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John J Mann - One of the best experts on this subject based on the ideXlab platform.
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Sex differences in the familial transmission of Mood Disorders.
Journal of affective disorders, 2006Co-Authors: Dianne Currier, Maria A. Oquendo, Mark J Mann, Hanga Galfalvy, John J MannAbstract:Mood Disorders exhibit familial transmission due to both environmental and genetic risk factors. Mood Disorders are more common in women, yet the role of gender in the familial transmission of Mood Disorders is unclear. This study examines rates of Mood Disorder transmission to offspring based on the sex of affected parent, sex of offspring and role of clinical factors, such as childhood abuse history, comorbid psychiatric Disorders, and traits of aggression and impulsivity. Risk of transmission of Mood Disorder to offspring from females and males was compared in a sample of 272 probands with a major Mood Disorder using generalized estimating equations (GEE). Demographic and clinical characteristics of all male and female probands were compared. Characteristics that differed in probands were entered into the model to obtain an unbiased test of gender differences in transmission rate. Multivariate GEE models, one for male probands and one for female probands, were used to test for risk factors in transmission of Mood Disorder. Familial transmission rate of Mood Disorders from female probands was almost double that of males. There was no difference in transmission to male or female offspring. For male probands, offspring Mood Disorder was independently associated with earlier age of proband Mood Disorder onset, greater number of proband years ill, and proband history of childhood abuse. For female probands, offspring Mood Disorder was associated with higher aggression scores in probands. We did not directly interview offspring and also had limited data on psychopathology in co-parents. This is a cross-sectional study and cannot account for emergence of illness in offspring in the future. The two-fold higher rate of maternal transmission of Mood Disorder may reflect differences in regulation of maternal and paternal transmission of Mood Disorder. Future studies need to determine the relative contribution of genetic and non-genetic factors and identify the factors responsible for higher rates of transmission of Mood Disorders by females with a Mood Disorder.
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suicidal behavior in bipolar Mood Disorder clinical characteristics of attempters and nonattempters
Journal of Affective Disorders, 2000Co-Authors: Maria A. Oquendo, Christine Waternaux, Beth S Brodsky, Bruce Parsons, Gretchen L Haas, Kevin M Malone, John J MannAbstract:OBJECTIVE: Bipolar Disorder is associated with a higher frequency of attempted suicide than most other psychiatric Disorders. The reasons are unknown. This study compared bipolar subjects with a history of a suicide attempt to those without such a history, assessing suicidal behavior qualitatively and quantitatively, and examining possible demographic, psychopathologic and familial risk factors. METHODS: Patients (ages 18 to 75) with a DSM III-R Bipolar Disorder (n = 44) diagnosis determined by a structured interview for Axis I Disorders were enrolled. Acute psychopathology, hopelessness, protective factors, and traits of aggression and impulsivity were measured. The number, method and degree of medical damage was assessed for suicide attempts, life-time. RESULTS: Bipolar suicide attempters had more life-time episodes of major depression, and twice as many were in a current depressive or mixed episode, compared to bipolar nonattempters. Attempters reported more suicidal ideation immediately prior to admission, and fewer reasons for living even when the most recent suicide attempt preceded the index hospitalization by more than six months. Attempters had more lifetime aggression and were more likely to be male. However, attempters did not differ from nonattempters on lifetime impulsivity. LIMITATIONS: The generalizability of the results is limited because this is a study of inpatients with a history of suicide attempts. Patients with Bipolar I and NOS Disorders were pooled and a larger sample is needed to look at differences. We could not assess psychopathology immediately prior to the suicide attempt because, only half of the suicide attempters had made attempts in the six months prior to admission. Patients with current comorbid substance abuse were excluded. No suicide completers were studied. CONCLUSIONS: Bipolar subjects with a history of suicide attempt experience more episodes of depression, and react to them by having severe suicidal ideation. Their diathesis for acting on feelings of anger or suicidal ideation is suggested by a higher level of lifetime aggression and a pattern of repeated suicide attempts.
