High frequency visual stimuli induced functional networks of emotion recognition: an MEG study
Poster No:
1944
Submission Type:
Abstract Submission
Authors:
RUOCHU XIONG1, Masashi Kinoshita2, Kenji Yoshiki2, Riho Nakajima3, Mitsutoshi Nakada2
Institutions:
1Department of Neurosurgery, Graduate School of Medical Sciences, Kanazawa University, Kanazawa, Japan, 2Department of Neurosurgery, Kanazawa University, Kanazawa, Japan, 3Department of Occupational Therapy, Kanazawa University, Kanazawa, Japan
First Author:
RUOCHU XIONG
Department of Neurosurgery, Graduate School of Medical Sciences, Kanazawa University
Kanazawa, Japan
Department of Neurosurgery, Graduate School of Medical Sciences, Kanazawa University
Kanazawa, Japan
Co-Author(s):
Introduction:
Researchers have studied emotion recognition with various modalities, but results vary depending on different tasks and analysis methods. In the field of EEG, fast periodic visual stimulation (FPVS) was established to stably activate the functional networks apart from the primary visual cortex (V1) (Rossion & Boremanse, 2011)(Keil et al., 2011). Although MEG studies have proved the validity of FPVS (Minarik et al., 2023)(Parkkonen et al., 2008), most studies chose low frequencies that might entrain endogenous activity. However, high frequency stimuli avoid entrainment of endogenous gamma activity (Duecker et al., 2021). In this study, we applied high-frequency FPVS in MEG for emotion recognition. We hypothesized that high frequency visual stimuli would activate a related functional network, besides V1, during the emotion recognition task.
Methods:
We collected MEG data from 22 healthy subjects. The task started with a 2s emotional word, followed by 2 possible choices of eyes with basic emotions lasting for 4s, then a 2s blank screen as baseline. Subjects were asked to fixate on the figure corresponding to the emotional word. By modulating the amplitude of RGB code, we tagged the figure corresponding to the word with 80Hz, and the inconsistent one with 90Hz. After MEG scan, participants recorded their figure choices on paper.
Using Brainstorm and Matlab, we calculated the averaged time-frequency (TF) map for V1, in the Desikan-Killiany atlas, called "pericalcarine". Source activity at 80Hz, 90Hz, and all frequencies was estimated. White matter tracts were reconstructed based on imaginary coherence results of connectivity analysis. Statistical analysis was t-test comparing the activity of viewing figures and viewing blank (baseline: -4.0000s to -2.0005s), with a significance threshold of 0.0001.
Using Brainstorm and Matlab, we calculated the averaged time-frequency (TF) map for V1, in the Desikan-Killiany atlas, called "pericalcarine". Source activity at 80Hz, 90Hz, and all frequencies was estimated. White matter tracts were reconstructed based on imaginary coherence results of connectivity analysis. Statistical analysis was t-test comparing the activity of viewing figures and viewing blank (baseline: -4.0000s to -2.0005s), with a significance threshold of 0.0001.
Results:
Behavioral data confirmed that all subjects correctly recognized the emotion. There was a strong and stable activation at 80Hz from 0s to 4s (viewing figures) on the TF map of V1. On the 80Hz source maps, we divided the activated areas into 5 groups according to the begin of their activation time: 0s~0.1s: bilateral pericalcarine, lateral occipital, cuneus, lingual area; 0.1s~0.2s: bilateral parahippocampal, fusiform, precuneus, and entorhinal area; 0.2s~0.6s: bilateral isthmus cingulate, inferior temporal, inferior parietal, and superior parietal, temporal pole, insular, superior temporal sulcus, posterior cingulate cortex, and middle temporal gyrus; 0.6s~1.0s: right parstriangularis, and left medial orbitofrontal area; 1.0s~: right superior temporal, bilateral paracentral, right lateral orbitofrontal, right postcentral, right precentral, and right caudal anterior cingulate area. From Group 1 to 5, the maximum statistic value of these activation areas gradually decreased. These groups roughly corresponded to five stages of task performance: visual processing, face recognition and memory retrieving, emotion perceiving, execution of selection task, and maintenance of attention. While no clear 90Hz activity was observed in the TF map, the 90Hz source map indicated activations similar to, but weaker than those at 80Hz. The source map of all frequencies showed that the activity was mainly in 0.6s from 0s. Connectivity analysis highlighted the role of the corpus callosum and anterior commissure in facial emotion recognition.
Conclusions:
In this study, we identified a bilateral temporospatial network for facial emotion recognition, mediated by the splenium of corpus callosum and the anterior commissure. This network spanned the occipital lobes, temporal lobes, cingulate gyrus, and insula-likely core regions for processing facial expressions-before engaging the frontal lobe to execute the task. Our findings suggest that the upper limit of usable tagging frequency may depend on task complexity; thus, researchers should optimize the frequency for their specific design.
Emotion, Motivation and Social Neuroscience:
Emotional Perception 2
Novel Imaging Acquisition Methods:
MEG 1
Keywords:
Emotions
MEG
Other - fast periodic visual stimulation
1|2Indicates the priority used for review
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3. Minarik, T., Berger, B., & Jensen, O. (2023). Optimal parameters for rapid (invisible) frequency tagging using MEG. NeuroImage, 281, 120389. https://doi.org/10.1016/j.neuroimage.2023.120389
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