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Meinke C, Lueken U, Walter H, Hilbert K. Predicting treatment outcome based on resting-state functional connectivity in internalizing mental disorders: A systematic review and meta-analysis. Neurosci Biobehav Rev 2024; 160:105640. [PMID: 38548002 DOI: 10.1016/j.neubiorev.2024.105640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 02/29/2024] [Accepted: 03/21/2024] [Indexed: 04/07/2024]
Abstract
Predicting treatment outcome in internalizing mental disorders prior to treatment initiation is pivotal for precision mental healthcare. In this regard, resting-state functional connectivity (rs-FC) and machine learning have often shown promising prediction accuracies. This systematic review and meta-analysis evaluates these studies, considering their risk of bias through the Prediction Model Study Risk of Bias Assessment Tool (PROBAST). We examined the predictive performance of features derived from rs-FC, identified features with the highest predictive value, and assessed the employed machine learning pipelines. We searched the electronic databases Scopus, PubMed and PsycINFO on the 12th of December 2022, which resulted in 13 included studies. The mean balanced accuracy for predicting treatment outcome was 77% (95% CI: [72%- 83%]). rs-FC of the dorsolateral prefrontal cortex had high predictive value in most studies. However, a high risk of bias was identified in all studies, compromising interpretability. Methodological recommendations are provided based on a comprehensive exploration of the studies' machine learning pipelines, and potential fruitful developments are discussed.
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Affiliation(s)
- Charlotte Meinke
- Department of Psychology, Humboldt-Universität zu Berlin, Germany.
| | - Ulrike Lueken
- Department of Psychology, Humboldt-Universität zu Berlin, Germany; German Center for Mental Health (DZPG), partner site Berlin/Potsdam, Germany.
| | - Henrik Walter
- Charité Universtätsmedizin Berlin, corporate member of FU Berlin and Humboldt Universität zu Berlin, Department of Psychiatrie and Psychotherapy, CCM, Germany.
| | - Kevin Hilbert
- Department of Psychology, Humboldt-Universität zu Berlin, Germany; Department of Psychology, Health and Medical University Erfurt, Germany.
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Bai Y, Chang D, Ren H, Ju M, Wang Y, Chen B, Li H, Liu X, Li D, Huo X, Guo X, Tong M, Tan Y, Yao H, Han B. Engagement of N6-methyladenisine methylation of Gng4 mRNA in astrocyte dysfunction regulated by CircHECW2. Acta Pharm Sin B 2024; 14:1644-1660. [PMID: 38572093 PMCID: PMC10985031 DOI: 10.1016/j.apsb.2024.01.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Revised: 12/08/2023] [Accepted: 01/05/2024] [Indexed: 04/05/2024] Open
Abstract
The N6-methyladenosine (m6A) modification is the most prevalent modification of eukaryotic mRNAs and plays a crucial role in various physiological processes by regulating the stability or function of target mRNAs. Accumulating evidence has suggested that m6A methylation may be involved in the pathological process of major depressive disorder (MDD), a common neuropsychiatric disorder with an unclear aetiology. Here, we found that the levels of the circular RNA HECW2 (circHECW2) were significantly increased in the plasma of both MDD patients and the chronic unpredictable stress (CUS) mouse model. Notably, the downregulation of circHECW2 attenuated astrocyte dysfunction and depression-like behaviors induced by CUS. Furthermore, we demonstrated that the downregulation of circHECW2 increased the expression of the methylase WTAP, leading to an increase in Gng4 expression via m6A modifications. Our findings provide functional insight into the correlation between circHECW2 and m6A methylation, suggesting that circHECW2 may represent a potential target for MDD treatment.
