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.

Resting-state functional connectivity disruption between the left and right pallidum as a biomarker for subthreshold depression.

Although the identification of late adolescents with subthreshold depression (StD) may provide a basis for developing effective interventions that could lead to a reduction in the prevalence of StD and prevent the development of major depressive disorder, knowledge about the neural basis of StD remains limited. The purpose of this study was to develop a generalizable classifier for StD and to shed light on the underlying neural mechanisms of StD in late adolescents. Resting-state functional magnetic resonance imaging data of 91 individuals (30 StD subjects, 61 healthy controls) were included to build an StD classifier, and eight functional connections were selected by using the combination of two machine learning algorithms. We applied this biomarker to an independent cohort (n = 43) and confirmed that it showed generalization performance (area under the curve = 0.84/0.75 for the training/test datasets). Moreover, the most important functional connection was between the left and right pallidum, which may be related to clinically important dysfunctions in subjects with StD such as anhedonia and hyposensitivity to rewards. Investigation of whether modulation of the identified functional connections can be an effective treatment for StD may be an important topic of future research.

The neuroanatomy of social trust predicts depression vulnerability.

Trust attitude is a social personality trait linked with the estimation of others' trustworthiness. Trusting others, however, can have substantial negative effects on mental health, such as the development of depression. Despite significant progress in understanding the neurobiology of trust, whether the neuroanatomy of trust is linked with depression vulnerability remains unknown. To investigate a link between the neuroanatomy of trust and depression vulnerability, we assessed trust and depressive symptoms and employed neuroimaging to acquire brain structure data of healthy participants. A high depressive symptom score was used as an indicator of depression vulnerability. The neuroanatomical results observed with the healthy sample were validated in a sample of clinically diagnosed depressive patients. We found significantly higher depressive symptoms among low trusters than among high trusters. Neuroanatomically, low trusters and depressive patients showed similar volume reduction in brain regions implicated in social cognition, including the dorsolateral prefrontal cortex (DLPFC), dorsomedial PFC, posterior cingulate, precuneus, and angular gyrus. Furthermore, the reduced volume of the DLPFC and precuneus mediated the relationship between trust and depressive symptoms. These findings contribute to understanding social- and neural-markers of depression vulnerability and may inform the development of social interventions to prevent pathological depression.

Modeling Heterogeneous Brain Dynamics of Depression and Melancholia Using Energy Landscape Analysis.

Our current understanding of melancholic depression is shaped by its position in the depression spectrum. The lack of consensus on how it should be treated-whether as a subtype of depression, or as a distinct disorder altogethe-interferes with the recovery of suffering patients. In this study, we analyzed brain state energy landscape models of melancholic depression, in contrast to healthy and non-melancholic energy landscapes. Our analyses showed significant group differences on basin energy, basin frequency, and transition dynamics in several functional brain networks such as basal ganglia, dorsal default mode, and left executive control networks. Furthermore, we found evidences suggesting the connection between energy landscape characteristics (basin characteristics) and depressive symptom scores (BDI-II and SHAPS). These results indicate that melancholic depression is distinguishable from its non-melancholic counterpart, not only in terms of depression severity, but also in brain dynamics.

Resting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training.

Neurofeedback (NF) aptitude, which refers to an individual's ability to change brain activity through NF training, has been reported to vary significantly from person to person. The prediction of individual NF aptitudes is critical in clinical applications to screen patients suitable for NF treatment. In the present study, we extracted the resting-state functional brain connectivity (FC) markers of NF aptitude, independent of NF-targeting brain regions. We combined the data from fMRI-NF studies targeting four different brain regions at two independent sites (obtained from 59 healthy adults and six patients with major depressive disorder) to collect resting-state fMRI data associated with aptitude scores in subsequent fMRI-NF training. We then trained the multiple regression models to predict the individual NF aptitude scores from the resting-state fMRI data using a discovery dataset from one site and identified six resting-state FCs that predicted NF aptitude. Subsequently, the reproducibility of the prediction model was validated using independent test data from another site. The identified FC model revealed that the posterior cingulate cortex was the functional hub among the brain regions and formed predictive resting-state FCs, suggesting that NF aptitude may be involved in the attentional mode-orientation modulation system's characteristics in task-free resting-state brain activity.

