Functional coupling between auditory memory and verbal transformations.

The ability to parse sound mixtures into coherent auditory objects is fundamental to cognitive functions, such as speech comprehension and language acquisition. Yet, we still lack a clear understanding of how auditory objects are formed. To address this question, we studied a speech-specific case of perceptual multistability, called verbal transformations (VTs), in which a variety of verbal forms is induced by continuous repetition of a physically unchanging word. Here, we investigated the degree to which auditory memory through sensory adaptation influences VTs. Specifically, we hypothesized that when memory persistence is longer, participants are able to retain the current verbal form longer, resulting in sensory adaptation, which in turn, affects auditory perception. Participants performed VT and auditory memory tasks on different days. In the VT task, Japanese participants continuously reported their perception while listening to a Japanese word (2- or 3-mora in length) played repeatedly for 5 min. In the auditory memory task, a different sequence of three morae, e.g., /ka/, /hi/, and /su/, was presented to each ear simultaneously. After some period (0-4 s), participants were visually cued to recall one of the sequences, i.e., in the left or right ear. We found that delayed recall accuracy was negatively correlated with the number of VTs, particularly under 2-mora conditions. This suggests that memory persistence is important for formation and selection of perceptual objects.

Associations of conservatism and jumping to conclusions biases with aberrant salience and default mode network.

AIM: While conservatism bias refers to the human need for more evidence for decision-making than rational thinking expects, the jumping to conclusions (JTC) bias refers to the need for less evidence among individuals with schizophrenia/delusion compared to healthy people. Although the hippocampus-midbrain-striatal aberrant salience system and the salience, default mode (DMN), and frontoparietal networks ("triple networks") are implicated in delusion/schizophrenia pathophysiology, the associations between conservatism/JTC and these systems/networks are unclear. METHODS: Thirty-seven patients with schizophrenia and 33 healthy controls performed the beads task, with large and small numbers of bead draws to decision (DTD) indicating conservatism and JTC, respectively. We performed independent component analysis (ICA) of resting functional magnetic resonance imaging (fMRI) data. For systems/networks above, we investigated interactions between diagnosis and DTD, and main effects of DTD. We similarly applied ICA to structural and diffusion MRI to explore the associations between DTD and gray/white matter. RESULTS: We identified a significant main effect of DTD with functional connectivity between the striatum and DMN, which was negatively correlated with delusion severity in patients, indicating that the greater the anti-correlation between these networks, the stronger the JTC and delusion. We further observed the main effects of DTD on a gray matter network resembling the DMN, and a white matter network connecting the functional and gray matter networks (all P < 0.05, family-wise error [FWE] correction). Function and gray/white matter showed no significant interactions. CONCLUSION: Our results support the novel association of conservatism and JTC biases with aberrant salience and default brain mode.

Dynamic Transitions Between Brain States Predict Auditory Attentional Fluctuations.

Achievement of task performance is required to maintain a constant level of attention. Attentional level fluctuates over the course of daily activities. However, brain dynamics leading to attentional fluctuation are still unknown. We investigated the underlying mechanisms of sustained attention using functional magnetic resonance imaging (fMRI). Participants were scanned with fMRI while performing an auditory, gradual-onset, continuous performance task (gradCPT). In this task, narrations gradually changed from one to the next. Participants pressed a button for frequent Go trials (i.e., male voices) as quickly as possible and withheld responses to infrequent No-go trials (i.e., female voices). Event-related analysis revealed that frontal and temporal areas, including the auditory cortex, were activated during successful and unsuccessful inhibition of predominant responses. Reaction-time (RT) variability throughout the auditory gradCPT was positively correlated with signal changes in regions of the dorsal attention network: superior frontal gyrus and superior parietal lobule. Energy landscape analysis showed that task-related activations could be clustered into different attractors: regions of the dorsal attention network and default mode network. The number of alternations between RT-stable and erratic periods increased with an increase in transitions between attractors in the brain. Therefore, we conclude that dynamic transitions between brain states are closely linked to auditory attentional fluctuations.

Model retraining and information sharing in a supply chain with long-term fluctuating demands.

