Evidence map›Paper›PMID 34415651›Full record

ArticleHuman brain mapping2021

Mixed-effects multilevel analysis followed by canonical correlation analysis is an effective fMRI tool for the investigation of idiosyncrasies.

Sungman Jo, Hyun-Chul Kim, Niv Lustig, Gang Chen, Jong-Hwan Lee

Abstract read
In one paragraph

Article in Human brain mapping, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
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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

5 authors.

Sungman JoDepartment of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.
Hyun-Chul KimDepartment of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.
Niv LustigDepartment of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.
Gang ChenScientific and Statistical Computing Core, NIMH/NIH/DHHS, Bethesda, Maryland.
Jong-Hwan LeeDepartment of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.ORCID 0000-0002-8902-6009

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We report that regions-of-interest (ROIs) associated with idiosyncratic individual behavior can be identified from functional magnetic resonance imaging (fMRI) data using statistical approaches that explicitly model individual variability in neuronal activations, such as mixed-effects multilevel analysis (MEMA). We also show that the relationship between neuronal activation in fMRI and behavioral data can be modeled using canonical correlation analysis (CCA). A real-world dataset for the neuronal response to nicotine use was acquired using a custom-made MRI-compatible apparatus for the smoking of electronic cigarettes (e-cigarettes). Nineteen participants smoked e-cigarettes in an MRI scanner using the apparatus with two experimental conditions: e-cigarettes with nicotine (ECIG) and sham e-cigarettes without nicotine (SCIG) and subjective ratings were collected. The right insula was identified in the ECIG condition from the χ

Indexed as

Functional NeuroimagingAdultCerebral CortexCravingElectronic Nicotine Delivery SystemsFemaleHumansMachine LearningMagnetic Resonance ImagingMaleMultilevel AnalysisTobacco Use DisorderYoung Adultcanonical correlation analysiselectronic cigarettefunctional MRIHuman Connectome Projectmixed-effects multilevel analysisnicotine craving

Identifiers

PMID34415651
PMCPMC8519860

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.