Profiles and the impact of affective temperaments on alcohol use disorder: a cross-sectional study.

AIMS: This study aimed to explore the profiles and impact of affective temperaments, together with social and clinical backgrounds, including affective symptoms, in patients with alcohol use disorder (AUD). METHODS: This study included 314 low-risk drinkers and 257 patients with AUD. To assess affective temperament, we used the short version of the temperament evaluation of Memphis, Pisa, Paris, and San Diego. To evaluate depressive and mixed symptoms, the quick inventory of depressive symptomatology self-report Japanese version and 12-item questionnaire for the quantitative assessment of the depressive mixed state were used. We compared the profiles of affective temperaments as well as social and clinical backgrounds, including affective symptoms, between the two groups and further performed logistic regression analyses to explore the factors contributing to AUD. RESULTS: Our analysis showed higher cyclothymic, hyperthymic, and irritable temperament scores and lower depressive temperament scores in patients with AUD than that in nonclinical drinkers. Regarding other social and clinical backgrounds, patients with AUD were less educated and employed and more experienced depressive and mixed symptoms. Logistic regression analysis identified hyperthymic temperament as a positive contributor and depressive temperament as a negative contributor to AUD. CONCLUSIONS: Our findings indicated potential bipolarity in patients with AUD, as manifested by a more hyperthymic temperament in contrast to less depressive temperament. Despite their self-perceived adaptive temperament profiles, patients showed poorer social outcomes and more affective symptoms. This gap may be partly explained by a lack of insight unique to AUD psychology, which potentially disturbs problem recognition.

The status of MRI databases across the world focused on psychiatric and neurological disorders.

Neuroimaging databases for neuro-psychiatric disorders enable researchers to implement data-driven research approaches by providing access to rich data that can be used to study disease, build and validate machine learning models, and even redefine disease spectra. The importance of sharing large, multi-center, multi-disorder databases has gradually been recognized in order to truly translate brain imaging knowledge into real-world clinical practice. Here, we review MRI databases that share data globally to serve multiple psychiatric or neurological disorders. We found 42 datasets consisting of 23,293 samples from patients with psychiatry and neurological disorders and healthy controls; 1245 samples from mood disorders (major depressive disorder and bipolar disorder), 2015 samples from developmental disorders (autism spectrum disorder, attention-deficit hyperactivity disorder), 675 samples from schizophrenia, 1194 samples from Parkinson's disease, 5865 samples from dementia (including Alzheimer's disease), We recognize that large, multi-center databases should include governance processes that allow data to be shared across national boundaries. Addressing technical and regulatory issues of existing databases can lead to better design and implementation and improve data access for the research community. The current trend toward the development of shareable MRI databases will contribute to a better understanding of the pathophysiology, diagnosis and assessment, and development of early interventions for neuropsychiatric disorders.

Examining the usefulness of the brain network marker program using fMRI for the diagnosis and stratification of major depressive disorder: a non-randomized study protocol.

BACKGROUND: Although many studies have reported the biological basis of major depressive disorder (MDD), none have been put into practical use. Recently, we developed a generalizable brain network marker for MDD diagnoses (diagnostic marker) across multiple imaging sites using resting-state functional magnetic resonance imaging (rs-fMRI). We have planned this clinical trial to establish evidence for the practical applicability of this diagnostic marker as a medical device. In addition, we have developed generalizable brain network markers for MDD stratification (stratification markers), and the verification of these brain network markers is a secondary endpoint of this study. METHODS: This is a non-randomized, open-label study involving patients with MDD and healthy controls (HCs). We will prospectively acquire rs-fMRI data from 50 patients with MDD and 50 HCs and anterogradely verify whether our diagnostic marker can distinguish between patients with MDD and HCs. Furthermore, we will longitudinally obtain rs-fMRI and clinical data at baseline and 6 weeks later in 80 patients with MDD treated with escitalopram and verify whether it is possible to prospectively distinguish MDD subtypes that are expected to be effectively responsive to escitalopram using our stratification markers. DISCUSSION: In this study, we will confirm that sufficient accuracy of the diagnostic marker could be reproduced for data from a prospective clinical study. Using longitudinally obtained data, we will also examine whether the "brain network marker for MDD diagnosis" reflects treatment effects in patients with MDD and whether treatment effects can be predicted by "brain network markers for MDD stratification". Data collected in this study will be extremely important for the clinical application of the brain network markers for MDD diagnosis and stratification. TRIAL REGISTRATION: Japan Registry of Clinical Trials ( jRCTs062220063 ). Registered 12/10/2022.

