Linking minimal and detailed models of <scp>CA1</scp> microcircuits reveals how theta rhythms emerge and their frequencies controlled

Chatzikalymniou, Alexandra P.; Gumus, Melisa; Skinner, Frances K.

doi:10.1002/hipo.23364

Cited by 11 publications

(33 citation statements)

References 65 publications

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“…In that work, we distinguished the PYR cell population, and not any of the inhibitory cell populations, as the theta rhythm initiators. Further, we showed the promotion of increased PYR cell clustering as the recurrent excitation increased (see Figure 6 in 39 ). The importance of having these ’rare’ recurrent connections for the presence of theta rhythms was already noted by Bezaire et al 32 .…”

Section: Resultsmentioning

confidence: 69%

“…Here, in removing particular connections, we show the contribution of specific cellular pathways in the generation of theta rhythms. As already noted above, the initiation of the theta rhythm is known to be due to the PYR cell population 39 . The requirement 4/22 of recurrent connectivity is shown in FIGURE 2C where connections between PYR cells are removed to reveal the loss of theta rhythms.…”

Section: Dissecting the Theta/gamma Fsm Circuitmentioning

confidence: 75%

“…As was previously shown in 32,33,39 , recurrent connections (w PY R→PY R ) are needed for the existence of theta rhythms. To visualize this, we compute 2-D difference heat maps that represent the subtraction of the normalized gamma power from the normalized theta power.…”

Section: Prm Theta Frequency Relationships Agree With Predictions And...mentioning

confidence: 75%

“…A 10% ’slice’ of the FSM 32 is used in 39 . It is created from the FSM by reducing the volume and the number of cells by a tenth, as well as dividing all the connections in the network by a factor of ten so that we have a ‘slice’ and not a normalization of the FSM.…”

Section: Methodsmentioning

confidence: 99%

“…In this paper, we build on our previous work 39 and take a deep dive into the FSM of Bezaire and colleagues 32 . We leverage these examinations to develop precise hypotheses of how theta and gamma rhythms are generated, interact with each other, and how the different cell types mediate these interactions.…”

Section: Introductionmentioning

confidence: 99%

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Cholecystokinin-expressing (CCK+) basket cells are key controllers of theta-gamma coupled rhythms in the hippocampus

Chatzikalymniou

Sengupta

Lefebvre

et al. 2022

Preprint

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It has been shown that different inhibitory cell types underlie brain rhythmic and cross-frequency coupling output, but exactly how inhibitory cell types manifest their contributions to these outputs is far from clear. Brain rhythms and their couplings are functionally important in cognition and behaviour, and thus it is essential that we determine and understand these contributions. To do this, one needs simultaneous access to multiple cell types and population brain output in functionally relevant states. This is extremely challenging to do with experiments alone, and here we use mathematical models of different types to gain insight into the contributions of the different inhibitory cell types. We focus on theta and gamma rhythms in the hippocampus and use a previously developed detailed model to develop precise hypotheses of how these rhythms are generated and coupled. We find critical contributions by four different cell types - pyramidal cells, parvalbumin expressing (PV+) basket cells (BCs), cholecystokinin-expressing (CCK+) BCs and bistratified cells. We develop a reduced population rate model (PRM) based on these hypotheses and do extensive explorations and visualizations of the PRM to obtain testable predictions. We find that CCK+BCs exhibit a much higher degree of control relative to PV+BCs for theta or gamma rhythm dominance and thus their coupling, and that coupling from PV+BCs to CCK+BCs more strongly affects theta frequencies relative to coupling from CCK+BCs to PV+BCs. As it is now possible to target these specific inhibitory cell types in the behaving animal, experimentally testing these hypotheses and predictions would be possible. Moreover, our work shows that a variety of mathematical model types creates new insights that would not be revealed with a single model type.

