Background
Mental health disorders like depression and anxiety are associated with dysregulated brain activity. Current treatments, such as medication and open-loop brain stimulation, often have delayed effects and work differently for each person. Closed-loop neuromodulation is a promising new approach that adjusts stimulation in real-time based on a patient’s neural signals, allowing for more personalised and immediate care. This project aims to combine this technology with advanced computational models that mimic the brain’s own biological networks to create a new generation of intelligent therapeutic systems.
Novelty & Importance
This research is novel in its direct application of biologically inspired neural network models to psychiatric neuromodulation. While closed-loop systems exist for movement disorders, their use in psychiatry is limited. Our approach uses models based on hippocampal-cortical brain topology to not just react to, but also predict pathological states, enabling pre-emptive stimulation. This creates a truly adaptive system that learns and personalises treatment continuously, aiming to significantly improve the efficacy of therapies like Deep Brain Stimulation (DBS) and Transcranial Magnetic Stimulation (TMS) for mental health conditions.
Aims & Objectives
The primary aim is to develop and validate a biologically inspired neural network model to guide closed-loop stimulation for psychiatric disorders. Key objectives are:
1. To develop a computational model that simulates circuit dysfunction in psychiatric disorders.
2. To design a closed-loop algorithm that dynamically adjusts stimulation based on the model’s predictions and real-time neural data.
3. To validate the system’s ability to suppress abnormal neural activity in simulated and pre-clinical experimental settings.
References
1. Soleimani, G., et al. (2023). Closing the loop between brain and electrical stimulation: towards precision neuromodulation treatments. Translational Psychiatry, 13(1), 279.
2. Chen, G., Scherr, F., & Maass, W. (2022). A data-based large-scale model for primary visual cortex enables brain-like robust and versatile visual processing. Science Advances, 8(44), eabq7592.
3. Sourmpis, C., et al. (2024). Biologically informed cortical models predict optogenetic perturbations. bioRxiv, 2024.09.