Hemispheric Lateralization in Older Adults Who Habitually Play Darts: 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 this from the total score, thus requiring the simultaneous performance of two tasks: exercise and calculation. This is expected to maintain and improve cognitive function, and whether this continual darts 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 the 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 there was no difference between the expert older and the younger groups. Our results suggest that the Wellness Darts game possibly promotes hemispheric lateralization.

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 (FF) 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: 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 tentatively suggested that FF interaction in acquainted pairs did not increase IBS and supported the idea that IBS is affected by 'with whom we interact and how'.

Demarcation Line Determination for Diagnosis of Gastric Cancer Disease Range Using Unsupervised Machine Learning in Magnifying Narrow-Band Imaging.

BACKGROUND AND AIMS: It is important to determine an accurate demarcation line (DL) between the cancerous lesions and background mucosa in magnifying narrow-band imaging (M-NBI)-based diagnosis. However, it is difficult for novice endoscopists. We aimed to automatically determine the accurate DL using a machine learning method. METHODS: We used an unsupervised machine learning approach to determine the DLs. Our method consists of the following four steps: (1) an M-NBI image is segmented into superpixels using simple linear iterative clustering; (2) the image features are extracted for each superpixel; (3) the superpixels are grouped into several clusters using the k-means method; and (4) the boundaries of the clusters are extracted as DL candidates. The 23 M-NBI images of 11 cases were used for performance evaluation. The evaluation investigated the similarity of the DLs identified by endoscopists and our method, and the Euclidean distance between the two DLs was calculated. For the single case of 11 cases, the histopathological examination was also conducted to evaluate the proposed system. RESULTS: The average Euclidean distances for the 11 cases were 10.65, 11.97, 7.82, 8.46, 8.59, 9.72, 12.20, 9.06, 22.86, 8.45, and 25.36. The results indicated that the proposed method could identify similar DLs to those identified by experienced doctors. Additionally, it was confirmed that the proposed system could generate pathologically valid DLs by increasing the number of clusters. CONCLUSIONS: Our proposed system can support the training of inexperienced doctors as well as enrich the knowledge of experienced doctors in endoscopy.

Thresholding Approach for Low-Rank Correlation Matrix Based on MM Algorithm.

BACKGROUND: Low-rank approximation is used to interpret the features of a correlation matrix using visualization tools; however, a low-rank approximation may result in an estimation that is far from zero, even if the corresponding original value is zero. In such a case, the results lead to a misinterpretation. METHODS: To 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. RESULTS: Through numerical simulation, we found that the false positive rate (FPR), interpretability, 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, while the FPR of the tandem approach was 0.285. CONCLUSIONS: We 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 using a heatmap, thereby avoiding result misinterpretations. We demonstrated the superiority of the proposed method through both numerical simulations and real examples.

U-Net Convolutional Neural Network for Segmenting the Corneal Endothelium in a Mouse Model of Fuchs Endothelial Corneal Dystrophy.

PURPOSE: The purpose of this study was to assess the U-Net-based convolutional neural network performance for segmenting corneal endothelium and guttae of Fuchs endothelial corneal dystrophy. METHODS: Twenty-eight images of corneal endothelial cells and guttae of Col8a2L450W/L450W knock-in mice were obtained by specular microscopy. We used 20 images as training data to develop the U-Net for analyzing guttae and cell borders. The proposed network was validated using independent test data of 8 images. Cell density, hexagonality, and coefficient of variation were calculated from the predicted cell borders and compared with ground truth. RESULTS: U-Net allowed the prediction of cell borders and guttae, and overlays of those segmentations on specular microscopy images highly corresponded to ground truth. The average number of guttae per field was 6.25 ± 8.07 for ground truth and 6.25 ± 7.87 when predicted by the network (Pearson correlation coefficient 0.989, P = 3.25 × 10 -6 ). The guttae areas were 1.60% ± 1.79% by manual determination and 1.90% ± 2.02% determined by the network (Pearson correlation coefficient 0.970, P = 6.72 × 10 -5 ). Cell density, hexagonality, and coefficient of variation analyzed by the proposed network for cell borders showed very strong correlations with ground truth (Pearson correlation coefficient 0.989, P = 3.23 × 10 -6 , Pearson correlation coefficient 0.978, P = 2.66 × 10 -5 , and Pearson correlation coefficient 0.936, P = 6.20 × 10 -4 , respectively). CONCLUSIONS: We demonstrated proof of concept for application of U-Net for objective analysis of corneal endothelial cells and guttae in Fuchs endothelial corneal dystrophy, based on limited ground truth data.

