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.

Development of a stroke risk score with MRI asymptomatic brain lesions attributes to evaluate prognostic vascular events.

BACKGROUND: The use of a combination of stroke predictors, such as clinical factors and asymptomatic lesions on brain magnetic resonance imaging (MRI), may improve the accuracy of stroke risk prediction. Therefore, we attempted to develop a stroke risk score for healthy individuals. METHODS: We investigated the presence of cerebral stroke in 2365 healthy individuals who underwent brain dock screening at the Health Science Center in Shimane. We examined the factors that contributed to stroke and attempted to determine the risk of stroke by comparing background factors and MRI findings. RESULTS: The following items were found to be significant risk factors for stroke: age (≥60 years), hypertension, subclinical cerebral infarction, deep white matter lesion, and microbleeds. Each item was scored with 1 point, and the hazard ratios for the risk of developing stroke based on the group with 0 points were 17.2 (95% confidence interval [CI] 2.31-128) for 3 points, 18.1 (95% CI 2.03-162) for 4 points, and 102 (95% CI 12.6-836) for 5 points. CONCLUSIONS: A precise stroke prediction score biomarker can be obtained by combining MRI findings and clinical factors.

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.

Altered Feedback-Related Negativity in Mild Cognitive Impairment.

INTRODUCTION: Feedback-related negativity (FRN) is electrical brain activity related to the function of monitoring behavior and its outcome. FRN is generated by negative feedback input, such as punishment or monetary loss, and its potential is distributed maximally over the frontal-central part of the skull. Our previous study demonstrated that FRN latency was delayed and that the amplitude was increased in patients with mild Alzheimer's disease (AD). As mild cognitive impairment (MCI) is considered to be a prodromal stage of AD, we speculated that FRN would also be altered in MCI, as in AD. The aim of this study is to examine whether MCI patients showed changes in FRN during a gambling task. METHODS: Thirteen MCI patients and thirteen age-matched healthy elderly individuals participated in a simple gambling task and underwent neuro-psychological assessments. The participants were asked to choose one out of two options and randomly received positive or negative feedback to their response. An EEG was recorded during the task, and FRN was obtained by subtracting the positive feedback-related activity from the negative feedback-related activity. RESULTS: The reaction time to probe stimuli was comparable in the two groups. The group comparisons revealed that the FRN amplitude was significantly larger for the MCI group than for the healthy elderly (F(1,24) = 6.4, ηp2 = 0.22, p = 0.019), but there was no group difference in the FRN latency. The FRN amplitude at the frontocentral electrode positively correlated with the mini-mental state examination score (Spearman's rhopartial = 0.41, p = 0.043). The finding of increased FRN amplitude in MCI was consistent with the previous finding in AD. CONCLUSION: Our findings indicate that monitoring dysfunction might also be involved in the prodromal stage of dementia.

Increase in cerebral microbleeds and cognitive decline.

BACKGROUND: In spite of increasing evidence of the clinical importance of cerebral microbleeds (CMBs), the relationship between CMBs and cognitive impairment is still controversial. In addition, there are very limited prior data regarding the prospective association of additional CMBs over time with a decline in cognitive function. This study thus aimed to investigate the effects of newly detected CMBs on cognitive decline in a Japanese health examination cohort. PATIENTS AND METHODS: We performed a prospective cohort study involving 769 Japanese participants (mean age, 61.6 years) with a mean follow-up of 7.3 ± 3.5 years. CMBs were classified according to their locations. Cognitive functions were evaluated using Okabe's Intelligence Scale, Koh's block design test, and the Wisconsin Card Sorting Test. Multiple linear regression analyses were performed to examine the relationship between the newly detected CMBs and cognitive decline. RESULTS: Fifty-six (7.3%) participants (16 had new strictly lobar cerebral microbleeds and 40 had new deep or infratentorial cerebral microbleeds) developed new CMBs during the follow-up period. In multivariable analysis, newly detected strictly lobar CMBs were associated with a greater decline in the Wisconsin Card Sorting Test in the categories achieved (ß: - 0.862 [95% CI: - 1.325, - 0.399]; P < 0.0001), greater increase in perseverative errors of Nelson (ß: 0.603 [95% CI: 0.023, 1.183]; P = 0.04), and greater increase in the difficulty with maintaining set (ß: 1.321 [95% CI: 0.801, 1.842]; P < 0.0001). CONCLUSIONS: Strictly lobar CMBs over time were associated with a decline in executive function.

Verification of the brain network marker of major depressive disorder: Test-retest reliability and anterograde generalization performance for newly acquired data.

