Multiscale co-simulation of deep brain stimulation with brain networks in neurodegenerative disorders
Bibliographic record
Abstract
Deep brain stimulation (DBS) has been used successfully as symptomatic treatment in several neurodegenerative disorders, including Parkinson’s disease (PD). However, the mechanisms of its activity inside the brain network are unclear. Many virtual DBS models investigate the dynamics of a subnetwork surrounding the basal ganglia (BG) as a spiking network has been attracting a growing body of research in neuroscience. Connectomic data, on the other hand, show that DBS has a wide range of impacts on many distinct cortical and subcortical sites. Notably, the nonlinear reaction–diffusion multiscale mathematical models demonstrate the effectiveness of capturing crucial disease characteristics and are used to simulate large-scale brain activity. The BG and associated nuclei comprise many subcortical cell groups in the brain, and their couplings commonly revealed MRI-based assessments of the strength of anatomical connections. We have developed a hybrid modelling formalism and a unique co-simulation technique that allows us to compute electrodiffusive ion dynamics for the cortex-BG-thalamus (BGTH) brain network model within a large-scale brain connectome. We collect data from the Human Connectome Project (HCP) and propose a closed-loop DBS approach based on the brain network model. Moreover, we select regions in the parameter space that reflect the healthy and Parkinsonian states as well as the impact of DBS on the subthalamic nucleus (STN) and globus pallidus internus (GPi) neurons. We predicted that if we apply the DBS to the system described by the temporal model, the brain maintains a healthy state until 0.05ms for STN neurons and 0.035ms for GPi neurons. A local regulatory mechanism known as feedback inhibition control (FIC) points to the existence of underlying network dynamics in the white matter of connected brain regions. The model showed unanticipated effects that in the presence of diffusion, the human brain maintained a healthy state for a long time after the DBS had been applied to STN and GPi neurons. This research helps us better understand the changes in brain activity caused by DBS and enhances this clinical therapy, thus shedding new light on the importance of DBS mechanisms in BGTH brain network models of neurodegenerative disorders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".