Editorial: New technologies for detection, monitoring and treatment of Parkinson's disease, volume II
Bibliographic record
Abstract
In (Joza et al., 2022) the authors examine the role of the pedunculopontine nucleus (PPN) in gait control. The study suggests that abnormalities in PPN connectivity, particularly in the right hemisphere involving regions such as the caudate nucleus and amygdala, contribute to gait dysfunction in PD. This finding highlights the importance of investigating overall brain networks and connectivity patterns to understand the neural basis of gait impairment. In addition, (Guo et al., 2022) provide an overview of gait analysis as a tool for both detecting and monitoring PD. Gait impairments can serve as clinical signs for early PD detection and can be objectively measured, allowing for pervasive monitoring of patients in daily life. The paper discusses various gait analysis systems and automatic recognition methods, highlighting the importance of personalized interventions and smart devices for improving gait performance in PD.The three other papers on this Topic describe novel brain imaging-based biomarkers for PD. Two papers emphasize the importance of incorporating subregions of the basal ganglia into the analysis. (Pan et al., 2022) introduces a method for detecting PD using PET imaging. By analyzing dopaminergic activity in different subregions of the striatum, Pan et al. found that specific standardized uptake value ratios (SUVRs) had high diagnostic accuracy for PD. (Liu et al., 2022) focuses on the diagnostic value of 18F-FP-DTBZ PET imaging for PD. This study demonstrates that the posterior dorsal putamen (PDP) can effectively differentiate PD patients from healthy controls. The last paper (Shih et al., 2023) highlights the use of diffusion tensor imaging (DTI) to study white matter alterations in PD. DTI enables the detection of early axonal changes in PD, which may play a critical role in the disease's pathophysiology. The review emphasizes the significance of DTI in differentiating PD subtypes, understanding the progression of the disease, and optimizing treatment strategies such as deep brain stimulation (DBS).In summary, these papers collectively contribute to the broader understanding of gait impairment in PD and its underlying brain imaging mechanisms. They highlight the potential of advanced imaging techniques, gait analysis systems, and DTI in diagnosing and monitoring PD, as well as guiding personalized interventions. The findings have implications for improving the quality of life for individuals with PD and advancing research in the field of neurodegenerative disorders.It is important to consider some weaknesses, limitations, and potential biases that may affect the interpretation of the results of the accepted papers.• Sample Size: all the studies have relatively small sample sizes, which may limit the generalizability of the findings. This is likely due to the relatively large expense associated with imaging studies.Larger-scale studies involving more diverse populations would be valuable to confirm and strengthen the observed correlations and diagnostic accuracy. • Cross-sectional Design: The majority of the mentioned studies adopt a cross-sectional design, which captures a snapshot of the participant's condition at a specific point in time. Longitudinal studies that track individuals over time would provide more insights into the progression and predictive value of the observed biomarkers and connectivity patterns. • Heterogeneity of Parkinson's Disease: Parkinson's disease is a complex and heterogeneous disorder with various subtypes and clinical manifestations. The studies mentioned may not capture the full spectrum of PD, potentially limiting the generalizability of the findings to all PD patients.Overall this Topic has exemplified the importance of new technologies in detecting and monitoring PD, with the ultimate goal of improving the lives of people living with the disease.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.017 | 0.018 |
| Insufficient payload (model declined to judge) | 0.022 | 0.021 |
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".