# Towards a biophysical model of human cortical neurophysiological signatures that incorporates cellular and cell type biophysics, transcriptomics, and morphology

> **NIH NIH R01** · CEDARS-SINAI MEDICAL CENTER · 2024 · $547,234

## Abstract

Project Summary
The past decade has seen an unprecedented accumulation of data at the single-cell level giving rise to unique,
multimodal sets of gene expression patterns, morphologies, and electrophysiology characteristics. This high-
resolution census of neuronal types in the brain constitutes an essential step towards a mechanistic
understanding of circuits and brain computations. However, linking cellular identity and activity to circuits and
neurophysiological signals remains daunting. The central goal of this proposal is to develop a set of publicly
available computational models, tools and resources that allow testing mechanistic hypotheses about the role
of different neuronal types in neural circuit function, that can be used to analyze neural data, and can serve to
identify potential biomarkers of cortical function in the brain. The proposed approach starts with building
rigorous computational models of individual human neocortical neurons linking transcription, morphology, and
physiology. Next, we connect neurons into cortical circuits, emulate their activity and simulate the associated
neurophysiological signals generated from those circuits. At all steps, the models are guided by, and compared
to, existing state-of-the-art measurements. As a proof-of-principle, we use a human cortical circuit model to
study the impact of the hyperpolarization-activated non-specific cation current, Ih, carried by h-channels.
Specifically, we recently showed that Ih differences within human pyramidal FREM3 neurons alter their
synaptic integration properties. The existence of an Ih depth gradient along human layer 2/3 in FREM3
neurons in fact is a fundamental organizing principle of the supragranular cortex. Even so, its role and how it
affects circuit processing and associated neurophysiological signals remains unknown. The human cortical
circuit model provides an ideal testbed where hypotheses about the role of the FREM3 Ih depth gradient can
be tested. First, we hypothesize that while fundamental spike properties of FREM3 models remain broadly
unaffected by deep vs. superficial FREM3 Ih-differences, the circuit activity will be substantially affected and a
coincidence detection gradient will emerge along the cortical depth axis (facilitated by the Ih gradient in
FREM3). Furthermore, we hypothesize that deep in the circuit where FREM3 has both increased Ih and
coincidence detection, the local extracellular signals will exhibit lower power in the slower bands and increased
power in faster ones. Accordingly, we expect decreased Ih (superficial neurons) to lead to a relative increase in
power in theta-slow gamma and a decrease in the mid-high gamma band. To test these hypotheses, we will
instantiate specific perturbations in the Ih conductance of FREM3 in our circuit model while leaving all other
model parameters unperturbed. We expect that these tools, which will be shared with the scientific community,
will facilitate the dialogue between the study of c...

## Key facts

- **NIH application ID:** 10865067
- **Project number:** 5R01NS130126-02
- **Recipient organization:** CEDARS-SINAI MEDICAL CENTER
- **Principal Investigator:** Constantinos Anastassiou
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $547,234
- **Award type:** 5
- **Project period:** 2023-06-15 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10865067

## Citation

> US National Institutes of Health, RePORTER application 10865067, Towards a biophysical model of human cortical neurophysiological signatures that incorporates cellular and cell type biophysics, transcriptomics, and morphology (5R01NS130126-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10865067. Licensed CC0.

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