Thalamic control of sensory processing and spindles in a biophysical somatosensory thalamoreticular circuit model of wakefulness and sleep

Iavarone, Elisabetta; Simko, Jane; Shi, Ying; Bertschy, Marine; García‐Amado, María; Litvak, Polina; Kaufmann, Anna-Kristin; O’Reilly, Christian; Amsalem, Oren; Abdellah, Marwan; Chevtchenko, Grigori; Coste, B.; Courcol, Jean-Denis; Ecker, András; Favreau, Cyrille; Fleury, Adrien Christian; Geit, Werner Van; Gevaert, Michael; Guerrero, Nadir Román; Herttuainen, Joni; Ivaska, Genrich; Kerrien, Samuel; King, James G.; Kumbhar, Pramod; Lurie, Patrycja; Magkanaris, Ioannis; Muddapu, Vignayanandam Ravindernath; Nair, Jayakrishnan; Pereira, F.L.; Perin, Rodrigo; Petitjean, Fabien; Ranjan, Rajnish; Reimann, Michael; Soltuzu, Liviu; Sy, Mohameth François; Tuncel, Mustafa Anil; Ulbrich, Alexander; Wolf, M.; Clascá, Francisco; Markram, Henry; Hill, Sean

doi:10.1016/j.celrep.2023.112200

Cited by 10 publications

(7 citation statements)

References 196 publications

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“…After identifying all potential synapses, a subsequent pathway-specific pruning step discards some to match the known bouton densities (Table S7) and number of synapses per single axon connection (Table S9). This algorithm has been demonstrated to accurately recreate local connectivity (Markram et al, 2015; Reimann et al, 2022; Iavarone et al, 2023) as well as higher-order topological features (Gal et al, 2017). The resulting intrinsic connectome consisted of about 821 million synapses.…”

Section: Resultsmentioning

confidence: 99%

“…To initiate a community effort of this magnitude requires an approach that standardizes data curation and integration of diverse datasets from different labs and uses these curated data to construct and simulate a scalable and reproducible circuit automatically. A reconstruction and simulation methodology was introduced and applied at the microcircuit scale, for the neocortex (Markram et al, 2015) and the thalamus (Iavarone et al, 2023) and at full-scale for a whole neocortical area (Reimann et al, 2022; Isbister et al, 2023). However, these models relied primarily on datasets collected specifically for the purpose rather than data sought from and curated with the help of the scientific community.…”

Section: Introductionmentioning

confidence: 99%

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Community-based Reconstruction and Simulation of a Full-scale Model of Region CA1 of Rat Hippocampus

Romani,

Antonietti,

Bella

et al. 2023

Preprint

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The CA1 region of the hippocampus is one of the most studied regions of the rodent brain, thought to play an important role in cognitive functions such as memory and spatial navigation. Despite a wealth of experimental data on its structure and function, it can be challenging to reconcile information obtained from diverse experimental approaches. To address this challenge, we present a community-driven, full-scalein silicomodel of the rat CA1 that integrates a broad range of experimental data, from synapse to network, including the reconstruction of its principal afferents, the Schaffer collaterals, and a model of the effects that acetylcholine has on the system. We have tested and validated each model component and the final network model, and made input data, assumptions, and strategies explicit and transparent. The flexibility of the model allows scientists to address a range of scientific questions. In this article, we describe the methods used to set up simulations that reproduce and extendin vitroandin vivoexperiments. Among several applications in the article, we focus on theta rhythm, a prominent hippocampal oscillation associated with various behavioral correlates and use our computer model to reproduce and reconcile experimental findings. Finally, we make data, code and model available through the hippocampushub.eu portal, which also provides an extensive set of analyses of the model and a user-friendly interface to facilitate adoption and usage. This neuroscience community-driven model represents a valuable tool for integrating diverse experimental data and provides a foundation for further research into the complex workings of the hippocampal CA1 region.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Community-based Reconstruction and Simulation of a Full-scale Model of Region CA1 of Rat Hippocampus

Romani,

Antonietti,

Bella

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

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“…This approach can capture the complex electrical signaling that occurs within neurons and can provide a more accurate representation of how neurons interact with one another in neural circuits. Mesoscale dynamical phenomena such as oscillations are usually studied with this approach as emergent properties of networks containing hundreds or thousands of multicompartmental neurons, designed according to known architectural features of specific brain structures such as cortex (Hay et al, 2011), thalamus (Iavarone et al, 2023), or hippocampus (Chatzikalymniou et al, 2021). Interestingly, despite the prominence of this general modelling paradigm in computational neuroscience, there are (to our knowledge) no established and/or consistently explored models of multicompartmental circuit models of EEG alpha.…”

