Poster No:
527
Submission Type:
Abstract Submission
Authors:
Alexander Sokolov1, Annika Resch1, Valentina Romagnano2, Andreas Fallgatter3, Christoph Braun4, Marina Pavlova5
Institutions:
1University of Tübingen Medical School, Tübingen, Baden-Württemberg, 2University of Tubingen Medical School, Tubingen, Baden-Wurtemberg, 3UNiversity of Tubingen Medical School, Tubingen, Baden-Wurtemberg, 4University of Tubingen Medical School, Tubingen, Baden-Wuertemberg, 5Eberhard karls University of Tübingen, Tübingen, Baden-Würtemberg
First Author:
Co-Author(s):
Annika Resch
University of Tübingen Medical School
Tübingen, Baden-Württemberg
Christoph Braun
University of Tubingen Medical School
Tubingen, Baden-Wuertemberg
Introduction:
Schizophrenia (SZ) is a chronic mental disorder that affects one in 300 people worldwide. Reading emotions from faces is vital for adaptive social behavior and interpersonal interactions. Social cognition and, specifically, recognition of facial affect is altered in SZ patients. Here, we asked whether and if so, how brain communication, as quantified by magnetoencephalography (MEG) in the γ-frequency range (>30 Hz), during facial affect recognition is altered in male SZ patients compared with matched typically developing (TD) individuals.
Methods:
MEG recording was accomplished in 2 groups of participants (in total, 56 persons aged 30 years: 28 male patients with paranoid SZ and 28 person-by-person matched TD individuals) who were shown a set of dynamic point-light faces with either angry or happy expressions (Pavlova et al., 2022). On each trial, participants identified the expression they saw by pressing respective keys. Both behavioral data and neuromagnetic traces were recorded and analyzed using in-house MATLAB scripts and the FieldTrip toolbox. Individual MEG data was preprocessed (including visual rejection and independent component analysis, ICA) to remove muscular, ocular, and cardiac artifacts from both components and trials. Then, the time-frequency representation (TFR) analysis was conducted for correct and incorrect trials in the frequency range of 30–95 Hz, both relative to baseline and for group contrasts. Statistical analysis of TFR data was performed using cluster-based nonparametric permutation tests. A 2-tailed cluster-based test statistic was employed with a cluster-α=.05 and a required minimum cluster size of two neighboring channels to correct for family-wise error (FWE) rates. Source localization based on time windows and frequency ranges of the significant clusters was performed using a beamformer approach as implemented in the FieldTrip. Finally, in both groups, data-driven connectivity analysis (Romagnano et al., 2024) was conducted for correct emotion recognition and γ-frequency bands exhibiting significant differences in spectral power between conditions, assuming increased coherence indicates communication between the engaged brain regions. Overall, 5 bilateral regions of interest (ROIs) were chosen: the lateral occipital cortex, LOC, fusiform gyrus, FFG, superior temporal gyrus, STG, insula, INS, and inferior frontal gyrus, IFG. In each participant, directionality of the connections (leading or lagging) and their changes over time (i.e., time course) were assessed by computing the phase slope index (PSI) for the connections among the ROIs over the entire stimulus duration in 0.4-s windows with an overlap of 0.1 s. Finally, 2-tailed sign tests assessed connections' significance and their directionality.
Results:
SZ patients exhibited lower emotion recognition rates compared to matched TD individuals. In SZ, significant correct-incorrect contrasts occurred in the high-γ range (80-95 Hz). Source localization revealed a network of several brain regions (including the LOC, INS, FFG, and IFG) with an activation maximum in the right INS. In SZ patients, connectivity analysis indicated extensive feedback connectivity at high-γ frequencies of 85-90 Hz and moderate latencies (>600 ms) from the bilateral IFG to the right INS and left LOC compared to TD individuals, in which feedforward connections both from the right LOC to the right INS and STG and from the left INS to bilateral IFG prevailed.
Conclusions:
The findings indicate that SZ patients exhibit poorer emotion recognition from dynamic point-light faces and for correctly recognized emotions, mostly feedback brain connectivity at moderate latencies compared to their TD peers, who show primarily feedforward connections. The outcome suggests alterations in social cognition and neural communication in SZ that warrant future neuroimaging work for better understanding of neurobiological roots of this devastating mental condition.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Emotion, Motivation and Social Neuroscience:
Social Cognition
Novel Imaging Acquisition Methods:
MEG 2
Keywords:
Emotions
Psychiatric Disorders
Schizophrenia
Other - Social Cognition; Moving Faces; Emotion Recognition
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Task-activation
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
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Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
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Were any animal research approved by the relevant IACUC or other animal research panel?
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Please indicate which methods were used in your research:
MEG
Behavior
Provide references using APA citation style.
Pavlova, M.A., Romagnano, V., Kubon, J., Isernia, S., Fallgatter, A.J., & Sokolov, A.N. (2022). Ties between autistic traits and body, face, and eye reading. Frontiers in Neuroscience, 16: Article 997263. https://doi.org/10.3389/fnins.2022.997263
Romagnano, V., Kubon, J., Sokolov, A.N., Fallgatter, A.J., Braun, C., & Pavlova, M.A. (2024). Brain networks underwriting face pareidolia. Proceedings of the National Academy of Sciences of the USA, 121(16): Article e2401196121. https://doi.org/10.1073/pnas.2401196121
No