Demand forecasting based on empirical data is a viable approach for optimizing a supply chain. However, in this approach, a model constructed from past data occasionally becomes outdated due to long-term changes in the environment, in which case the model should be updated (i.e., retrained) using the latest data. In this study, we examine the effects of updating models in a supply chain using a minimal setting. We demonstrate that when each party in the supply chain has its own forecasting model, uncoordinated model retraining causes the bullwhip effect even if a very simple replenishment policy is applied. Our results also indicate that sharing the forecasting model among the parties involved significantly reduces the bullwhip effect.

Modelling state-transition dynamics in resting-state brain signals by the hidden Markov and Gaussian mixture models.

Recent studies have proposed that one can summarize brain activity into dynamics among a relatively small number of hidden states and that such an approach is a promising tool for revealing brain function. Hidden Markov models (HMMs) are a prevalent approach to inferring such neural dynamics among discrete brain states. However, the impact of assuming Markovian structure in neural time series data has not been sufficiently examined. Here, to address this situation and examine the performance of the HMM, we compare the model with the Gaussian mixture model (GMM), which is with no temporal regularization and thus a statistically simpler model than the HMM, by applying both models to synthetic time series generated from empirical resting-state functional magnetic resonance imaging (fMRI) data. We compared the GMM and HMM for various sampling frequencies, lengths of recording per participant, numbers of participants and numbers of independent component signals. We find that the HMM attains a better accuracy of estimating the hidden state than the GMM in a majority of cases. However, we also find that the accuracy of the GMM is comparable to that of the HMM under the condition that the sampling frequency is reasonably low (e.g., TR = 2.88 or 3.60 s) or the data are relatively short. These results suggest that the GMM can be a viable alternative to the HMM for investigating hidden-state dynamics under this condition.

Presynaptic inhibition of dopamine neurons controls optimistic bias.

Regulation of reward signaling in the brain is critical for appropriate judgement of the environment and self. In Drosophila, the protocerebral anterior medial (PAM) cluster dopamine neurons mediate reward signals. Here, we show that localized inhibitory input to the presynaptic terminals of the PAM neurons titrates olfactory reward memory and controls memory specificity. The inhibitory regulation was mediated by metabotropic gamma-aminobutyric acid (GABA) receptors clustered in presynaptic microdomain of the PAM boutons. Cell type-specific silencing the GABA receptors enhanced memory by augmenting internal reward signals. Strikingly, the disruption of GABA signaling reduced memory specificity to the rewarded odor by changing local odor representations in the presynaptic terminals of the PAM neurons. The inhibitory microcircuit of the dopamine neurons is thus crucial for both reward values and memory specificity. Maladaptive presynaptic regulation causes optimistic cognitive bias.

Closer to critical resting-state neural dynamics in individuals with higher fluid intelligence.

According to the critical brain hypothesis, the brain is considered to operate near criticality and realize efficient neural computations. Despite the prior theoretical and empirical evidence in favor of the hypothesis, no direct link has been provided between human cognitive performance and the neural criticality. Here we provide such a key link by analyzing resting-state dynamics of functional magnetic resonance imaging (fMRI) networks at a whole-brain level. We develop a data-driven analysis method, inspired from statistical physics theory of spin systems, to map out the whole-brain neural dynamics onto a phase diagram. Using this tool, we show evidence that neural dynamics of human participants with higher fluid intelligence quotient scores are closer to a critical state, i.e., the boundary between the paramagnetic phase and the spin-glass (SG) phase. The present results are consistent with the notion of "edge-of-chaos" neural computation.

Methodology and theoretical basis of forward genetic screening for sleep/wakefulness in mice.

The regulatory network of genes and molecules in sleep/wakefulness remains to be elucidated. Here we describe the methodology and workflow of the dominant screening of randomly mutagenized mice and discuss theoretical basis of forward genetics research for sleep in mice. Our high-throughput screening employs electroencephalogram (EEG) and electromyogram (EMG) to stage vigilance states into a wake, rapid eye movement sleep (REMS) and non-REM sleep (NREMS). Based on their near-identical sleep/wake behavior, C57BL/6J (B6J) and C57BL/6N (B6N) are chosen as mutagenized and counter strains, respectively. The total time spent in the wake and NREMS, as well as the REMS episode duration, shows sufficient reproducibility with small coefficients of variance, indicating that these parameters are most suitable for quantitative phenotype-driven screening. Coarse linkage analysis of the quantitative trait, combined with whole-exome sequencing, can identify the gene mutation associated with sleep abnormality. Our simulations calculate the achievable LOD score as a function of the phenotype strength and the numbers of mice examined. A pedigree showing a mild decrease in total wake time resulting from a heterozygous point mutation in the Cacna1a gene is described as an example.

