Psychometric properties of the Japanese version of the standardised assessment of personality abbreviated scale

This study was undertaken to translate the Standardised Assessment of Personality - Abbreviated Scale (SAPAS) into Japanese and to evaluate its validity and reliability. SAPAS is one of the most rapid tools for assessing personality disorder (PD) and has excellent sensitivity and good specificity, whereas other PD assessment tools require such a significant investment of time that they are infeasible for large surveys or routine clinical practice. Customary assessment in clinical practice ideally incorporates screening for PD, as it is associated with a substantial public health burden, including premature mortality and increased health service utilization. Furthermore, PD's status as a key prognostic variable of mental disorders also drives PD screening. While SAPAS has been translated into several languages, there has been no Japanese version. Therefore, we translated SAPAS into Japanese (SAPAS-J) and evaluated its reliability and validity. Study 1 recruited undergraduates to reveal its test-retest reliability. Although its internal consistency was not high, since the intent of the original SAPAS was to assess the broad character of personality disorder with the fewest possible items, minimal correlations between items were reasonable. We tested two factorial models, the single-factor model and the higher-order-single-factor model, and the latter offered better fitting. This higher-order model contained a three-factor structure corresponding to clusters described in DSM-5. It measures general PD traits as a common higher-order latent variable comprising those factors. Correlations of SAPAS-J with the much longer PD screening questionnaire in Study 1 and depressive and anxiety symptoms in Study 2 from the general population support its validity. Although validation for the clinical use of SAPAS-J is limited, our research with non-clinical populations demonstrated sufficient validity to justify its use in the context of psychopathological analog research. Since PD is understood as a continuum, the severity of which is distributed dimensionally, the analog study recruiting from the general population and attempting to reveal psychopathological mechanisms of PD is meaningful.

Precision Mapping of Thalamic Deep Brain Stimulation Lead Positions Associated With the Microlesion Effect in Tourette Syndrome

BACKGROUND

The microlesion effect refers to the improvement of clinical symptoms after deep brain stimulation (DBS) lead placement and is suggested to indicate optimal lead placement. Very few studies have reported its implications in neuropsychiatric disorders.

OBJECTIVE

To evaluate the magnitude of the microlesion effect in Tourette syndrome and the relationship between the microlesion effect and the anatomic location of implanted DBS leads.

METHODS

Six male patients were included. Their median age at surgery and follow-up period were 25 years (range, 18-47) and 12 months (range, 6-24), respectively. All patients were videotaped pre- and postoperatively, and tic frequencies were counted. We also analyzed the precision of lead placement and evaluated the normative connectome associated with the microlesion area.

RESULTS

The microlesion effect was observed as an improvement in tic symptoms in all patients, and the long-term clinical outcomes were favorable. The median motor tic frequency was 20.2 tics/min (range, 9.7-60) at baseline and decreased to 3.2 tics/min (1.2-11.3) in patients on postoperative day 1 ( P = .043) and to 5.7 tics/min (range, 1.9-16.6) in patients on postoperative day 7 ( P = .028). Phonic tic tended to improve immediately after surgery although the changes were not significant. Image analyses revealed that the precise position of the electrode was directed toward the anteromedial centromedian nucleus. Normative connectome analysis demonstrated connections between improvement-related areas and wide areas of the prefrontal cortex.

CONCLUSION

This study shows that the microlesion effect may seem as an immediate improvement after optimal DBS lead placement in patients with Tourette syndrome.

Generalizable neuromarker for autism spectrum disorder across imaging sites and developmental stages: A multi-site study

Autism spectrum disorder (ASD) is a lifelong condition, and its underlying biological mechanisms remain elusive. The complexity of various factors, including inter-site and development-related differences, makes it challenging to develop generalizable neuroimaging-based biomarkers for ASD. This study used a large-scale, multi-site dataset of 730 Japanese adults to develop a generalizable neuromarker for ASD across independent sites and different developmental stages. Our adult ASD neuromarker achieved successful generalization for the US and Belgium adults and Japanese adults. The neuromarker demonstrated significant generalization for children and adolescents. We identified 141 functional connections (FCs) important for discriminating individuals with ASD from TDCs. Finally, we mapped schizophrenia (SCZ) and major depressive disorder (MDD) onto the biological axis defined by the neuromarker and explored the biological continuity of ASD with SCZ and MDD. We observed that SCZ, but not MDD, was located proximate to ASD on the biological dimension defined by the ASD neuromarker. The successful generalization in multifarious datasets and the observed relations of ASD with SCZ on the biological dimensions provide new insights for a deeper understanding of ASD.

Generalizable neuromarker for autism spectrum disorder across imaging sites and developmental stages: A multi-site study

Autism spectrum disorder (ASD) is a lifelong condition, and its underlying biological mechanisms remain elusive. The complexity of various factors, including inter-site and development-related differences, makes it challenging to develop generalizable neuroimaging-based biomarkers for ASD. This study used a large-scale, multi-site dataset of 730 Japanese adults to develop a generalizable neuromarker for ASD across independent sites (U.S., Belgium, and Japan) and different developmental stages (children and adolescents). Our adult ASD neuromarker achieved successful generalization for the US and Belgium adults (area under the curve [AUC] = 0.70) and Japanese adults (AUC = 0.81). The neuromarker demonstrated significant generalization for children (AUC = 0.66) and adolescents (AUC = 0.71; all P < 0.05 , family-wise-error corrected). We identified 141 functional connections (FCs) important for discriminating individuals with ASD from TDCs. These FCs largely centered on social brain regions such as the amygdala, hippocampus, dorsomedial and ventromedial prefrontal cortices, and temporal cortices. Finally, we mapped schizophrenia (SCZ) and major depressive disorder (MDD) onto the biological axis defined by the neuromarker and explored the biological continuity of ASD with SCZ and MDD. We observed that SCZ, but not MDD, was located proximate to ASD on the biological dimension defined by the ASD neuromarker. The successful generalization in multifarious datasets and the observed relations of ASD with SCZ on the biological dimensions provide new insights for a deeper understanding of ASD.

