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Abstract Background Electronic gambling machines (EGMs) in gambling venues cause gambling-related harm and are a public health concern. This study focused on pachinko parlours as gambling venues and income-generating crimes as gambling-related harm. We aimed to verify whether income-generating crime rates increase in proximity to pachinko parlours and during the opening and post-closing periods of pachinko parlours relative to the pre-opening periods. Methods We used crime records spanning 6.5 years, including data on the opening and closing days of pachinko parlours for 6.5 years. We also sampled the addresses of convenience stores, bowling alleys, and households with official land prices all over Japan. The dependent variable was the daily income-generating crime incidence rate. Areas within 0.5 km, 0.5–1 km, 1–5 km, and 5–10 km radii of the pachinko parlours were the independent variables. The pre-, opening-, and post-closing periods of the pachinko parlours were also independent variables. The covariates included the number of convenience stores and always open pachinko parlours near pachinko parlours. Data were analysed using an analysis of variance (ANOVA) and covariance (ANCOVA). We also used differences-in-differences analysis (DD) to reveal the increase in income-generating crime rates in neighbourhoods exposed to the opening or closing of pachinko parlours. Results The daily income-generating crime incidence rate was significantly higher in areas within 0.5–1 km and 1–5 km radii of pachinko parlours than in those within 0.5 km and 5–10 km radii of them. The daily income-generating crime incidence rate was also significantly higher during the opening and post-closing periods than during the pre-opening period, even when controlling for the number of convenience stores and always open pachinko parlours. In particular, fraud crime rates increased with the opening and closing of pachinko parlours. Conclusions The highest income-generating crime incidence rate was observed within a 0.5–1 km and 1–5 km radius of pachinko parlours. The opening of pachinko parlours also increased income-generating crime incidence rates, which increased after closing. Pachinko parlours are considered to be creating public harm because the corporate activities of these parlours make the youth in their neighbourhood perpetrators of fraud and older adults its victims. Future research should examine the current findings using official crime records.

IntroductionAnemia is a risk factor for dementia development. However, few studies have examined the relationship between brain volume and anemia. This study aimed to analyze the association between anemia and brain volume using magnetic resonance imaging data from brain health checkups.MethodParticipants underwent brain health checkups between January 2015 and March 2022. Blood samples were collected to measure hemoglobin concentrations and mean corpuscular volumes. The modified Mini-Mental State Examination (MMSE) was used to evaluate cognitive function. Magnetic resonance images were analyzed using voxel-based Morphometry to evaluate the overall patterns of brain volume. After extracting the principal components (PCs) from PC analysis, we investigated their association with MMSE scores and anemia.ResultsThis study included 1,029 participants and identified principal components, representing smaller volume in the frontal lobe (PC1), and smaller volume in the limbic system to the temporal lobe (PC2). A higher PC2 score was significantly associated with a lower MMSE score. Male participants with anemia had smaller bilateral PC1 volumes and left hippocampal volumes, and female participants with anemia had smaller bilateral PC2 volumes and hippocampus volumes.DiscussionPC2 may represent the extent of disease affecting limbic system volume, such as Alzheimer’s disease. Our results suggest that anemia may be associated with smaller volumes in the limbic system, especially in women. Further studies are required to determine which type of anemia is more strongly correlated with smaller brain volumes.

Background: Specific bacterial species in the oral cavity contribute to cerebral hemorrhage and microbleeds. The relationship between oral bacterial flora and asymptomatic brain lesions (ABL) remains unclear. This study aimed to investigate this relationship in a healthy Japanese cohort. Methods: This cross-sectional study included participants who underwent health examinations at our Brain Dock facility between October 2020 and March 2021. The oral microbiomes of participants with and without ABL were compared using magnetic resonance imaging. To extensively assess the oral bacterial flora, the differences in genes and species compositions between the ABL and noBL (without brain lesions) groups were statistically evaluated via extensive analysis using 16S rRNA gene-based cloning. Results: Among 143 patients, 48.3 % had ABL. In the univariate analyses, Fusobacterium and Leptotrichia were associated with ABL (P = 0.017 and P

