Generalizable and transportable resting-state neural signatures characterized by functional networks, neurotransmitters, and clinical symptoms in autism.

Autism spectrum disorder (ASD) is a lifelong condition with elusive biological mechanisms. The complexity of factors, including inter-site and developmental differences, hinders the development of a generalizable neuroimaging classifier for ASD. Here, we developed a classifier for ASD using a large-scale, multisite resting-state fMRI dataset of 730 Japanese adults, aiming to capture neural signatures that reflect pathophysiology at the functional network level, neurotransmitters, and clinical symptoms of the autistic brain. Our adult ASD classifier was successfully generalized to adults in the United States, Belgium, and Japan. The classifier further demonstrated its successful transportability to children and adolescents. The classifier contained 141 functional connections (FCs) that were important for discriminating individuals with ASD from typically developing controls. These FCs and their terminal brain regions were associated with difficulties in social interaction and dopamine and serotonin, respectively. Finally, we mapped attention-deficit/hyperactivity disorder (ADHD), schizophrenia (SCZ), and major depressive disorder (MDD) onto the biological axis defined by the ASD classifier. ADHD and SCZ, but not MDD, were located proximate to ASD on the biological dimensions. Our results revealed functional signatures of the ASD brain, grounded in molecular characteristics and clinical symptoms, achieving generalizability and transportability applicable to the evaluation of the biological continuity of related diseases.

Predicting anxiety state using smartphone-based passive sensing.

This study predicts the change of stress levels using real-world and online behavioral features extracted from smartphone log information. Previous studies of stress detection using smartphone data focused on a single feature and did not consider all features simultaneously. We propose a method to extract a co-occurring combination of a user's real-world and online behavioral features by converting raw sensor data into categorical features. We conducted an experiment in which the State Trait Anxiety Inventory (STAI) was used to assess the anxiety-related stress levels of 20 healthy participants. The participants installed a log-collecting application on their smartphones and answered the STAI questions once a day for one month. The proposed method showed an F-score of 74.2%, which is 4.0% higher than the F-score of previous studies (70.2%) that used single non-combined features. The results demonstrate that anxiety-related stress levels can be predicted using combined features extracted from smartphone log data.

Aberrant sense of agency induced by delayed prediction signals in schizophrenia: a computational modeling study.

Aberrant sense of agency (SoA, a feeling of control over one's own actions and their subsequent events) has been considered key to understanding the pathology of schizophrenia. Behavioral studies have demonstrated that a bidirectional (i.e., excessive and diminished) SoA is observed in schizophrenia. Several neurophysiological and theoretical studies have suggested that aberrancy may be due to temporal delays (TDs) in sensory-motor prediction signals. Here, we examined this hypothesis via computational modeling using a recurrent neural network (RNN) expressing the sensory-motor prediction process. The proposed model successfully reproduced the behavioral features of SoA in healthy controls. In addition, simulation of delayed prediction signals reproduced the bidirectional schizophrenia-pattern SoA, whereas three control experiments (random noise addition, TDs in outputs, and TDs in inputs) demonstrated no schizophrenia-pattern SoA. These results support the TD hypothesis and provide a mechanistic understanding of the pathology underlying aberrant SoA in schizophrenia.

Distorted neurocomputation by a small number of extra-large spines in psychiatric disorders.

Human genetics strongly support the involvement of synaptopathy in psychiatric disorders. However, trans-scale causality linking synapse pathology to behavioral changes is lacking. To address this question, we examined the effects of synaptic inputs on dendrites, cells, and behaviors of mice with knockdown of SETD1A and DISC1, which are validated animal models of schizophrenia. Both models exhibited an overrepresentation of extra-large (XL) synapses, which evoked supralinear dendritic and somatic integration, resulting in increased neuronal firing. The probability of XL spines correlated negatively with working memory, and the optical prevention of XL spine generation restored working memory impairment. Furthermore, XL synapses were more abundant in the postmortem brains of patients with schizophrenia than in those of matched controls. Our findings suggest that working memory performance, a pivotal aspect of psychiatric symptoms, is shaped by distorted dendritic and somatic integration via XL spines.

Passive Way of Measuring QOL/Well-Being Levels Using Smartphone Log.