Section: Discussionmentioning

confidence: 99%

A comprehensive investigation of intracortical and corticothalamic models of alpha rhythms

Bastiaens,

Momi,

Griffiths

2024

Preprint

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Alpha rhythms are a robust phenomenon prominently observed in posterior resting state electroencephalogram (EEG) that has been shown to play a key role in a number of cognitive processes. However, the underlying mechanisms behind their generation is poorly understood. Here, we showcase the most concrete, mathematically-expressed theoretical foundations for understanding the neural mechanisms underlying the alpha rhythmogenesis. The neural population models of interest are Jansen-Rit (JR), Moran-David-Friston (MDF), Robinson-Rennie-Wright (RRW), and Liley-Wright (LW). Common elements between all models are identified, such as the description of each neural population in the form of a second-order differential equation with a potential-to-rate operator represented as a sigmoid and a rate-to-potential operator usually expressed as an impulse response. Even though these models have major differences, they can be meaningfully compared by associating parameters of analogous biological significance, which we summarize with a unified parameter table. With these correspondences, rate constants and connectivity parameter space is explored to identify common patterns between similar behaviors, such as the role of excitatory-inhibitory interactions in the generation of oscillations. Through stability analysis, two different alpha generation mechanisms were identified: one noise-driven and one self-sustaining oscillation in the form of a limit cycle emerging due to a Andronov-Hopf bifurcation. This work contributes to improving our mechanistic and theoretical understanding on candidate theories of alpha rhythmogenesis.

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“…Empirically, shifts in the excitatory and inhibitory firing rates of various neural populations have been implicated in the generation of sleep spindles, initiated by a transition in the thalamic reticular nucleus [28, 99]. The circuit mechanisms and underlying mathematical structure of spindle generation in these detailed thalamic models [100] and the coarser-grained corticothalamic models [50, 49] may be related, but are not identical. They could be best understood as either complementary or competing candidate theories of this prominent phenomenon observed in human sleep EEG.…”

Section: Discussionmentioning

confidence: 99%

Corticothalamic modelling of sleep neurophysiology with applications to mobile EEG

Morshedzadeh,

Kadak,

Bastiaens

et al. 2024

Preprint

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Recent developments in mathematical modelling of EEG data enable estimation and tracking of otherwise-inaccessible neurophysiological parameters over the course of a night’s sleep. Likewise, advancements in wearable electronics have enabled easier & more affordable at-home collection of sleep EEG data. The convergence of these two advances, namely neurophysiological modelling for mobile sleep EEG, has the potential to significantly improve sleep assessments in research and the clinic. However, this subject area has received limited attention in existing literature. To address this, we used an established mathematical model of the corticothalamic system to analyze EEG power spectra from 5 datasets, spanning from in-lab, research-grade systems to at-home mobile EEG devices. In the present work, we compare the convergent and divergent features of the data and the estimated physiological model parameters. While data quality and characteristics differ considerably, several key patterns consistent with previous theoretical and empirical work are observed. During the transition from lighter to deeper NREM stages, i) the exponent of the aperiodic (1/f) spectral component is increased, ii) bottom-up thalamocortical drive is reduced, iii) corticocortical connection strengths are increased. This effect, which we observe in healthy individuals across all 5 datasets, is interestingly absent in individuals taking SSRI antidepressants, suggesting possible effects of ascending neuromodulatory systems on corticothalamic oscillations. Our results provide a proof-of-principle for the utility and feasibility of this physiological modelling-based approach to analyzing data from mobile EEG devices, providing a mechanistic measure of brain physiology during sleep at home or in the lab.

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Thalamic control of sensory processing and spindles in a biophysical somatosensory thalamoreticular circuit model of wakefulness and sleep

Cited by 10 publications

References 196 publications

Community-based Reconstruction and Simulation of a Full-scale Model of Region CA1 of Rat Hippocampus

Community-based Reconstruction and Simulation of a Full-scale Model of Region CA1 of Rat Hippocampus

A comprehensive investigation of intracortical and corticothalamic models of alpha rhythms

Corticothalamic modelling of sleep neurophysiology with applications to mobile EEG

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