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Record W4411713200 · doi:10.1093/braincomms/fcaf151

Motor and sensory neurophysiology in relation to [18F]FDG PET imaging in children with dystonia

2025· article· en· W4411713200 on OpenAlexaff
Stavros Tsagkaris, Verity M. McClelland, Doreen Fialho, Daniel E. Lumsden, Margaret Kaminska, Éric Guedj, Alexander Hammers, Jean‐Pierre Lin

Bibliographic record

VenueBrain Communications · 2025
Typearticle
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsSt. Thomas Hospital
FundersCentre For Medical Engineering, King’s College LondonGuy's and St Thomas' NHS Foundation TrustRosetrees TrustManchester Biomedical Research CentreKing's College LondonEngineering and Physical Sciences Research CouncilNew York State Department of HealthNational Institute for Health and Care ResearchAction Medical ResearchKing’s College LondonKing's College Hospital NHS Foundation Trust
KeywordsDystoniaSomatosensory systemHypermetabolismClinical neurophysiologyFluorodeoxyglucoseNeuroscienceMedicinePsychologyNeurophysiologyNeuroimagingPositron emission tomographyElectroencephalographyInternal medicine

Abstract

fetched live from OpenAlex

Abstract The network model of dystonia reconciles many of the neuroanatomical and electrophysiological abnormalities identified as potential pathophysiological factors. Recent [18F]fluorodeoxyglucose-PET brain imaging findings support this concept, revealing distinct patterns of abnormal brain metabolism in specific dystonia aetiologies. However, it is unclear how changes in specific neural pathways alter brain glucose metabolism. This observational study investigates patterns of brain metabolism using [18F]fluorodeoxyglucose-PET imaging in children with dystonia, awake-resting during the uptake period, in relation to measures of brain function using standard neurophysiological tests of motor and sensory pathway integrity. Central motor conduction times, somatosensory evoked potentials and [18F] fluorodeoxyglucose-PET scans were obtained in children with dystonia or dystonic-dyskinetic cerebral palsy undergoing standard clinical assessment for bilateral pallidal deep brain stimulation between 2007 and 2018 in Evelina London Children’s Hospital. Data from 109 children aged 2.8–18.8 years were analysed retrospectively. Patients were divided into groups based on their neurophysiology results as follows: both tests normal (NN; 67), both abnormal (AA; 11), normal central motor conduction times/abnormal somatosensory evoked potentials (NA; 20), abnormal central motor conduction times/normal somatosensory evoked potentials (AN; 11). Groups were compared with a control group comprising [18F]fluorodeoxyglucose-PET scans from 39 healthy adults using Statistical Parametric Mapping 12 with age and groupwise global means as covariates. Taking into account groupwise global uptake, all four groups shared relative hypermetabolism in parietal areas, postcentral and precentral gyri. In addition, mild peri-insular hypometabolism was seen in the NN group. The NA group showed marked regional hypometabolism bilaterally in the thalami, globi pallidi, putamina, heads of the caudate nuclei and areas of peri-sylvian cortex. The AN group had hypometabolism in the thalami and posterior globi pallidi, the posterior putamina and areas of peri-sylvian cortex. The AA group also exhibited hypometabolism in the medial thalami and some areas of frontal and peri-insular cortex. Across the whole cohort, abnormal somatosensory evoked potentials were strongly associated with thalamic hypometabolism, with no marked differences for abnormal central motor conduction times. Brain metabolism patterns in dystonia relate to neurophysiological abnormalities in our study. Relative parietal hypermetabolism is more common than recognized previously, while thalamic hypometabolism is prominent in those with abnormal sensory pathway function. The findings support the network model of dystonia and emphasize the importance of multi-modal assessment in providing detailed phenotyping, which could inform individualized management strategies.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.234

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.290
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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