Distinctive alterations in the mesocorticolimbic circuits in various psychiatric disorders.

AIM: Increasing evidence suggests that psychiatric disorders are linked to alterations in the mesocorticolimbic dopamine-related circuits. However, the common and disease-specific alterations remain to be examined in schizophrenia (SCZ), major depressive disorder (MDD), and autism spectrum disorder (ASD). Thus, this study aimed to examine common and disease-specific features related to mesocorticolimbic circuits. METHODS: This study included 555 participants from four institutes with five scanners: 140 individuals with SCZ (45.0% female), 127 individuals with MDD (44.9%), 119 individuals with ASD (15.1%), and 169 healthy controls (HC) (34.9%). All participants underwent resting-state functional magnetic resonance imaging. A parametric empirical Bayes approach was adopted to compare estimated effective connectivity among groups. Intrinsic effective connectivity focusing on the mesocorticolimbic dopamine-related circuits including the ventral tegmental area (VTA), shell and core parts of the nucleus accumbens (NAc), and medial prefrontal cortex (mPFC) were examined using a dynamic causal modeling analysis across these psychiatric disorders. RESULTS: The excitatory shell-to-core connectivity was greater in all patients than in the HC group. The inhibitory shell-to-VTA and shell-to-mPFC connectivities were greater in the ASD group than in the HC, MDD, and SCZ groups. Furthermore, the VTA-to-core and VTA-to-shell connectivities were excitatory in the ASD group, while those connections were inhibitory in the HC, MDD, and SCZ groups. CONCLUSION: Impaired signaling in the mesocorticolimbic dopamine-related circuits could be an underlying neuropathogenesis of various psychiatric disorders. These findings will improve the understanding of unique neural alternations of each disorder and will facilitate identification of effective therapeutic targets.

Drinking behavior patterns may be associated with persistent depressive symptoms after alcohol abstinence in alcohol use disorder.

AIM: This study examined the association between drinking behavior patterns and depressive symptoms after alcohol abstinence in patients with alcohol use disorder (AUD). METHOD: We recruited 102 AUD inpatients with baseline depressive symptoms, indicated by scores ≥6 on the Quick Inventory of Depressive Symptomatology Self-Report Japanese Version (QIDS-SR-J) pre-detoxification. Post-4-week abstinence, remission was defined as QIDS-SR-J scores <6. Patients were classified into remitted (n = 51) and persistent (n = 51) groups. Comparative analyses were conducted using patient profiles and the Drinking Behavior Pattern 20-item Questionnaire (DBP-20). Logistic regression identified factors related to post-abstinence persistent depression. Receiver operating characteristic curve analysis determined DBP-20 cutoff scores differentiating between persistent and remitted depression. RESULTS: The persistent group exhibited higher scores in the DBP-20 "coping with negative affect" subscale. Logistic regression showed low education, unemployment, and using alcohol for coping as significant factors for persistent depression. Conversely, an automatic drinking pattern indicated natural remission post-abstinence. A subscale score of ≥8 in alcohol use for coping, especially among unemployed patients, predicted persistent depression (sensitivity 86.8%, positive predictive value 73.3%). CONCLUSION: Unemployed patients with AUD using alcohol to cope with negative affect may experience residual depression even after detoxification. In contrast, patients with AUD with predominantly automatic drinking behavior may exhibit natural remission post-abstinence.

