Neuroimaging of Freezing of Gait
Bibliographic record
Abstract
Functional brain imaging techniques appear ideally suited to explore the pathophysiology of freezing of gait (FOG). In the last two decades, techniques based on magnetic resonance or nuclear medicine imaging have found a number of structural changes and functional disconnections between subcortical and cortical regions of the locomotor network in patients with FOG. FOG seems to be related in part to disruptions in the "executive-attention" network along with regional tissue loss including the premotor area, inferior frontal gyrus, precentral gyrus, the parietal and occipital areas involved in visuospatial functions of the right hemisphere. Several subcortical structures have been also involved in the etiology of FOG, principally the caudate nucleus and the locomotor centers in the brainstem. Maladaptive neural compensation may present transiently in the presence of acute conflicting motor, cognitive or emotional stimulus processing, thus causing acute network overload and resulting in episodic impairment of stepping.In this review we will summarize the state of the art of neuroimaging research for FOG. We will also discuss the limitations of current approaches and delineate the next steps of neuroimaging research to unravel the pathophysiology of this mysterious motor phenomenon.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".