Validity, reliability, and correlates of the Smartphone Addiction Scale-Short Version among Japanese adults

BACKGROUND

The short version of the smartphone addiction scale (SAS-SV) is widely used to measure problematic smartphone use (PSU). This study examined the validity and reliability of the SAS-SV among Japanese adults, as well as cross-sectional and longitudinal associations with relevant mental health traits and problems.

METHODS

Datasets from a larger project on smartphone use and mental health were used to conduct two studies. Participants were adults aged over 20 years who carried a smartphone.

RESULTS

Study 1 (n = 99,156) showed the acceptable internal consistency and structural validity of the SAS-SV with a bifactor model with three factors. For the test-retest reliability of the SAS-SV, the intraclass correlation coefficient (ICC) was .70, 95% CI [.69, 70], when the SAS-SV was measured seven and twelve months apart (n = 20,389). Study 2 (n = 3419) revealed that when measured concurrently, the SAS-SV was strongly positively correlated with another measure of PSU and moderately correlated with smartphone use time, problematic internet use (PIU), depression, the attentional factor of impulsiveness, and symptoms related to attention-deficit hyperactivity disorder and obsessive-compulsive disorder. When measured 12 months apart, the SAS-SV was positively strongly associated with another measure of PSU and PIU and moderately associated with depression.

DISCUSSION

The structural validity of the SAS-SV appeared acceptable among Japanese adults with the bifactor model. The reliability of the SAS-SV was demonstrated in the subsequent seven- and twelve-month associations.

CONCLUSION

The cross-sectional and longitudinal associations of the SAS-SV provided further evidence regarding PSU characteristics.

Distinctive alterations in the mesocorticolimbic circuits in various psychiatric disorders

AIM

Increasing evidence suggests that psychiatric disorders are linked to alterations in the mesocorticolimbic dopamine-related circuits. However, the common and disease-specific alterations remain to be examined in schizophrenia (SCZ), major depressive disorder (MDD), and autism spectrum disorder (ASD). Thus, this study aimed to examine common and disease-specific features related to mesocorticolimbic circuits.

METHODS

This study included 555 participants from four institutes with five scanners: 140 individuals with SCZ (45.0% female), 127 individuals with MDD (44.9%), 119 individuals with ASD (15.1%), and 169 healthy controls (HC) (34.9%). All participants underwent resting-state functional magnetic resonance imaging. A parametric empirical Bayes approach was adopted to compare estimated effective connectivity among groups. Intrinsic effective connectivity focusing on the mesocorticolimbic dopamine-related circuits including the ventral tegmental area (VTA), shell and core parts of the nucleus accumbens (NAc), and medial prefrontal cortex (mPFC) were examined using a dynamic causal modeling analysis across these psychiatric disorders.

RESULTS

The excitatory shell-to-core connectivity was greater in the all patients than in the HC group. The inhibitory shell-to-VTA and shell-to-mPFC connectivities were greater in the ASD group than in the HC, MDD, and SCZ groups. Furthermore, the VTA-to-core and VTA-to-shell connectivities were excitatory in the ASD group, while those connections were inhibitory in the HC, MDD, and SCZ groups.

CONCLUSION

Impaired signaling in the mesocorticolimbic dopamine-related circuits could be an underlying neuropathogenesis of various psychiatric disorders. These findings will improve the understanding of unique neural alternations of each disorder and will facilitate identification of effective therapeutic targets. This article is protected by copyright. All rights reserved.

Aberrant Large-Scale Network Interactions Across Psychiatric Disorders Revealed by Large-Sample Multi-Site Resting-State Functional Magnetic Resonance Imaging Datasets

BACKGROUND AND HYPOTHESIS

Dynamics of the distributed sets of functionally synchronized brain regions, known as large-scale networks, are essential for the emotional state and cognitive processes. However, few studies were performed to elucidate the aberrant dynamics across the large-scale networks across multiple psychiatric disorders. In this paper, we aimed to investigate dynamic aspects of the aberrancy of the causal connections among the large-scale networks of the multiple psychiatric disorders.

STUDY DESIGN

We applied dynamic causal modeling (DCM) to the large-sample multi-site dataset with 739 participants from 4 imaging sites including 4 different groups, healthy controls, schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BD), to compare the causal relationships among the large-scale networks, including visual network, somatomotor network (SMN), dorsal attention network (DAN), salience network (SAN), limbic network (LIN), frontoparietal network, and default mode network.

STUDY RESULTS

DCM showed that the decreased self-inhibitory connection of LIN was the common aberrant connection pattern across psychiatry disorders. Furthermore, increased causal connections from LIN to multiple networks, aberrant self-inhibitory connections of DAN and SMN, and increased self-inhibitory connection of SAN were disorder-specific patterns for SCZ, MDD, and BD, respectively.

CONCLUSIONS

DCM revealed that LIN was the core abnormal network common to psychiatric disorders. Furthermore, DCM showed disorder-specific abnormal patterns of causal connections across the 7 networks. Our findings suggested that aberrant dynamics among the large-scale networks could be a key biomarker for these transdiagnostic psychiatric disorders.

