Detecting State Changes in Functional Neuronal Connectivity using Factorial Switching Linear Dynamical Systems

2024-11-06

AI Summary

Traditional neural functional connectivity modeling relies on a “single-state activation” assumption, which contradicts empirical evidence of concurrent, parallel activity across multiple subnetworks. To address this limitation, we propose the Factorized Switching Linear Dynamical System (F-SLDS), the first framework to integrate a factorized hidden Markov model into time-varying connectivity analysis—enabling asynchronous, independent switching among multiple latent subnetworks. Our method employs a scalable variational inference algorithm based on the Concrete relaxation for robust and efficient posterior estimation of latent variables. On synthetic data, F-SLDS accurately recovers ground-truth connectivity structures. Applied to in vitro microelectrode array recordings, it uncovers stage-wise, non-monotonic evolution of functional connectivity during neural maturation. This work transcends conventional single-state paradigms, providing an interpretable and scalable framework for characterizing complex, dynamic neural circuitry.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Probabilistic Circuits and Graphical Models
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Social Networks and Social Media: Computational social science
• Web Mining and Content Analysis: Models for Web evolution

Abstract

A key question in brain sciences is how to identify time-evolving functional connectivity, such as that obtained from recordings of neuronal activity over time. We wish to explain the observed phenomena in terms of latent states which, in the case of neuronal activity, might correspond to subnetworks of neurons within a brain or organoid. Many existing approaches assume that only one latent state can be active at a time, in contrast to our domain knowledge. We propose a switching dynamical system based on the factorial hidden Markov model. Unlike existing approaches, our model acknowledges that neuronal activity can be caused by multiple subnetworks, which may be activated either jointly or independently. A change in one part of the network does not mean that the entire connectivity pattern will change. We pair our model with scalable variational inference algorithm, using a concrete relaxation of the underlying factorial hidden Markov model, to effectively infer the latent states and model parameters. We show that our algorithm can recover ground-truth structure and yield insights about the maturation of neuronal activity in microelectrode array recordings from in vitro neuronal cultures.

Problem

• Detecting time-evolving functional connectivity in neuronal activity
• Modeling multiple simultaneously active latent neuronal subnetworks
• Inferring latent states and parameters with scalable variational inference

Innovation

• Factorial Switching Linear Dynamical Systems
• Scalable variational inference algorithm
• Concrete relaxation of factorial HMM