Modeling Spatial-Temporal Dependencies in Emotion-Related fMRI: A Nonparametric Bayesian Application to the NeuroEmo Dataset.

Nonparametric Bayesian general linear model analysis of culturally relevant film stimuli yields nuanced insights into emotion-brain dynamics, controls Type I error without arbitrary thresholds, and uncovers interindividual heterogeneity, offering a robust tool for affective neuroimaging.

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PubMed
Published
2025-12-31
Primary Topic
Face Recognition and Perception
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article
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article

Modeling Spatial-Temporal Dependencies in Emotion-Related fMRI: A Nonparametric Bayesian Application to the NeuroEmo Dataset.

Reza Taherian, Farzaneh Amanpour, Hamid Alavi Majd, Seyyed Mohammad Tabatabaei
PubMed
Face Recognition and Perception
article

Modeling Spatial-Temporal Dependencies in Emotion-Related fMRI: A Nonparametric Bayesian Application to the NeuroEmo Dataset.

Reza Taherian, Farzaneh Amanpour, Hamid Alavi Majd, Seyyed Mohammad Tabatabaei
article en

Abstract

Nonparametric Bayesian general linear model analysis of culturally relevant film stimuli yields nuanced insights into emotion-brain dynamics, controls Type I error without arbitrary thresholds, and uncovers interindividual heterogeneity, offering a robust tool for affective neuroimaging.

PubMedVol. 46(8)
Mashhad University of Medical Sciences (IR), Shahid Beheshti University of Medical Sciences (IR)
Reduced inequalities
Openalex Percentile: Top 27%
Face Recognition and Perception
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