Ecological momentary assessment of mind-wandering: meta-analysis and systematic review

Mind-wandering (MW) is a universal human phenomenon and revealing its nature contributes to understanding consciousness. The ecological momentary assessment (EMA), in which subjects report a momentary mental state, is a suitable method to investigate MW in a natural environment. Previous studies employed EMA to study MW and attempted to answer the most fundamental question: How often do we let our minds wander? However, reported MW occupancies vary widely among studies. Further, while some experimental settings may induce bias in MW reports, these designs have not been explored. Therefore, we searched PubMed and Web of Science for articles published until the end of 2020 and systematically reviewed 25 articles, and performed meta-analyses on 17 of them. Our meta-analysis found that people spend 34.504% of daily life in mind-wandering, and meta-regression revealed that using subject smartphones for EMA, frequent sampling, and long experimental duration significantly affect MW reports. This result indicates that EMA using subject smartphones may tend to collect sampling under habitual smartphone use. Furthermore, these results indicate the existence of reactivity, even in MW research. We provide fundamental knowledge of MW and discuss rough standards for EMA settings in future MW studies.

Memory trace imbalance in reinforcement and punishment systems can reinforce implicit choices leading to obsessive-compulsive behavior

We may view most of our daily activities as rational action selections; however, we sometimes reinforce maladaptive behaviors despite having explicit environmental knowledge. In this study, we model obsessive-compulsive disorder (OCD) symptoms as implicitly learned maladaptive behaviors. Simulations in the reinforcement learning framework show that agents implicitly learn to respond to intrusive thoughts when the memory trace signal for past actions decays differently for positive and negative prediction errors. Moreover, this model extends our understanding of therapeutic effects of behavioral therapy in OCD. Using empirical data, we confirm that patients with OCD show extremely imbalanced traces, which are normalized by serotonin enhancers. We find that healthy participants also vary in their obsessive-compulsive tendencies, consistent with the degree of imbalanced traces. These behavioral characteristics can be generalized to variations in the healthy population beyond the spectrum of clinical phenotypes.

Cross-scanner reproducibility and harmonization of a diffusion MRI structural brain network: A traveling subject study of multi-b acquisition

Characterization of brain networks by diffusion MRI (dMRI) has rapidly evolved, and there are ongoing movements toward data sharing and multi-center studies. To extract meaningful information from multi-center data, methods to correct for the bias caused by scanner differences, that is, harmonization, are urgently needed. In this work, we report the cross-scanner differences in structural network analyses using data from nine traveling subjects (four males and five females, 21-49 years-old) who underwent scanning using four 3T scanners (public database available from the Brain/MINDS Beyond Human Brain MRI project (http://mriportal.umin.jp/)). The reliability and reproducibility were compared to those of data from another set of four subjects (all males, 29-42 years-old) who underwent scan-rescan (interval, 105-147 days) with the same scanner as well as scan-rescan data from the Human Connectome Project database. The results demonstrated that the reliability of the edge weights and graph theory metrics was lower for data including different scanners, compared to the scan-rescan with the same scanner. Besides, systematic differences between scanners were observed, indicating the risk of bias in comparing networks obtained from different scanners directly. We further demonstrate that it is feasible to reduce inter-scanner variabilities while preserving the inter-subject differences among healthy individuals by modeling the scanner effects at the level of network matrices, when traveling-subject data are available for calibration between scanners. The present data and results are expected to serve as a basis for developing and evaluating novel harmonization methods.

Functions of the ventromedial prefrontal cortex in emotion regulation under stress

Recent neuroimaging studies suggest that the ventromedial prefrontal cortex (vmPFC) contributes to regulation of emotion. However, the adaptive response of the vmPFC under acute stress is not understood. We used fMRI to analyse brain activity of people viewing and rating the emotional strength of emotional images after acute social stress. Here, we show that the vmPFC is strongly activated by highly emotional images, indicating its involvement in emotional regulation, and that the midbrain is activated as a main effect of stress during the emotional response. vmPFC activation also exhibits individual differences in behavioural scores reflecting individual reactions to stress. Moreover, functional connectivity between the vmPFC and midbrain under stress reflects stress-induced emotion regulation. Those results suggest that the functions of the network including the vmPFC in emotion regulation is affected by stress depending on the individuals' level of reaction to the stress.

A multi-site, multi-disorder resting-state magnetic resonance image database

Machine learning classifiers for psychiatric disorders using resting-state functional magnetic resonance imaging (rs-fMRI) have recently attracted attention as a method for directly examining relationships between neural circuits and psychiatric disorders. To develop accurate and generalizable classifiers, we compiled a large-scale, multi-site, multi-disorder neuroimaging database. The database comprises resting-state fMRI and structural images of the brain from 993 patients and 1,421 healthy individuals, as well as demographic information such as age, sex, and clinical rating scales. To harmonize the multi-site data, nine healthy participants (“traveling subjects”) visited the sites from which the above datasets were obtained and underwent neuroimaging with 12 scanners. All participants consented to having their data shared and analyzed at multiple medical and research institutions participating in the project, and 706 patients and 1,122 healthy individuals consented to having their data disclosed. Finally, we have published four datasets: 1) the SRPBS Multi-disorder Connectivity Dataset 2), the SRPBS Multi-disorder MRI Dataset (restricted), 3) the SRPBS Multi-disorder MRI Dataset (unrestricted), and 4) the SRPBS Traveling Subject MRI Dataset.

Measurement(s)	mental or behavioural disorder • brain measurement • Demographic Data
Technology Type(s)	functional magnetic resonance imaging • magnetic resonance imaging • Resting State Functional Connectivity Magnetic Resonance Imaging
Factor Type(s)	age • sex • site • disorder
Sample Characteristic - Organism	Homo sapiens

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.14716329

Case Report: GPi DBS for Non-parkinsonian Midline Tremor: A Normative Connectomic Comparison to a Failed Thalamic DBS

Introduction: The clinical efficacy of deep brain stimulation (DBS) for midline tremor has been heterogenous. Here, we present an atypical case with facial and palatal tremor treated with DBS. We aimed to show the difference between the fibers affected by stimulation of the two targets [globus pallidus interna (GPi) and ventral intermediate (Vim) thalamic nucleus] using a normative connectome analysis.