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.

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.

Age-related white matter changes revealed by a whole-brain fiber-tracking method in bipolar disorder compared to major depressive disorder and healthy controls.

AIM: Several studies have reported altered age-associated changes in white matter integrity in bipolar disorder (BD). However, little is known as to whether these age-related changes are illness-specific. We assessed disease-specific effects by controlling for age and investigated age-associated changes and Group × Age interactions in white matter integrity among major depressive disorder (MDD) patients, BD patients, and healthy controls. METHODS: Healthy controls (n = 96; age range, 20-77 years), MDD patients (n = 101; age range, 25-78 years), and BD patients (n = 58; age range, 22-76 years) participated in this study. Fractional anisotropy (FA) derived from diffusion tensor imaging in 54 white matter tracts were compared after controlling for the linear and quadratic effect of age using a generalized linear model. Age-related effects and Age × Group interactions were also assessed in the model. RESULTS: The main effect of group was significant in the left column and body of the fornix after controlling for both linear and quadratic effects of age, and in the left body of the corpus callosum after controlling for the quadratic effect of age. BD patients exhibited significantly lower FA relative to other groups. There was no Age × Group interaction in the tracts. CONCLUSION: Significant FA reductions were found in BD patients after controlling for age, indicating that abnormal white matter integrity in BD may occur at a younger age rather than developing progressively with age.

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.

White matter abnormalities and cognitive function in euthymic patients with bipolar disorder and major depressive disorder.

OBJECTIVES: In recent years, a growing number of diffusion tensor imaging (DTI) studies have compared white matter integrity between patients with major depressive disorder (MDD) and bipolar disorder (BD). However, few studies have examined the pathophysiological significance of different degrees of white matter abnormalities between the two disorders. The present study comprehensively assessed white matter integrity among healthy controls (HC) and euthymic patients with MDD and BD using whole-brain tractography and examined associations between white matter integrity and cognitive functioning. METHODS: We performed neurocognitive examinations and DTI with 30 HCs, 30 patients with MDD, and 30 patients with BD. We statistically evaluated white matter integrity and cognitive function differences across the three groups, assessing associations between white matter integrities and cognitive function. RESULTS: The BD group showed lower fractional anisotropy (FA) for the corpus callosum body, as well as lower, sustained attention and set-shifting scores compared to the other groups. FA for the left body of the corpus callosum was correlated with sustained attention in patients with BD. CONCLUSIONS: The significant reduction of white matter integrity in the corpus callosum in BD, compared to MDD, was associated with an impairment of sustained attention. This result promotes the understanding of the significance of white matter integrity in mood disorders.

Publisher Correction: Primary functional brain connections associated with melancholic major depressive disorder and modulation by antidepressants.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Antidepressive effect of left dorsolateral prefrontal cortex neurofeedback in patients with major depressive disorder: A preliminary report.

Background Real-time functional magnetic resonance imaging neurofeedback (rtfMRI-nf) have recently attracted attention as a novel, individualized treatment method for major depressive disorder (MDD). In this study, the antidepressant effect of neurofeedback training for left dorsolateral prefrontal cortex (DLPFC) activity was examined. Methods Six patients with MDD completed 5 days of neurofeedback training sessions. In each session, the patients observed a BOLD signal within their left DLPFC as a line graph, and attempted to up-regulate the signal using the graphical cue. Primary outcome measures were clinical scales of severity of depression and rumination. Results After neurofeedback training, the clinical measures were improved significantly. In addition, patient proficiency for neurofeedback training was related significantly to the improvement of the rumination symptom. Limitations Study limitations include the lack of a control group or condition, the lack of transfer run, and the small number of participants. Conclusions This small sample study suggests the possible efficacy of DLPFC activity regulation training for the treatment of MDD. As a next step, a sham-controlled randomized clinical trial is needed to confirm the antidepressive effect of left DLPFC neurofeedback.

