MétaCan
Menu
Back to cohort
Record W4206497009 · doi:10.1093/brain/awac009

A neural network for tics: insights from causal brain lesions and deep brain stimulation

2022· review· en· W4206497009 on OpenAlexaff
Bassam Al‐Fatly, J. Fischer, Juan-Carlos Baldermann, Christina Hennen, Veerle Visser‐Vandewalle, Clemens Neudorfer, Davide Martino, Jing Li, Tim Bouwens, Linda Ackermanns, Albert F.G. Leentjens, Nadya Pyatigorskaya, Yulia Worbe, Michael Fox, Andrea A. Kühn

Bibliographic record

VenueBrain · 2022
Typereview
Languageen
FieldPsychology
TopicObsessive-Compulsive Spectrum Disorders
Canadian institutionsOntario Brain InstituteUniversity of Calgary
FundersNational Institute of Mental HealthNational Institute on AgingH. Lundbeck A/SDeutsches Zentrum für Luft- und RaumfahrtVolkswagen FoundationCharité – Universitätsmedizin BerlinDeutsche ForschungsgemeinschaftInternational Parkinson and Movement Disorder SocietyBioMarin PharmaceuticalEU Joint Programme – Neurodegenerative Disease ResearchBerlin Institute of HealthNancy Lurie Marks Family FoundationNational Institutes of HealthMedtronic
KeywordsDeep brain stimulationTicsNeuroscienceBrain stimulationStimulationNeuroimagingPsychologyMedicinePathologyParkinson's disease

Abstract

fetched live from OpenAlex

Brain lesions are a rare cause of tic disorders. However, they can provide uniquely causal insights into tic pathophysiology and can also inform on possible neuromodulatory therapeutic targets. Based on a systematic literature review, we identified 22 cases of tics causally attributed to brain lesions and employed 'lesion network mapping' to interrogate whether tic-inducing lesions would be associated with a common network in the average human brain. We probed this using a normative functional connectome acquired in 1000 healthy participants. We then examined the specificity of the identified network by contrasting tic-lesion connectivity maps to those seeding from 717 lesions associated with a wide array of neurological and/or psychiatric symptoms within the Harvard Lesion Repository. Finally, we determined the predictive utility of the tic-inducing lesion network as a therapeutic target for neuromodulation. Specifically, we collected retrospective data of 30 individuals with Tourette disorder, who underwent either thalamic (n = 15; centromedian/ventrooralis internus) or pallidal (n = 15; anterior segment of globus pallidus internus) deep brain stimulation and calculated whether connectivity between deep brain stimulation sites and the lesion network map could predict clinical improvements. Despite spatial heterogeneity, tic-inducing lesions mapped to a common network map, which comprised the insular cortices, cingulate gyrus, striatum, globus pallidus internus, thalami and cerebellum. Connectivity to a region within the anterior striatum (putamen) was specific to tic-inducing lesions when compared with control lesions. Connectivity between deep brain stimulation electrodes and the lesion network map was predictive of tic improvement, regardless of the deep brain stimulation target. Taken together, our results reveal a common brain network involved in tic generation, which shows potential as a therapeutic target for neuromodulation.

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)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.050
GPT teacher head0.360
Teacher spread0.311 · 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 designNot applicable
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

Citations84
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueBrainSame topicObsessive-Compulsive Spectrum DisordersFrench-language works237,207