Case Report: A 76-year-old woman with a 4-year history of severe facial and palatal tremor due to craniofacial dystonia. Following a failed bilateral Vim DBS, explantation of preexisting leads and implantation of bilateral GPi leads resulted in the resolution of tremor symptoms following a failed bilateral Vim DBS. We performed a normative connectome analysis using the volume of tissue activated (VTA) as a region of interest. The results revealed that the fiber tracts associated with VTA of GPi DBS had connections with the facial area of the motor cortex while the Vim DBS did not.

Conclusion: This case study suggests the possibility that GPi DBS may be considered for midline tremor, and that the normative connectome analysis may possibly offer clues as to the structures underpinning a positive response. We may refine targets for some of the more difficult to control symptoms such as the midline tremor in this case.

Comparison of traveling-subject and ComBat harmonization methods for assessing structural brain characteristics

Multisite magnetic resonance imaging (MRI) is increasingly used in clinical research and development. Measurement biases—caused by site differences in scanner/image‐acquisition protocols—negatively influence the reliability and reproducibility of image‐analysis methods. Harmonization can reduce bias and improve the reproducibility of multisite datasets. Herein, a traveling‐subject (TS) dataset including 56 T1‐weighted MRI scans of 20 healthy participants in three different MRI procedures—20, 19, and 17 subjects in Procedures 1, 2, and 3, respectively—was considered to compare the reproducibility of TS‐GLM, ComBat, and TS‐ComBat harmonization methods. The minimum participant count required for harmonization was determined, and the Cohen's d between different MRI procedures was evaluated as a measurement‐bias indicator. The measurement‐bias reduction realized with different methods was evaluated by comparing test–retest scans for 20 healthy participants. Moreover, the minimum subject count for harmonization was determined by comparing test–retest datasets. The results revealed that TS‐GLM and TS‐ComBat reduced measurement bias by up to 85 and 81.3%, respectively. Meanwhile, ComBat showed a reduction of only 59.0%. At least 6 TSs were required to harmonize data obtained from different MRI scanners, complying with the imaging protocol predetermined for multisite investigations and operated with similar scan parameters. The results indicate that TS‐based harmonization outperforms ComBat for measurement‐bias reduction and is optimal for MRI data in well‐prepared multisite investigations. One drawback is the small sample size used, potentially limiting the applicability of ComBat. Investigation on the number of subjects needed for a large‐scale study is an interesting future problem.

Neuroanatomical considerations for optimizing thalamic deep brain stimulation in Tourette syndrome

OBJECTIVE

Deep brain stimulation (DBS) of the centromedian thalamic nucleus has been reportedly used to treat severe Tourette syndrome, yielding promising outcomes. However, it remains unclear how DBS electrode position and stimulation parameters modulate the specific area and related networks. The authors aimed to evaluate the relationships between the anatomical location of stimulation fields and clinical responses, including therapeutic and side effects.

METHODS

The authors collected data from 8 patients with Tourette syndrome who were treated with DBS. The authors selected the active contact following threshold tests of acute side effects and gradually increased the stimulation intensity within the therapeutic window such that acute and chronic side effects could be avoided at each programming session. The patients were carefully interviewed, and stimulation-induced side effects were recorded. Clinical outcomes were evaluated using the Yale Global Tic Severity Scale, the Yale-Brown Obsessive-Compulsive Scale, and the Hamilton Depression Rating Scale. The DBS lead location was evaluated in the normalized brain space by using a 3D atlas. The volume of tissue activated was determined, and the associated normative connective analyses were performed to link the stimulation field with the therapeutic and side effects.

RESULTS

The mean follow-up period was 10.9 ± 3.9 months. All clinical scales showed significant improvement. Whereas the volume of tissue activated associated with therapeutic effects covers the centromedian and ventrolateral nuclei and showed an association with motor networks, those associated with paresthesia and dizziness were associated with stimulation of the ventralis caudalis and red nucleus, respectively. Depressed mood was associated with the spread of stimulation current to the mediodorsal nucleus and showed an association with limbic networks.

CONCLUSIONS

This study addresses the importance of accurate implantation of DBS electrodes for obtaining standardized clinical outcomes and suggests that meticulous programming with careful monitoring of clinical symptoms may improve outcomes.

Intergenerational transmission of the patterns of functional and structural brain networks

There is clear evidence of intergenerational transmission of life values, cognitive traits, psychiatric disorders, and even aspects of daily decision making. To investigate biological substrates of this phenomenon, the brain has received increasing attention as a measurable biomarker and potential target for intervention. However, no previous study has quantitatively and comprehensively investigated the effects of intergenerational transmission on functional and structural brain networks. Here, by employing an unusually large cohort dataset (N = 84 parent-child dyads; 45 sons, 39 daughters, 81 mothers, and 3 fathers), we show that patterns of functional and structural brain networks are preserved over a generation. We also demonstrate that several demographic factors and behavioral/physiological phenotypes have a relationship with brain similarity. Collectively, our results provide a comprehensive picture of neurobiological substrates of intergenerational transmission and demonstrate the usability of our dataset for investigating the neurobiological substrates of intergenerational transmission.

