Tsubaki, T. | bioRxiv Science

bioRxiv2025

Effects of stimulus modality and response type on oddball stimulus discrimination using polarity-considered EEG microstate labeling

ObjectiveBrain-computer interfaces (BCIs) require effective feature extraction and dimensionality reduction from multidimensional brain signals. Electroencephalogram (EEG) microstate analysis offers a fast and noise-resistant approach by classifying the states of brain signals into spatial distribution patterns (templates). Each EEG segment was assigned the template with the highest spatial correlation, reducing the information to a one-dimensional representation. However, prior BCI studies have often ignored the polarity of spatial distributions in these templates. Incorporating polarity during labeling may enhance classification performance. This study investigated the effectiveness of polarity-considered microstate labeling for classifying infrequent stimuli in an auditory-visual oddball task with implications for BCI applications. MethodEEG recordings were analyzed using polarity-considered microstate labeling to classify infrequent stimuli. This study examined the effects of stimulus modality (auditory or visual), modality conditions (unimodal: stimulus and response in the same modality; cross-modal: stimulus and response in different modalities), and response type (key-press task vs. mental counting task) on classification accuracy. Machine learning models were used for classification, including support vector machine, random forest, logistic regression, XGBoost, CatBoost and K-means methods. ResultsPolarity-considered labeling outperformed the non-polarity approach, especially in decision-tree-based models (20.1% improvement in the key-press task and 22.2% improvement in the mental counting task). A significant interaction was observed between stimulus modality and response type, with the highest accuracy achieved when the infrequent stimuli in the key-press task involved cross-modal visual information. ConclusionThe findings suggest that polarity-considered microstate labeling enhances EEG-based classification. This approach has potential applications in BCI, such as in P300 spellers using cross-modal auditory-visual stimuli.

neuroscience↗

bioRxiv2023

Gravitational and mechanical forces drive mitochondrial translation through the cell adhesion-FAK axis

Life on Earth has evolved in a form suitable for the gravitational force of 1 x g. Although the pivotal role of gravity in gene expression has been revealed by multiomics approaches in space-flown samples and astronauts, the molecular details of how mammalian cells harness gravity have remained unclear. Here, we showed that mitochondria utilize gravity to activate protein synthesis within the organelle. Genome-wide ribosome profiling revealed reduced mitochondrial translation in mammalian cells and Caenorhabditis elegans under both microgravity at the International Space Station and simulated microgravity in a 3D-clinostat on the ground. We found that attenuation of cell adhesion through laminin-integrin interactions causes the phenotype. The downstream signaling pathway including FAK, RAC1, PAK1, BAD, and Bcl-2 family proteins in the cytosol, and mitochondrial fatty acid synthesis (mtFAS) pathway in the matrix maintain mitochondrial translation at high level. Mechanistically, a decreased level of mitochondrial malonyl-CoA, which is consumed by activated mtFAS, leads to a reduction in the malonylation of the translational machinery and an increase in the initiation and elongation of in organello translation. Consistent with the role of integrin as a mechanosensor, we observed a decrease in mitochondrial translation via the minimization of mechanical stress in mouse skeletal muscle. Our work provides mechanistic insights into how cells convert gravitational and mechanical forces into translation in an energy-producing organelle.

molecular biology↗