Maria A. Oquendo - One of the best experts on this subject based on the ideXlab platform.
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Sex differences in the familial transmission of Mood Disorders.
Journal of Affective Disorders, 2006Co-Authors: Dianne Currier, Maria A. Oquendo, Hanga Galfalvy, Mark Mann, J. John MannAbstract:Abstract Background Mood Disorders exhibit familial transmission due to both environmental and genetic risk factors. Mood Disorders are more common in women, yet the role of gender in the familial transmission of Mood Disorders is unclear. This study examines rates of Mood Disorder transmission to offspring based on the sex of affected parent, sex of offspring and role of clinical factors, such as childhood abuse history, comorbid psychiatric Disorders, and traits of aggression and impulsivity. Methods Risk of transmission of Mood Disorder to offspring from females and males was compared in a sample of 272 probands with a major Mood Disorder using generalized estimating equations (GEE). Demographic and clinical characteristics of all male and female probands were compared. Characteristics that differed in probands were entered into the model to obtain an unbiased test of gender differences in transmission rate. Multivariate GEE models, one for male probands and one for female probands, were used to test for risk factors in transmission of Mood Disorder. Results Familial transmission rate of Mood Disorders from female probands was almost double that of males. There was no difference in transmission to male or female offspring. For male probands, offspring Mood Disorder was independently associated with earlier age of proband Mood Disorder onset, greater number of proband years ill, and proband history of childhood abuse. For female probands, offspring Mood Disorder was associated with higher aggression scores in probands. Limitations We did not directly interview offspring and also had limited data on psychopathology in co-parents. This is a cross-sectional study and cannot account for emergence of illness in offspring in the future. Conclusions The two-fold higher rate of maternal transmission of Mood Disorder may reflect differences in regulation of maternal and paternal transmission of Mood Disorder. Future studies need to determine the relative contribution of genetic and non-genetic factors and identify the factors responsible for higher rates of transmission of Mood Disorders by females with a Mood Disorder.
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Sex differences in the familial transmission of Mood Disorders.
Journal of affective disorders, 2006Co-Authors: Dianne Currier, Maria A. Oquendo, Mark J Mann, Hanga Galfalvy, John J MannAbstract:Mood Disorders exhibit familial transmission due to both environmental and genetic risk factors. Mood Disorders are more common in women, yet the role of gender in the familial transmission of Mood Disorders is unclear. This study examines rates of Mood Disorder transmission to offspring based on the sex of affected parent, sex of offspring and role of clinical factors, such as childhood abuse history, comorbid psychiatric Disorders, and traits of aggression and impulsivity. Risk of transmission of Mood Disorder to offspring from females and males was compared in a sample of 272 probands with a major Mood Disorder using generalized estimating equations (GEE). Demographic and clinical characteristics of all male and female probands were compared. Characteristics that differed in probands were entered into the model to obtain an unbiased test of gender differences in transmission rate. Multivariate GEE models, one for male probands and one for female probands, were used to test for risk factors in transmission of Mood Disorder. Familial transmission rate of Mood Disorders from female probands was almost double that of males. There was no difference in transmission to male or female offspring. For male probands, offspring Mood Disorder was independently associated with earlier age of proband Mood Disorder onset, greater number of proband years ill, and proband history of childhood abuse. For female probands, offspring Mood Disorder was associated with higher aggression scores in probands. We did not directly interview offspring and also had limited data on psychopathology in co-parents. This is a cross-sectional study and cannot account for emergence of illness in offspring in the future. The two-fold higher rate of maternal transmission of Mood Disorder may reflect differences in regulation of maternal and paternal transmission of Mood Disorder. Future studies need to determine the relative contribution of genetic and non-genetic factors and identify the factors responsible for higher rates of transmission of Mood Disorders by females with a Mood Disorder.