Brain/MINDS beyond human brain MRI project: A protocol for multi-level harmonization across brain disorders throughout the lifespan

Psychiatric and neurological disorders are afflictions of the brain that can affect individuals throughout their lifespan. Many brain magnetic resonance imaging (MRI) studies have been conducted; however, imaging-based biomarkers are not yet well established for diagnostic and therapeutic use. This article describes an outline of the planned study, the Brain/MINDS Beyond human brain MRI project (BMB-HBM, FY2018 ~ FY2023), which aims to establish clinically-relevant imaging biomarkers with multi-site harmonization by collecting data from healthy traveling subjects (TS) at 13 research sites. Collection of data in psychiatric and neurological disorders across the lifespan is also scheduled at 13 sites, whereas designing measurement procedures, developing and analyzing neuroimaging protocols, and databasing are done at three research sites. A high-quality scanning protocol, Harmonization Protocol (HARP), was established for five high-quality 3 T scanners to obtain multimodal brain images including T1 and T2-weighted, resting-state and task functional and diffusion-weighted MRI. Data are preprocessed and analyzed using approaches developed by the Human Connectome Project. Preliminary results in 30 TS demonstrated cortical thickness, myelin, functional connectivity measures are comparable across 5 scanners, suggesting sensitivity to subject-specific connectome. A total of 75 TS and more than two thousand patients with various psychiatric and neurological disorders are scheduled to participate in the project, allowing a mixed model statistical harmonization. The HARP protocols are publicly available online, and all the imaging, demographic and clinical information, harmonizing database will also be made available by 2024. To the best of our knowledge, this is the first project to implement a prospective, multi-level harmonization protocol with multi-site TS data. It explores intractable brain disorders across the lifespan and may help to identify the disease-specific pathophysiology and imaging biomarkers for clinical practice.

The DecNef collection, fMRI data from closed-loop decoded neurofeedback experiments

Decoded neurofeedback (DecNef) is a form of closed-loop functional magnetic resonance imaging (fMRI) combined with machine learning approaches, which holds some promises for clinical applications. Yet, currently only a few research groups have had the opportunity to run such experiments; furthermore, there is no existing public dataset for scientists to analyse and investigate some of the factors enabling the manipulation of brain dynamics. We release here the data from published DecNef studies, consisting of 5 separate fMRI datasets, each with multiple sessions recorded per participant. For each participant the data consists of a session that was used in the main experiment to train the machine learning decoder, and several (from 3 to 10) closed-loop fMRI neural reinforcement sessions. The large dataset, currently comprising more than 60 participants, will be useful to the fMRI community at large and to researchers trying to understand the mechanisms underlying non-invasive modulation of brain dynamics. Finally, the data collection size will increase over time as data from newly run DecNef studies will be added.

Measurement(s)	functional brain measurement
Technology Type(s)	3 T MRI scanner • functional magnetic resonance imaging • Neurofeedback • machine learning
Factor Type(s)	type of task performed: visual, preference, perceptual, or memory task
Sample Characteristic - Organism	Homo sapiens

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13578008

Common Brain Networks Between Major Depressive-Disorder Diagnosis and Symptoms of Depression That Are Validated for Independent Cohorts

Large-scale neuroimaging data acquired and shared by multiple institutions are essential to advance neuroscientific understanding of pathophysiological mechanisms in psychiatric disorders, such as major depressive disorder (MDD). About 75% of studies that have applied machine learning technique to neuroimaging have been based on diagnoses by clinicians. However, an increasing number of studies have highlighted the difficulty in finding a clear association between existing clinical diagnostic categories and neurobiological abnormalities. Here, using resting-state functional magnetic resonance imaging, we determined and validated resting-state functional connectivity related to depression symptoms that were thought to be directly related to neurobiological abnormalities. We then compared the resting-state functional connectivity related to depression symptoms with that related to depression diagnosis that we recently identified. In particular, for the discovery dataset with 477 participants from 4 imaging sites, we removed site differences using our recently developed harmonization method and developed a brain network prediction model of depression symptoms (Beck Depression Inventory-II [BDI] score). The prediction model significantly predicted BDI score for an independent validation dataset with 439 participants from 4 different imaging sites. Finally, we found 3 common functional connections between those related to depression symptoms and those related to MDD diagnosis. These findings contribute to a deeper understanding of the neural circuitry of depressive symptoms in MDD, a hetero-symptomatic population, revealing the neural basis of MDD.

Generalizable brain network markers of major depressive disorder across multiple imaging sites

Many studies have highlighted the difficulty inherent to the clinical application of fundamental neuroscience knowledge based on machine learning techniques. It is difficult to generalize machine learning brain markers to the data acquired from independent imaging sites, mainly due to large site differences in functional magnetic resonance imaging. We address the difficulty of finding a generalizable marker of major depressive disorder (MDD) that would distinguish patients from healthy controls based on resting-state functional connectivity patterns. For the discovery dataset with 713 participants from 4 imaging sites, we removed site differences using our recently developed harmonization method and developed a machine learning MDD classifier. The classifier achieved an approximately 70% generalization accuracy for an independent validation dataset with 521 participants from 5 different imaging sites. The successful generalization to a perfectly independent dataset acquired from multiple imaging sites is novel and ensures scientific reproducibility and clinical applicability.

Time preference and personal value: a population-based cross-sectional study in Japan

Objective

This study aimed to investigate the association between time preference (i.e., time discounting and hyperbolic time discounting) and personal values (the areas of priority values and commitment to value) in a sample of adult community residents in Japan.

Methods

Data from respondents (N = 2787) who completed the wave 1 and 3 surveys of a three-wave panel study of adult community residents in municipalities in Tokyo and suburban areas spanning 2010–2017 were analysed. Time discount rate and hyperbolic discount were measured using a three-item choice-based scale at the wave 1. Areas of priority value at present and at age 15 were measured by 11 questions for different value areas at the wave 3; the commitment to value at present and age 15 was measured by the Personal Value Questionnaire-II (PVQ-II) at the wave 3. Linear regression analyses were conducted of priority areas of values and commitment to value on time preference indicators, adjusting for sociodemographic variables and childhood socioeconomic status.

