It is well known that depressive disorder is heterogeneous, yet little is known about its neurophysiological subtypes. In the present study, we identified neurophysiological subtypes of depression related to specific neural substrates. We performed cluster analysis for 134 subjects (67 depressive subjects and 67 controls) using a high-dimensional dataset consisting of resting state functional connectivity measured by functional MRI, clinical questionnaire scores, and various biomarkers. Applying a newly developed, multiple co-clustering method to this dataset, we identified three subtypes of depression that are characterized by functional connectivity between the right Angular Gyrus (AG) and other brain areas in default mode networks, and Child Abuse Trauma Scale (CATS) scores. These subtypes are also related to Selective Serotonin-Reuptake Inhibitor (SSRI) treatment outcomes, which implies that we may be able to predict effectiveness of treatment based on AG-related functional connectivity and CATS.

Recent experiments have shown that optogenetic activation of serotonin neurons in the dorsal raphe nucleus (DRN) in mice enhances patience in waiting for future rewards. Here, we show that serotonin effect in promoting waiting is maximized by both high probability and high timing uncertainty of reward. Optogenetic activation of serotonergic neurons prolongs waiting time in no-reward trials in a task with 75% food reward probability, but not with 50 or 25% reward probabilities. Serotonin effect in promoting waiting increases when the timing of reward presentation becomes unpredictable. To coherently explain the experimental data, we propose a Bayesian decision model of waiting that assumes that serotonin neuron activation increases the prior probability or subjective confidence of reward delivery. The present data and modeling point to the possibility of a generalized role of serotonin in resolving trade-offs, not only between immediate and delayed rewards, but also between sensory evidence and subjective confidence.

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We propose a novel method for multiple clustering, which is useful for analysis of high-dimensional data containing heterogeneous types of features. Our method is based on nonparametric Bayesian mixture models in which features are automatically partitioned (into views) for each clustering solution. This feature partition works as feature selection for a particular clustering solution, which screens out irrelevant features. To make our method applicable to high-dimensional data, a co-clustering structure is newly introduced for each view. Further, the outstanding novelty of our method is that we simultaneously model different distribution families, such as Gaussian, Poisson, and multinomial distributions in each cluster block, which widens areas of application to real data. We apply the proposed method to synthetic and real data, and show that our method outperforms other multiple clustering methods both in recovering true cluster structures and in computation time. Finally, we apply our method to a depression dataset with no true cluster structure available, from which useful inferences are drawn about possible clustering structures of the data.

Hashtags, which started to be widely used since 2007, are always utilized to mark keywords in tweets to categorize messages and form conversation for topics in Twitter. However, it is hard for users to use hashtags for sharing their opinions/interests/comments for their interesting topics. In this paper, we present a new approach for recommending news-topic oriented hashtags to help Twitter users easily join the conversation about news topics in Twitter. We first detect topic-specific informative words co-occurring with a given target word, which we call characteristic co-occurrence words, from news articles to form a vector for representing the news topic. Then by creating a hashtag vector based on tweets with the same hashtag, we calculate the similarity between these two vectors and recommend hashtags of high similarity scores with the news topic. Experimental results show that our approach could recommend hashtags which are highly relevant to the news topics, helping users share their tweets with others in Twitter.