Bridging the Micro-Macro Gap between Single-Molecular Behavior and Bulk Hydrolysis Properties of Cellulase.

The microscopic kinetics of enzymes at the single-molecule level often deviate considerably from those expected from bulk biochemical experiments. Here, we propose a coarse-grained-model approach to bridge this gap, focusing on the unexpectedly slow bulk hydrolysis of crystalline cellulose by cellulase, which constitutes a major obstacle to mass production of biofuels and biochemicals. Building on our previous success in tracking the movements of single molecules of cellulase on crystalline cellulose, we develop a mathematical description of the collective motion and function of enzyme molecules hydrolyzing the surface of cellulose. Model simulations robustly explained the experimental findings at both the microscopic and macroscopic levels and revealed a hitherto-unknown mechanism causing a considerable slowdown of the reaction, which we call the crowding-out effect. The size of the cellulase molecule impacted significantly on the collective dynamics, whereas the rate of molecular motion on the surface did not.

A case of adenoid cystic carcinoma of the breast.

An 85-year old woman who had a large tumor in the left breast came to our out-patient clinic. Computed tomography showed multiple lung tumors in addition to a huge tumor in the left breast. A needle biopsy brought about a histological diagnosis of ductal carcinoma. A simple mastectomy was performed and a histological examination using the resected specimen demonstrated a coexistence of an adenoid structure and a false ductal structure according the histologic characteristics of adenoid cystic carcinoma, which is quite rare among breast tumors. J. Med. Invest. 65:289-291, August, 2018.

Age-related changes in the ease of dynamical transitions in human brain activity.

Executive functions, a set of cognitive processes that enable flexible behavioral control, are known to decay with aging. Because such complex mental functions are considered to rely on the dynamic coordination of functionally different neural systems, the age-related decline in executive functions should be underpinned by alteration of large-scale neural dynamics. However, the effects of age on brain dynamics have not been firmly formulated. Here, we investigate such age-related changes in brain dynamics by applying "energy landscape analysis" to publicly available functional magnetic resonance imaging data from healthy younger and older human adults. We quantified the ease of dynamical transitions between different major patterns of brain activity, and estimated it for the default mode network (DMN) and the cingulo-opercular network (CON) separately. We found that the two age groups shared qualitatively the same trajectories of brain dynamics in both the DMN and CON. However, in both of networks, the ease of transitions was significantly smaller in the older than the younger group. Moreover, the ease of transitions was associated with the performance in executive function tasks in a doubly dissociated manner: for the younger adults, the ability of executive functions was mainly correlated with the ease of transitions in the CON, whereas that for the older adults was specifically associated with the ease of transitions in the DMN. These results provide direct biological evidence for age-related changes in macroscopic brain dynamics and suggest that such neural dynamics play key roles when individuals carry out cognitively demanding tasks.

Clustering Coefficients for Correlation Networks.

Graph theory is a useful tool for deciphering structural and functional networks of the brain on various spatial and temporal scales. The clustering coefficient quantifies the abundance of connected triangles in a network and is a major descriptive statistics of networks. For example, it finds an application in the assessment of small-worldness of brain networks, which is affected by attentional and cognitive conditions, age, psychiatric disorders and so forth. However, it remains unclear how the clustering coefficient should be measured in a correlation-based network, which is among major representations of brain networks. In the present article, we propose clustering coefficients tailored to correlation matrices. The key idea is to use three-way partial correlation or partial mutual information to measure the strength of the association between the two neighboring nodes of a focal node relative to the amount of pseudo-correlation expected from indirect paths between the nodes. Our method avoids the difficulties of previous applications of clustering coefficient (and other) measures in defining correlational networks, i.e., thresholding on the correlation value, discarding of negative correlation values, the pseudo-correlation problem and full partial correlation matrices whose estimation is computationally difficult. For proof of concept, we apply the proposed clustering coefficient measures to functional magnetic resonance imaging data obtained from healthy participants of various ages and compare them with conventional clustering coefficients. We show that the clustering coefficients decline with the age. The proposed clustering coefficients are more strongly correlated with age than the conventional ones are. We also show that the local variants of the proposed clustering coefficients (i.e., abundance of triangles around a focal node) are useful in characterizing individual nodes. In contrast, the conventional local clustering coefficients were strongly correlated with and therefore may be confounded by the node's connectivity. The proposed methods are expected to help us to understand clustering and lack thereof in correlational brain networks, such as those derived from functional time series and across-participant correlation in neuroanatomical properties.