Results

After excluding those with missing responses, data from 1880 and 1958 respondents were subject to analyses on time discounting and hyperbolic time discounting, respectively. Time discount rate was significantly and negatively associated with the value area of maintaining a stable life at present. Hyperbolic time discounting was significantly and negatively associated with the commitment to value at age 15.

Conclusion

There may be an association between time preference and personal values. Time discounting and hyperbolic time discounting may be associated with different aspects of personal values, i.e., area of priority values and commitment to value, respectively.

Assessing the hierarchy of personal values among adolescents: A comparison of rating scale and paired comparison methods

INTRODUCTION

For assessing personal values, the rating scale method may not adequately reflect the hierarchical structure of personal values and tends to be influenced by response style bias. The paired comparison method is considered a promising alternative approach, because it engages comparative judgment and may reduce response style biases. The present study aimed to compare these two methods for assessing the hierarchy of personal values among adolescents.

METHODS

A total of 191 community-dwelling adolescents aged 12-15 years old completed the rating scale and paired comparison version of the Brief Personalized Value Inventory. Descriptive statistics and latent class analyses were used to assess the difference between the rating scale and paired comparison methods.

RESULTS

The two methods yielded similar rankings and means for personal values. The number of subgroups identified by latent class analysis was higher in the paired comparison method than in the rating scale method (10-class vs. 5-class). In the results using the rating scale method, there was a subgroup with high scores on all personal values items.

CONCLUSIONS

The paired comparison method captured substantially more heterogeneity in the hierarchy of personal values among adolescents compared to the rating scale, which may be influenced by response style bias.

State-unspecific patterns of whole-brain functional connectivity from resting and multiple task states predict stable individual traits

An increasing number of functional magnetic resonance imaging (fMRI) studies have revealed potential neural substrates of individual differences in diverse types of brain function and dysfunction. Although most previous studies have inherently focused on state-specific characterizations of brain networks and their functions, several recent studies reported on the potential state-unspecific nature of functional brain networks, such as global similarities across different experimental conditions or states, including both task and resting states. However, no previous studies have carried out direct, systematic characterizations of state-unspecific brain networks, or their functional implications. Here, we quantitatively identified several modes of state-unspecific individual variations in whole-brain functional connectivity patterns, called "Common Neural Modes" (CNMs), from a large-scale fMRI database including eight task/resting states. Furthermore, we tested how CNMs accounted for variability in individual cognitive measures. The results revealed that three CNMs were robustly extracted under various dimensions of features used. Each of these CNMs was preferentially correlated with different aspects of representative cognitive measures, reflecting stable individual traits. Importantly, the association between CNMs and cognitive measures emerged from brain connectivity data alone ("unsupervised"), whereas previous related studies have explicitly used both connectivity and cognitive measures to build their prediction models ("supervised"). The three CNMs were also able to predict several life outcomes, including income and life satisfaction, and achieved the highest level of performance when combined with a conventional cognitive measure. Our findings highlight the importance of state-unspecific brain networks in characterizing fundamental individual variation.

Population-neuroscience study of the Tokyo TEEN Cohort (pn-TTC): Cohort longitudinal study to explore the neurobiological substrates of adolescent psychological and behavioral development

AIM

Adolescence is a crucial stage of psychological development and is critically vulnerable to the onset of psychopathology. Our understanding of how the maturation of endocrine, epigenetics, and brain circuit may underlie psychological development in adolescence, however, has not been integrated. Here, we introduce our research project, the population-neuroscience study of the Tokyo TEEN Cohort (pn-TTC), a longitudinal study to explore the neurobiological substrates of development during adolescence.

METHODS

Participants in the first wave of the pn-TTC (pn-TTC-1) study were recruited from those of the TTC study, a large-scale epidemiological survey in which 3171 parent-adolescent pairs were recruited from the general population. Participants underwent psychological, cognitive, sociological, and physical assessment. Moreover, adolescents and their parents underwent magnetic resonance imaging (MRI; structural MRI, resting-state functional MRI, and magnetic resonance spectroscopy), and adolescents provided saliva samples for hormone analysis and for DNA analysis including epigenetics. Furthermore, the second wave (pn-TTC-2) followed similar methods as in the first wave.

RESULTS

A total of 301 parent-adolescent pairs participated in the pn-TTC-1 study. Moreover, 281 adolescents participated in the pn-TTC-2 study, 238 of whom were recruited from the pn-TTC-1 sample. The instruction for data request is available at: http://value.umin.jp/data-resource.html.

CONCLUSION

The pn-TTC project is a large-scale and population-neuroscience-based survey with a plan of longitudinal biennial follow up. Through this approach we seek to elucidate adolescent developmental mechanisms according to biopsychosocial models. This current biomarker research project, using minimally biased samples recruited from the general population, has the potential to expand the new research field of population neuroscience.

Harmonization of resting-state functional MRI data across multiple imaging sites via the separation of site differences into sampling bias and measurement bias

When collecting large amounts of neuroimaging data associated with psychiatric disorders, images must be acquired from multiple sites because of the limited capacity of a single site. However, site differences represent a barrier when acquiring multisite neuroimaging data. We utilized a traveling-subject dataset in conjunction with a multisite, multidisorder dataset to demonstrate that site differences are composed of biological sampling bias and engineering measurement bias. The effects on resting-state functional MRI connectivity based on pairwise correlations because of both bias types were greater than or equal to psychiatric disorder differences. Furthermore, our findings indicated that each site can sample only from a subpopulation of participants. This result suggests that it is essential to collect large amounts of neuroimaging data from as many sites as possible to appropriately estimate the distribution of the grand population. Finally, we developed a novel harmonization method that removed only the measurement bias by using a traveling-subject dataset and achieved the reduction of the measurement bias by 29% and improvement of the signal-to-noise ratios by 40%. Our results provide fundamental knowledge regarding site effects, which is important for future research using multisite, multidisorder resting-state functional MRI data.