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 brake reaction time to random beeps while driving as an objective variable. As a result, we were able to construct a prediction model with the mean absolute error of 5.58 × 102 ms for the BRT 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 of 12 models that showed significant accuracy 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 driver's brain activity during actual driving. These results are expected to contribute to the development of safe driving systems and elucidate the neural basis of distracted driving.

Bayesian approach for predicting responses to therapy from high-dimensional time-course gene expression profiles.

BACKGROUND: Historical and updated information provided by time-course data collected during an entire treatment period proves to be more useful than information provided by single-point data. Accurate predictions made using time-course data on multiple biomarkers that indicate a patient's response to therapy contribute positively to the decision-making process associated with designing effective treatment programs for various diseases. Therefore, the development of prediction methods incorporating time-course data on multiple markers is necessary. RESULTS: We proposed new methods that may be used for prediction and gene selection via time-course gene expression profiles. Our prediction method consolidated multiple probabilities calculated using gene expression profiles collected over a series of time points to predict therapy response. Using two data sets collected from patients with hepatitis C virus (HCV) infection and multiple sclerosis (MS), we performed numerical experiments that predicted response to therapy and evaluated their accuracies. Our methods were more accurate than conventional methods and successfully selected genes, the functions of which were associated with the pathology of HCV infection and MS. CONCLUSIONS: The proposed method accurately predicted response to therapy using data at multiple time points. It showed higher accuracies at early time points compared to those of conventional methods. Furthermore, this method successfully selected genes that were directly associated with diseases.

Potential of automatic diagnosis system with linked color imaging for diagnosis of Helicobacter pylori infection.

BACKGROUND AND AIM: It is necessary to establish universal methods for endoscopic diagnosis of Helicobacter pylori (HP) infection, such as computer-aided diagnosis. In the present study, we propose a multistage diagnosis algorithm for HP infection. METHODS: The aims of this study are to: (i) to construct an interpretable automatic diagnostic system using a support vector machine for HP infection; and (ii) to compare the diagnosis capability of our artificial intelligence (AI) system with that of endoscopists. Presence of an HP infection determined through linked color imaging (LCI) was learned through machine learning. Trained classifiers automatically diagnosed HP-positive and -negative patients examined using LCI. We retrospectively analyzed the new images from 105 consecutive patients; 42 were HP positive, 46 were post-eradication, and 17 were uninfected. Five endoscopic images per case taken from different areas were read into the AI system, and used in the HP diagnosis. RESULTS: Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the diagnosis of HP infection using the AI system were 87.6%, 90.4%, 85.7%, 80.9%, and 93.1%, respectively. Accuracy of the AI system was higher than that of an inexperienced doctor, but there was no significant difference between the diagnosis of experienced physicians and the AI system. CONCLUSIONS: The AI system can diagnose an HP infection with significant accuracy. There remains room for improvement, particularly for the diagnosis of post-eradication patients. By learning more images and considering a diagnosis algorithm for post-eradication patients, our new AI system will provide diagnostic support, particularly to inexperienced physicians.

A Functional NIRS Study of Brain Functional Networks Induced by Social Time Coordination.

The ability to coordinate one's behavior with the others' behavior is essential to achieve a joint action in daily life. In this paper, the brain activity during synchronized tapping task was measured using functional near infrared spectroscopy (fNIRS) to investigate the relationship between time coordination and brain function. Furthermore, using brain functional network analysis based on graph theory, we examined important brain regions and network structures that serve as the hub when performing the synchronized tapping task. Using the data clustering method, two types of brain function networks were extracted and associated with time coordination, suggesting that they were involved in expectation and imitation behaviors.