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Family History of Suicidal Behavior and Mood Disorders in Probands With Mood Disorders
American Journal of Psychiatry, 2005Co-Authors: J. John Mann, Jonathan Bortinger, Maria A. Oquendo, Dianne Currier, Shuhua Li, David A. BrentAbstract:OBJECTIVE: First-degree relatives of persons with Mood Disorder who attempt suicide are at greater risk for Mood Disorders and attempted or completed suicide. This study examined the shared and distinctive factors associated with familial Mood Disorders and familial suicidal behavior. METHOD: First-degree relatives’ history of DSM-IV–defined Mood Disorder and suicidal behavior was recorded for 457 Mood Disorder probands, of whom 81% were inpatients and 62% were female. Probands’ lifetime severity of aggression and impulsivity were rated, and probands’ reports of childhood physical or sexual abuse, suicide attempts, and age at onset of Mood Disorder were recorded. Univariate and multivariate analyses were carried out to identify predictors of suicidal acts in first-degree relatives. RESULTS: A total of 23.2% of the probands with Mood Disorder who had attempted suicide had a first-degree relative with a history of suicidal behavior, compared with 13.2% of the probands with Mood Disorder who had not attempte...
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suicidal behavior in bipolar Mood Disorder clinical characteristics of attempters and nonattempters
Journal of Affective Disorders, 2000Co-Authors: Maria A. Oquendo, Christine Waternaux, Beth S Brodsky, Bruce Parsons, Gretchen L Haas, Kevin M Malone, John J MannAbstract:OBJECTIVE: Bipolar Disorder is associated with a higher frequency of attempted suicide than most other psychiatric Disorders. The reasons are unknown. This study compared bipolar subjects with a history of a suicide attempt to those without such a history, assessing suicidal behavior qualitatively and quantitatively, and examining possible demographic, psychopathologic and familial risk factors. METHODS: Patients (ages 18 to 75) with a DSM III-R Bipolar Disorder (n = 44) diagnosis determined by a structured interview for Axis I Disorders were enrolled. Acute psychopathology, hopelessness, protective factors, and traits of aggression and impulsivity were measured. The number, method and degree of medical damage was assessed for suicide attempts, life-time. RESULTS: Bipolar suicide attempters had more life-time episodes of major depression, and twice as many were in a current depressive or mixed episode, compared to bipolar nonattempters. Attempters reported more suicidal ideation immediately prior to admission, and fewer reasons for living even when the most recent suicide attempt preceded the index hospitalization by more than six months. Attempters had more lifetime aggression and were more likely to be male. However, attempters did not differ from nonattempters on lifetime impulsivity. LIMITATIONS: The generalizability of the results is limited because this is a study of inpatients with a history of suicide attempts. Patients with Bipolar I and NOS Disorders were pooled and a larger sample is needed to look at differences. We could not assess psychopathology immediately prior to the suicide attempt because, only half of the suicide attempters had made attempts in the six months prior to admission. Patients with current comorbid substance abuse were excluded. No suicide completers were studied. CONCLUSIONS: Bipolar subjects with a history of suicide attempt experience more episodes of depression, and react to them by having severe suicidal ideation. Their diathesis for acting on feelings of anger or suicidal ideation is suggested by a higher level of lifetime aggression and a pattern of repeated suicide attempts.
Hans A Hofmann - One of the best experts on this subject based on the ideXlab platform.