A prediction model of working memory across health and psychiatric disease using whole-brain functional connectivity

Working memory deficits are present in many neuropsychiatric diseases with diagnosis-related severity. However, it is unknown whether this common behavioral abnormality is a continuum explained by a neural mechanism shared across diseases or a set of discrete dysfunctions. Here, we performed predictive modeling to examine working memory ability (WMA) as a function of normative whole-brain connectivity across psychiatric diseases. We built a quantitative model for letter three-back task performance in healthy participants, using resting state functional magnetic resonance imaging (rs-fMRI). This normative model was applied to independent participants (N = 965) including four psychiatric diagnoses. Individual’s predicted WMA significantly correlated with a measured WMA in both healthy population and schizophrenia. Our predicted effect size estimates on WMA impairment were comparable to previous meta-analysis results. These results suggest a general association between brain connectivity and working memory ability applicable commonly to health and psychiatric diseases.

Deficient neural activity subserving decision-making during reward waiting time in intertemporal choice in adult attention-deficit hyperactivity disorder

AIM

Impulsivity, which significantly affects social adaptation, is an important target behavioral characteristic in interventions for attention-deficit hyperactivity disorder (ADHD). Typically, people are willing to wait longer to acquire greater rewards. Impulsivity in ADHD may be associated with brain dysfunction in decision-making involving waiting behavior under such situations. We tested the hypothesis that brain circuitry during a period of waiting (i.e., prior to the acquisition of reward) is altered in adults with ADHD.

METHODS

The participants included 14 medication-free adults with ADHD and 16 healthy controls matched for age, sex, IQ, and handedness. The behavioral task had participants choose between a delayed, larger monetary reward and an immediate, smaller monetary reward, where the reward waiting time actually occurred during functional magnetic resonance imaging measurement. We tested for group differences in the contrast values of blood-oxygen-level dependent signals associated with the length of waiting time, calculated using the parametric modulation method.

RESULTS

While the two groups did not differ in the time discounting rate, the delay-sensitive contrast values were significantly lower in the caudate and visual cortex in individuals with ADHD. The higher impulsivity scores were significantly associated with lower delay-sensitive contrast values in the caudate and visual cortex.

CONCLUSION

These results suggest that deficient neural activity affects decision-making involving reward waiting time during intertemporal choice tasks, and provide an explanation for the basis of impulsivity in adult ADHD.

[Brain mechanisms of depression--preliminary evidence from fMRI studies]

Although brain monoamines serotonin, noradrenaline, and dopamine have been repeatedly shown to be linked to depression, it remains unclear how monoamine dysfunction is mechanistically related to symptoms of depression. We hypothesized that imbalances in the networks of regions innervated by monoamines disrupt patients' learning and decision-making abilities, and this disruption could, in turn, lead to symptoms of depression. We have conducted functional magnetic resonance imaging (fMRI) studies on learning and decision-making, mainly focusing on the role of serotonin. Our results suggest that parallel organization for reward prediction at different time scales in the striatum is under differential modulation by serotonin, and that depression is associated with a diminished recruitment of the dorsal striatum, involved in long-term reward prediction. Based on these findings, the brain mechanisms of depression are discussed.

Neural and personality correlates of individual differences related to the effects of acute tryptophan depletion on future reward evaluation

BACKGROUND/AIMS

In general, humans tend to discount the value of delayed reward. An increase in the rate of discounting leads to an inability to select a delayed reward over a smaller immediate reward (reward-delay impulsivity). Although deficits in the serotonergic system are implicated in this reward-delay impulsivity, there is individual variation in response to serotonin depletion. The aim of the present study was to investigate whether the effects of serotonin depletion on the ability to evaluate future reward are affected by individual personality traits or brain activation.

METHODS

Personality traits were assessed using the NEO-Five Factor Inventory and Temperament and Character Inventory. The central serotonergic levels of 16 healthy volunteers were manipulated by dietary tryptophan depletion. Subjects performed a delayed reward choice task that required the continuous estimation of reward value during functional magnetic resonance imaging scanning.

RESULTS

Discounting rates were increased in 9 participants, but were unchanged or decreased in 7 participants in response to tryptophan depletion. Participants whose discounting rate was increased by tryptophan depletion had significantly higher neuroticism and lower self-directedness. Furthermore, tryptophan depletion differentially affected the groups in terms of hemodynamic responses to the value of predicted future reward in the right insula.

CONCLUSIONS

These results suggest that individuals who have high neuroticism and low self-directedness as personality traits are particularly vulnerable to the effect of low serotonin on future reward evaluation accompanied by altered brain activation patterns.

[Effects of serotonin on delay discounting for rewards--an application for understanding of pathophysiology in psychiatric disorders]

In our daily life, we constantly make such choices between actions leading to rewards of various sizes after different delays. "Delay discounting" is a theoretical concept in which the "value" of reward R after delay. A steep rate of discounting results in impulsive choice, defined by an abnormally frequent choice of the more immediate reward. Our behavioral and neuroimaging results suggest that serotonin may adjust the rate of delayed reward discounting via the modulation of striatum in cortico-basal ganglia circuits in human. Our proposed role of serotonin may explain certain aspects of impulsivity in psychiatric disorders such as major depression, panic disorder or obsessive-compulsive disorder, that are known to effectively relieve symptoms by selective serotonin reuptake inhibitors. Future experiments using delayed reward paradigms could be designed to study impulsivity in these patients.

