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Record W2987041778 · doi:10.1101/828301

Impaired Hippocampal-cortical interactions during sleep and memory reactivation without consolidation in a mouse model of Alzheimer’s disease

2019· preprint· en· W2987041778 on OpenAlexafffund
Sarah Danielle Benthem, Ivan Skelin, Shawn C. Moseley, J. R. Dixon, A. S. Melilli, Lucía Molina, Bruce L. McNaughton, Aaron A. Wilber

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2019
Typepreprint
Languageen
FieldNeuroscience
TopicSleep and Wakefulness Research
Canadian institutionsUniversity of Lethbridge
FundersNational Institute on AgingCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaDefense Advanced Research Projects Agency
KeywordsMemory consolidationHippocampal formationNeuroscienceHippocampusPsychologyImpaired memoryNeuroplasticityCognition

Abstract

fetched live from OpenAlex

Abstract Spatial learning is impaired in preclinical Alzheimer’s disease (AD). We reported similar impairments in 3xTg-AD mice learning a spatial reorientation task . Memory reactivation during sleep is critical for learning related plasticity, and memory consolidation is correlated with hippocampal sharp wave ripple (SWR) density, cortical delta waves (DWs), and their temporal coupling - postulated as a physiological substrate of memory consolidation. Finally, hippocampal-cortical dyscoordination is prevalent in individuals with AD. Thus, we hypothesized impaired memory consolidation mechanisms in hippocampal-cortical networks could account for spatial memory deficits. We assessed sleep architecture, SWR/DW dynamics and memory reactivation in a mouse model of tauopathy and amyloidosis implanted with a recording array targeting isocortex and hippocampus. Mice underwent daily recording sessions of rest-task-rest while learning the spatial reorientation task . We assessed memory reactivation by matching activity patterns from the approach to the unmarked reward zone to patterns during slow wave sleep (SWS). AD mice had more SWS, but reduced SWR density. The increased SWS compensated for reduced SWR density so there was no reduction in SWR number. Conversely, DW density was not reduced so the number of DWs was increased. In control mice hippocampal SWR-cortical DW coupling was strengthened in post-task-sleep and was correlated with performance on the spatial reorientation task the following day. However, in AD mice SWR-DW coupling was reduced and not correlated with behavior, suggesting behavioral decoupling. Thus, reduced SWR-DW coupling may cause impaired learning in AD and may serve as a biomarker for early AD related changes. Significance Statement Understanding the relationship between network dynamics and cognition early in Alzheimer’s disease progression is critical for identifying therapeutic targets for earlier treatment. We assessed hippocampal-cortical interactions during sleep in AD mice as a potential cause of early spatial learning and memory deficits. We identified compensatory sleep changes in AD mice, that ameliorated some brain dysfunction. Despite the compensatory changes, impaired spatial navigation and impaired hippocampal–cortical (sharp wave ripple-delta wave) interactions were apparent in AD mice. In control but not AD mice hippocampal-cortical interactions were correlated with performance on the spatial task, the following day, suggesting a potential mechanism of impaired consolidation in AD mice. Thus, changes in hippocampal-cortical brain dynamics during sleep may underlie early memory deficits in AD.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
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 head0.047
GPT teacher head0.283
Teacher spread0.237 · 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 designBench or experimental
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

Citations0
Published2019
Admission routes2
Has abstractyes

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