Successful treatment with guanfacine in a long-COVID case manifesting marked cognitive impairment.

BACKGROUND: Persistent cognitive impairment is a serious consequence of the post-COVID condition. However, there have been no established effective treatments for this pathophysiology supported by sufficient evidence. CASE PRESENTATION: A 32-year-old woman became aware of difficulty in word recalling, reading, and writing as well as difficulty in completing various household multitasks 3 weeks after the COVID-19 infection. Although blood tests, magnetic resonance imaging, electroencephalography, and Kohs block design test were all within normal limits, completion time by trail making test (TMT) A or B was markedly delayed. Finally, she was referred to our hospital 3 months after the infection. At baseline, the THINC integrated tool (THINC-it), a digital battery consisting of the five-item version of the perceived deficit questionnaire (PDQ-5), choice reaction time (CRT), 1-back test, digit symbol substitution test (DSST), and TMT-B, revealed poor capability in attention, working memory, and executive function. Also, near-infrared spectroscopy (NIRS) demonstrated no activation in frontal or temporal regions during verbal fluency task. Extended-release guanfacine (GXR) 2 mg/day was initiated and a month later was elevated up to 4 mg/day as a maintenance dose. The PDQ-5, CRT, 1-back test, DSST, and TMT-B were dramatically improved 1 month after GXR treatment. NIRS finding was also normalized after 2 months of treatment. These effects were successfully maintained throughout the 6-month follow-up period. CONCLUSION: GXR may be helpful in improving subjective/objective cognitive functioning and frontotemporal brain activity in long-COVID patients manifesting apparent cognitive impairment.

Cluster analysis of patients with alcohol use disorder featuring alexithymia, depression, and diverse drinking behavior.

AIM: This study aimed to identify subgroups of alcohol use disorder (AUD) based on a multidimensional combination of alexithymia, depression, and diverse drinking behavior. METHOD: We recruited 176 patients with AUD, which were initially divided into non-alexithymic (n = 130) and alexithymic (n = 46) groups using a cutoff score of 61 on the Toronto Alexithymia Scale (TAS-20). Subsequently, the profiles of the two groups were compared. Thereafter, a two-stage cluster analysis using hierarchical and K-means methods was performed with the Z-scores from the TAS-20, the Quick Inventory of Depressive Symptomatology Self-Report Japanese Version, the 12-item questionnaire for quantitative assessment of depressive mixed state, and the 20-item questionnaire for drinking behavior pattern. RESULTS: In the first analysis, Alexithymic patients with AUD showed greater depressive symptoms and more pathological drinking behavior patterns than those without alexithymia. Cluster analysis featuring alexithymia, depression, and drinking behavior identified three subtypes: Cluster 1 (core AUD type) manifesting pathological drinking behavior highlighting automaticity; Cluster 2 (late-onset type) showing relatively late-onset alcohol use and fewer depressive symptoms or pathological drinking behavior; and Cluster 3 (alexithymic type) characterized by alexithymia, depression, and pathological drinking behavior featuring greater coping with negative affect. CONCLUSION: The multidimensional model with alexithymia, depression, and diverse drinking behavior provided possible practical classification of AUD. The alexithymic subtype may require more caution, and additional support for negative affect may be necessary due to accompanying mood problems and various maladaptive drinking behaviors.

Associations between relapse and drinking behaviors in patients with alcohol use disorders: A 6-month prospective study.

BACKGROUND: Habitual behaviors, rather than goal-oriented behaviors, mainly characterize drinking patterns in patients with alcohol use disorder (AUD). However, few studies have focused on the influence of drinking behavior on AUD relapse. This prospective study examined associations between drinking behavior patterns and alcohol-use relapse using the 20-item questionnaire for drinking behavior patterns (DBP-20). METHODS: We enrolled patients with AUD and compared the cohort's demographic data and 6-month outcomes based on the DBP-20 and the Alcohol Use Disorders Identification Test between two groups (alcohol use relapse vs. abstinence). We also assessed the results for significant factors related to relapse. RESULTS: We included 105 patients with AUD. More patients in the relapse group (n = 63) were active smokers and lived alone, while fewer took medication with cyanamide or disulfiram than those in the abstinence group (n = 42). The DBP-20 automaticity subscale score was higher in the relapse group than that in the abstinence group. Current smoker, living alone, and automatic drinking habits were significantly associated with AUD relapse. CONCLUSIONS: Automaticity may be a risky drinking behavior that leads to future relapse in patients with AUD, justifying behavioral strategies to combat automatic drinking for relapse prevention.

