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.

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.

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 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.

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.

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.

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.

Pavlovian-based neurofeedback enhances meta-awareness of mind-wandering.

Absorption in mind-wandering (MW) may worsen our mood and can cause psychological disorders. Researchers indicate the possibility that meta-awareness of MW prevents these mal-effects and enhances favorable consequences of MW, such as boosting creativity; thus, meta-awareness has attracted psychological and clinical attention. However, few studies have investigated the nature of meta-awareness of MW, because there has been no method to isolate and operate this ability. Therefore, we propose a new approach to manipulate the ability of meta-awareness. We used Pavlovian conditioning, tying to it an occurrence of MW and a neutral tone sound inducing the meta-awareness of MW. To perform paired presentations of the unconditioned stimulus (neutral tone) and the conditioned stimulus (perception accompanying MW), we detected participants' natural occurrence of MW via electroencephalogram and a machine-learning estimation method. The double-blinded randomized controlled trial with 37 participants found that a single 20-min conditioning session significantly increased the meta-awareness of MW as assessed by behavioral and neuroscientific measures. The core protocol of the proposed method is real-time feedback on participants' neural information, and in that sense, we can refer to it as neurofeedback. However, there are some differences from typical neurofeedback protocols, and we discuss them in this paper. Our novel classical conditioning is expected to contribute to future research on the modulation effect of meta-awareness on MW.

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.

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.

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.

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.

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.

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.

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.

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.

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.