Bibliographic record
Abstract
Deep brain stimulation (DBS) is an established method for treatment of symptoms of different neurological diseases like Parkinson's disease, essential tremor, dystonia, and epilepsy. It is based on implanting leads into deep brain structures with stereotactic neurosurgery and applying chronic electrical stimulation delivered from battery powered implantable pulse generator (IPG). Different disorders have different stimulation target structures. Different computational methods have been developed for modelling the effects of DBS and estimating the spread of stimulation. A relationship between the clinical effects, and spatial distribution of electric field and its coverage of anatomical structures has been observed. Optimizing the electric field coverage of certain structures can be used to optimize the therapeutic effects. This includes minimizing the stimulation of non-targeted regions and maximizing the stimulation of target regions. In this thesis three developed methods for estimating the electric field and volume of tissue activated (VTA) by applied stimulation were compared. First method was partly based on experimental results and derived analytical model for VTA. Second method was based on pre-computed and saved electric field library which were computed with finite element method (FEM) on a simplified patient model with isotropic and homogeneous electrical conductivity of tissue. Last method utilized FEM in volume conductor model which consisted of isotropic tissues of grey matter (GM) and white matter (WM). A heuristic electric field threshold used in several studies was used to evaluate VTA with second and third methods. Patient-specific models, for epilepsy patients (n=16) with DBS leads implanted bilaterally in anterior nucleus of thalamus (ANT), were derived from preoperative magnetic resonance imaging (MRI) and postoperative computed tomography (CT) data sets. For comparison purposes, the patient models were normalized by transforming them into common Montreal Neurological Institute (MNI) defined atlas space. In the initial phase theoretically optimal stimulation contacts were defined, based on simulations, and defined metrics. Results from these optimal simulations were compared against each other. As the third method, utilizing FEM, was most realistic of the three it was considered as ground truth. First and second method produced quite similar results, but the third method differed from the other two. First and second method seemed to generally underestimate the VTA significantly. As the computation time of FEM models was significantly longer, they are not feasible in clinical settings unlike the other two. However, when time is not of the essence the accuracy gain from utilizing FEM compensates for the demanded computational load and time. Simulation research has been usually performed with data of patients which have some other disease than epilepsy, these results could probably be used for optimization of epilepsy treatment. This would require simulations with patient-specific stimulation settings, instead of standardized, and comparing the obtained results with observed clinical effects.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".