Enhancing Multi-Center Generalization of Machine Learning-Based Depression Diagnosis From Resting-State fMRI.

Resting-state fMRI has the potential to help doctors detect abnormal behavior in brain activity and to diagnose patients with depression. However, resting-state fMRI has a bias depending on the scanner site, which makes it difficult to diagnose depression at a new site. In this paper, we propose methods to improve the performance of the diagnosis of major depressive disorder (MDD) at an independent site by reducing the site bias effects using regression. For this, we used a subgroup of healthy subjects of the independent site to regress out site bias. We further improved the classification performance of patients with depression by focusing on melancholic depressive disorder. Our proposed methods would be useful to apply depression classifiers to subjects at completely new sites.

Importance of the Habenula for Avoidance Learning Including Contextual Cues in the Human Brain: A Preliminary fMRI Study.

Human habenula studies are gradually advancing, primarily through the use of functional magnetic resonance imaging (fMRI) analysis of passive (Pavlovian) conditioning tasks as well as probabilistic reinforcement learning tasks. However, no studies have particularly targeted aversive prediction errors, despite the essential importance for the habenula in the field. Complicated learned strategies including contextual contents are involved in making aversive prediction errors during the learning process. Therefore, we examined habenula activation during a contextual learning task. We performed fMRI on a group of 19 healthy controls. We assessed the manually traced habenula during negative outcomes during the contextual learning task. The Beck Depression Inventory-Second Edition (BDI-II), the State-Trait-Anxiety Inventory (STAI), and the Temperament and Character Inventory (TCI) were also administered. The left and right habenula were activated during aversive outcomes and the activation was associated with aversive prediction errors. There was also a positive correlation between TCI reward dependence scores and habenula activation. Furthermore, dynamic causal modeling (DCM) analyses demonstrated the left and right habenula to the left and right hippocampus connections during the presentation of contextual stimuli. These findings serve to highlight the neural mechanisms that may be relevant to understanding the broader relationship between the habenula and learning processes.

Overlapping but Asymmetrical Relationships Between Schizophrenia and Autism Revealed by Brain Connectivity.

Although the relationship between schizophrenia spectrum disorder (SSD) and autism spectrum disorder (ASD) has long been debated, it has not yet been fully elucidated. The authors quantified and visualized the relationship between ASD and SSD using dual classifiers that discriminate patients from healthy controls (HCs) based on resting-state functional connectivity magnetic resonance imaging. To develop a reliable SSD classifier, sophisticated machine-learning algorithms that automatically selected SSD-specific functional connections were applied to Japanese datasets from Kyoto University Hospital (N = 170) including patients with chronic-stage SSD. The generalizability of the SSD classifier was tested by 2 independent validation cohorts, and 1 cohort including first-episode schizophrenia. The specificity of the SSD classifier was tested by 2 Japanese cohorts of ASD and major depressive disorder. The weighted linear summation of the classifier's functional connections constituted the biological dimensions representing neural classification certainty for the disorders. Our previously developed ASD classifier was used as ASD dimension. Distributions of individuals with SSD, ASD, and HCs s were examined on the SSD and ASD biological dimensions. We found that the SSD and ASD populations exhibited overlapping but asymmetrical patterns in the 2 biological dimensions. That is, the SSD population showed increased classification certainty for the ASD dimension but not vice versa. Furthermore, the 2 dimensions were correlated within the ASD population but not the SSD population. In conclusion, using the 2 biological dimensions based on resting-state functional connectivity enabled us to discover the quantified relationships between SSD and ASD.

Global connectivity and local excitability changes underlie antidepressant effects of repetitive transcranial magnetic stimulation.