Reinforcement learning account of network reciprocity.

Evolutionary game theory predicts that cooperation in social dilemma games is promoted when agents are connected as a network. However, when networks are fixed over time, humans do not necessarily show enhanced mutual cooperation. Here we show that reinforcement learning (specifically, the so-called Bush-Mosteller model) approximately explains the experimentally observed network reciprocity and the lack thereof in a parameter region spanned by the benefit-to-cost ratio and the node's degree. Thus, we significantly extend previously obtained numerical results.

Further evidence to demonstrate the significance of serum appearance of anti-p53 antibody as a marker for progressive potential in invasive ductal carcinoma of the breast.

BACKGROUND: Serum appearance of anti-p53 antibody (p53Ab) has been reported as an indicator for progressive potential of human tumor tumors including breast cancer. But its significance in breast cancer has not been discussed fully. METHODS: Relationship between serum appearance of p53Abs and representative data accounting for progressive potential in breast cancer, nuclear grade (NG), triple negative cancer, and the cumulative score based on these two data (TGS) was investigated among 129 women with invasive ductal carcinoma (IDC) of the breast, who had been treated with surgical resection. RESULTS: There was a significant correlation between appearance of p53Abs and recurrence of the tumors (P = 0.035). Significant correlation of serum appearance of p53Abs with negative expression of ER (P = 0.011), the proportion of TNBC (P = 0.013), NG (P = 0.017), and TGS (P = 0.0005). CONCLUSIONS: Preoperative serum appearance of p53Abs can be correlated with pathological nuclear grade, incidence of triple negative breast cancer, and TGS. These results might demonstrate more powerful significance of serum appearance of p53Abs as an indicator of progressive potential in IDC of the breast. J. Med. Invest. 64: 241-244, August, 2017.

Energy landscape analysis of neuroimaging data.

Computational neuroscience models have been used for understanding neural dynamics in the brain and how they may be altered when physiological or other conditions change. We review and develop a data-driven approach to neuroimaging data called the energy landscape analysis. The methods are rooted in statistical physics theory, in particular the Ising model, also known as the (pairwise) maximum entropy model and Boltzmann machine. The methods have been applied to fitting electrophysiological data in neuroscience for a decade, but their use in neuroimaging data is still in its infancy. We first review the methods and discuss some algorithms and technical aspects. Then, we apply the methods to functional magnetic resonance imaging data recorded from healthy individuals to inspect the relationship between the accuracy of fitting, the size of the brain system to be analysed and the data length.This article is part of the themed issue 'Mathematical methods in medicine: neuroscience, cardiology and pathology'.

Reinforcement Learning Explains Conditional Cooperation and Its Moody Cousin.

Direct reciprocity, or repeated interaction, is a main mechanism to sustain cooperation under social dilemmas involving two individuals. For larger groups and networks, which are probably more relevant to understanding and engineering our society, experiments employing repeated multiplayer social dilemma games have suggested that humans often show conditional cooperation behavior and its moody variant. Mechanisms underlying these behaviors largely remain unclear. Here we provide a proximate account for this behavior by showing that individuals adopting a type of reinforcement learning, called aspiration learning, phenomenologically behave as conditional cooperator. By definition, individuals are satisfied if and only if the obtained payoff is larger than a fixed aspiration level. They reinforce actions that have resulted in satisfactory outcomes and anti-reinforce those yielding unsatisfactory outcomes. The results obtained in the present study are general in that they explain extant experimental results obtained for both so-called moody and non-moody conditional cooperation, prisoner's dilemma and public goods games, and well-mixed groups and networks. Different from the previous theory, individuals are assumed to have no access to information about what other individuals are doing such that they cannot explicitly use conditional cooperation rules. In this sense, myopic aspiration learning in which the unconditional propensity of cooperation is modulated in every discrete time step explains conditional behavior of humans. Aspiration learners showing (moody) conditional cooperation obeyed a noisy GRIM-like strategy. This is different from the Pavlov, a reinforcement learning strategy promoting mutual cooperation in two-player situations.

