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.

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.

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.

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.

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.

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.

A common brain network among state, trait, and pathological anxiety from whole-brain functional connectivity.

Anxiety is one of the most common mental states of humans. Although it drives us to avoid frightening situations and to achieve our goals, it may also impose significant suffering and burden if it becomes extreme. Because we experience anxiety in a variety of forms, previous studies investigated neural substrates of anxiety in a variety of ways. These studies revealed that individuals with high state, trait, or pathological anxiety showed altered neural substrates. However, no studies have directly investigated whether the different dimensions of anxiety share a common neural substrate, despite its theoretical and practical importance. Here, we investigated a brain network of anxiety shared by different dimensions of anxiety in a unified analytical framework using functional magnetic resonance imaging (fMRI). We analyzed different datasets in a single scale, which was defined by an anxiety-related brain network derived from whole brain. We first conducted the anxiety provocation task with healthy participants who tended to feel anxiety related to obsessive-compulsive disorder (OCD) in their daily life. We found a common state anxiety brain network across participants (1585 trials obtained from 10 participants). Then, using the resting-state fMRI in combination with the participants' behavioral trait anxiety scale scores (879 participants from the Human Connectome Project), we demonstrated that trait anxiety shared the same brain network as state anxiety. Furthermore, the brain network between common to state and trait anxiety could detect patients with OCD, which is characterized by pathological anxiety-driven behaviors (174 participants from multi-site datasets). Our findings provide direct evidence that different dimensions of anxiety have a substantial biological inter-relationship. Our results also provide a biologically defined dimension of anxiety, which may promote further investigation of various human characteristics, including psychiatric disorders, from the perspective of anxiety.

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.

Neural mechanisms of gain-loss asymmetry in temporal discounting.

Humans typically discount future gains more than losses. This phenomenon is referred to as the "sign effect" in experimental and behavioral economics. Although recent studies have reported associations between the sign effect and important social problems, such as obesity and incurring multiple debts, the biological basis for this phenomenon remains poorly understood. Here, we hypothesized that enhanced loss-related neural processing in magnitude and/or delay representation are causes of the sign effect. We examined participants performing intertemporal choice tasks involving future gains or losses and compared the brain activity of those who exhibited the sign effect and those who did not. When predicting future losses, significant differences were apparent between the two participant groups in terms of striatal activity representing delay length and in insular activity representing sensitivity to magnitude. Furthermore, participants with the sign effect exhibited a greater insular response to the magnitude of loss than to that of gain, and also a greater striatal response to the delay of loss than to that of gain. These findings may provide a new biological perspective for the development of novel treatments and preventive measures for social problems associated with the sign effect.

[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.

Surgical Concepts and Long-term Outcomes of Thalamic Deep Brain Stimulation in Patients with Severe Tourette Syndrome: A Single-center Experience.

Tourette syndrome (TS) is a developmental neuropsychiatric disorder that is characterized by tic movements. Deep brain stimulation (DBS) may be a treatment option for severe cases refractory to medical and behavioral therapies. In this study, we reviewed the surgical techniques used for DBS in patients with severe TS and its clinical outcomes and sought to determine the optimal surgical procedure and current issues based on our experience and the literature. A total of 14 patients, consisting of 13 men and 1 woman, who underwent centromedian thalamic DBS and were followed up for a mean duration of 2.3 ± 1.0 years, participated in this study. The mean Yale Global Tic Severity Scale severity score significantly improved from 41.4 ± 7.0 at baseline to 19.8 ± 11.4 at 6 months (P = 0.01) and 12.7 ± 6.2 at the last follow-up (P < 0.01). Moreover, the mean Yale Global Tic Severity Scale impairment score significantly improved from 47.1 ± 4.7 at baseline to 23.1 ± 11.1 at 6 months (P < 0.01) and 7.6 ± 2.9 at the last follow-up (P < 0.01). However, there were problems with continuous postoperative monitoring (three cases were lost to follow-up) and surgery-related adverse events, including one case each of lead misplacement and a delayed intracerebral hemorrhage due to severe self-injurious tics. This study aimed to highlight not only the clinical efficacy of DBS for TS but also its challenges. Clinicians should understand the three-dimensional brain anatomy so that they can perform precise surgical procedures, avoid adverse events, and achieve favorable outcomes of DBS for TS.

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.

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.

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 (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.

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.