Inter-individual discount factor differences in reward prediction are topographically associated with caudate activation

In general, humans tend to devalue a delayed reward. Such delay discounting is a theoretical and computational concept in which the discount factor influences the time scale of the trade-off between delay of reward and amount of reward. The discount factor relies on the individual's ability to evaluate the future reward. Using functional magnetic resonance imaging, we investigated brain mechanisms for reward valuation at different individual discount factors in a delayed reward choice task. In the task, participants were required to select small/immediate or large/delayed rewards to maximize the total reward over time. The discount factor for each participant individually was calculated from the behavioral data based on an exponential discounting model. The estimated value of a future reward increases as the expected delivery approaches, so the time course of these estimated values was computed based on each individual's discount factor; each was entered into the regression analysis as an explanatory (independent) variable. After the region of interest was narrowed anatomically to the caudate, a peak coordinate was detected in each individual. A correlation analysis revealed that the location of the peak along the dorsal-ventral axis in the right caudate was positively correlated with the discount factor. This implies that individuals who showed a larger discount factor had peak activations in a more dorsal part of the right caudate associated with future reward prediction. This evidence also suggests that a higher ability to delay reward prediction might be related to activation of the more dorsal caudate.

Serotonin differentially regulates short- and long-term prediction of rewards in the ventral and dorsal striatum

Background

The ability to select an action by considering both delays and amount of reward outcome is critical for maximizing long-term benefits. Although previous animal experiments on impulsivity have suggested a role of serotonin in behaviors requiring prediction of delayed rewards, the underlying neural mechanism is unclear.

Methodology/Principal Findings

To elucidate the role of serotonin in the evaluation of delayed rewards, we performed a functional brain imaging experiment in which subjects chose small-immediate or large-delayed liquid rewards under dietary regulation of tryptophan, a precursor of serotonin. A model-based analysis revealed that the activity of the ventral part of the striatum was correlated with reward prediction at shorter time scales, and this correlated activity was stronger at low serotonin levels. By contrast, the activity of the dorsal part of the striatum was correlated with reward prediction at longer time scales, and this correlated activity was stronger at high serotonin levels.

Conclusions/Significance

Our results suggest that serotonin controls the time scale of reward prediction by differentially regulating activities within the striatum.

Serotonin and the Evaluation of Future Rewards: Theory, Experiments, and Possible Neural Mechanisms

The ability to select an action by considering both delays and amount of reward outcome is critical for survival and well-being of animals and humans. Previous animal experiments suggest a role of serotonin in action choice by modulating the evaluation of delayed rewards. It remains unclear, however, through which neural circuits, and through what receptors and intracellular mechanisms, serotonin affects the evaluation of delayed rewards. Here, we review experimental studies and computational theory of decisions under delayed rewards, and propose that serotonin controls the timescale of reward prediction by regulating neural activity in the basal ganglia.

Humans can adopt optimal discounting strategy under real-time constraints

Critical to our many daily choices between larger delayed rewards, and smaller more immediate rewards, are the shape and the steepness of the function that discounts rewards with time. Although research in artificial intelligence favors exponential discounting in uncertain environments, studies with humans and animals have consistently shown hyperbolic discounting. We investigated how humans perform in a reward decision task with temporal constraints, in which each choice affects the time remaining for later trials, and in which the delays vary at each trial. We demonstrated that most of our subjects adopted exponential discounting in this experiment. Further, we confirmed analytically that exponential discounting, with a decay rate comparable to that used by our subjects, maximized the total reward gain in our task. Our results suggest that the particular shape and steepness of temporal discounting is determined by the task that the subject is facing, and question the notion of hyperbolic reward discounting as a universal principle.

Brain mechanism of reward prediction under predictable and unpredictable environmental dynamics

In learning goal-directed behaviors, an agent has to consider not only the reward given at each state but also the consequences of dynamic state transitions associated with action selection. To understand brain mechanisms for action learning under predictable and unpredictable environmental dynamics, we measured brain activities by functional magnetic resonance imaging (fMRI) during a Markov decision task with predictable and unpredictable state transitions. Whereas the striatum and orbitofrontal cortex (OFC) were significantly activated both under predictable and unpredictable state transition rules, the dorsolateral prefrontal cortex (DLPFC) was more strongly activated under predictable than under unpredictable state transition rules. We then modelled subjects' choice behaviours using a reinforcement learning model and a Bayesian estimation framework and found that the subjects took larger temporal discount factors under predictable state transition rules. Model-based analysis of fMRI data revealed different engagement of striatum in reward prediction under different state transition dynamics. The ventral striatum was involved in reward prediction under both unpredictable and predictable state transition rules, although the dorsal striatum was dominantly involved in reward prediction under predictable rules. These results suggest different learning systems in the cortico-striatum loops depending on the dynamics of the environment: the OFC-ventral striatum loop is involved in action learning based on the present state, while the DLPFC-dorsal striatum loop is involved in action learning based on predictable future states.

Prediction of immediate and future rewards differentially recruits cortico-basal ganglia loops

Evaluation of both immediate and future outcomes of one's actions is a critical requirement for intelligent behavior. Using functional magnetic resonance imaging (fMRI), we investigated brain mechanisms for reward prediction at different time scales in a Markov decision task. When human subjects learned actions on the basis of immediate rewards, significant activity was seen in the lateral orbitofrontal cortex and the striatum. When subjects learned to act in order to obtain large future rewards while incurring small immediate losses, the dorsolateral prefrontal cortex, inferior parietal cortex, dorsal raphe nucleus and cerebellum were also activated. Computational model-based regression analysis using the predicted future rewards and prediction errors estimated from subjects' performance data revealed graded maps of time scale within the insula and the striatum: ventroanterior regions were involved in predicting immediate rewards and dorsoposterior regions were involved in predicting future rewards. These results suggest differential involvement of the cortico-basal ganglia loops in reward prediction at different time scales.