Cerebral glucose metabolism during complex walking in Parkinson's disease with freezing of gait
Bibliographic record
Abstract
Freezing of gait is a common motor symptom in Parkinson's disease (PD) that is characterised by a transient inability to produce effective steps despite the intention to walk, leading to severe disability and falls.An important trigger of freezing episodes is complex gait such as walking while turning (i.e., steering of gait).This type of complex gait is believed to be problematic due to an increased demand for voluntary control compared to steady-state forward walking.Frontostriatal impairment in freezing of gait is associated with decreased automaticity of locomotion and impaired compensatory mechanisms involving cognitive circuits.It is not well understood what role these mechanisms have during complex gait known to induce freezing episodes.Therefore, the aim of this thesis was to determine if complex walking promotes the use of alternate neural circuits during an upright gait paradigm comparing steering of gait (i.e., complex locomotion) to straight walking (i.e., simple locomotion) in PD with and without freezing of gait.18 participants with PD in the off-medication state were included that were determined to be experiencing freezing of gait (FOG+, n=9, aged 68 ± 6) or not experiencing freezing (FOG-, n=9, aged 65 ± 5).All subjects underwent [18F]-fluoro-deoxy-glucose positron emission tomography ([18F]-FDG PET) imaging during two gait tasks, steering and straight walking.Cerebral glucose metabolism and spatiotemporal gait measures (i.e., stride length, stride velocity) were obtained.Region of interest and whole-brain voxel wise analysis was used to determine task-related change in cerebral glucose metabolism for steering between groups.Activations in significant regions were correlated with severity of freezing and gait impairment (i.e., stride length).vi
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".