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neuro transcriptomic signatures for Mood Disorder morbidity and suicide mortality
Journal of Psychiatric Research, 2020Co-Authors: Mbemba Jabbi, Dhivya Arasappan, Simon B Eickhoff, Stephen M Strakowski, Charles B Nemeroff, Hans A HofmannAbstract:Abstract Suicidal behaviors are strongly linked with Mood Disorders, but the specific neurobiological and functional gene-expression correlates for this linkage remain elusive. We performed neuroimaging-guided RNA-sequencing in two studies to test the hypothesis that imaging-localized gray matter volume (GMV) loss in Mood Disorders, harbors gene-expression changes associated with disease morbidity and related suicide mortality in an independent postmortem cohort. To do so, first, we conducted study 1 using an anatomical likelihood estimation (ALE) MRI meta-analysis including a total of 47 voxel-based morphometry (VBM) publications (i.e. 26 control versus (vs) major depressive Disorder (MDD) studies, and 21 control vs bipolar Disorder (BD) studies) in 2387 (living) participants. Study 1 meta-analysis identified a selective anterior insula cortex (AIC) GMV loss in Mood Disorders. We then used this results to guide study 2 postmortem tissue dissection and RNA-Sequencing of 100 independent donor brain samples with a life-time history of MDD (N = 30), BD (N = 37) and control (N = 33). In study 2, exploratory factor-analysis identified a higher-order factor representing number of Axis-1 diagnoses (e.g. substance use Disorders/psychosis/anxiety, etc.), referred to here as morbidity and suicide-completion referred to as mortality. Comparisons of case-vs-control, and factor-analysis defined higher-order-factor contrast variables revealed that the imaging-identified AIC GMV loss sub-region harbors differential gene-expression changes in high morbidity-&-mortality versus low morbidity-&-mortality cohorts in immune, inflammasome, and neurodevelopmental pathways. Weighted gene co-expression network analysis further identified co-activated gene modules for psychiatric morbidity and mortality outcomes. These results provide evidence that AIC anatomical signature for Mood Disorders are possible correlates for gene-expression abnormalities in Mood morbidity and suicide mortality.
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neuro transcriptomic signatures for Mood Disorder morbidity and suicide mortality
bioRxiv, 2020Co-Authors: Mbemba Jabbi, Dhivya Arasappan, Simon B Eickhoff, Stephen M Strakowski, Charles B Nemeroff, Hans A HofmannAbstract:Suicidal behaviors are strongly linked with Mood Disorders, but the specific neurobiological and functional gene-expression correlates for this linkage remain elusive. We performed neuroimaging-guided RNA-sequencing in two studies to test the hypothesis that imaging-localized gray matter volume (GMV) loss in Mood Disorders, harbors gene-expression changes associated with disease morbidity and related suicide mortality in an independent postmortem cohort. To do so, first, we conducted study 1 using an anatomical likelihood estimation (ALE) MRI meta-analysis including a total of 47 voxel-based morphometry (VBM) publications (i.e. 26 control>major depressive Disorder (MDD) studies, and 21 control>bipolar Disorder (BD) studies) in 2387 (living) participants. Study 1 meta-analysis identified a selective anterior insula cortex (AIC) GMV loss in Mood Disorders. We then used this results to guide study 2 postmortem tissue dissection and RNA-Sequencing of 100 independent donor brain samples with a life-time history of MDD (N=30), BD (N=37) and control (N=33). In study 2, exploratory factor-analysis identified a higher-order factor representing number of Axis-1 diagnoses (e.g. substance use Disorders/psychosis/anxiety, etc.), referred to here as morbidity and suicide-completion referred to as mortality. Comparisons of case-vs-control, and factor-analysis defined higher-order-factor contrast variables revealed that the imaging-identified AIC GMV loss sub-region harbors differential gene-expression changes in high morbidity-&-mortality versus low morbidity-&-mortality cohorts in immune, inflammasome, and neurodevelopmental pathways. Weighted gene co-expression network analysis further identified co-activated gene modules for psychiatric morbidity and mortality outcomes. These results provide evidence that AIC anatomical signature for Mood Disorders are possible correlates for gene-expression abnormalities in Mood morbidity and suicide mortality.
Ming-hsiang Su - One of the best experts on this subject based on the ideXlab platform.
