Task‐specificity in focal dystonia is shaped by aberrant diversity of a functional network kernel
Bibliographic record
Abstract
OBJECTIVES: Task-specific focal dystonia selectively affects the motor control during skilled and highly learned behaviors. Recent data suggest the role of neural network abnormalities in the development of the pathophysiological dystonic cascade. METHODS: We used resting-state functional MRI and analytic techniques rooted in network science and graph theory to examine the formation of abnormal subnetwork of highly influential brain regions, the functional network kernel, and its influence on aberrant dystonic connectivity specific to affected body region and skilled motor behavior. RESULTS: We found abnormal embedding of sensorimotor cortex and prefrontal thalamus in dystonic network kernel as a hallmark of task-specific focal dystonia. Dependent on the affected body region, aberrant functional specialization of the network kernel included regions of motor control management in focal hand dystonia (writer's cramp, musician's focal hand dystonia) and sensorimotor processing in laryngeal dystonia (spasmodic dysphonia, singer's laryngeal dystonia). Dependent on skilled motor behavior, the network kernel featured altered connectivity between sensory and motor execution circuits in musician's dystonia (musician's focal hand dystonia, singer's laryngeal dystonia) and abnormal integration of sensory feedback into motor planning and executive circuits in non-musician's dystonia (writer's cramp, spasmodic dysphonia). CONCLUSIONS: Our study identified specific traits in disorganization of large-scale neural connectivity that underlie the common pathophysiology of task-specific focal dystonia while reflecting distinct symptomatology of its different forms. Identification of specialized regions of information transfer that influence dystonic network activity is an important step for future delineation of targets for neuromodulation as a potential therapeutic option of task-specific focal dystonia. © 2018 International Parkinson and Movement Disorder Society.
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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 | 0.002 |
| 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 | 0.000 | 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".