Elastic net-based prediction of IFN-ß treatment response of patients with multiple sclerosis using time series microarray gene expression profiles.

INF-ß has been widely used to treat patients with multiple sclerosis (MS) in relapse. Accurate prediction of treatment response is important for effective personalization of treatment. Microarray data have been frequently used to discover new genes and to predict treatment responses. However, conventional analytical methods suffer from three difficulties: high-dimensionality of datasets; high degree of multi-collinearity; and achieving gene identification in time-course data. The use of Elastic net, a sparse modelling method, would decrease the first two issues; however, Elastic net is currently unable to solve these three issues simultaneously. Here, we improved Elastic net to accommodate time-course data analyses. Numerical experiments were conducted using two time-course microarray datasets derived from peripheral blood mononuclear cells collected from patients with MS. The proposed methods successfully identified genes showing a high predictive ability for INF-ß treatment response. Bootstrap sampling resulted in an 81% and 78% accuracy for each dataset, which was significantly higher than the 71% and 73% accuracy obtained using conventional methods. Our methods selected genes showing consistent differentiation throughout all time-courses. These genes are expected to provide new predictive biomarkers that can influence INF-ß treatment for MS patients.

Revealing Changes in Brain Functional Networks Caused by Focused-Attention Meditation Using Tucker3 Clustering.

This study examines the effects of focused-attention meditation on functional brain states 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 the eight brain regions, Frontal Inf Oper L, Occipital Inf R, ParaHippocampal R, Cerebellum 10 R, Cingulum Mid R, Cerebellum Crus1 L, Occipital Inf L, and Paracentral Lobule R increased through the meditation. Our study also provided the framework of data-driven brain functional analysis and confirmed its effectiveness on analyzing neural basis of focused-attention meditation.

Development of Cell Analysis Software to Evaluate Fibroblastic Changes in Cultivated Corneal Endothelial Cells for Quality Control.

PURPOSE: To develop software to evaluate the fibroblastic morphological changes in cultured human corneal endothelial cells (HCECs) as a quality control measure for use in tissue engineering therapy. METHODS: Software was designed to recognize cell borders, to approximate cell shape as an ellipse, and to calculate the aspect ratio of the ellipse as an indicator of severity of the fibroblastic morphological change. Using the designed software, 60 phase contrast images of polygonal HCECs and 60 phase contrast images of fibroblastic HCECs were analyzed. The correlations of the aspect ratio and other parameters (cell density, percentage of cells surrounded by 6 cells, and coefficient of variation) were evaluated. RESULTS: Cell shapes were recognized based on phase contrast images and were approximated as ellipses by software. The average aspect ratio was significantly higher (34.9% ± 6.1%) in fibroblastic HCECs than in polygonal HCECs (24.4% ± 2.3%) (P < 0.01). The aspect ratio showed a correlation with cell density, with the percentage of cells surrounded by 6 neighboring cells, and with the coefficient of variation (Pearson correlation coefficients, -0.84, -0.38, and 0.66, respectively). CONCLUSIONS: We propose that fibroblastic alteration of HCECs can be evaluated by the cell morphology based on the aspect ratio. Software developed in this study, which can analyze the frequency and severity of fibroblastic alteration, will be useful for nondestructive assessment of cells destined for use in cell-based therapy for corneal endothelial decompensation.

Functional near-infrared spectroscopy study of the neural correlates between auditory environments and intellectual work performance.