Age-related cognitive decline is accelerated in alcohol use disorder.

This study aimed to examine potential cognitive impairments in patients with alcohol use disorder (AUD), and explore the factors affecting them. We recruited 97 inpatients with AUD, showing superficially normal cognitive function (mini-mental state examination score ≥24) for this study. We assessed cognitive function after a 4-week post-abstinence period using the Brief Assessment of Cognition in Schizophrenia-Japanese version (BACS-J). Relationships between BACS-J subcategory/composite raw scores and Z-scores (deviation from standard data in healthy Japanese) and background factors such as age, sex, education, smoking status, mini-mental state examination score, body mass index, systolic blood pressure, severity of depression, alcohol consumption, and laboratory findings were analyzed. Multiple regression analysis showed that the age (p < 0.001) and total bilirubin level (p = 0.014) were worsening factors for the BACS-J composite raw score, whereas education (p < 0.001) was a protective factor. An inverse correlation was apparent between the age and the composite Z-score of the BACS-J (r = -0.431, p < 0.001). Receiver operating characteristic (ROC) analysis identified 53 years as the cutoff age for predicting more than -2SD cognitive decline from the normal standard, with a high negative predictive value (95%). Patients with AUD aged ≥53 years showed more pronounced impairments in verbal memory, working memory, verbal fluency, and attention than those younger than 53 years (p < 0.05). These findings clearly demonstrate accelerated age-related cognitive decline in patients with AUD, especially those aged ≥53 years, suggesting the necessity of early intervention in patients with AUD to prevent progressive cognitive impairment and preserve their quality of life.

Elevated Brain-Derived Neurotrophic Factor Levels During Depressive Mixed States.

OBJECTIVE: Neurotrophin-like brain-derived neurotrophic factor (BDNF) and pro-inflammatory cytokines may modulate the pathophysiology of mood disorders. Although several studies show alterations in these biomarkers during the depressive, manic, and euthymic states of mood disorders, evidence is lacking for those in a mixed state. Therefore, this study aimed to investigate the relationship between the depressive mixed state (DMX) and peripheral neurobiological factors. METHODS: We enrolled 136 patients with major depressive episodes. Depressive symptoms were assessed using the Quick Inventory of Depressive Symptomatology Self-Report Japanese version (QIDS-SR-J). The severity of DMX was assessed using the self-administered 12-item questionnaire (DMX-12). Categorical screening as DMX-positive (n=54) was determined by a cutoff score of 13 or more in the specific eight symptoms from the DMX-12; the remaining were DMX-negative (n=82). Serum BDNF, tumor necrosis factor-α, highsensitivity C-reactive protein, and interleukin-6 levels were measured. RESULTS: When comparing biomarkers between the DMX-positive and DMX-negative groups, higher serum BDNF concentration in the DMX-positive group than in the DMX-negative group was the only significant finding (p=0.009). A positive correlation existed between the total score of the eight specific symptoms of DMX-12 and the BDNF concentration (r=0.190, p=0.027). After adjustment for confounders, logistic regression analysis revealed that BDNF (odds ratio [OR]=1.07, 95% confidence interval [CI]=1.00-1.14, p=0.045), bipolar diagnosis (OR=3.43, 95% CI=1.36-8.66, p=0.009), and total QIDS-SR-J score (OR=1.29, 95% CI=1.15-1.43, p<0.001) were significantly associated with DMX positivity. CONCLUSION: BDNF was positively associated with DMX severity, suggesting that higher BDNF concentrations may be involved in the pathophysiology of DMX.

