Data-driven multiscale model of macaque auditory thalamocortical circuits reproduces in vivo dynamics

Dura-Bernal, Salvador; Griffith, Erica Y.; Barczak, Annamaria; O’Connell, Monica N.; McGinnis, Tammy; Moreira, Joao V.S.; Schroeder, Charles E.; Lytton, William W.; Lakatos, Peter; Neymotin, Samuel A.

doi:10.1016/j.celrep.2023.113378

Cited by 5 publications

(2 citation statements)

References 141 publications

(203 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Rössert et al (2017) describes the adjustments to synaptic parameters required for such an endeavor that could be easily implemented as part of the operations described in Figure 1D2 in the context of the transplant functionality (Figure 1C, blue). Together with the recent publications of openly available large-scale models (Billeh et al, 2020;Dura-Bernal et al, 2023;Isbister et al, 2023), this makes our work also relevant for the point neuron model community.…”

Section: Discussionmentioning

confidence: 60%

A connectome manipulation framework for the systematic and reproducible study of structure function relationships through simulations

Pokorny,

Awile,

Isbister

et al. 2024

Preprint

View full text Add to dashboard Cite

Synaptic connectivity at the neuronal level is characterized by highly non-random features. Hypotheses about their role can be developed by correlating structural metrics to functional features. But to prove causation, manipulations of connectivity would have to be studied. However, the fine-grained scale at which non-random trends are expressed makes this approach challenging to pursue experimentally. Simulations of neuronal networks provide an alternative route to study arbitrarily complex manipulations in morphologically and biophysically detailed models. Here, we present Connectome-Manipulator, a Python framework for rapid connectome manipulations of large-scale network models in SONATA format. In addition to creating or manipulating the connectome of a model, it provides tools to fit parameters of stochastic connectivity models against existing connectomes. This enables rapid replacement of any existing connectome with equivalent connectomes at different levels of complexity, or transplantation of connectivity features from one connectome to another, for systematic study. We employed the framework in a detailed model of rat somatosensory cortex in two exemplary use cases: transplanting interneuron connectivity trends from electron microscopy data and creating simplified connectomes of excitatory connectivity. We ran a series of network simulations and found diverse shifts in the activity of individual neuron populations causally linked to these manipulations.

show abstract

Section: Discussionmentioning

confidence: 60%

A connectome manipulation framework for the systematic and reproducible study of structure function relationships through simulations

Pokorny,

Awile,

Isbister

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Detailed biophysical models can capture key properties of EEG [29][30][31] , and thus provide powerful tools for linking cellular and circuit mechanisms to brain activity and clinically-relevant signals. We previously showed in silico that α5-PAM would recover cortical activity, function and EEG spectral profile in detailed models of human depression microcircuits with reduced SST interneuron inhibition, and we highlighted EEG biomarkers that can monitor α5-PAM efficacy 11 .…”

Section: Introductionmentioning

confidence: 99%

Therapeutic dose prediction of α5-GABA receptor modulation from simulated EEG of depression severity

Guet-McCreight,

Mazza,

Prevot

et al. 2024

Preprint

View full text Add to dashboard Cite

Treatment for major depressive disorder (depression) often has partial efficacy and a large portion of patients are treatment resistant. Recent studies implicate reduced somatostatin (SST) interneuron inhibition in depression, and new pharmacology boosting this inhibition via positive allosteric modulators of α5-GABAA receptors (α5-PAM) offers a promising effective treatment. However, testing the effect of α5-PAM on human brain activity is limited, meriting the use of detailed simulations. We utilized our previous detailed computational models of human depression microcircuits with reduced SST interneuron inhibition and α5-PAM effects, to simulate EEG of virtual subjects across depression severity and α5-PAM doses. We developed machine learning models that predicted optimal dose from EEG with high accuracy and recovered microcircuit activity and EEG. This study provides dose prediction models for α5-PAM administration based on EEG biomarkers of depression severity. Given limitations in doing the above in the living human brain, the results and tools we developed will facilitate translation of α5-PAM treatment to clinical use.

show abstract

In-silico testing of new pharmacology for restoring inhibition and human cortical function in depression

Guet-McCreight,

Chameh,

Mazza

et al. 2024

Commun Biol

View full text Add to dashboard Cite

Reduced inhibition by somatostatin-expressing interneurons is associated with depression. Administration of positive allosteric modulators of α5 subunit-containing GABAA receptor (α5-PAM) that selectively target this lost inhibition exhibit antidepressant and pro-cognitive effects in rodent models of chronic stress. However, the functional effects of α5-PAM on the human brain in vivo are unknown, and currently cannot be assessed experimentally. We modeled the effects of α5-PAM on tonic inhibition as measured in human neurons, and tested in silico α5-PAM effects on detailed models of human cortical microcircuits in health and depression. We found that α5-PAM effectively recovered impaired cortical processing as quantified by stimulus detection metrics, and also recovered the power spectral density profile of the microcircuit EEG signals. We performed an α5-PAM dose-response and identified simulated EEG biomarker candidates. Our results serve to de-risk and facilitate α5-PAM translation and provide biomarkers in non-invasive brain signals for monitoring target engagement and drug efficacy.

show abstract

Data-driven multiscale model of macaque auditory thalamocortical circuits reproduces in vivo dynamics

Cited by 5 publications

References 141 publications

A connectome manipulation framework for the systematic and reproducible study of structure function relationships through simulations

A connectome manipulation framework for the systematic and reproducible study of structure function relationships through simulations

Therapeutic dose prediction of α5-GABA receptor modulation from simulated EEG of depression severity

In-silico testing of new pharmacology for restoring inhibition and human cortical function in depression

Contact Info

Product

Resources

About