Jamming transitions in force-based models for pedestrian dynamics.

Force-based models describe pedestrian dynamics in analogy to classical mechanics by a system of second order ordinary differential equations. By investigating the linear stability of two main classes of forces, parameter regions with unstable homogeneous states are identified. In this unstable regime it is then checked whether phase transitions or stop-and-go waves occur. Results based on numerical simulations show, however, that the investigated models lead to unrealistic behavior in the form of backwards moving pedestrians and overlapping. This is one reason why stop-and-go waves have not been observed in these models. The unrealistic behavior is not related to the numerical treatment of the dynamic equations but rather indicates an intrinsic problem of this model class. Identifying the underlying generic problems gives indications how to define models that do not show such unrealistic behavior. As an example we introduce a force-based model which produces realistic jam dynamics without the appearance of unrealistic negative speeds for empirical desired walking speeds.

Rerupture of nonparasitic liver cyst treated with cyst fenestration: a case report.

We herein describe a case involving spontaneous rerupture of a nonparasitic liver cyst successfully treated with cyst fenestration and an omental flap. A 59-year-old Japanese woman was transferred to our hospital for evaluation of acute abdominal pain. She had a history of conservative treatment with antibiotics for spontaneous rupture of a liver cyst 1 month previously. On arrival, she exhibited abdominal tenderness and muscular defense. Enhanced computed tomography showed ascites and a large ruptured hepatic cyst (diameter of 10 cm). We diagnosed rerupture of a liver cyst and performed laparotomy for cyst fenestration and intraperitoneal drainage. During the operation, we found the perforation site on the ventral side of the cyst and brown, muddled ascitic fluid. Cholangiography showed no bile leakage on the inner wall. Pathological investigation revealed no evidence of malignancy. The patient recovered without any adverse events and was discharged on postoperative day 8. No recurrences or complications occurred for 2 years.

Potential global jamming transition in aviation networks.

In this paper, we propose a nonlinear transport model for an aviation network. The takeoff rate from an airport is characterized by the degree of ground congestion. Due to the effect of surface congestion, the performance of an airport deteriorates because of inefficient configurations of waiting aircraft on the ground. Using a simple transport model, we performed simulations on a United States airport network and found a global jamming transition induced by local surface congestion. From a physical perspective, the mechanism of the transition is studied analytically and the resulting aircraft distribution is discussed considering system dynamics. This study shows that the knowledge of the relationship between a takeoff rate and a congestion level on the ground is vital for efficient air traffic operations.

Glasgow Prognostic Score (GPS) can be a useful indicator to determine prognosis of patients with colorectal carcinoma.

The Glasgow Prognostic Score (GPS), an inflammation-based score, has been used to predict the biologic behavior of malignant tumors. The aim of the current study was to elucidate a further significance of GPS in colorectal carcinoma. Correlation of GPS and modified GPS (mGPS), which are composed of combined score provided for serum elevation of C-reactive protein and hypoalbuminemia examined before surgical treatment, with clinicopathologic features was investigated in 272 patients with colorectal carcinoma. Survival of GPS 1 patients was significantly worse than that of GPS 0 patients (P= 0.009), and survival of GPS 2 patients was significantly worse than that of GPS 1 patients (P < 0.0001). Similarly, survival of mGPS 1 patients was significantly worse than that of mGPS 0 patients (P = 0.009), and survival of mGPS 2 patients was significantly worse than that of mGPS 1 patients (P = 0.0006). Multivariate analysis demonstrated that GPS (P < 0.0001) as well as tumor stage (P= 0.004) and venous invasion (P = 0.011) were factors independently associated with worse prognosis. Both GPS and mGPS could classify outcome of patients with a clear stratification, and could be applied as prognostic indicators in colorectal carcinoma.