INTRODUCTION: Many people spend a considerable amount of time performing intellectual activities within auditory environments that affect work efficiency. To investigate auditory environments that improve working efficiency, we investigated the relationship between brain activity and performance of the number memory task in environments with and without white noise using functional near-infrared spectroscopy (fNIRS). METHODS: Twenty-nine healthy subjects (aged 21.9 ± 1.4 years) performed the number memory task in both the white noise and silent environments. Cerebral blood flow changes during the task were measured using an ETG-7100 fNIRS system (Hitachi, Ltd., Tokyo, Japan). The psychological states of the subjects were also estimated by subjective ratings of the pleasantness of the auditory environment. Then, they were divided into three groups based on their task scores. The differences in the cerebral blood flow (CBF) changes, functional connection strength, and the subjects' feelings of pleasantness to the noise between the subject groups were analyzed and discussed. RESULTS: The first group felt that the white noise was pleasant, which strengthened the bilateral functional connections between the brain regions related to the memory task. Therefore, the subjects' task performance improved in the white noise environment. Although the second group felt that the white noise was uncomfortable, the frontal regions related to attention control were more activated in the white noise environment to sustain the task performance in the noisy environment. The third group felt that the white noise was unpleasant, and their CBF decreased in that environment, which was associated with deteriorated task performance. CONCLUSIONS: Task performance was closely related to the subjects' feelings of pleasantness to the noise. The results of the analysis of the CBF changes and functional connectivity suggested that the effects of the white noise on brain activity differed among the three groups.

Automated Extraction of Human Functional Brain Network Properties Associated with Working Memory Load through a Machine Learning-Based Feature Selection Algorithm.

Working memory (WM) load-dependent changes of functional connectivity networks have previously been investigated by graph theoretical analysis. However, the extraordinary number of nodes represented within the complex network of the human brain has hindered the identification of functional regions and their network properties. In this paper, we propose a novel method for automatically extracting characteristic brain regions and their graph theoretical properties that reflect load-dependent changes in functional connectivity using a support vector machine classification and genetic algorithm optimization. The proposed method classified brain states during 2- and 3-back test conditions based upon each of the three regional graph theoretical metrics (degree, clustering coefficient, and betweenness centrality) and automatically identified those brain regions that were used for classification. The experimental results demonstrated that our method achieved a >90% of classification accuracy using each of the three graph metrics, whereas the accuracy of the conventional manual approach of assigning brain regions was only 80.4%. It has been revealed that the proposed framework can extract meaningful features of a functional brain network that is associated with WM load from a large number of nodal graph theoretical metrics without prior knowledge of the neural basis of WM.

Development of Cell Analysis Software for Cultivated Corneal Endothelial Cells.

PURPOSE: To develop analysis software for cultured human corneal endothelial cells (HCECs). METHODS: Software was designed to recognize cell borders and to provide parameters such as cell density, coefficient of variation, and polygonality of cultured HCECs based on phase contrast images. Cultured HCECs with high or low cell density were incubated with Ca-free and Mg-free phosphate-buffered saline for 10 minutes to reveal the cell borders and were then analyzed with software (n = 50). RESULTS: Phase contrast images showed that cell borders were not distinctly outlined, but these borders became more distinctly outlined after phosphate-buffered saline treatment and were recognized by cell analysis software. The cell density value provided by software was similar to that obtained using manual cell counting by an experienced researcher. Morphometric parameters, such as the coefficient of variation and polygonality, were also produced by software, and these values were significantly correlated with cell density (Pearson correlation coefficients -0.62 and 0.63, respectively). CONCLUSIONS: The software described here provides morphometric information from phase contrast images, and it enables subjective and noninvasive quality assessment for tissue engineering therapy of the corneal endothelium.

Analyzing Brain Functions by Subject Classification of Functional Near-Infrared Spectroscopy Data Using Convolutional Neural Networks Analysis.

Functional near-infrared spectroscopy (fNIRS) is suitable for noninvasive mapping of relative changes in regional cortical activity but is limited for quantitative comparisons among cortical sites, subjects, and populations. We have developed a convolutional neural network (CNN) analysis method that learns feature vectors for accurate identification of group differences in fNIRS responses. In this study, subject gender was classified using CNN analysis of fNIRS data. fNIRS data were acquired from male and female subjects during a visual number memory task performed in a white noise environment because previous studies had revealed that the pattern of cortical blood flow during the task differed between males and females. A learned classifier accurately distinguished males from females based on distinct fNIRS signals from regions of interest (ROI) including the inferior frontal gyrus and premotor areas that were identified by the learning algorithm. These cortical regions are associated with memory storage, attention, and task motor response. The accuracy of the classifier suggests stable gender-based differences in cerebral blood flow during this task. The proposed CNN analysis method can objectively identify ROIs using fNIRS time series data for machine learning to distinguish features between groups.