Repetitive transcranial magnetic stimulation (rTMS) is a commonly- used treatment for major depressive disorder (MDD). However, our understanding of the mechanism by which TMS exerts its antidepressant effect is minimal. Furthermore, we lack brain signals that can be used to predict and track clinical outcome. Such signals would allow for treatment stratification and optimization. Here, we performed a randomized, sham-controlled clinical trial and measured electrophysiological, neuroimaging, and clinical changes before and after rTMS. Patients (N = 36) were randomized to receive either active or sham rTMS to the left dorsolateral prefrontal cortex (dlPFC) for 20 consecutive weekdays. To capture the rTMS-driven changes in connectivity and causal excitability, resting fMRI and TMS/EEG were performed before and after the treatment. Baseline causal connectivity differences between depressed patients and healthy controls were also evaluated with concurrent TMS/fMRI. We found that active, but not sham rTMS elicited (1) an increase in dlPFC global connectivity, (2) induction of negative dlPFC-amygdala connectivity, and (3) local and distributed changes in TMS/EEG potentials. Global connectivity changes predicted clinical outcome, while both global connectivity and TMS/EEG changes tracked clinical outcome. In patients but not healthy participants, we observed a perturbed inhibitory effect of the dlPFC on the amygdala. Taken together, rTMS induced lasting connectivity and excitability changes from the site of stimulation, such that after active treatment, the dlPFC appeared better able to engage in top-down control of the amygdala. These measures of network functioning both predicted and tracked clinical outcome, potentially opening the door to treatment optimization.

Primary functional brain connections associated with melancholic major depressive disorder and modulation by antidepressants.

The limited efficacy of available antidepressant therapies may be due to how they affect the underlying brain network. The purpose of this study was to develop a melancholic MDD biomarker to identify critically important functional connections (FCs), and explore their association to treatments. Resting state fMRI data of 130 individuals (65 melancholic major depressive disorder (MDD) patients, 65 healthy controls) were included to build a melancholic MDD classifier, and 10 FCs were selected by our sparse machine learning algorithm. This biomarker generalized to a drug-free independent cohort of melancholic MDD, and did not generalize to other MDD subtypes or other psychiatric disorders. Moreover, we found that antidepressants had a heterogeneous effect on the identified FCs of 25 melancholic MDDs. In particular, it did impact the FC between left dorsolateral prefrontal cortex (DLPFC)/inferior frontal gyrus (IFG) and posterior cingulate cortex (PCC)/precuneus, ranked as the second 'most important' FC based on the biomarker weights, whilst other eight FCs were normalized. Given that left DLPFC has been proposed as an explicit target of depression treatments, this suggest that the limited efficacy of antidepressants might be compensated by combining therapies with targeted treatment as an optimized approach in the future.

Predicting Ventral Striatal Activation During Reward Anticipation From Functional Connectivity at Rest.

Reward anticipation is essential for directing behavior toward positively valenced stimuli, creating motivational salience. Task-related activation of the ventral striatum (VS) has long been used as a target for understanding reward function. However, some subjects may not be able to perform the respective tasks because of their complexity or subjects' physical or mental disabilities. Moreover, task implementations may differ, which results in limited comparability. Hence, developing a task-free method for evaluating neural gain circuits is essential. Research has shown that fluctuations in neuronal activity at rest denoted individual differences in the brain functional networks. Here, we proposed novel models to predict the activation of the VS during gain anticipation, using the functional magnetic resonance imaging data of 45 healthy subjects acquired during a monetary incentive delay task and under rest. In-sample validation and held-out data were used to estimate the generalizability of the models. It was possible to predict three measures of reward activation (sensitivity, average, maximum) from resting-state functional connectivity (Pearson's r = 0.38-0.54 in validation data). Especially high contributions to the models were observed from the default mode network. These findings highlight the potential of using functional connectivity at rest as a task-free alternative for predicting activation in the VS, offering a possibility to estimate reward response in the broader sampling of subject populations.

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.