Biology subjects

Tanioka, K.

Publications and source records attributed to Tanioka, K..

10 recordsLinked to original sources

bioRxiv2026

Robust Cooperative Learning for Multiview Regression via Soft-IPOD

Multiview data analysis has become increasingly important for integrating heterogeneous information from multiple data sources. Cooperative learning provides a flexible framework for multiview integration by bridging early and late fusion strategies. However, its use of squared loss makes it sensitive to outliers. In this study, we propose robust cooperative learning based on the soft-IPOD framework. The proposed method introduces an observation-specific outlier absorption vector into the cooperative learning objective, thereby reducing the influence of outlying observations while preserving information sharing across views. The resulting optimization problem can be formulated as a weighted Lasso-type problem using an augmented design matrix and can be naturally extended to more than two data views. Numerical simulations and a real-data application to labor onset prediction demonstrate the predictive performance of the proposed method in the presence of outliers.

bioinformatics↗

bioRxiv2026

Cooperative Learning with Penalized Linear Mixed-Effects Models for High-Dimensional Clustered Multiview Data

In biomedical research, multiple types of high-dimensional data, such as genomic, transcriptomic, proteomic, and metabolomic data, are increasingly collected from the same subjects. Integrating these multiple data views can improve prediction by exploiting shared or complementary information across the views. Cooperative learning provides an agreement-based framework for multiview supervised learning by encouraging predictions obtained from individual views to be similar. However, the original framework assumes independent observations and therefore does not account for clustered structures, such as repeated measurements obtained from the same subject. To address this limitation, we propose Cooperative Learning with a penalized Linear Mixed Model (CL-pLMM) for high-dimensional multiview data with a clustered structure. CL-pLMM replaces the ordinary prediction loss in cooperative learning with a covariance-weighted loss that accounts for within-cluster dependence, while retaining the agreement penalty between views and a Lasso penalty for variable selection. We further show that its objective function can be represented as a penalized linear mixed-effects model applied to augmented data, allowing existing estimation procedures to be used. The performance of CL-pLMM is evaluated through simulation studies under various signal and dependence settings and an application to longitudinal proteomic and metabolomic data for predicting the time to spontaneous labor.

bioinformatics↗

bioRxiv2023

Estimation of a treatment effect based on a modified covariates method with L0 norm

AO_SCPLOWBSTRACTC_SCPLOWIn randomized clinical trials, we assumed the situation that the new treatment is not adequate compared to the control treatment as result. However, it is unknown if the new treatment is ineffective for all patients or if it is effective for only a subgroup of patients with specific characteristics. If such a subgroup exists and can be detected, the patients can receive effective therapy. To detect subgroups, we need to estimate treatment effects. To achieve this, various treatment effect estimation methods have been proposed based on the sparse regression method. However, these methods are affected by noise. Therefore, we propose new treatment effect estimation approaches based on the modified covariate method, one using lasso regression and the other ridge regression, using the L0 norm. The proposed approach was evaluated through numerical simulation and real data examples.

bioinformatics↗

bioRxiv2022

Effects of habitual dart training on hemispheric lateralization in older adults: a cross-sectional study using functional near-infrared spectroscopy

Exercise training integrating physical and cognitive activities is gaining attention because of its potential benefits for brain health. This study focuses on exercise training using a dart game called Wellness Darts. Wellness Darts is a sport involving throwing darts and walking to pull them out of the board, memorizing the score, and subtracting it from the total score, thus allowing the performance of two tasks simultaneously: exercise and calculation. It is expected to maintain and improve cognitive function, and whether the continual dart training affects brain function is of great interest. Before conducting the longitudinal study revealing its effect on brain function, we aimed to cross-sectionally confirm the difference in hemispheric lateralization between expert and non-expert players. Functional near-infrared spectroscopy (fNIRS) was used to measure brain activity for three groups: an expert older group who practiced darts continually, a non-expert older control group, and a non-expert younger control group. Their brain activity patterns were quantified by lateralization index (LI) and compared between groups. The results showed that the younger and the expert older groups had significantly higher LI values than the non-expert older group and did not differ between the expert older and the younger groups. Our results suggest that the Wellness Darts possibly promote hemispheric lateralization.

neuroscience↗

bioRxiv2022

Predicting the Degree of Distracted Driving Based on fNIRS Functional Connectivity: A Pilot Study

Distracted driving is one of the main causes of traffic accidents. By predicting the attentional state of drivers, it is possible to prevent distractions and promote safe driving. In this study, we developed a model that could predict the degree of distracted driving based on brain activity. Changes in oxyhemoglobin concentrations were measured in drivers while driving a real car using functional near-infrared spectroscopy (fNIRS). A regression model was constructed for each participant using functional connectivity as an explanatory variable and break reaction time to random beeps while driving as an objective variable. As a result, we were able to construct a prediction model for 11 of the 12 participants. Furthermore, the regression model with the highest prediction accuracy for each participant was analyzed to gain a better understanding of the neural basis of distracted driving. The 11 models were classified into five clusters by hierarchical clustering based on their functional connectivity edges used in each cluster. The results showed that the combinations of the dorsal attention network (DAN)-sensory motor network (SMN) and DAN-ventral attention network (VAN) connections were common in all clusters and that these networks were essential to predict the degree of distraction in complex multitask driving. They also confirmed the existence of multiple types of prediction models with different within- and between-network connectivity patterns. These results indicate that it is possible to predict the degree of distracted driving based on the brain activity of the driver during actual driving. These results are expected to contribute to the development of safe driving systems and elucidation of the neural basis of distracted driving.

