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.
To verify that high-frequency FPVS can generate the same frequency activity, we calculated the time-frequency (TF) map for V1, in the Desikan-Killiany atlas, called "pericalcarine" using Brainstorm and Matlab. Source activity at 80Hz, 90Hz, and all frequencies was estimated. 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.
To verify that high-frequency FPVS can generate the same frequency activity, we calculated the time-frequency (TF) map for V1, in the Desikan-Killiany atlas, called "pericalcarine" using Brainstorm and Matlab. Source activity at 80Hz, 90Hz, and all frequencies was estimated. 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~0.01s: bilateral pericalcarine, lateral occipital, cuneus, lingual, and parahippocampal area; 0.2s~0.3s: bilateral fusiform, precuneus, and entorhinal area; 0.3s~0.5s: right inferior temporal, bilateral isthmus cingulate, inferior parietal, and superior parietal, right temporal pole, insular, superior temporal sulcus, and posterior cingulate cortex; 0.6s~1.0s: left inferior temporal, bilateral transverse temporal, left superior temporal sulcus, right posterior cingulate, right middle temporal, left insular, left medial orbitofrontal area; 1.0s~: bilateral superior temporal, left middle temporal, paracentral, right lateral orbitofrontal, left temporal pole, 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.
Conclusions:
Besides V1, high-frequency FPVS also induced activity with the same frequency in functional networks related to emotion recognition. We further hypothesized based on our results that FPVS first activates V1 and visual processing areas, then to the connected functional areas, with frequency keeping the same but activity getting weaker after being processed in functional areas. The frontal lobe exhibited relatively low statistical activity in this study. Our results highlight the temporal lobe, insular, and limbic cortex as key regions for emotion perception, challenging previous studies that emphasized the frontal lobe's role in emotion recognition.
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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Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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