Novel connectivity map normalization procedure for improved quantitative investigation of structural thalamic connectivity in temporal lobe epilepsy patients
Bibliographic record
Abstract
BACKGROUND: Connectivity studies targeting the thalamus have revealed patterns of atrophy and deafferentiation in temporal lobe epilepsy (TLE). The thalamus can be parcellated using probabilistic tractography to demonstrate regions of cortical connectivity; however, sensitivity to smaller or less connected regions is low. PURPOSE/HYPOTHESIS: To investigate thalamic structural connectivity in a wider range of cortical and limbic structures in TLE patients using a novel connectivity map normalization procedure. STUDY TYPE: Retrospective. POPULATION/SUBJECTS: Patients (N = 23) with medication-resistant TLE and 34 healthy age-matched controls. FIELD STRENGTH/SEQUENCE: mapping balanced steady-state free precession was used. Images were acquired at 3T. ASSESSMENT: Probabilistic tractography and a novel normalization procedure allowed comparison of groups with respect to thalamic connected volume, quantitative MRI, and diffusion tensor imaging (DTI) metrics. STATISTICAL TESTS: Independent samples t-test, Cohen's d, and Mann-Whitney tests. RESULTS: Following normalization, significant differences in thalamic connected volumes were found in left TLE vs. controls bilaterally within the posterior parahippocampal gyrus (L: P = 0.007, confidence interval [CI]: [173.306,1044.41], effect size [ES] = 1.072; R: P = 0.017, CI: [98.677,947.653], ES = 0.945), and contralaterally in the anterior temporal neocortex (P = 0.01, CI: (-2348.09, -333.719), ES = -1.021). This procedure revealed differences in thalamic connected volumes, where previously published procedures could not, and provided a basis for exploratory analysis of quantitative MRI and DTI metrics. DATA CONCLUSION: The novel connectivity map normalization scheme proposed here successfully allowed comparison between a wider range of cortical and limbic structures. Multiple volumetric and quantitative MRI and DTI-related differences between TLE patients and controls were revealed following normalization. With validation from a larger cohort, thalamo-temporal connection aberrancies may become useful biomarkers of disease states and probabilistic tractography as a procedure for identification of thalamic targets in modulatory therapies for TLE. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:1529-1539.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".