The mechanism of chronic knee osteoarthritis (OA) pain and postoperative pain due to knee arthroplasty has not been elucidated. This could be involved neuroplasticity in brain connectivity. To clarify the mechanism of chronic knee OA pain and postoperative pain, we examined the relationship between resting-state functional connectivity (rs-FC) and clinical measurements in knee OA before and after knee arthroplasty, focusing on rs-FCs with the anterior insular cortex (aIC) as the key region. Fifteen patients with knee OA underwent resting-state functional magnetic resonance imaging and clinical measurements shortly before and 6 months after knee arthroplasty, and 15 age- and sex-matched control patients underwent an identical protocol. Seed-to-voxel analysis was performed to compare the clinical measurements and changed rs-FCs, using the aIC as a seed region, between the preoperative and postoperative patients, as well as between the operative and control patients. In preoperative patients, rs-FCs of the aIC to the OFC, frontal pole, subcallosal area, and medial frontal cortex increased compared with those of the control patients. The strength of rs-FC between the left aIC and right OFC decreased before and after knee arthroplasty. The decrease in rs-FC between the left aIC and right OFC was associated with decreased subjective pain score. Our study showed a correlation between longitudinally changed rs-FC and clinical measurement before and after knee arthroplasty. Rs-FC between the aIC and OFC have the potential to elucidate the mechanisms of knee OA pain and postoperative pain due to knee arthroplasty.

Abstract 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.

Functional connectivity neurofeedback (FCNef) is a technique that modulates synchronous neural activity through training and is being investigated as a potential novel treatment for patients suffering from treatment-resistant depression (TRD). An FCNef protocol, based on the analysis of resting-state functional imaging data from a large cohort of depressed individuals, has been proposed to promote negative functional connectivity between the dorsolateral prefrontal cortex and the posterior cingulate cortex (DLPFC-PCC FC). This study aimed to assess the therapeutic efficacy and practicality of the protocol in a small sample of TRD patients. Of the six patients recruited, five completed the FCNef sessions. Depression and rumination scores significantly improved post-treatment, however, there were no significant changes in DLPFC-PCC FC. The study demonstrated efficacy of FCNef in ameliorating depressive symptoms, yet, it also indicated that the training itself may be burdensome for depressed patients, as evidenced by participants reporting fatigue (one of whom dropped out). Thus, a more efficient and less burdensome protocol is needed for future investigations and applications.

Abstract 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.

Abstract 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.

Abstract 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.

Abstract 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 (β = −6.82, 95%CI: [−13.27 to −0.38], p

Measurement(s) mental or behavioural disorder • brain measurement • Demographic Data Technology Type(s) functional magnetic resonance imaging • magnetic resonance imaging • Resting State Functional Connectivity Magnetic Resonance Imaging Factor Type(s) age • sex • site • disorder Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14716329

Abstract Since optimal treatment at an early stage leads to remission of symptoms and recovery of function, putative biomarkers leading to early diagnosis and prediction of therapeutic responses are desired. The current study aimed to use a metabolomic approach to extract metabolites involved in both the diagnosis of major depressive disorder (MDD) and the prediction of therapeutic response for escitalopram. We compared plasma metabolites of MDD patients (n = 88) with those in healthy participants (n = 88) and found significant differences in the concentrations of 20 metabolites. We measured the Hamilton Rating Scale for Depression (HRSD) on 62 patients who completed approximately six-week treatment with escitalopram before and after treatment and found that kynurenic acid and kynurenine were significantly and negatively associated with HRSD reduction. Only one metabolite, kynurenic acid, was detected among 73 metabolites for overlapped biomarkers. Kynurenic acid was lower in MDD, and lower levels showed a better therapeutic response to escitalopram. Kynurenic acid is a metabolite in the kynurenine pathway that has been widely accepted as being a major mechanism in MDD. Overlapping biomarkers that facilitate diagnosis and prediction of the treatment response may help to improve disease classification and reduce the exposure of patients to less effective treatments in MDD.

