Evidence map›Paper›PMID 42020504›Full record

ArticleScientific reports2026

Large-scale brain network interactions differentiate sweet, sour, and salty food cues during visual processing.

Manyoel Lim, Min Jung Kim

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Manyoel LimFood Processing Research Group, Food Convergence Research Division, Korea Food Research Institute, Wanju-gun, 55365, Republic of Korea. manyoell@kfri.re.kr.
Min Jung KimAging Research Group, Food Functionality Research Division, Korea Food Research Institute, Wanju-gun, 55365, Republic of Korea. mjkim14@kfri.re.kr.

Funding

Korea Food Research Institute Main Research Program (E0232201)
6 · The paper itself

Abstract

Visual cues play an important role in food selection by providing early information about taste properties before consumption. However, the large-scale neural mechanisms underlying the perception of different tastes from visual food cues remain poorly understood. Using electroencephalography and whole-brain functional connectivity analysis, we conducted an exploratory, hypothesis-generating investigation of large-scale brain network responses to visual food cues associated with three basic taste qualities, including sweet, sour, and salty. We computed functional connectivity across 100 cortical parcels, and examined the distribution of significant connections across seven canonical brain networks. Brain network connectivity differed across taste categories. Sweet food images engaged default mode network connectivity with somatomotor and attention networks. Sour food images showed significant limbic and salience network connectivity, suggesting heightened affective evaluation. Salty food images evoked the most widespread engagement, with significant control network involvement alongside visual, default mode, and attention networks. Our findings suggest that large-scale network interactions vary with the inferred taste profile of visual food cues. This network-level characterization provides exploratory evidence for taste-associated brain-wide communication during visual food processing.

Indexed as

BrainCuesNerve NetTasteTaste PerceptionVisual PerceptionAdultBrain MappingElectroencephalographyFemaleFoodHumansMaleYoung AdultBrain networkElectroencephalographyFunctional connectivityTasteVisual food perception

Identifiers

PMID42020504
PMCPMC13270053

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.