Decreased Theta Power Reflects Disruption in Postural Control Networks of Fragile X Premutation Carriers
Bibliographic record
Abstract
FRM1 premutation carriers exhibit various subtle deficits in balance and stability, prior to the development of the movement disorder Fragile X Associated Tremor/Ataxia Syndrome (FXTAS). Force plate posturography has increasingly been combined with the temporal sensitive imaging methods such as EEG to offer insight into the neural mechanisms which govern postural control. This study investigated cortical theta power during continuous balance and its relationship to balance performance in Fragile X premutation carriers. Eight premutation carriers and 6 controls stood on a force platform under altered sensory and cognitive conditions while postural sway and high-density EEG data were simultaneously recorded. Carriers exhibited greater sway area when sensory input was reduced (p=0.01) and cognitive load was increased (p=0.01), as well as significantly reduced frontal theta power compared to the Control Group. The relationship between theta power and postural control seen in the control group may indicate an increase in error detection caused by reduced visual input and greater discrepancies between expected and actual balance state. While the lower theta power in frontal regions of carriers may indicate a disruption in neural networks underpinning postural control. Such results provide new insight into the neural correlates of balance control in Fragile X premutation carriers.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".