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.

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.

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.

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.

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.

Abstract 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.

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.

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.

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.

BackgroundBehavioural activation is an efficient treatment for depression and can improve intrinsic motivation. Previous studies have revealed that the frontostriatal circuit is involved in intrinsic motivation; however, there are no data on how behavioural activation affects the frontostriatal circuit.AimsWe aimed to investigate behavioural activation-related changes in the frontostriatal circuit.MethodFifty-nine individuals with subthreshold depression were randomly assigned to either the intervention or non-intervention group. The intervention group received five weekly behavioural activation sessions. The participants underwent functional magnetic resonance imaging scanning on two separate occasions while performing a stopwatch task based on intrinsic motivation. We investigated changes in neural activity and functional connectivity after behavioural activation.ResultsAfter behavioural activation, the intervention group had increased activation and connectivity in the frontostriatal region compared with the non-intervention group. The increased activation in the right middle frontal gyrus was correlated with an improvement of subjective sensitivity to environmental rewards.ConclusionsBehavioural activation-related changes to the frontostriatal circuit advance our understanding of psychotherapy-induced improvements in the neural basis of intrinsic motivation.Declaration of interestNone.

Abstract Complex relationships between depression and chronic pain have been reported in previous studies. However, only a few neuroimaging studies have investigated similarities and differences in neural systems underlying them. We examined the brain functions in the resting state of 43 patients with depression, 41 patients with chronic pain (somatoform pain disorder) and 41 healthy controls, by using regional homogeneity (ReHo) and functional connectivity analysis. Depressive symptoms were assessed by using the Beck Depression Inventory-Second Edition (BDI-II). ReHo values for the dorsolateral prefrontal cortex (DLPFC) significantly decreased for chronic pain patients, and functional connectivity between the DLPFC and thalamus decreased only for these patients. These findings are indicative of distinct brain functions related to depression and chronic pain. Understanding these differences would further elucidate the pathophysiology of these conditions.

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.

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.

Background: The neurophysiological mechanisms of cognitive reactivity, the primary vulnerability factor of major depressive disorder (MDD) recurrence, remain unclear in individuals with recovered MDD (rMDD). Because gamma-band responses (GBRs) can be used to measure cognitive processing, they may also be useful for elucidating the mechanisms underlying cognitive reactivity. Identifying these mechanisms may permit the development of an index for predicting and preempting MDD recurrence. Here, to identify the neurophysiological mechanisms of cognitive reactivity, we examined the characteristics of the GBRs evoked/induced by emotional words in participants with and without rMDD after inducing a negative mood.Methods: Thirty-three healthy control participants and 18 participants with rMDD completed a lexical emotion identification task during electroencephalography along with assessments of cognitive reactivity after negative mood induction.Results: No between-group differences were identified for the task reaction times; however, the rMDD group had significantly higher cognitive reactivity scores than did the control group. Furthermore, the power of late GBRs to positive words was significantly greater in the rMDD group, with the greater power of late GBRs being related to higher cognitive reactivity.Limitations: Considering the population studied, our findings cannot be completely generalized to populations other than adolescents, people with rMDD, and those without a history of co-morbid disorders and early life stress.Conclusions: Our findings indicate that the dysfunction of neural circuits related to higher-order processes like memory and attention might underlie cognitive reactivity. Altered late GBRs to positive information may be persistent biomarkers of the depression recurrence risk.