BACKGROUND: Recently, we developed a generalizable brain network marker for the diagnosis of major depressive disorder (MDD) across multiple imaging sites using resting-state functional magnetic resonance imaging. Here, we applied this brain network marker to newly acquired data to verify its test-retest reliability and anterograde generalization performance for new patients. METHODS: We tested the sensitivity and specificity of our brain network marker of MDD using data acquired from 43 new patients with MDD as well as new data from 33 healthy controls (HCs) who participated in our previous study. To examine the test-retest reliability of our brain network marker, we evaluated the intraclass correlation coefficients (ICCs) between the brain network marker-based classifier's output (probability of MDD) in two sets of HC data obtained at an interval of approximately 1 year. RESULTS: Test-retest correlation between the two sets of the classifier's output (probability of MDD) from HCs exhibited moderate reliability with an ICC of 0.45 (95 % confidence interval,0.13-0.68). The classifier distinguished patients with MDD and HCs with an accuracy of 69.7 % (sensitivity, 72.1 %; specificity, 66.7 %). LIMITATIONS: The data of patients with MDD in this study were cross-sectional, and the clinical significance of the marker, such as whether it is a state or trait marker of MDD and its association with treatment responsiveness, remains unclear. CONCLUSIONS: The results of this study reaffirmed the test-retest reliability and generalization performance of our brain network marker for the diagnosis of MDD.

Day-to-day regularity and diurnal switching of physical activity reduce depression-related behaviors: a time-series analysis of wearable device data.

BACKGROUND: Wearable devices have been widely used in research to understand the relationship between habitual physical activity and mental health in the real world. However, little attention has been paid to the temporal variability in continuous physical activity patterns measured by these devices. Therefore, we analyzed time-series patterns of physical activity intensity measured by a wearable device and investigated the relationship between its model parameters and depression-related behaviors. METHODS: Sixty-six individuals used the wearable device for one week and then answered a questionnaire on depression-related behaviors. A seasonal autoregressive integral moving average (SARIMA) model was fitted to the individual-level device data and the best individual model parameters were estimated via a grid search. RESULTS: Out of 64 hyper-parameter combinations, 21 models were selected as optimal, and the models with a larger number of affiliations were found to have no seasonal autoregressive parameter. Conversely, about half of the optimal models indicated that physical activity on any given day fluctuated due to the previous day's activity. In addition, both irregular rhythms in day-to-day activity and low-level of diurnal variability could lead to avoidant behavior patterns. CONCLUSION: Automatic and objective physical activity data from wearable devices showed that diurnal switching of physical activity, as well as day-to-day regularity rhythms, reduced depression-related behaviors. These time-series parameters may be useful for detecting behavioral issues that lie outside individuals' subjective awareness.

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.

Left DLPFC activity is associated with plasma kynurenine levels and can predict treatment response to escitalopram in major depressive disorder.

AIM: To establish treatment response biomarkers that reflect the pathophysiology of depression, it is important to use an integrated set of features. This study aimed to determine the relationship between regional brain activity at rest and blood metabolites related to treatment response to escitalopram to identify the characteristics of depression that respond to treatment. METHODS: Blood metabolite levels and resting-state brain activity were measured in patients with moderate to severe depression (n = 65) before and after 6-8 weeks of treatment with escitalopram, and these were compared between Responders and Nonresponders to treatment. We then examined the relationship between blood metabolites and brain activity related to treatment responsiveness in patients and healthy controls (n = 36). RESULTS: Thirty-two patients (49.2%) showed a clinical response (>50% reduction in the Hamilton Rating Scale for Depression score) and were classified as Responders, and the remaining 33 patients were classified as Nonresponders. The pretreatment fractional amplitude of low-frequency fluctuation (fALFF) value of the left dorsolateral prefrontal cortex (DLPFC) and plasma kynurenine levels were lower in Responders, and the rate of increase of both after treatment was correlated with an improvement in symptoms. Moreover, the fALFF value of the left DLPFC was significantly correlated with plasma kynurenine levels in pretreatment patients with depression and healthy controls. CONCLUSION: Decreased resting-state regional activity of the left DLPFC and decreased plasma kynurenine levels may predict treatment response to escitalopram, suggesting that it may be involved in the pathophysiology of major depressive disorder in response to escitalopram treatment.

Pulse pressure is associated with cognitive performance in Japanese non-demented population: a cross-sectional study.