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.

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.

Aberrant Large-Scale Network Interactions Across Psychiatric Disorders Revealed by Large-Sample Multi-Site Resting-State Functional Magnetic Resonance Imaging Datasets.

BACKGROUND AND HYPOTHESIS: Dynamics of the distributed sets of functionally synchronized brain regions, known as large-scale networks, are essential for the emotional state and cognitive processes. However, few studies were performed to elucidate the aberrant dynamics across the large-scale networks across multiple psychiatric disorders. In this paper, we aimed to investigate dynamic aspects of the aberrancy of the causal connections among the large-scale networks of the multiple psychiatric disorders. STUDY DESIGN: We applied dynamic causal modeling (DCM) to the large-sample multi-site dataset with 739 participants from 4 imaging sites including 4 different groups, healthy controls, schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BD), to compare the causal relationships among the large-scale networks, including visual network, somatomotor network (SMN), dorsal attention network (DAN), salience network (SAN), limbic network (LIN), frontoparietal network, and default mode network. STUDY RESULTS: DCM showed that the decreased self-inhibitory connection of LIN was the common aberrant connection pattern across psychiatry disorders. Furthermore, increased causal connections from LIN to multiple networks, aberrant self-inhibitory connections of DAN and SMN, and increased self-inhibitory connection of SAN were disorder-specific patterns for SCZ, MDD, and BD, respectively. CONCLUSIONS: DCM revealed that LIN was the core abnormal network common to psychiatric disorders. Furthermore, DCM showed disorder-specific abnormal patterns of causal connections across the 7 networks. Our findings suggested that aberrant dynamics among the large-scale networks could be a key biomarker for these transdiagnostic psychiatric disorders.

Development of a 20-item questionnaire for drinking behavior pattern (DBP-20) toward personalized behavioral approaches for alcohol use disorder.

Although screening tools are available for alcohol use disorders (AUD), such as the Alcohol Use Disorders Identification Test (AUDIT), these tools do not directly characterize individual drinking behavior for patients with AUD. Therefore, the aim of this study was to develop a new self-report questionnaire to identify the characteristics of drinking behavior patterns in patients with AUD.The study team developed a self-administered 20-item questionnaire for drinking behavior pattern (DBP-20) based on semi-structured interviews of patients with AUD. The DBP-20 and AUDIT were administered to 232 patients with AUD and 222 normal drinkers (1 ≤ AUDIT <20) as controls. Exploratory factor analysis of the DBP-20 was conducted for patients with AUD, followed by comparisons of its item and subscale scores between patients with AUD and controls. Correlations of AUDIT with total and subscale scores of the DBP-20 were also analyzed. Receiver operating characteristic (ROC) analyses for the DBP-20 and its subscales were performed to distinguish patients with AUD from controls.Exploratory factor analysis revealed a multidimensional 4-factor model of the DBP-20: coping with negative affect, automaticity, enhancement, and social use. Significant differences in DBP-20 total and subscale scores were observed for patients with AUD versus controls for all factors, except the social use subscale. Both the coping with negative affect and automaticity subscale scores as well as total DBP-20 scores were highly correlated with AUDIT scores. Total DBP-20 scores showed the greatest sensitivity, negative predictive value, and area under the ROC curve to distinguish patients with AUD from normal drinkers.Drinking as a means of coping with negative affect and automaticity may be specific for patients with AUD. DBP-20 may help patients with AUD to be aware of their own targeted problematic drinking behaviors and to seek their personalized behavioral approaches in a collaborative relationship with therapists.

Prevalence and profile of depressive mixed state in patients with autism spectrum disorder.