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Section: Resultsmentioning

confidence: 69%

Section: Dissecting the Theta/gamma Fsm Circuitmentioning

confidence: 75%

Section: Prm Theta Frequency Relationships Agree With Predictions And...mentioning

confidence: 75%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Cholecystokinin-expressing (CCK+) basket cells are key controllers of theta-gamma coupled rhythms in the hippocampus

Chatzikalymniou

Sengupta

Lefebvre

et al. 2022

Preprint

Self Cite

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EEG band power and phase‐amplitude coupling in patients with Dravet syndrome

Hall,

Bashir,

Tsuboyama

et al. 2024

Annals of the Child Neurology Society

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ObjectiveDravet syndrome (DS) is an epileptic encephalopathy caused by haploinsufficiency of the SCN1A gene. SCN1A gene deficiency limits the firing rates of fast‐spiking inhibitory interneurons, which should reflect in abnormal aggregate network oscillatory electroencephalography (EEG) activity that can be measured by spectral power and phase‐amplitude coupling (PAC) analysis. In this retrospective pilot study, we tested whether spectral EEG frequency band power and PAC metrics distinguish children with DS from age‐matched controls, an early step toward establishing EEG markers of target engagement by gene or drug therapy.MethodsEEG data were collected from patients with DS (N = 6) and age‐matched control pediatric participants (N = 11) and analyzed for cumulative spectral power and PAC and classification capacity of these metrics, by logistic regression analysis. For this initial spectral and PAC analysis, we focused on sleep EEG, where myogenic artifact is minimal and where δ–γ and θ–γ coupling is otherwise expected to be robust.ResultsCumulative δ (1– <4 Hz) and θ (4–7 Hz) power was significantly reduced in the DS group, compared with age‐matched controls (p = 0.001 and p = 0.02, respectively). The δ power was a stronger classifier of separating DS from controls than θ power, with 87% and 83% accuracy, respectively. The γ power trended toward significant reduction (p = 0.08) in the DS group. We found significantly lower PAC between 1–2 Hz phase and 63–80 Hz amplitude in patients with DS compared with the age‐matched controls (p = 0.003), with 78% classification accuracy between groups for PAC.InterpretationIn this pilot study assessing EEG patterns during sleep, we found lower δ–θ power and PAC in patients with DS versus controls, which may reflect abnormal aggregate macroscale network communication patterns resulting from SCN1A deficiency. These measures may be useful metrics of therapeutic target engagement, particularly if the therapy restores the underlying DS pathophysiology. The sorting capacity of these metrics distinguished patients with DS from patients without DS and may in turn facilitate near‐future development of disease and therapy target engagement biomarkers in this syndrome.

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A dynamical computational model of theta generation in hippocampal circuits to study theta-gamma oscillations during neurostimulation

Vardalakis

Aussel

Rougier

et al. 2023

Preprint

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Neurostimulation of the hippocampal formation has shown promising results for modulating memory but the underlying mechanisms remain unclear. In particular, the effects on hippocampal theta-nested gamma oscillations and theta phase reset, which are both crucial for memory processes, are unknown. Moreover, these effects cannot be investigated using current computational models, which consider theta oscillations with a fixed amplitude and phase velocity. Here, we developed a novel computational model that includes the medial septum, represented as a set of abstract Kuramoto oscillators producing a dynamical theta rhythm with phase reset, and the hippocampal formation, composed of biophysically-realistic neurons and able to generate theta-nested gamma oscillations under theta drive. We showed that this system can exhibit bistability in a specific range of parameters and that a single stimulation pulse could switch the network behavior from non-oscillatory to a state producing theta-nested gamma oscillations. Next, we demonstrated that for a theta input too weak to generate theta-nested gamma oscillations, pulse train stimulation at the theta frequency could restore seemingly physiological oscillations. Importantly, the presence of phase reset influenced whether these two effects depended on the phase at which stimulation onset was delivered, which has practical implications for designing neurostimulation protocols that are triggered by the phase of ongoing theta oscillations. This novel model opens new avenues for studying the effects of neurostimulation on the hippocampal formation. Furthermore, our hybrid approach that combines different levels of abstraction could be extended in future work to other neural circuits that produce dynamical brain rhythms.

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Linking minimal and detailed models of CA1 microcircuits reveals how theta rhythms emerge and their frequencies controlled

Cited by 11 publications

References 65 publications

Cholecystokinin-expressing (CCK+) basket cells are key controllers of theta-gamma coupled rhythms in the hippocampus

Cholecystokinin-expressing (CCK+) basket cells are key controllers of theta-gamma coupled rhythms in the hippocampus

EEG band power and phase‐amplitude coupling in patients with Dravet syndrome

A dynamical computational model of theta generation in hippocampal circuits to study theta-gamma oscillations during neurostimulation

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