summary : This study investigates the perception of silence by employing the auditory illusion paradigm. The hypothesis posits that if silence can be perceived, then the same auditory illusion would exist. Therefore, silence was used instead of auditory stimuli to induce auditory illusions. Participants reported experiencing the same illusions as in the normal auditory illusion paradigm
summary: They proposed a novel GCN based deep learning model to diagnose autism spectrum disorder. They used spatial-temporal features from fMRI data and used Graph convolution network with attention. They achieved SOTA performance.
summary :Here they recorded simultaneous EEG-fMRI data from participants viewing affective pictures. Applying multivariate analyses including SVM and RSA. They found perceptual processing of affective pictures began ~100ms in the visual cortex, where affect-specific representation began to form ~200ms. The neural representation of affective scenes is sustained rather than dynamic.
summary : In this paper, they tried to diagnose ASD using semi-supervised autoencoder (SSAE). SSAE tries to reconstruct the features that are specifically important for downstream task (ASD vs HC), which is conceptually different than conventional AE. With sparsity constraints, the model can overcome SOTA performance in ABIDE classification. With a little bit of fine-tuning(ex. sparsity control), this method could be applied for our research using CHA data or p-factor related future research.
summary : In this work, they utilize multimodal features of spoken words, speech prosody and facial expression from Aff-WIld2 dataset. They combine these features using a transformer-based fusion module which makes the output embedding features of sequences of images, audio and text. Integrated output feature is then processed in MLP layer for Action Unit (AU) detection and also facial expression recognition.
summary : In this work, they propose the first video-based multimodal spontaneous emotion recognition that combines facial expressions with physiological data to derive the advantages of each modality. The feature vector of facial expression is fused with physiological signals including iPPG signal and HRV. The feature-level fusioned input is then processed in a 3D Xception-net based DNN model.
summary : This paper investigated how the neural representations for learning would change during rapid behavioral changes. Various cortical regions contribute to the representational changes, notably DLPFC and ACC representing uncertainty, and OFC for representations of (rapidly) shifting contexts.
Summary: First, they found WM performance is related to FPN and DMN coupling. Furthermore, they found two sub-networks of FPN and showed how their activity, FC, and SC are related to the integrative processing of complex cognition using HCP 2-back task.
summary : This paper introduced single-cell graph neural network (scGNN) to provide a hypothesis-free deep learning framework for scRNA-Seq analysis. This formulates and aggregates cell-cell relationships with GNN and models heterogeneous gene expressio naptterns using a left-truncated mixture Gaussian model. They integrates three iterative multi-modal autoencoders and outperforms existing tools for gene imputation and cell clustering.
Summary: In this paper, they investigate facial micro-expression that is getting much attention recently. They propose a novel spatio-temporal transformer architecture - the first purely transformer based approach for micro-expression recognition. It captures both local and global spatio-temporal patterns of video in an end-to-end way. This model is currently SOTA in MER(Micro-Expression Recognition) task.