iDC-cortex-model

This is a morphological approximation model that investigates how ionic direct current (iDC) modulates the membrane potential in rat cortical neurons. The code generates:

1. Extracellular potential $V_e$ produced by a disk electrode on the pia mater and the corresponding mirror‐estimate $\Delta V_m$ for multiple neuron types (Fig. 3A–B).
2. Population‐level heatmaps via weighted‐sum interpolation of soma potentials, and layer‐wise averages as a function of lateral distance (Fig. 3C–E).

Written and maintained by Runming Wang and Gene Fridman, August 2024 - June 2025.

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Prerequisites

• MATLAB R2020a or later (no external toolboxes required).
• Download or clone this repository locally.
• Ensure the working directory contains the iDC_paper_model.m script.

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Script Overview

All code is contained in a single MATLAB script (iDC_paper_model.m) with two independent sections. You can run each section in isolation by selecting its code block and pressing Run Section (in the MATLAB Editor) or by copying it into the Command Window.

Section 1: Ve & ΔVm for Neuron Types (Fig. 3A–B)

This section computes and plots:

• Extracellular potential (Ve) on a 2D cortical cross‐section (±1.1 mm laterally, 0–2.2 mm depth) using a point‐source summation from a 250 µm‐diameter disk electrode.
• Mirror‐estimate $\Delta V_m$ for 12 neuron morphologies (8 excitatory, 4 inhibitory) represented as vertical rods. Each rod’s length covers its dendritic span and a black marker indicates the soma.

To run:

1. Open the script in MATLAB.
2. Highlight the code under Section 1 (lines 1–108).
3. Press Run Section.
4. Examine figures titled “V_e (V)” and each neuron’s $\Delta V_m$ subplot.

Section 2: Weighted 2D Interpolation & Layer‐wise Averages (Fig. 3C–E)

This section simulates a population of rods randomly scattered according to known layer‐specific densities, computes each soma’s $\Delta V_m$, and generates:

• A 2D cross-section with randomly scattered neuron rods.
• A heatmap of weighted‐sum soma potential change of all scattered neurons on a finer grid (200×200) using a Gaussian kernel.
• Layer‐wise average potential changes at four lateral distances (0.2, 0.55, 0.9, 1.25 mm).

To run:

1. Highlight the code under Section 2 (lines 109–end).
2. Press Run Section.
3. Examine the figures:
   • “Combined Randomly Scattered Neurons”
   • “2D Weighted Sum of Soma Potentials”
   • “Depth‑Layer Averages of Weighted Soma Potentials”

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Adjusting Parameters

• iDC electrode current (Icenter_amp): default anodic +20 µA.
• iDC electrode radius (electrode_radius): default 0.125mm.
• iDC electrode position (electrode_center): default [0,0,0] mm.
• Grid resolution (dx, dy): finer resolution increases accuracy at cost of speed.
• Neuron types: edit neuronTypes to reflect alternative neuron types/morphologies.
• Neuron densities: edit densities_percent to reflect alternative relative neuron densities.
• Gaussian kernel (sigma): controls spatial smoothing of weighted-sum interpolation.

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License

This project is released under the MIT License—see LICENSE for details. Feel free to adapt and extend for your own research.