PURPOSE: The present study aimed to clarify prevalence and profile of depressive mixed state (DMX) in depressed individuals with autism spectrum disorder (ASD). PATIENTS AND METHODS: The Quick Inventory of Depressive Symptomatology Self-Report Japanese version (QIDS-SR-J) and global assessment of functioning (GAF) were administered to 182 consecutive patients (36 ASD and 146 non-ASD subjects) with a major depressive episode (MDE). DMX was categorically diagnosed according to the criteria for mixed depression (MD) by Benazzi and mixed features (MF) specifier by DSM-5. Severity of DMX was assessed by the self-administered 12-item questionnaire for DMX (DMX-12). Clinical backgrounds and incidence/severity of DMX were compared between the ASD and non-ASD groups. RESULTS: ASD patients showed higher prevalence of MD than non-ASD patients (36.1% versus 18.5%). Mood lability, distractibility, impulsivity, aggression, irritability, dysphoria and risk-taking behavior as mixed symptoms were more prevalent in ASD patients than those in non-ASD patients, together with higher scores of total DMX-12 and its disruptive emotion/behavior cluster. Multiple regression analysis revealed significant contribution of ASD to the disruptive emotion/behavior symptoms. CONCLUSION: Careful monitoring and management of potential DMX are warranted in depressed ASD individuals.

The 12-Item Self-Rating Questionnaire for Depressive Mixed State (DMX-12) for Screening of Mixed Depression and Mixed Features.

For simultaneous screening of mixed features (MF) by DSM-5 and mixed depression (MD) by Benazzi, useful symptoms were extracted from our 12-item dimensional scale for depressive mixed state (DMX-12). Subjects were 190 consecutive cases with major depressive episode (MDE) who visited our clinic. Associations between symptomatological combinations of the DMX-12 and MF or MD were analyzed using receiver operating characteristic (ROC). The rate of MF was 4.2% while that of MD was 22.6%. Eight symptoms (overreactivity, inner tension, racing/crowded thought, impulsivity, irritability, aggression, risk-taking behavior, and dysphoria) with their AUC > 0.6 for ROC curves were specially focused on distinguishing patients with MF or MD from non-mixed patients. By using these 8 symptoms, 40.5% of the overall patients were screened as positive at the same cut-off value (≥13) for both MD and MF. The AUC of ROC curve and sensitivity/specificity were well balanced together with sufficient negative predictive values. The abovementioned 8 symptoms seem to be helpful for primary screening and negative check of DMX with considerable severity during MDE.

Development of the 12-item questionnaire for quantitative assessment of depressive mixed state (DMX-12).

BACKGROUND: Conventional categorical criteria have limitations in assessing the prevalence and severity of depressive mixed state (DMX). Thus, we have developed a new scale for screening and quantification of DMX and examined the symptomatological structure and severity of DMX in individuals with major depressive episode (MDE). METHODS: Subjects were 154 patients with MDE (57 males and 97 females; age 13-83 years). Our original Japanese version of the self-administered 12-item questionnaire to assess DMX (DMX-12), together with the Quick Inventory of Depressive Symptomatology Self-Report Japanese version (QIDS-SR-J) and global assessment of functioning, were administered to each participant. The symptomatological structure of the DMX-12 was examined by exploratory factor analysis. Multiple regression analyses were used to analyze factors contributing to the DMX-12 scale. The relationships of this scale with categorical diagnoses (mixed depression by Benazzi and mixed features by DSM-5) were also investigated. RESULTS: A three-factor model of the DMX-12 was extracted from exploratory factor analysis, namely, "spontaneous instability", "vulnerable responsiveness", and "disruptive emotion/behavior". Multiple regression analyses revealed that age was negatively correlated with total DMX-12 score, while bipolarity and the QIDS-SR-J score were positively correlated. A higher score on the disruptive emotion/behavior subscale was observed in patients with mixed depression and mixed features. CONCLUSION: The DMX-12 seems to be useful for screening DMX in conjunction with conventional categorical diagnoses. Severely depressed younger subjects with potential bipolarity are more likely to develop DMX. The disruptive emotion/behavior subscale of the DMX-12 may be the most helpful in distinguishing patients with DMX from non-mixed patients.