BACKGROUND: Growing evidence suggests that vascular risk factors, especially hypertension, relate not only to cardiovascular disease but also to cognitive impairment. However, the impact of pulse pressure on cognitive function remains controversial. In this study, we evaluated the associations between pulse pressure and cognitive function in a Japanese health examination cohort using propensity matching analysis. METHODS: We examined 2,546 individuals with a mean age of 60.8 ± 10.3 years who voluntarily participated in health examination. Clinical variables included pulse pressure, and brain magnetic resonance imaging (MRI). We divided the participants into the high and low pulse pressure groups with a pre-defined cut-off value of 65 mmHg and evaluated their physical examination data, cognitive functions including Okabe's test, Kohs' test, and silent brain lesions using propensity matching. To clarify whether pulse pressure and blood pressure have different implications for cognitive function, a mediating analysis was also conducted. RESULTS: From the 2,546 subjects, 439 (17.2%) were in the high PP group. The propensity matching algorithm produced 433 pairs of patients with similar propensities. Higher pulse pressure corresponded to lower Okabe and Kohs' scores (44.3 ± 7.1 vs 42.7 ± 7.5; p = 0.002, 97.9 ± 18.0 vs 95.0 ± 18.1 p = 0.019, respectively). The relationship between pulse pressure and cognitive impairment was not significantly mediated by systolic blood pressure. We observed no significant associations between silent brain lesions and pulse pressure. CONCLUSION: High pulse pressure was associated with lower cognitive performance without systolic blood pressure mediation in Japanese subjects without dementia.

Decreased physical activity with subjective pleasure is associated with avoidance behaviors.

The main hypothesis for the relation between physical activity and mental health is that autonomous motivation, such as subjective pleasure for the activity, plays an important role. However, no report has described empirical research designed to examine the role of subjective pleasure in the relation between objectively measured physical activity and psychological indexes. We used accelerometers to collect data indicating participants' physical activity intensity during a week. Participants recorded their subjective pleasure of activity per hour. In 69% of them, the individual correlation coefficients between physical activity and pleasure in an hour (an index of Physical Activity-Pleasure; PA-PL) were positive (r = 0.22, 95%Cl = [0.11-0.38]), indicating that pleasant sensations increased concomitantly with increasing physical activity. Conversely, 31% participants exhibited negative values of PA-PL, which means that the increase in physical activity had the opposite effect, decreasing pleasure. Multiple linear regression analysis showed that avoidance/rumination behaviors decreased significantly with increased PA-PL (ß = -6.82, 95%CI: [-13.27 to -0.38], p < .05). These results indicate that subjective pleasure attached to the PA is more important than the PA amount for reducing depressive behavior.

Altered resting-state functional connectivity of the frontal-striatal circuit in elderly with apathy.

Apathy is defined as reduction of goal-directed behaviors and a common nuisance syndrome of neurodegenerative and psychiatric disease. The underlying mechanism of apathy implicates changes of the front-striatal circuit, but its precise alteration is unclear for apathy in healthy aged people. The aim of our study is to investigate how the frontal-striatal circuit is changed in elderly with apathy using resting-state functional MRI. Eighteen subjects with apathy (7 female, 63.7 ± 3.0 years) and eighteen subjects without apathy (10 female, 64.8 ± 3.0 years) who underwent neuropsychological assessment and MRI measurement were recruited. We compared functional connectivity with/within the striatum between the apathy and non-apathy groups. The seed-to-voxel group analysis for functional connectivity between the striatum and other brain regions showed that the connectivity was decreased between the ventral rostral putamen and the right dorsal anterior cingulate cortex/supplementary motor area in the apathy group compared to the non-apathy group while the connectivity was increased between the dorsal caudate and the left sensorimotor area. Moreover, the ROI-to-ROI analysis within the striatum indicated reduction of functional connectivity between the ventral regions and dorsal regions of the striatum in the apathy group. Our findings suggest that the changes in functional connectivity balance among different frontal-striatum circuits contribute to apathy in elderly.

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.

Personality classification enhances blood metabolome analysis and biotyping for major depressive disorders: two-species investigation.

BACKGROUND: The relationship between depression and personality has long been suggested, however, biomarker investigations for depression have mostly overlooked this connection. METHODS: We collected personality traits from 100 drug-free patients with major depressive disorders (MDD) and 100 healthy controls based on the Five-Factor Model (FFM) such as Neuroticism (N) and Extraversion (E), and also obtained 63 plasma metabolites profiles by LCMS-based metabolome analysis. RESULTS: Partitional clustering analysis using the NEO-FFI data classified all subjects into three major clusters. Eighty-six subjects belonging to Cluster 1 (C1: less personality-biased group) constituted half of MDD patients and half of healthy controls. C2 constituted 50 subjects mainly MDD patients (N high + E low), and C3 constituted 64 subjects mainly healthy subjects (N low + E high). Using metabolome information, the machine learning model was optimized to discriminate MDD patients from healthy controls among all subjects and C1, respectively. The performance of the model for all subjects was moderate (AUC = 0. 715), while the performance was extremely improved when limited to C1 (AUC = 0. 907). Tryptophan-pathway plasma metabolites including tryptophan, serotonin and kynurenine were significantly lower in MDD patients especially among C1. We also validated metabolomic findings using a social-defeat mice model of stress-induced depression. LIMITATIONS: A case-control study design and sample size is not large. CONCLUSIONS: Our results suggest that personality classification enhances blood biomarker analysis for MDD patients and further translational investigations should be conducted to clarify the biological relationship between personality traits, stress and depression.

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.