Evidence map›Paper›PMID 42817975›Full record

ArticlebioRxiv : the preprint server for biology2026

Decoding instrumental lever pressing from prefrontal, accumbens, and hippocampal local field potentials: conservation across sex, task, and dopamine depletion.

Abhijith Mankili, Alev Ecevitoglu, Gayle A Edelstein, Naixin Ren, Renee A Rotolo, James J Chrobak, John D Salamone, Ian H Stevenson

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In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

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No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Abhijith MankiliDepartment of Psychological Sciences, University of Connecticut, Storrs, CT 06269-1020.ORCID 0000-0002-0620-259X
Alev EcevitogluDepartment of Psychological Sciences, University of Connecticut, Storrs, CT 06269-1020.ORCID 0000-0003-2457-8424
Gayle A EdelsteinDepartment of Psychological Sciences, University of Connecticut, Storrs, CT 06269-1020.ORCID 0009-0002-7553-1001
Naixin RenDepartment of Psychological Sciences, University of Connecticut, Storrs, CT 06269-1020.ORCID 0000-0002-3074-281X
Renee A RotoloDepartment of Psychological Sciences, University of Connecticut, Storrs, CT 06269-1020.ORCID 0000-0002-0432-7663
James J ChrobakDepartment of Psychological Sciences, University of Connecticut, Storrs, CT 06269-1020.ORCID 0000-0002-2733-2975
John D SalamoneDepartment of Psychological Sciences, University of Connecticut, Storrs, CT 06269-1020.ORCID 0000-0001-6435-9635
Ian H StevensonDepartment of Psychological Sciences, University of Connecticut, Storrs, CT 06269-1020.ORCID 0000-0002-1428-5946

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

During operant behavior, oscillatory activity is coordinated across multiple brain regions and can change under different task conditions. In this study, we use a decoding approach to characterize these patterns and predict lever pressing behavior in rats from local field potentials (LFP) recorded bilaterally in the hippocampus, nucleus accumbens, and prefrontal cortex. We extract LFP features for band power and peak frequency and predict behavior across animals and conditions with a Poisson Generalized Linear Model (GLM). We use data from both male and female rats performing either fixed-ratio (FR40) or progressive (PROG) operant lever-pressing tasks, under vehicle (VEH) or tetrabenazine (TBZ), a vesicular monoamine transport (VMAT-2) inhibitor that depletes dopamine and induces depressive-like motivation dysfunction. We find that we can accurately predict lever pressing from multi-region LFP within-animals on a timescale of seconds with >30% variance explained. Although within-animal and within-condition predictions are highest, we also find, interestingly, that these decoders generalize across animals and across drug, sex, and task conditions, suggesting a stable association between LFP and behavior. We then evaluate whether LFP features can be used to decode long-term task variables, such as the number of presses since or until the next reinforcer. We find that LFP features can predict these long-term task variables, but that decoding relies on different LFP features for prediction than those used for immediate lever pressing. Altogether these results suggest that there are robust distributed patterns of LFP associated with lever-pressing behavior under multiple drug, task, and sex conditions.

Identifiers

PMID42817975
PMCPMC13621978

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.