P089 Parameter identifiability in model-based inference for neurodegenerative diseases: noninvasive stimulation
Jan Fousek*¹
¹ Central European Institute of Technology (CEITEC), Masaryk University, Brno, Czech Republic
*Email: jan.fousek@ceitec.muni.cz Introduction
Tracking the trajectories of progression of patients with neurodegenerative diseases remains a challenging task. While employing connectome-based models can improve the performance of machine-learning-based classification [1], the identifiability of relevant parameters can be challenging when using only data features derived from spontaneous (resting state) data [2]. Here, in the context of Alzheimer disease (AD), we explore an alternative approach based on perturbations, namely using the response to single pulse transcranial magnetic stimulation recorded by EEG.
Methods First, a whole-brain model using a normative human connectome was set up together with an EEG forward solution in order to replicate TMS evoked potential (TEP) [3] following precuneus stimulation [4]. Next, to define the trajectory of the AD in parameter space, we used previously established trajectory of progression of AD capturing how the evolution of the spatial profile of the proteinopathy expresses is reflected in the altered model parameters [5]. Using simulation-based inference, we then tried to recover the parameters using synthetic data simulated along the AD progression trajectory, and assessed the shrinkage of the posterior distributions, and the precision of the point estimates. Results The model successfully reproduced the TEP patterns found in the empirical data. Along the progression trajectory, the model parameters remained identifiable, showing significant shrinkage of the posterior distribution with respect to the prior and small distance of the mean values from the ground-truth. Additionally, while we observed some correlation between the estimated parameters (hinting to a certain degree of degeneracy), it did not impact the performance of the inference. Discussion Here we demonstrate that the brain response to the noninvasive stimulation is informative enough to allow effective parameter inference in connectome-based models. The workflow can be easily adapted to different data-features derived from the TEPs, as well as different stimulation targets. As a natural next step, this approach will be benchmarked and validated in empirical datasets on individual subject data.
Acknowledgements Jan Fousek receives funding from the European Union’s Horizon Europe research and innovation programme under the Marie Sklodowska-Curie grant agreement No 101130827. References [1] https://doi.org/10.1002/trc2.12303 [2] https://doi.org/10.1088/2632-2153/ad6230 [3] https://doi.org/10.3389/fninf.2013.00010 [4] https://doi.org/10.1016/j.clinph.2024.09.007 [5] https://doi.org/10.1523/ENEURO.0345-23.2023
