Biology subjects

Fasse, A.

Publications and source records attributed to Fasse, A..

3 recordsLinked to original sources

bioRxiv2026

BraiNN: A Modern Simulator for Clinically Feasible Personalized Whole-Brain Network Modeling

Personalized whole-brain modeling aims to transform treatment planning for neurological disorders by enabling patient-specific simulations of brain network dynamics. Neural mass models (NMMs) offer a tractable compromise between biophysical detail and computational cost and can be directly linked to macroscopic observables such as EEG. However, scaling NMMs to whole-brain networks with realistic connectivity, conduction delays, and cortical surface resolution--and fitting them to individual patient data--imposes computational demands that existing frameworks cannot meet at clinically relevant timescales. Here we introduce BraiNN, a JAX-based Python framework for large-scale neural mass modeling that achieves speedups of up to two to three orders of magnitude over existing tools by leveraging GPU/TPU-accelerated, XLA-compiled array computation. BraiNN combines a region-level Jansen-Rit network with a subject-specific cortical surface mesh of coupled neural mass models and biophysically grounded EEG forward modeling via reciprocity-based lead fields. Its fully differentiable computational graph enables a hybrid personalization pipeline that pairs Bayesian optimization for global parameter exploration with gradient-based refinement, completing EEG-driven spectral fitting of an eight-dimensional parameter space in approximately 2-3 hours on a single consumer GPU--compared to multiple days with conventional neural mass modeling software. Numerical verification against established benchmarks confirms that BraiNN faithfully reproduces canonical synchronization and bifurcation dynamics of Jansen-Rit networks. By reducing the time requirements for personalizing a high-detail whole-brain surface model from days to a few hours on consumer-grade hardware, BraiNN brings personalized brain network modeling closer to practical use in clinical contexts. We anticipate that BraiNN will serve as a foundation for patient-specific digital twins and EEG-guided neuromodulation planning.

neuroscience↗

bioRxiv2025

Shaping a Collaborative, Sustainable, Accessible, and Reproducible Future for Computational Modeling

The o2S2PARC platform is an open-source, extensible, and scalable cloud-based platform developed in the context of the U.S. National Institutes of Health SPARC program to support collaborative, sustainable, FAIR (findable, accessible, interoperable, reusable) and reproducible computational modeling and analysis. This publication presents the main features of o2S2PARC, its underlying approaches and philosophy, innovative aspects of the developed technologies, while also drawing attention to its rapid adoption. The paper showcases a variety of applications and use cases enabled by the platform. These include hybrid electromagnetic-electrophysiology simulations of neural interfaces, personalized brain and spinal cord stimulation planning, in silico device safety assessments, the training and application of AI systems (e.g., for model-predictive control and medical image segmentation), hybridized surrogate modeling and multi-objective optimization in high-dimensional parameter spaces, sensitive and unbiased validation of measurement devices, and interactive data analysis as paper supplements.

neuroscience↗

bioRxiv2025

SpIC3D imaging: Spinal In-situ Contrast 3D imaging

High-definition visualization techniques are critical for understanding the neuroanatomy of the spinal cord, an essential structure for sensorimotor and autonomic functions, in both healthy and pathological conditions. Magnetic resonance imaging (MRI) is a common method for visualizing neural structures in 3D. However, techniques for spinal cord MRI have historically achieved limited visualization of rootlets and nerves, especially at lower spinal levels, due to their highly complex and compact organization. Here we developed a spinal in-situ contrast 3D imaging (SpIC3D) method that allows visualization of spinal compartments in fixed animal and human specimens with ultra-high resolution at various spinal levels. Using SpIC3D, we achieved quantification of neuronal cell density in dorsal root ganglia, multi-segment identification of individual rootlets and roots, and volumetric reconstruction of multiple spinal structures for computational modeling. SpIC3D provides a basis for accelerated spinal pathology characterization and personalized spinal cord stimulation treatments.

neuroscience↗