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Cell-Coupled Long Short-Term Memory With $L$ -Skip Fusion Mechanism for Mood Disorder Detection Through Elicited Audiovisual Features
IEEE Transactions on Neural Networks and Learning Systems, 2020Co-Authors: Ming-hsiang Su, Chung-hsien Wu, Kun-yi Huang, Tsung-hsien YangAbstract:In early stages, patients with bipolar Disorder are often diagnosed as having unipolar depression in Mood Disorder diagnosis. Because the long-term monitoring is limited by the delayed detection of Mood Disorder, an accurate and one-time diagnosis is desirable to avoid delay in appropriate treatment due to misdiagnosis. In this paper, an elicitation-based approach is proposed for realizing a one-time diagnosis by using responses elicited from patients by having them watch six emotion-eliciting videos. After watching each video clip, the conversations, including patient facial expressions and speech responses, between the participant and the clinician conducting the interview were recorded. Next, the hierarchical spectral clustering algorithm was employed to adapt the facial expression and speech response features by using the extended Cohn-Kanade and eNTERFACE databases. A denoizing autoencoder was further applied to extract the bottleneck features of the adapted data. Then, the facial and speech bottleneck features were input into support vector machines to obtain speech emotion profiles (EPs) and the modulation spectrum (MS) of the facial action unit sequence for each elicited response. Finally, a cell-coupled long short-term memory (LSTM) network with an L-skip fusion mechanism was proposed to model the temporal information of all elicited responses and to loosely fuse the EPs and the MS for conducting Mood Disorder detection. The experimental results revealed that the cell-coupled LSTM with the L-skip fusion mechanism has promising advantages and efficacy for Mood Disorder detection.
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Exploring Macroscopic Fluctuation of Facial Expression for Mood Disorder Classification
2018 First Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia), 2018Co-Authors: Qian-bei Hong, Chung-hsien Wu, Ming-hsiang Su, Kun-yi HuangAbstract:In clinical diagnosis of Mood Disorder, a large portion of bipolar Disorder patients (BDs) are misdiagnosed as unipolar depression (UDs). Clinicians have confirmed that BDs generally show "reduced affect'' during clinical treatment. Thus, it is expected to build an objective and one-time diagnosis system for diagnosis assistance by using machine-learning techniques. In this study, facial expressions of BD, UD and control group (C) elicited by emotional video clips are collected for exploring temporal fluctuation characteristics of intensities of facial muscles expression among the three groups. The differences of facial expressions among Mood Disorders are investigated by observing macroscopic fluctuations. To deal with these problems, the corresponding methods for feature extraction and modeling are proposed. From the viewpoint of macroscopic facial expression, action unit (AU) is applied for describing the temporal transformation of muscles. Then, modulation spectrum is used for extracting short-term variation of AU. The multilayer perceptron (MLP)-based Disorder prediction model is then applied to obtain the prediction results. For evaluation of the proposed method, 12 subjects for three group are included in the K-fold (K=12) cross validation experiments. The experiment results reached 61.1% classification accuracy, and outperformed the other baseline methods.
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Mood Disorder identification using deep bottleneck features of elicited speech
2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2017Co-Authors: Kun-yi Huang, Chung-hsien Wu, Ming-hsiang Su, Chia-hui ChouAbstract:In the diagnosis of mental health Disorder, a large portion of the Bipolar Disorder (BD) patients is likely to be misdiagnosed as Unipolar Depression (UD) on initial presentation. As speech is the most natural way to express emotion, this work focuses on tracking emotion profile of elicited speech for short-term Mood Disorder identification. In this work, the Deep Scattering Spectrum (DSS) and Low Level Descriptors (LLDs) of the elicited speech signals are extracted as the speech features. The hierarchical spectral clustering (HSC) algorithm is employed to adapt the emotion database to the Mood Disorder database to alleviate the data bias problem. The denoising autoencoder is then used to extract the bottleneck features of DSS and LLDs for better representation. Based on the bottleneck features, a long short term memory (LSTM) is applied to generate the time-varying emotion profile sequence. Finally, given the emotion profile sequence, the HMM-based identification and verification model is used to determine Mood Disorder. This work collected the elicited emotional speech data from 15 BDs, 15 UDs and 15 healthy controls for system training and evaluation. Five-fold cross validation was employed for evaluation. Experimental results show that the system using the bottleneck feature achieved an identification accuracy of 73.33%, improving by 8.89%, compared to that without bottleneck features. Furthermore, the system with verification mechanism, improving by 4.44%, outperformed that without verification.
