Leveraging Julia's automated differentiation and symbolic computation to increase spectral DCM flexibility and speed.
David HofmannAnthony G ChesebroChris RackauckasLilianne Rivka Mujica-ParodiKarl John FristonAlan EdelmanHelmut H StreyPublished in: bioRxiv : the preprint server for biology (2023)
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 SPM12, 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.
Keyphrases
- optical coherence tomography
- electronic health record
- deep learning
- machine learning
- big data
- healthcare
- primary care
- functional connectivity
- autism spectrum disorder
- resting state
- high throughput
- minimally invasive
- magnetic resonance
- dual energy
- quality improvement
- magnetic resonance imaging
- density functional theory
- data analysis
- neural network
- single cell