Intensity- and frequency-specific effects of transcranial alternating current stimulation are explained by network dynamics

Journal of Neural Engineering, Journal Year: 2024, Volume and Issue: 21(2), P. 026024 - 026024

Published: March 26, 2024

Abstract Objective . Transcranial alternating current stimulation (tACS) can be used to non-invasively entrain neural activity and thereby cause changes in local oscillatory power. Despite its increased use cognitive clinical neuroscience, the fundamental mechanisms of tACS are still not fully understood. Approach We developed a computational neuronal network model two-compartment pyramidal neurons (PY) inhibitory interneurons, which mimic cortical circuits. modeled with electric field strengths that achievable human applications. then simulated intrinsic measured entrainment investigate how modulates ongoing endogenous oscillations. Main results The intensity-specific effects non-linear. At low intensities (<0.3 mV mm −1 ), desynchronizes firing relative higher (>0.3 entrained exogenous field. further explore parameter space find oscillations also depends on frequency by following an Arnold tongue. Moreover, networks amplify tACS-induced via synaptic coupling effects. Our shows PY directly drive neurons. Significance presented this study provide mechanistic framework for understanding intensity- frequency-specific oscillating fields networks. This is crucial rational selection studies

Language: Английский

---

A physical neural mass model framework for the analysis of oscillatory generators from laminar electrophysiological recordings

NeuroImage, Journal Year: 2023, Volume and Issue: 270, P. 119938 - 119938

Published: Feb. 10, 2023

Cortical function emerges from the interactions of multi-scale networks that may be studied at a high level using neural mass models (NMM) represent mean activity large numbers neurons. Here, we provide first new framework called laminar NMM, or LaNMM for short, where combine conduction physics with NMMs to simulate electrophysiological measurements. Then, employ this infer location oscillatory generators laminar-resolved data collected prefrontal cortex in macaque monkey. We define minimal model capable generating coupled slow and fast oscillations, optimize LaNMM-specific parameters fit multi-contact recordings. rank candidate an optimization evaluates match between functional connectivity (FC) data, FC is defined by covariance bipolar voltage measurements different cortical depths. The family best solutions reproduces observed electrophysiology selecting locations pyramidal cells their synapses result generation superficial layers across most depths, line recent literature proposals. In closing, discuss how hybrid modeling can more generally used circuitry.

Language: Английский

---

Exact low-dimensional description for fast neural oscillations with low firing rates

Physical review. E, Journal Year: 2024, Volume and Issue: 109(1)

Published: Jan. 31, 2024

Recently, low-dimensional models of neuronal activity have been exactly derived for large networks deterministic, quadratic integrate-and-fire (QIF) neurons. Such firing rate (FRM) describe the emergence fast collective oscillations (>30 Hz) via frequency locking a subset neurons to global oscillation frequency. However, suitability such realistic states is seriously challenged by fact that during episodes oscillations, discharges are often very irregular and low rates compared Here we extend theory derive exact FRM QIF include noise show stochastic displaying at governed same evolution equations as deterministic networks. Our results reconcile two traditionally confronted views on synchronization upgrade applicability broad range biologically states.

Language: Английский

---

Pulse Shape and Voltage-Dependent Synchronization in Spiking Neuron Networks

Neural Computation, Journal Year: 2024, Volume and Issue: 36(8), P. 1476 - 1540

Published: July 19, 2024

Abstract Pulse-coupled spiking neural networks are a powerful tool to gain mechanistic insights into how neurons self-organize produce coherent collective behavior. These use simple neuron models, such as the θ-neuron or quadratic integrate-and-fire (QIF) neuron, that replicate essential features of real dynamics. Interactions between modeled with infinitely narrow pulses, spikes, rather than more complex dynamics synapses. To make these biologically plausible, it has been proposed they must also account for finite width which can have significant impact on network However, derivation and interpretation pulses contradictory, pulse shape is largely unexplored. Here, I take comprehensive approach coupling in QIF θ-neurons. argue activate voltage-dependent synaptic conductances show implement them their effect last through phase after spike. Using an exact low-dimensional description globally coupled neurons, prove instantaneous interactions oscillations emerge due effective mean voltage. analyze by means family smooth functions arbitrary symmetric asymmetric shapes. For resulting voltage not very synchronizing but slightly skewed spike readily generate oscillations. The results unveil synchronization mechanism at heart emergent behavior, facilitated complementary traditional transmission networks.