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Exploring microscopic fluctuation of facial expression for Mood Disorder classification
2017 International Conference on Orange Technologies (ICOT), 2017Co-Authors: Ming-hsiang Su, Chung-hsien Wu, Kun-yi Huang, Qian-bei Hong, Hsin-min WangAbstract:In clinical diagnosis of Mood Disorder, depression is one of the most common psychiatric Disorders. There are two major types of Mood Disorders: major depressive Disorder (MDD) and bipolar Disorder (BPD). A large portion of BPD are misdiagnosed as MDD in the diagnostic of Mood Disorders. Short-term detection which could be used in early detection and intervention is thus desirable. This study investigates microscopic facial expression changes for the subjects with MDD, BPD and control group (CG), when elicited by emotional video clips. This study uses eight basic orientations of motion vector (MV) to characterize the subtle changes in microscopic facial expression. Then, wavelet decomposition is applied to extract entropy and energy of different frequency bands. Next, an autoencoder neural network is adopted to extract the bottleneck features for dimensionality reduction. Finally, the long short term memory (LSTM) is employed for modeling the long-term variation among different Mood Disorders types. For evaluation of the proposed method, the elicited data from 36 subjects (12 for each of MDD, BPD and CG) were considered in the K-fold (K=12) cross validation experiments, and the performance for distinguishing among MDD, BPD and CG achieved 67.7% accuracy.
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Detection of Mood Disorder using modulation spectrum of facial action unit profiles
2016 International Conference on Orange Technologies (ICOT), 2016Co-Authors: Tsung-hsien Yang, Chung-hsien Wu, Ming-hsiang Su, Chia-cheng ChangAbstract:In Mood Disorder diagnosis, bipolar Disorder (BD) patients are often misdiagnosed as unipolar depression (UD) on initial presentation. It is crucial to establish an accurate distinction between BD and UD to make an accurate and early diagnosis, leading to improvements in treatment. In this work, facial expressions of the subjects are collected when they were watching the eliciting emotional video clips. In Mood Disorder detection, first, facial features extracted from the DISFA database are used to train a support vector machine (SVM) for generating facial action unit (AU) profiles. The modulation spectrum characterizing the fluctuation of AU profile sequence over a video segment are further extracted and then used for Mood Disorder detection using an ANN model. Comparative experiments clearly show the promising advantage of the modulation spectrum features for Mood Disorder detection.
Jean Endicott - One of the best experts on this subject based on the ideXlab platform.
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validity of an abbreviated quality of life enjoyment and satisfaction questionnaire q les q 18 for schizophrenia schizoaffective and Mood Disorder patients
Quality of Life Research, 2005Co-Authors: Michael S Ritsner, Rena Kurs, Anatoly Gibel, Yael Ratner, Jean EndicottAbstract:We sought to identify a core subset of Quality of Life Enjoyment and Satisfaction Questionnaire (Q-LES-Q) items that maintains the validity and psychometric properties of the basic version. A parsimonious subset of items from the Q-LES-Q that can accurately predict the basic Q-LES-Q domain mean scores was sought and evaluated in 339 inpatients meeting DSM-IV criteria for schizophrenia, schizoaffective, and Mood Disorders. Three additional data sets were used for validation. Assessments included Q-LES-Q, Quality of Life Scale, Lancashire Quality of Life Profile, rating scales for psychopathology, medication side effects, and self-reported emotional distress, self-esteem, self-efficacy, and social support. We found that 18-items predicted basic Q-LES-Q domains (physical health, subjective feelings, leisure time activities, social relationships) and general index scores with high accuracy. Q-LES-Q-18 showed high reliability, validity, and stability of test-retest ratings. Thus, Q-LES-Q-18, a brief, self-administered questionnaire may aid in monitoring quality of life outcomes of schizophrenia, schizoaffective, and Mood Disorder patients.