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Affiliation(s)
- Ying Bai
- Department of Pharmacology, Jiangsu Provincial Key Laboratory of Critical Care Medicine, School of Medicine, Southeast University, Nanjing 210009, China
| | - Di Chang
- Department of Radiology, Zhongda Hospital, Jiangsu Key Laboratory of Molecular and Functional Imaging, Medical School of Southeast University, Nanjing 210009, China
| | - Hui Ren
- Department of Pharmacology, Jiangsu Provincial Key Laboratory of Critical Care Medicine, School of Medicine, Southeast University, Nanjing 210009, China
| | - Minzi Ju
- Department of Pharmacology, Jiangsu Provincial Key Laboratory of Critical Care Medicine, School of Medicine, Southeast University, Nanjing 210009, China
| | - Yu Wang
- Department of Pharmacology, Jiangsu Provincial Key Laboratory of Critical Care Medicine, School of Medicine, Southeast University, Nanjing 210009, China
| | - Biling Chen
- Department of Pharmacology, Jiangsu Provincial Key Laboratory of Critical Care Medicine, School of Medicine, Southeast University, Nanjing 210009, China
| | - Han Li
- Department of Pharmacology, Jiangsu Provincial Key Laboratory of Critical Care Medicine, School of Medicine, Southeast University, Nanjing 210009, China
| | - Xue Liu
- Department of Pharmacology, Jiangsu Provincial Key Laboratory of Critical Care Medicine, School of Medicine, Southeast University, Nanjing 210009, China
| | - Daxing Li
- Department of Pharmacology, Jiangsu Provincial Key Laboratory of Critical Care Medicine, School of Medicine, Southeast University, Nanjing 210009, China
| | - Xinchen Huo
- Department of Pharmacology, Jiangsu Provincial Key Laboratory of Critical Care Medicine, School of Medicine, Southeast University, Nanjing 210009, China
| | - Xiaofei Guo
- Department of Pharmacology, Jiangsu Provincial Key Laboratory of Critical Care Medicine, School of Medicine, Southeast University, Nanjing 210009, China
| | - Mengze Tong
- Department of Pharmacology, Jiangsu Provincial Key Laboratory of Critical Care Medicine, School of Medicine, Southeast University, Nanjing 210009, China
| | - Ying Tan
- Department of Pharmacology, Jiangsu Provincial Key Laboratory of Critical Care Medicine, School of Medicine, Southeast University, Nanjing 210009, China
| | - Honghong Yao
- Department of Pharmacology, Jiangsu Provincial Key Laboratory of Critical Care Medicine, School of Medicine, Southeast University, Nanjing 210009, China
- Co-Innovation Center of Neuroregeneration, Nantong University, Nantong 226001, China
- Institute of Life Sciences, Key Laboratory of Developmental Genes and Human Disease, Southeast University, Nanjing 210096, China
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Bing Han
- Department of Pharmacology, Jiangsu Provincial Key Laboratory of Critical Care Medicine, School of Medicine, Southeast University, Nanjing 210009, China
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Pilmeyer J, Lamerichs R, Ramsaransing F, Jansen JFA, Breeuwer M, Zinger S. Improved clinical outcome prediction in depression using neurodynamics in an emotional face-matching functional MRI task. Front Psychiatry 2024; 15:1255370. [PMID: 38585483 PMCID: PMC10996064 DOI: 10.3389/fpsyt.2024.1255370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Accepted: 03/06/2024] [Indexed: 04/09/2024] Open
Abstract
Introduction Approximately one in six people will experience an episode of major depressive disorder (MDD) in their lifetime. Effective treatment is hindered by subjective clinical decision-making and a lack of objective prognostic biomarkers. Functional MRI (fMRI) could provide such an objective measure but the majority of MDD studies has focused on static approaches, disregarding the rapidly changing nature of the brain. In this study, we aim to predict depression severity changes at 3 and 6 months using dynamic fMRI features. Methods For our research, we acquired a longitudinal dataset of 32 MDD patients with fMRI scans acquired at baseline and clinical follow-ups 3 and 6 months later. Several measures were derived from an emotion face-matching fMRI dataset: activity in brain regions, static and dynamic functional connectivity between functional brain networks (FBNs) and two measures from a wavelet coherence analysis approach. All fMRI features were evaluated independently, with and without demographic and clinical parameters. Patients were divided into two classes based on changes in depression severity at both follow-ups. Results The number of coherence clusters (nCC) between FBNs, reflecting the total number of interactions (either synchronous, anti-synchronous or causal), resulted in the highest predictive performance. The nCC-based classifier achieved 87.5% and 77.4% accuracy for the 3- and 6-months change in severity, respectively. Furthermore, regression analyses supported the potential of nCC for predicting depression severity on a continuous scale. The posterior default mode network (DMN), dorsal attention network (DAN) and two visual networks were the most important networks in the optimal nCC models. Reduced nCC was associated with a poorer depression course, suggesting deficits in sustained attention to and coping with emotion-related faces. An ensemble of classifiers with demographic, clinical and lead coherence features, a measure of dynamic causality, resulted in a 3-months clinical outcome prediction accuracy of 81.2%. Discussion The dynamic wavelet features demonstrated high accuracy in predicting individual depression severity change. Features describing brain dynamics could enhance understanding of depression and support clinical decision-making. Further studies are required to evaluate their robustness and replicability in larger cohorts.
