Application of MRI and electrovestibulography on Alzheimer's disease: diagnosis, monitoring and predicting response to treatment
Bibliographic record
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder characterized by gradual loss of memory and cognition. Diagnosing people with pure AD from those subjects who have mixed conditions of AD and cerebrovascular disease (AD-CVD) is a challenging research problem. Another interesting research problem is the monitoring of changes in the comorbid depression of participants with AD when repetitive transcranial magnetic stimulation (rTMS) is applied to improve their cognition. Furthermore, the rTMS has a demanding treatment protocol, and its efficacy for AD is uncertain; a method capable of predicting rTMS responses at baseline would be of great interest. In this thesis, we separately utilized magnetic resonance imaging (MRI) and electrovestibulography (EVestG) to address the above research questions; in particular, using MRI analysis investigating plausible differences between AD and AD-CVD, using EVestG investigating the impact of rTMS on depression comorbidity amongst individuals with AD who received rTMS treatment, and also using MRI analysis predicting patients’ response to rTMS treatment at baseline. This thesis consists of four studies. Firstly, we conducted voxel-based morphometry on brain MRI data of participants with AD and AD-CVD and controls. In addition to significantly lower gray matter or white matter volumes of AD and AD-CVD compared to controls, a potential differential trend was shown in MRI between AD and AD-CVD. Secondly, we calculated the EVestG-driven depression features of participants with AD and investigated whether their changes were associated with cognition change following rTMS treatment. The EVestG results showed that cognitive improvement following rTMS treatment was not likely due to the improvement in their depression status. Thirdly, significant features from the gray matter of the dorsolateral prefrontal cortex were found, resulting in only 69% accuracy in predicting rTMS efficacy. Fourthly, the MRI-driven histogram-based radiomic features were extracted, and the significant features of rTMS efficacy were selected in a data-driven manner, which resulted in 81.9% accuracy in classifying responders and non-responders to active rTMS. Overall, the studies presented here would provide a way of differentiating AD and AD-CVD, monitoring the quantitative status of depression in AD subjects undergoing rTMS treatment, and aiding in designing personalized treatment techniques for future AD participants.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".