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Updated: Sep 11, 2025

Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy
Published on: June 15, 2022
Increasing spectral DCM flexibility and speed by leveraging Julia's ModelingToolkit and automated differentiation
David Hofmann1,2, Anthony G Chesebro1,3, Chris Rackauckas2
1Laufer Center for Physical and Quantitative Biology, State University of New York at Stony Brook, Stony Brook, NY, United States.
None:
Using neuroimaging and electrophysiological data to infer neural parameter estimations from theoretical circuits requires solving the inverse problem. Here, we provide a new Julia language package designed to i) compose complex dynamical models in a simple and modular way with ModelingToolkit.jl, ii) implement parameter fitting based on spectral dynamic causal modeling (sDCM) using the Laplace approximation, analogous to MATLAB implementation in SPM, and iii) leverage Julia's unique strengths to increase accuracy and speed by employing Automatic Differentiation during the fitting procedure. To illustrate the utility of our flexible modular approach, we provide a method to improve correction for fMRI scanner field strengths (1.5T, 3T, 7T) when fitting models to real data.
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