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Affiliation(s)
- Jesper Pilmeyer
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
- Department of Research and Development, Epilepsy Centre Kempenhaeghe, Heeze, Netherlands
| | - Rolf Lamerichs
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
- Department of Research and Development, Epilepsy Centre Kempenhaeghe, Heeze, Netherlands
- Department of Medical Image Acquisitions, Philips Research, Eindhoven, Netherlands
| | - Faroeq Ramsaransing
- Department of Research and Development, Epilepsy Centre Kempenhaeghe, Heeze, Netherlands
- Department of Psychiatry, Amsterdam University Medical Center, Amsterdam, Netherlands
| | - Jacobus F. A. Jansen
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
- Department of Radiology and Nuclear Medicine, Maastricht University, Maastricht, Netherlands
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Marcel Breeuwer
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
- Department of Magnetic Resonance Research & Development - Clinical Science, Philips Healthcare, Best, Netherlands
| | - Svitlana Zinger
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
- Department of Research and Development, Epilepsy Centre Kempenhaeghe, Heeze, Netherlands
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Liu J, Guan J, Xiong J, Wang F. Effects of Transcranial Magnetic Stimulation Combined with Sertraline on Cognitive Level, Inflammatory Response and Neurological Function in Depressive Disorder Patients with Non-suicidal Self-injury Behavior. Actas Esp Psiquiatr 2024; 52:28-36. [PMID: 38454900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 03/09/2024]
Abstract
BACKGROUND Depressive disorder is a chronic mental illness characterized by persistent low mood as its primary clinical symptom. Currently, psychotherapy and drug therapy stand as the primary treatment modalities in clinical practice, offering a certain degree of relief from negative emotions for patients. Nevertheless, sole reliance on drug therapy exhibits a delayed impact on neurotransmitters, and long-term usage often results in adverse side effects such as nausea, drowsiness, and constipation, significantly impeding medication adherence. This study aims to investigate the impact of combining transcranial magnetic stimulation with sertraline on the cognitive level, inflammatory response, and neurological function in patients with depressive disorder who engage in non-suicidal self-injury (NSSI) behavior. METHODS A total of 130 depressive patients NSSI behavior, who were admitted to our hospital from December 2020 to February 2023, were selected as the subjects for this research. The single-group (65 cases) received treatment with oral sertraline hydrochloride tablets, while the combination group (65 cases) underwent repetitive transcranial magnetic stimulation (rTMS) in conjunction with sertraline. The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) was utilized to assess the depression status and cognitive function levels of both groups. Additionally, the enzyme-linked immunosorbent assay (ELISA) was employed to measure serum levels of inflammatory factors, including tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), and interleukin-6 (IL-6). Furthermore, serum levels of neurotransmitters (norepinephrine (NE), dopamine (DA), 5-hydroxytryptamine (5-HT)) and neuro-cytokines (brain-derived neurotrophic factor (BDNF), nerve growth factor (NGF), glial fibrillary acidic protein (GFAP)) were assessed. The clinical effects of the interventions on both groups were then evaluated. RESULTS Following the treatment, the combination group exhibited significantly higher levels of immediate memory, delayed memory, attention, visual function, and language function compared to the single group, with statistically significant differences (p < 0.05). Additionally, the serum levels of TNF-α, IL-1β, IL-6, and GFAP in the combination group were lower than those in the single group, while the levels of BDNF and NGF were higher in the combination group compared to the single group. These differences were also statistically significant (p < 0.05). Simultaneously, the total clinical effective rate in the combination group reached 95.38%, surpassing the 84.61% observed in the single group, and the disparity between the two groups was statistically significant (p < 0.05). CONCLUSIONS The combined use of rTMS and sertraline in treating patients with depressive disorder exhibiting NSSI behavior has proven to be effective in enhancing cognitive function, mitigating inflammatory responses, and elevating levels of neurotransmitters and nerve cytokines in the patients.