neuroscience↗

bioRxiv2022

Analyzing the relationship between distracted driving and eye movement using multimodal data collected during car driving

Human error is the leading cause of traffic accidents and originates from the distraction caused by various factors, such as the drivers physical condition and mental state. One of the significant factors causing driver distraction is the presence of stress. In a previous study, multiple stressors were used to examine distraction while driving. Multiple stressors were given to the driver and the corresponding driver biometric data were obtained, and a multimodal dataset was published thereafter. In this study, we reiterate the results of existing studies and investigated the relationship between gaze variability while driving and stressor intervention, which has not yet been examined. We also examined whether biometric and vehicle information can estimate the presence or absence of secondary tasks during driving.

bioengineering↗

bioRxiv2021

Thresholding Approach for Low-Rank Correlation Matrix based on MM algorithm

BackgroundLow-rank approximation is used for interpreting the features of a correlation matrix using visualization tools; however, a low-rank approximation may result in estimation that is far from zero even if the corresponding original value is zero. In such a case, the results lead to a misinterpretation. MethodsTo overcome this, we propose a novel approach to estimate a sparse low-rank correlation matrix based on threshold values. We introduce a new cross-validation function to tune the corresponding threshold values. To calculate the value of a function, the MM algorithm is used to estimate the sparse low-rank correlation matrix, and a grid search was performed to select the threshold values. ResultsThrough numerical simulation, we found that the false positive rate (FPR) and average relative error of the proposed method were superior to those of the tandem approach. For the application of microarray gene expression, the FPRs of the proposed approach with d = 2, 3, and 5 were 0.128, 0.139, and 0.197, respectively, whereas the FPR of the tandem approach was 0.285. ConclusionsWe propose a novel approach to estimate sparse low-rank correlation matrices. The advantage of the proposed method is that it provides results that are interpretable through the use of a heatmap, thereby avoiding result misinterpretations. We demonstrated the superiority of the proposed method through both numerical simulations and real examples.

bioinformatics↗

bioRxiv2021

Interpersonal brain synchronization during face-to-face economic exchange between acquainted dyads

Interpersonal brain synchronization (IBS) has been observed during social interactions and involves various factors, such as familiarity with the partner and type of social activity. A previous study has shown that face-to-face interactions in pairs of strangers increase IBS. However, it is unclear whether this can be observed when the nature of the interacting partners is different. Herein, we aimed to extend these findings to pairs of acquaintances. Neural activity in the frontal and temporal regions was recorded using functional near-infrared spectroscopy hyperscanning. Participants played an ultimatum game that required virtual economic exchange in two experimental settings: the face-to-face and face-blocked conditions. Random pair analysis confirmed whether IBS was induced by social interaction. Contrary to the aforementioned study, our results did not show any cooperative behavior or task-induced IBS increase. Conversely, the random pair analysis results revealed that the pair-specific IBS was significant only in the task condition at the left and right superior frontal, middle frontal, orbital superior frontal, right superior temporal, precentral, and postcentral gyri. Our results revealed that face-to-face interaction in acquainted pairs did not increase IBS and supported the idea that IBS is affected by "with whom we interact and how."

neuroscience↗

bioRxiv2021

Low rank approximation of difference between correlation matrices by using inner product

IntroductionIn the domain of functional magnetic resonance imaging (fMRI) data analysis, given two correlation matrices between regions of interest (ROIs) for the same subject, it is important to reveal relatively large differences to ensure accurate interpretations. However, clustering results based only on difference tend to be unsatisfactory, and interpreting features is difficult because the difference suffers from noise. Therefore, to overcome these problems, we propose a new approach for dimensional reduction clustering. MethodsOur proposed dimensional reduction clustering approach consists of low rank approximation and a clustering algorithm. The low rank matrix, which reflects the difference, is estimated from the inner product of the difference matrix, not only the difference. In addition, the low rank matrix is calculated based on the majorize-minimization (MM) algorithm such that the difference is bounded from 1 to 1. For the clustering process, ordinal k-means is applied to the estimated low rank matrix, which emphasizes the clustering structure. ResultsNumerical simulations show that, compared with other approaches that are based only on difference, the proposed method provides superior performance in recovering the true clustering structure. Moreover, as demonstrated through a real data example of brain activity while performing a working memory task measured by fMRI, the proposed method can visually provide interpretable community structures consisted of well-known brain functional networks which can be associated with human working memory system. ConclusionsThe proposed dimensional reduction clustering approach is a very useful tool for revealing and interpreting the differences between correlation matrices, even if the true difference tends to be relatively small.

bioinformatics↗

bioRxiv2019

Short-term effects on brain functional network caused by focused-attention meditation revealed by Tucker3 clustering on graph theoretical metrics

This study examines the short-term effects of focused-attention meditation on functional brain state in novice meditators. There are a number of feature metrics for functional brain states, such as functional connectivity, graph theoretical metrics, and amplitude of low frequency fluctuation (ALFF). It is necessary to choose appropriate metrics and also to specify the region of interests (ROIs) from a number of brain regions. Here, we use a Tucker3 clustering method, which simultaneously selects the feature vectors (graph theoretical metrics and fractional ALFF) and the ROIs that can discriminate between resting and meditative states based on the characteristics of the given data. In this study, breath-counting meditation, one of the most popular forms of focused-attention meditation, was used and brain activities during resting and meditation states were measured by functional magnetic resonance imaging. The results indicated that the clustering coefficients of eight brain regions tended to increase through the meditation. Our results reveal that short-term effects of breath-counting meditation can be explained by network density changes in these eight brain regions.

neuroscience↗