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Record W2790159617 · doi:10.3390/brainsci8020023

The Emerging Role of Tractography in Deep Brain Stimulation: Basic Principles and Current Applications

2018· review· en· W2790159617 on OpenAlexaff
Nelson B. Rodrigues, Karim Mithani, Ying Meng, Nir Lipsman, Clement Hamani

Bibliographic record

VenueBrain Sciences · 2018
Typereview
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsTractographyDiffusion MRINeuroscienceDeep brain stimulationWhite matterComputer sciencePsychologyMedicineMagnetic resonance imagingPathologyRadiology

Abstract

fetched live from OpenAlex

Diffusion tensor imaging (DTI) is an MRI-based technique that delineates white matter tracts in the brain by tracking the diffusion of water in neural tissue. This methodology, known as "tractography", has been extensively applied in clinical neuroscience to explore nervous system architecture and diseases. More recently, tractography has been used to assist with neurosurgical targeting in functional neurosurgery. This review provides an overview of DTI principles, and discusses current applications of tractography for improving and helping develop novel deep brain stimulation (DBS) targets.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score0.301

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.086
GPT teacher head0.397
Teacher spread0.312 · 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 designOther design
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

Citations34
Published2018
Admission routes1
Has abstractyes

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