Previous findings suggest that negative emotions could influence abnormal sensory perception in burning mouth syndrome (BMS). However, few studies have investigated the underlying neural mechanisms associated with BMS. We examined activation of brain regions in response to intraoral tactile stimuli when modulated by angry facial expressions. We performed functional magnetic resonance imaging on a group of 27 BMS patients and 21 age-matched healthy controls. Tactile stimuli were presented during different emotional contexts, which were induced via the continuous presentation of angry or neutral pictures of human faces. BMS patients exhibited higher tactile ratings and greater activation in the postcentral gyrus during the presentation of tactile stimuli involving angry faces relative to controls. Significant positive correlations between changes in brain activation elicited by angry facial images in the postcentral gyrus and changes in tactile rating scores by angry facial images were found for both groups. For BMS patients, there was a significant positive correlation between changes in tactile-related activation of the postcentral gyrus elicited by angry facial expressions and pain intensity in daily life. Findings suggest that neural responses in the postcentral gyrus are more strongly affected by angry facial expressions in BMS patients, which may reflect one possible mechanism underlying impaired somatosensory system function in this disorder.

We propose a novel method for multiple clustering, which is useful for analysis of high-dimensional data containing heterogeneous types of features. Our method is based on nonparametric Bayesian mixture models in which features are automatically partitioned (into views) for each clustering solution. This feature partition works as feature selection for a particular clustering solution, which screens out irrelevant features. To make our method applicable to high-dimensional data, a co-clustering structure is newly introduced for each view. Further, the outstanding novelty of our method is that we simultaneously model different distribution families, such as Gaussian, Poisson, and multinomial distributions in each cluster block, which widens areas of application to real data. We apply the proposed method to synthetic and real data, and show that our method outperforms other multiple clustering methods both in recovering true cluster structures and in computation time. Finally, we apply our method to a depression dataset with no true cluster structure available, from which useful inferences are drawn about possible clustering structures of the data.

In diagnostic applications of statistical machine learning methods to brain imaging data, common problems include data high-dimensionality and co-linearity, which often cause over-fitting and instability. To overcome these problems, we applied partial least squares (PLS) regression to resting-state functional magnetic resonance imaging (rs-fMRI) data, creating a low-dimensional representation that relates symptoms to brain activity and that predicts clinical measures. Our experimental results, based upon data from clinically depressed patients and healthy controls, demonstrated that PLS and its kernel variants provided significantly better prediction of clinical measures than ordinary linear regression. Subsequent classification using predicted clinical scores distinguished depressed patients from healthy controls with 80% accuracy. Moreover, loading vectors for latent variables enabled us to identify brain regions relevant to depression, including the default mode network, the right superior frontal gyrus, and the superior motor area.

Diagnosis of psychiatric disorders based on brain imaging data is highly desirable in clinical applications. However, a common problem in applying machine learning algorithms is that the number of imaging data dimensions often greatly exceeds the number of available training samples. Furthermore, interpretability of the learned classifier with respect to brain function and anatomy is an important, but non-trivial issue. We propose the use of logistic regression with a least absolute shrinkage and selection operator (LASSO) to capture the most critical input features. In particular, we consider application of group LASSO to select brain areas relevant to diagnosis. An additional advantage of LASSO is its probabilistic output, which allows evaluation of diagnosis certainty. To verify our approach, we obtained semantic and phonological verbal fluency fMRI data from 31 depression patients and 31 control subjects, and compared the performances of group LASSO (gLASSO), and sparse group LASSO (sgLASSO) to those of standard LASSO (sLASSO), Support Vector Machine (SVM), and Random Forest. Over 90% classification accuracy was achieved with gLASSO, sgLASSO, as well as SVM; however, in contrast to SVM, LASSO approaches allow for identification of the most discriminative weights and estimation of prediction reliability. Semantic task data revealed contributions to the classification from left precuneus, left precentral gyrus, left inferior frontal cortex (pars triangularis), and left cerebellum (c rus1). Weights for the phonological task indicated contributions from left inferior frontal operculum, left post central gyrus, left insula, left middle frontal cortex, bilateral middle temporal cortices, bilateral precuneus, left inferior frontal cortex (pars triangularis), and left precentral gyrus. The distribution of normalized odds ratios further showed, that predictions with absolute odds ratios higher than 0.2 could be regarded as certain.