MétaCan
Menu
Back to cohort
Record W2291457528 · doi:10.1002/hbm.23109

Contribution of insula in Parkinson's disease: A quantitative meta‐analysis study

2016· review· en· W2291457528 on OpenAlexafffund
Marion Criaud, Leigh Christopher, Philippe Boulinguez, Bénédicte Ballanger, Anthony E. Lang, Sang S. Cho, Sylvain Houle, Antonio P. Strafella

Bibliographic record

VenueHuman Brain Mapping · 2016
Typereview
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsToronto Western HospitalUniversity of TorontoCentre for Addiction and Mental Health
FundersCanadian Institutes of Health Research
KeywordsInsulaInsular cortexNeurosciencePsychologyCognitionSensory systemDiseaseParkinson's diseaseMedicinePathology

Abstract

fetched live from OpenAlex

The insula region is known to be an integrating hub interacting with multiple brain networks involved in cognitive, affective, sensory, and autonomic processes. There is growing evidence suggesting that this region may have an important role in Parkinson's disease (PD). Thus, to investigate the functional organization of the insular cortex and its potential role in parkinsonian features, we used a coordinate-based quantitative meta-analysis approach, the activation likelihood estimation. A total of 132 insular foci were selected from 96 published experiments comprising the five functional categories: cognition, affective/behavioral symptoms, bodily awareness/autonomic function, sensorimotor function, and nonspecific resting functional changes associated with the disease. We found a significant convergence of activation maxima related to PD in different insular regions including anterior and posterior regions bilaterally. This study provides evidence of an important functional distribution of different domains within the insular cortex in PD, particularly in relation to nonmotor aspects, with an influence of medication effect.

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 imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.013
Bibliometrics0.0030.004
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.214
GPT teacher head0.394
Teacher spread0.181 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations53
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueHuman Brain MappingSame topicFunctional Brain Connectivity StudiesFrench-language works237,207