Research on mental health states involves paying increasing attention to changes in daily life. Researchers have attempted to understand such daily changes by relying on self-reporting through frequent assessment using devices (smartphones); however, they are mostly focused on a single aspect of mental health. Assessing the mental health of a person from various perspectives may help in the primary prevention of mental illness and the comprehensive measurement of mental health. In this study, we used users' smartphone logs to build a model to estimate whether the scores on three types of questionnaires related to quality of life and well-being would increase compared to the previous week (fluctuation model) and whether they would be higher compared to the average for that user (interval model). Sixteen participants completed three questionnaires once per week, and their smartphone logs were recorded over the same period. Based on the results, estimation models were built, and the F-score ranged from 0.739 to 0.818. We also analyzed the features that the estimation model emphasized. Information related to "physical activity," such as acceleration and tilt of the smartphone, and "environment," such as atmospheric pressure and illumination, were given more weight in the estimation than information related to "cyber activity," such as usage of smartphone applications. In particular, in the Positive and Negative Affect Schedule (PANAS), 9 out of 10 top features in the fluctuation model and 7 out of 10 top features in the interval model were related to activities in the physical world, suggesting that short-term mood may be particularly heavily influenced by subjective activities in the human physical world.

Reliability and diagnostic validity for schizophrenia of the Japanese version of the Bonn Scale for Assessment of Basic Symptoms (BSABS).

Schizophrenia is defined by operative diagnostic criteria in DSM-IV with some typical symptoms as hallucinations and duration of the disease. Huber focused on the subjective experience of patients and coined the term "basic symptoms" and created BSABS. Our study investigated the reliability and the diagnostic validity of the 5 clusters of BSABS for DSM-IV-based diagnosis of schizophrenia with a cohort of 105 patients. Good inter-rater reliability was obtained except for one item D.10. As evaluated by Spearman's rank correlation coefficients, among the 5 clusters excluding Cluster 2, internal consistency was good. This suggests that, although each cluster is heterogeneous, cluster symptoms are the expression of physiological and biological disturbances of schizophrenia. Receiver Operating Characteristic Curve analysis was also used to show the ability of each cluster to discriminate schizophrenia. Results showed that the area representing the powers in discriminate schizophrenia of Cluster 4 "Adynamia", which is considered related to the dynamic aspect of thinking, was highest, at 0.739. Cluster 1 "Information processing disturbances" which has a predictive ability for schizophrenia showed 0.714 and Cluster 3 "Impaired tolerance to normal stress" showed 0.711. Our findings suggest that, although these clusters symptoms differ from DSM-IV criteria, they are related to fundamental process of schizophrenia. Use of some of these three clusters with other neurophysiological markers could allow clinical evaluation of schizophrenia from a new perspective.

Computational Psychiatry Research Map (CPSYMAP): A New Database for Visualizing Research Papers.

The field of computational psychiatry is growing in prominence along with recent advances in computational neuroscience, machine learning, and the cumulative scientific understanding of psychiatric disorders. Computational approaches based on cutting-edge technologies and high-dimensional data are expected to provide an understanding of psychiatric disorders with integrating the notions of psychology and neuroscience, and to contribute to clinical practices. However, the multidisciplinary nature of this field seems to limit the development of computational psychiatry studies. Computational psychiatry combines knowledge from neuroscience, psychiatry, and computation; thus, there is an emerging need for a platform to integrate and coordinate these perspectives. In this study, we developed a new database for visualizing research papers as a two-dimensional "map" called the Computational Psychiatry Research Map (CPSYMAP). This map shows the distribution of papers along neuroscientific, psychiatric, and computational dimensions to enable anyone to find niche research and deepen their understanding ofthe field.

[Simulation of Schizophrenia].

There have been so many and various researches of schizophrenia since the early days of modern psychiatry, but the pathology remains unexplained, and the treatmens not established. One of the reasons is the complexity of a brain which makes it difficult to form a bridge between biological findings and symptoms. Computational psychiatry has been recently expected to overcome this difficulty. In the field, a brain is seen as an information-processing system, and the dynamics of a brain are expressed using mathematical models. In addition, mental disorders are expressed as changes including parameters in the models. Here, we introduce some studies conducted in Japan.

