Assessment of Structural Connectivity and Brain Volumes after tDCS in Stroke: A Machine-learning Method
Bibliographic record
Abstract
Background: Stroke causes numerous symptoms, including impaired motor skills, sensation languages, and cognitive functions. Previous studies revealed non-invasive brain stimulation techniques could enhance sensory, cognitive, and motor function. Objective: This study aimed to evaluate the effectiveness of transcranial direct current stimulation on functional communication, motor learning, and cognitive function in patients with ischemic stroke. Methods and Materials: The research method of this study was quasi-experimental with pre-test and post-test designs with three groups. Twenty-four patients were enrolled at the beginning. After written consent before the intervention, the Fugl-Meyer Assessment, Montreal Cognitive Assessment Test, and Mini-Mental State Exam were performed at three different time points. Furthermore, functional and structural neuroimaging (DTI, fMRI) were exploited before and after the intervention. Regarding the intervention process, transcranial direct current stimulation (tDCS) was used for twelve 30-minute daily sessions for the patients. Data were analyzed via in-house MATLAB-SPM12, FSL, and scikit-learn. Results: The figures for the FMA test of the active group increased after the intervention (P<0.05). Additionally, the figure for both screening tests increased after the treatment in the active group (P<0.05). Regarding the results of DTI, a significant difference was found in some regions, such as the right inferior occipital. Moreover, the best results were achieved by Random Forest, CatBoost, and XGBoost models in classifying groups by DTI data. Conclusion: Transcranial direct-current stimulation has been proven to be an effective rehabilitation for post-stroke impairments. We assessed the different structural and functional neuroimaging methods to determine which could display the effect of tDCS.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".