in-or anti-phase synchronization depending on the frequency and the distribution of time-delays. Phase lags are shown to result from inhomogeneous network interactions, so that stronger connected nodes generally phase lag behind the weaker. Phase-lags in large scale brain synchronization PLOS Computational Biology | https://doi.
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