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Record W4387158863 · doi:10.1101/2023.09.28.560002

Differential Reorganization of Episodic and Semantic Memory Systems in Epilepsy-Related Mesiotemporal Pathology

2023· preprint· en· W4387158863 on OpenAlexafffund
Donna Gift Cabalo, Jordan DeKraker, Jessica Royer, Ke Xie, Shahin Tavakol, Raúl Rodríguez‐Cruces, Andrea Bernasconi, Neda Bernasconi, Alexander G. Weil, Raluca Pana, Birgit Frauscher, Lorenzo Caciagli, Elizabeth Jefferies, Jonathan Smallwood, Boris C. Bernhardt

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2023
Typepreprint
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustineQueen's UniversityMcGill UniversityMontreal Neurological Institute and Hospital
FundersFonds de Recherche du Québec - SantéHospital for Sick ChildrenCanadian Institutes of Health ResearchChina Scholarship CouncilBrain Research UKMcGill UniversityRéseau en Bio-Imagerie du QuebecCanada First Research Excellence FundCanada Research ChairsNatural Sciences and Engineering Research Council of CanadaFondation Brain Canada
KeywordsEpisodic memorySemantic memoryTemporal lobeNeurosciencePsychologyHippocampal sclerosisHippocampal formationNeocortexContext (archaeology)EpilepsyCognitionBiology

Abstract

fetched live from OpenAlex

A bstract Declarative memory encompasses episodic and semantic divisions. Episodic memory captures singular events with specific spatiotemporal relationships, while semantic memory houses context-independent knowledge. Behavioral and functional neuroimaging studies have revealed common and distinct neural substrates of both memory systems, implicating mesiotemporal lobe (MTL) regions and distributed neocortices. Here, we studied a population of patients with unilateral temporal lobe epilepsy (TLE) and variable degrees of MTL pathology as a human disease model to explore declarative memory system reorganization, and to examine neurocognitive associations. Our cohort included 20 patients with TLE as well as 60 age and sex-matched healthy controls, who underwent episodic and semantic retrieval tasks during a functional MRI session. Tasks were closely matched in terms of stimuli and trial design. Capitalizing on connectome gradient mapping techniques, we derived task-based functional topographies during episodic and semantic memory states, both in the MTL and in neocortical networks. Comparing neocortical and hippocampal functional gradients between TLE patients and healthy controls, we observed topographic reorganization during episodic but not semantic memory states, characterized by marked gradient compression in lateral temporal and midline parietal cortices in both hemispheres, cooccurring with an expansion of anterior-posterior hippocampal differentiation ipsilateral to the MTL pathology. These findings suggest that episodic processes are supported by a distributed network, implicating both hippocampus and neocortex, and such alterations can provide a compact signature of state-dependent reorganization in conditions associated with MTL damage such as TLE. Leveraging microstructural and morphological MRI proxies of MTL pathology, we furthermore observed that pathological markers selective to the hippocampus are associated with TLE-related functional reorganization. Moreover, correlation analysis and statistical mediation models revealed that these functional alterations contributed to behavioral deficits in episodic memory in patients. Altogether, our findings point to a selective mesiotemporal and neocortical functional reorganization of episodic memory systems in patients with TLE, which consistently affects behavioral memory deficits. These findings point to consistent structure-function relationships in declarative memory and reaffirm the critical role of the MTL in episodic memory systems.

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)0.000
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.023
GPT teacher head0.225
Teacher spread0.202 · 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".

Quick stats

Citations5
Published2023
Admission routes2
Has abstractyes

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