Study reviews BNMs’ role in understanding brain disorders

A research team from the International Research Institute for Artificial Intelligence at Harbin Institute of Technology, Shenzhen, recently published a comprehensive review in the journal Health data science about the application of Brain Network Models (BNMs) in the medical sector. This study summarizes the recent developments and challenges in using BNMs to simulate brain activities, understand neuropathological mechanisms, evaluate therapeutic effects, and predict disease progression.

Brain Network Models (BNMs) are mathematical modeling tools based on neural networks that integrate structural connectivity (SC) and functional connectivity (FC) data to simulate dynamic changes in the brain under different neurological conditions. With advances in neuroimaging techniques, BNMs have become crucial in studying underlying mechanisms for neurological disorders such as epilepsy, Alzheimer’s disease (AD), and Parkinson’s disease (PD).

Led by Assistant Professor Chenfei Ye of the International Research Institute for Artificial Intelligence at Harbin Institute of Technology, Shenzhen, the team reviewed the current applications of BNMs in medicine. The review highlights how BNMs integrate multimodal neuroimaging data to simulate overall brain dynamics and proposes improvements such as adopting multimodal data fusion strategies to improve model accuracy in representing the complex functional architecture of the brain.

The team developed a disease-focused BNM workflow that shows how an individual’s structural brain connectome (SC) can be extracted from structural and diffusion-weighted MRI data and how functional connectivity (FC) can be inferred through statistical analyzes of data from MEG , EEG or fMRI. Then, by coupling local neural mass models (NMMs) with structural connectivity data, a global BNM is constructed to simulate large-scale brain activities.

The main value of BNMs lies in their ability to quantitatively analyze the abnormal network dynamics of the brain under different disease states, providing new opportunities for personalized treatment planning. The study suggests that future BNM development should focus more on individual differences and the integration of multimodal data to achieve more accurate disease diagnosis and therapeutic strategies.

The research team indicates that future work will focus on developing new BNM models capable of estimating a broader range of neurodynamic parameters, such as the distribution of presynaptic inputs, frequency-dependent synaptic depression, and intrinsic excitability of postsynaptic neurons. The ultimate goal is to apply these advanced modeling techniques in clinical practice to optimize treatment strategies.