Evidence map›Paper›PMID 33362477›Full record

ArticleFrontiers in cellular neuroscience2020

The Olfactory Bulb Facilitates Use of Category Bounds for Classification of Odorants in Different Intensity Groups.

Justin Losacco, Nicholas M George, Naoki Hiratani, Diego Restrepo

Open access · goldAbstract read
In one paragraph

Article in Frontiers in cellular neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.1field-weighted citation impact, top 58% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Behavioral Assays in the Study of Olfaction.Methods in molecular biology (Clifton, N.J.) · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 2 countries.

Justin LosaccoNeuroscience Graduate Program, University of Colorado Anschutz Medical Campus, Aurora, CO, United States.
Nicholas M GeorgeNeuroscience Graduate Program, University of Colorado Anschutz Medical Campus, Aurora, CO, United States.
Naoki HirataniGatsby Computational Neuroscience Unit, University College London, London, United Kingdom.
Diego RestrepoNeuroscience Graduate Program, University of Colorado Anschutz Medical Campus, Aurora, CO, United States.
University of Colorado Anschutz Medical Campus · USUniversity College London · GB

Funding

PERIPHERAL MECHANISMS OF OLFACTORY RECEPTIONR01DC000566 · NIDCD · UNIVERSITY OF COLORADO DENVER · PI RESTREPO, DIEGO · 1989 to 2022
$9.9M
Mitral cell phase precession encodes olfactory stimulus strengthF31DC016483 · NIDCD · UNIVERSITY OF COLORADO DENVER · PI LOSACCO, JUSTIN T · 2017 to 2018
$63k
Investigating axonal and glial adaptations to sensory manipulations in the olfactory systemF31DC018459 · NIDCD · UNIVERSITY OF COLORADO DENVER · PI GEORGE, NICHOLAS M · 2019 to 2020
$59k
NIDCD NIH HHS F31 DC016483NIDCD NIH HHS F31 DC018459NIDCD NIH HHS R01 DC000566
6 · The paper itself

Abstract

Signal processing of odor inputs to the olfactory bulb (OB) changes through top-down modulation whose shaping of neural rhythms in response to changes in stimulus intensity is not understood. Here we asked whether the representation of a high vs. low intensity odorant in the OB by oscillatory neural activity changed as the animal learned to discriminate odorant concentration ranges in a go-no go task. We trained mice to discriminate between high vs. low concentration odorants by learning to lick to the rewarded group (low or high). We recorded the local field potential (LFP) in the OB of these mice and calculated the theta-referenced beta or gamma oscillation power (theta phase-referenced power, or tPRP). We found that as the mouse learned to differentiate odorant concentrations, tPRP diverged between trials for the rewarded vs. the unrewarded concentration range. For the proficient animal, linear discriminant analysis was able to predict the rewarded odorant group and the performance of this classifier correlated with the percent correct behavior in the odor concentration discrimination task. Interestingly, the behavioral response and decoding accuracy were asymmetric as a function of concentration when the rewarded stimulus was shifted between the high and low odorant concentration ranges. A model for decision making motivated by the statistics of OB activity that uses a single threshold in a logarithmic concentration scale displays this asymmetry. Taken together with previous studies on the intensity criteria for decisions on odorant concentrations, our finding suggests that OB oscillatory events facilitate decision making to classify concentrations using a single intensity criterion.

Indexed as

decodinggo-no golearningodorant concentrationoscillations

Identifiers

PMID33362477
PMCPMC7759615
OpenAlexW3110964931

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.