Biology subjects

Goldman, J. S.

Publications and source records attributed to Goldman, J. S..

5 recordsLinked to original sources

bioRxiv2023

Asynchronous and slow-wave oscillatory states in connectome-based models of mouse, monkey and human cerebral cortex

Thanks to the availability of connectome data that map connectivity between multiple brain areas, it is now possible to build models of whole brain activity. At the same time, advances in mean-field techniques have led to biologically based population models that integrate biophysical features such as membrane conductances or synaptic conductances. In this paper, we show that this approach can lead to brain-wide models of mouse, macaque, and human. We illustrate this approach by showing the transition from wakefulness to sleep simulated with multi-scale models in the three species. We compare the level of synchrony between the three species and found that the mouse brain displays a higher overall synchrony of slow-waves compared to monkey and human brains. We show that these differences are due to the different delays of axonal signal propagation between regions associated to brain-size differences between the species. We also make the program code publicly available, which provides a set of open-source tools for simulating large-scale activity in the cerebral cortex of mouse, monkey, and human.

neuroscience↗

bioRxiv2023

High-density exploration of activity states in a multi-area brain model

Biophysically-grounded whole-brain models were built recently using tractography data to interconnect multiple mesoscopic models, which can simulate the dynamics of neuronal populations with only a few equations. Mean-field models of neural populations, specifically the Adapting AdEx mean-field, was used for this purpose because it can integrate key biophysical mechanisms such as spike-frequency adaptation and its regulation at cellular scales, to the emergence of brain-scale dynamics. Using this approach, with the Virtual Brain (TVB) environment, it has been possible to model the macroscopic transitions between brain states, described by variation in brain-scale dynamics between asynchronous and rapid dynamics during conscious brain states, and synchronized slow-waves, with Up-and-Down state dynamics during unconscious brain states, emerging from mechanisms at the cellular level. Transitions between brain states are driven by changes in neuromodulation that can be due to intrinsic regulation during sleep-wake cycles or extrinsic factors such as anesthetics, which, in turn, affect spike-frequency adaptation. Here, we perform a dense grid parameter exploration of the TVB-AdEx model, making use of High Performance Computing, to thoroughly explore the properties of this model. We find that there is a remarkable robustness of the effect of adaptation to induce synchronized slow-wave activity. Moreover, the occurrence of slow waves is often paralleled with a closer relation between functional and structural connectivity. We find that hyperpolarization can also generate unconscious-like synchronized Up and Down states, which may be a mechanism underlying the action of anesthetics. We conclude that the parameter space of the TVB-AdEx model reveals features identified experimentally in sleep and anesthesia.

neuroscience↗

bioRxiv2021

A comprehensive neural simulation of slow-wave sleep and highly responsive wakefulness dynamics

Hallmarks of neural dynamics during healthy human brain states span spatial scales from neuromodulators acting on microscopic ion channels to macroscopic changes in communication between brain regions. Developing a scale-integrated understanding of neural dynamics has therefore remained challenging. Here, we perform the integration across scales using mean-field modeling of Adaptive Exponential (AdEx) neurons, explicitly incorporating intrinsic properties of excitatory and inhibitory neurons. We report that when AdEx mean-field neural populations are connected via structural tracts defined by the human connectome, macroscopic dynamics resembling human brain activity emerge. Importantly, the model can qualitatively and quantitatively account for properties of empirical spontaneous and stimulus-evoked dynamics in the space, time, phase, and frequency domains. Remarkably, the model also reproduces brain-wide enhanced responsiveness and capacity to encode information particularly during wake-like states, as quantified using the perturbational complexity index. The model was run using The Virtual Brain (TVB) simulator, and is open-access in EBRAINS. This approach not only provides a scale-integrated understanding of brain states and their underlying mechanisms, but also open access tools to investigate brain responsiveness, toward producing a more unified, formal understanding of experimental data from conscious and unconscious states, as well as their associated pathologies.

neuroscience↗

bioRxiv2019

Modeling subcortical white matter stimulation

ObjectiveIntracranial electrical stimulation of subcortical axonal tracts is particularly useful during brain surgery, where mapping helps identify and excise dysfunctional tissue while avoiding damage to functional structures. Stimulation parameters are generally set empirically and consequences for the spatial recruitment of axons within subcortical tracts are not well identified. ApproachComputational modeling is employed to study the effects of stimulation parameters on the recruitment of axons: monophasic versus biphasic stimuli induced with monopolar versus bipolar electrodes, oriented orthogonal or parallel to the tract, for isotropic and anisotropic tracts. Main resultsThe area and depth of axonal activation strongly depend on tissue conductivity and electrode parameters. The largest activation area results from biphasic stimulation with bipolar electrodes oriented orthogonal to axonal fasciculi, for anisotropic and especially isotropic tracts. For anisotropic tracts, the maximal activation depth is similar regardless of whether a monopolar or bipolar electrode is employed. For isotropic tracts, bipolar parallel and monopolar stimulation activate axons deeper than orthogonal bipolar stimulation. Attention is warranted during monophasic stimulation: a blockade of action potentials immediately under cathodes and a propagation of action potentials under anodes are found. SignificanceConsidering the spatial patterns of blockade and activation present during monophasic stimulation with both monopolar and bipolar electrodes, biphasic stimulation is recommended to explore subcortical axon responses during intraoperative mapping. Finally, the precise effect of electrical stimulation depends on conductivity profiles of tracts, and as such, should be explicitly considered for each individual subject and tract undergoing intracranial mapping.

biophysics↗

bioRxiv2018

Two types of slow waves in anesthetized and sleeping brains

Sleep slow waves are known to participate in memory consolidation, yet slow waves occurring under anesthesia present no positive effects on memory. Here, we shed light onto this paradox, based on a combination of extracellular recordings in vivo, in vitro, and computational models. We find two types of slow waves, based on analyzing the temporal patterns of successive slow-wave events. The first type is consistently observed in natural slow-wave sleep, while the second is shown to be ubiquitous under anesthesia. Network models of spiking neurons predict that the two slow wave types emerge due to a different gain on inhibitory vs excitatory cells and that different levels of spike-frequency adaptation in excitatory cells can account for dynamical distinctions between the two types. This prediction was tested in vitro by varying adaptation strength using an agonist of acetylcholine receptors, which demonstrated a neuromodulatory switch between the two types of slow waves. Finally, we show that the first type of slow-wave dynamics is more sensitive to external stimuli, which can explain how slow waves in sleep and anesthesia differentially affect memory consolidation, as well as provide a link between slow-wave dynamics and memory diseases.

neuroscience↗