Language: Английский

---

Low-dimensional model for adaptive networks of spiking neurons

Physical review. E, Journal Year: 2025, Volume and Issue: 111(1)

Published: Jan. 24, 2025

We investigate a large ensemble of quadratic integrate-and-fire neurons with heterogeneous input currents and adaptation variables. Our analysis reveals that, for specific class adaptation, termed spike-frequency the high-dimensional system can be exactly reduced to low-dimensional ordinary differential equations, which describes dynamics three mean-field variables: population's firing rate, mean membrane potential, variable. The resulting rate equations (FREs) uncover key generic feature networks adaptation: Both center width distribution neurons' frequencies are reduced, this largely promotes emergence collective synchronization in network. findings further supported by bifurcation FREs, accurately captures spiking neuron network, including phenomena such as oscillations, bursting, macroscopic chaos.

Language: Английский

---

Preclinical insights into gamma-tACS: foundations for clinical translation in neurodegenerative diseases

Frontiers in Neuroscience, Journal Year: 2025, Volume and Issue: 19

Published: March 12, 2025

Gamma transcranial alternating current stimulation (gamma-tACS) represents a novel neuromodulation technique with promising therapeutic applications across neurodegenerative diseases. This mini-review consolidates recent preclinical and clinical findings, examining the mechanisms by which gamma-tACS influences neural oscillations, enhances synaptic plasticity, modulates neuroimmune responses. Preclinical studies have demonstrated capacity of to synchronize neuronal firing, support long-term neuroplasticity, reduce markers neuroinflammation, suggesting its potential counteract processes. Early indicate that may improve cognitive functions network connectivity, underscoring ability restore disrupted oscillatory patterns central performance. Given intricate multifactorial nature gamma development tailored, optimized tACS protocols informed extensive animal research is crucial. Overall, presents avenue for advancing treatments resilience in range conditions.

Language: Английский

---

Complex spatiotemporal oscillations emerge from transverse instabilities in large-scale brain networks

PLoS Computational Biology, Journal Year: 2023, Volume and Issue: 19(4), P. e1010781 - e1010781

Published: April 12, 2023

Spatiotemporal oscillations underlie all cognitive brain functions. Large-scale models, constrained by neuroimaging data, aim to trace the principles underlying such macroscopic neural activity from intricate and multi-scale structure of brain. Despite substantial progress in field, many aspects about mechanisms behind onset spatiotemporal dynamics are still unknown. In this work we establish a simple framework for emergence complex dynamics, including high-dimensional chaos travelling waves. The model consists network 90 regions, whose structural connectivity is obtained tractography data. each area governed Jansen mass normalize total input received node so it amounts same across areas. This assumption allows existence an homogeneous invariant manifold, i.e., set different stationary oscillatory states which nodes behave identically. Stability analysis these solutions unveils transverse instability synchronized state, gives rise types as chaotic alpha activity. Additionally, illustrate ubiquity route towards next generation models. Altogehter, our results unveil bifurcation landscape that underlies function

Language: Английский

---

Exact low-dimensional description for fast neural oscillations with low-firing rates

arXiv (Cornell University), Journal Year: 2022, Volume and Issue: unknown

Published: Jan. 1, 2022

Recently, low-dimensional models of neuronal activity have been exactly derived for large networks deterministic, Quadratic Integrate-and-Fire (QIF) neurons. Such firing rate (FRM) describe the emergence fast collective oscillations (>30~Hz) via frequency-locking a subset neurons to global oscillation frequency. However, suitability such realistic states is seriously challenged by fact that during episodes oscillations, discharges are often very irregular and low rates compared Here we extend theory derive exact FRM QIF include noise, show stochastic displaying at governed same evolution equations as deterministic networks. Our results reconcile two traditionally confronted views on synchronization, upgrade applicability broad range biologically states.

Language: Английский

---

Firing rate models for gamma oscillations in I-I and E-I networks

Journal of Computational Neuroscience, Journal Year: 2024, Volume and Issue: 52(4), P. 247 - 266

Published: Aug. 19, 2024

Language: Английский

---

The Algorithmic Agent Perspective and Computational Neuropsychiatry: From Etiology to Advanced Therapy in Major Depressive Disorder

Entropy, Journal Year: 2024, Volume and Issue: 26(11), P. 953 - 953

Published: Nov. 6, 2024

Major Depressive Disorder (MDD) is a complex, heterogeneous condition affecting millions worldwide. Computational neuropsychiatry offers potential breakthroughs through the mechanistic modeling of this disorder. Using Kolmogorov theory (KT) consciousness, we developed foundational model where algorithmic agents interact with world to maximize an Objective Function evaluating affective valence. Depression, defined in context by state persistently low valence, may arise from various factors-including inaccurate models (cognitive biases), dysfunctional (anhedonia, anxiety), deficient planning (executive deficits), or unfavorable environments. Integrating algorithmic, dynamical systems, and neurobiological concepts, map agent brain circuits functional networks, framing etiological routes linking depression biotypes. Finally, explore how stimulation, psychotherapy, plasticity-enhancing compounds such as psychedelics can synergistically repair neural optimize therapies using personalized computational models.

Language: Английский

---