Dysconnectivity of the Agency Network in Schizophrenia: A Functional Magnetic Resonance Imaging Study.

Background: Self-disturbances in schizophrenia have recently been explained by an abnormality in the sense of agency (SoA). The cerebral structures of SoA in healthy people are considered to mainly include the insula and inferior parietal lobule. In contrast, the functional lesion of aberrant SoA in schizophrenia is not yet fully understood. Considering the recent explanation of establishing SoA from the standpoint of associative learning, the "agency network" may include not only the insula and inferior parietal lobule but also the striatum. We hypothesized that aberrant SoA in schizophrenia is based on a deficit in the "agency network." Methods: Functional magnetic resonance imaging data were acquired while patients with schizophrenia (n = 15) and matched controls (n = 15) performed our adaptation method of agency attribution task on a trial-by-trial basis to assess participants' explicit experience of the temporal causal relationship between an action and an external event with temporal biases. Analysis of functional connectivity was done using the right supramarginal gyrus and the right middle frontal gyrus as seed regions. Results: In healthy controls, analyses revealed increased activation of the right inferior parietal lobule (mainly the supramarginal gyrus), right insula, and right middle frontal gyrus as an activation of the agency condition. We defined activated Brodmann areas shown in the agency condition of healthy controls as the seed region for connectivity analysis. The connectivity analysis revealed lower connectivity between the head of the left caudate nucleus and right supramarginal gyrus in the patients compared to healthy controls. Conclusions: This dysconnectivity of the agency network in schizophrenia may lead to self-disturbance through deficits in associative learning of SoA. These findings may explain why pathological function of the striatum in schizophrenia leads to self-disturbance.

Behavioral evidence of delayed prediction signals during agency attribution in patients with schizophrenia.

Self-disturbance, a core feature of schizophrenia, recently has been explained from the standpoint of an abnormal sense of agency (SoA). Previous studies showed that aberrant SoA in schizophrenia arise from imprecise predictions about the sensory consequences of actions. However, the nature of the malfunctioning predictions remains unclear. We examined the temporally "delayed" nature of inadequate predictions. We studied 30 patients with schizophrenia and 30 healthy controls. Our original SoA task evaluates explicit experience of the temporal causal relationship between an intentional action and an effect on a computer screen under the presence of temporal biases. We introduced an adaptation with a "trial-by-trial" method that prolonged or shortened the temporal biases. We hypothesized that delayed prediction signals in schizophrenia could lead to a match in timing between predictions and actual outcomes, resulting in self-agency. The adjustment courses to changing temporal biases were evaluated. Patients with schizophrenia continued to feel self-agency even when the adjusted temporal bias was longer than 1000ms. This result indicated that patient's prediction would be delayed in each trial. Our study empirically showed behavioral evidence for "delayed" prediction signals in a SoA paradigm for the first time.

Reduced sense of agency in chronic schizophrenia with predominant negative symptoms.

Self-disturbances in schizophrenia have been regarded as a fundamental vulnerability marker for this disease, and have begun to be studied from the standpoint of an abnormal "sense of agency (SoA)" in cognitive neuroscience. To clarify the nature of aberrant SoA in schizophrenia, it needs to be investigated in various clinical subtypes and stages. The residual type of chronic schizophrenia with predominant negative symptoms (NS) has never been investigated for SoA. Accordingly, we investigated SoA by an original agency attribution task in NS-predominant schizophrenia, and evaluated the dynamic interplay between the predictive and postdictive components of SoA in the optimal cue integration framework. We studied 20 patients with NS-predominant schizophrenia, and compared with 30 patients with paranoid-type schizophrenia and 35 normal volunteers. NS-predominant schizophrenia showed markedly diminished SoA compared to normal controls and paranoid-type schizophrenia, indicating a completely opposite direction in agency attribution compared with excessive SoA demonstrated in paranoid-type schizophrenia. Reduced SoA was detected in experimental studies of schizophrenia for the first time. According to the optimal cue integration framework, these results indicate that there was no increase in compensatory contributions of the postdictive processes despite the existence of inadequate predictions, contrary to the exaggerated postdictive component in paranoid-type schizophrenia.