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Record W4403588636 · doi:10.1162/imag_a_00356

Hippocampal microscopic fractional anisotropy is reduced in temporal lobe epilepsy

2024· article· en· W4403588636 on OpenAlexafffund
Nico J. J. Arezza, Hana H. Abbas, Caroline Chadwick, Ingrid S. Johnsrude, Jorge G. Burneo, Ana Suller Martí, Ali R. Khan, Corey A. Baron

Bibliographic record

VenueImaging Neuroscience · 2024
Typearticle
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsWestern University
FundersCanadian Institutes of Health ResearchCanada First Research Excellence FundNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsFractional anisotropyDiffusion MRITemporal lobeSubiculumWhite matterMagnetic resonance imagingAxonHippocampal formationEpilepsy surgeryEpilepsyNeuroscienceMedicineDentate gyrusPathologyPsychologyRadiology

Abstract

fetched live from OpenAlex

Surgical resection is the method of choice for treating drug-resistant focal temporal lobe epilepsy (TLE). Postsurgical outcomes are better when magnetic resonance imaging (MRI) findings can localize the seizure focus for resection. However, many patients are MR-negative, meaning the focus cannot be differentiated from normal tissue in relaxation-weighted MRI. Diffusion MRI shows promise as a preoperative marker of neuronal abnormalities due to its sensitivity to cellular changes such as axon damage, indexed by fractional anisotropy. Microscopic fractional anisotropy is a recently introduced diffusion MRI metric that is sensitive to axon integrity regardless of axon orientation in both gray and white matter. In contrast, regular fractional anisotropy is only sensitive to axon integrity in coherently oriented bundles of fibers. This work investigated whether microscopic fractional anisotropy is sensitive to hippocampal abnormalities in drug-resistant TLE. Diffusion MRI was performed on a 3T scanner in 19 patients (age = 31 ± 10 years) with drug-resistant TLE (of which 10 were MR-negative) and 18 healthy volunteers (age = 38 ± 15). A deep-learning method was employed to segment the hippocampus into smaller subregions corresponding to the subiculum, cornu ammonis (CA) 1, CA2/3, and CA4 plus dentate gyrus (DG). Mean measurements of subregion volume, diffusivity, fractional anisotropy, and microscopic fractional anisotropy were compared between cohorts. In a subset of the TLE cohort suspected to have unilateral pathology (n = 15, age = 32 ± 10 years), the percentage differences between measurements ipsilateral and contralateral to the epileptogenic zone were evaluated to assess asymmetry. Microscopic fractional anisotropy was reduced in the hippocampus of drug-resistant TLE patients relative to healthy volunteers. In subregion-specific analysis, microscopic fractional anisotropy was significantly reduced in only the CA4/DG region in patients compared with healthy volunteers, after corrections for multiple comparisons. In the 15 patients with suspected unilateral pathology, microscopic fractional anisotropy was reliably and statistically lower in the ipsilateral CA4/DG region than in the contralateral side. Significant differences were not observed between TLE patients and healthy volunteers, or between hemispheres for patients with suspected unilateral pathology, for the fractional anisotropy or volume metrics. Diffusion MRI may complement standard imaging procedures by detecting abnormalities in MRI-negative patients. Due to its ability to detect abnormality regardless of axon orientation, microscopic fractional anisotropy may improve seizure focus localization in surgical candidates.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.052
GPT teacher head0.385
Teacher spread0.333 · 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 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
Published2024
Admission routes2
Has abstractyes

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