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Affiliation(s)
- Jun Liu
- Psychosomatic Ward, Wuhan Mental Health Center, Wuhan Hospital for Psychotherapy, 430000 Wuhan, Hubei, China
| | - Juan Guan
- Early Intervention Ward 1, Wuhan Mental Health Centre, Wuhan Hospital for Psychotherapy, 430000 Wuhan, Hubei, China
| | - Jie Xiong
- Psychosomatic Ward, Wuhan Mental Health Center, Wuhan Hospital for Psychotherapy, 430000 Wuhan, Hubei, China
| | - Fang Wang
- Psychosomatic Ward, Wuhan Mental Health Center, Wuhan Hospital for Psychotherapy, 430000 Wuhan, Hubei, China
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Wade B, Pindale R, Camprodon J, Luccarelli J, Li S, Meisner R, Seiner S, Henry M. Individual Prediction of Optimal Treatment Allocation Between Electroconvulsive Therapy or Ketamine using the Personalized Advantage Index. Res Sq 2023:rs.3.rs-3682009. [PMID: 38077094 PMCID: PMC10705694 DOI: 10.21203/rs.3.rs-3682009/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Introduction Electroconvulsive therapy (ECT) and ketamine are two effective treatments for depression with similar efficacy; however, individual patient outcomes may be improved by models that predict optimal treatment assignment. Here, we adapt the Personalized Advantage Index (PAI) algorithm using machine learning to predict optimal treatment assignment between ECT and ketamine using medical record data from a large, naturalistic patient cohort. We hypothesized that patients who received a treatment predicted to be optimal would have significantly better outcomes following treatment compared to those who received a non-optimal treatment. Methods Data on 2526 ECT and 235 mixed IV ketamine and esketamine patients from McLean Hospital was aggregated. Depressive symptoms were measured using the Quick Inventory of Depressive Symptomatology (QIDS) before and during acute treatment. Patients were matched between treatments on pretreatment QIDS, age, inpatient status, and psychotic symptoms using a 1:1 ratio yielding a sample of 470 patients (n=235 per treatment). Random forest models were trained and predicted differential patientwise minimum QIDS scores achieved during acute treatment (min-QIDS) scores for ECT and ketamine using pretreatment patient measures. Analysis of Shapley Additive exPlanations (SHAP) values identified predictors of differential outcomes between treatments. Results Twenty-seven percent of patients with the largest PAI scores who received a treatment predicted optimal had significantly lower min-QIDS scores compared to those who received a non-optimal treatment (mean difference=1.6, t=2.38, q<0.05, Cohen's D=0.36). Analysis of SHAP values identified prescriptive pretreatment measures. Conclusions Patients assigned to a treatment predicted to be optimal had significantly better treatment outcomes. Our model identified pretreatment patient factors captured in medical records that can provide interpretable and actionable guidelines treatment selection.
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Affiliation(s)
- Benjamin Wade
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Ryan Pindale
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Joan Camprodon
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - James Luccarelli
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Shuang Li
- Department of Psychiatry, McLean Hospital, Belmont, MA, USA
| | - Robert Meisner
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, McLean Hospital, Belmont, MA, USA
| | - Stephen Seiner
- Department of Psychiatry, McLean Hospital, Belmont, MA, USA
| | - Michael Henry
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
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Ju Y, Wang M, Liu J, Liu B, Yan D, Lu X, Sun J, Dong Q, Zhang L, Guo H, Zhao F, Liao M, Zhang L, Zhang Y, Li L. Modulation of resting-state functional connectivity in default mode network is associated with the long-term treatment outcome in major depressive disorder. Psychol Med 2023; 53:5963-5975. [PMID: 36164996 DOI: 10.1017/s0033291722002628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
BACKGROUND Treatment non-response and recurrence are the main sources of disease burden in major depressive disorder (MDD). However, little is known about its neurobiological mechanism concerning the brain network changes accompanying pharmacotherapy. The present study investigated the changes in the intrinsic brain networks during 6-month antidepressant treatment phase associated with the treatment response and recurrence in MDD. METHODS Resting-state functional magnetic resonance imaging was acquired from untreated patients with MDD and healthy controls at baseline. The patients' depressive symptoms were monitored by using the Hamilton Rating Scale for Depression (HAMD). After 6 months of antidepressant treatment, patients were re-scanned and followed up every 6 months over 2 years. Traditional statistical analysis as well as machine learning approaches were conducted to investigate the longitudinal changes in macro-scale resting-state functional network connectivity (rsFNC) strength and micro-scale resting-state functional connectivity (rsFC) associated with long-term treatment outcome in MDD. RESULTS Repeated measures of the general linear model demonstrated a significant difference in the default mode network (DMN) rsFNC change before and after the 6-month antidepressant treatment between remitters and non-remitters. The difference in the rsFNC change over the 6-month antidepressant treatment between recurring and stable MDD was also specific to DMN. Machine learning analysis results revealed that only the DMN rsFC change successfully distinguished non-remitters from the remitters at 6 months and recurring from stable MDD during the 2-year follow-up. CONCLUSION Our findings demonstrated that the intrinsic DMN connectivity could be a unique and important target for treatment and recurrence prevention in MDD.
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Affiliation(s)
- Yumeng Ju
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Mi Wang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Jin Liu
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Bangshan Liu
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Danfeng Yan
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Xiaowen Lu
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Jinrong Sun
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Qiangli Dong
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Liang Zhang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Hua Guo
- Zhumadian Psychiatric Hospital, Zhumadian, Henan 463000, China
| | - Futao Zhao
- Zhumadian Psychiatric Hospital, Zhumadian, Henan 463000, China
| | - Mei Liao
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Li Zhang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Yan Zhang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
| | - Lingjiang Li
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, China
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Huang X, Sun Y, Wu A, Zhang X. Differences in the prevalence and clinical correlates of comorbid suicide attempts in patients with early- and late-onset major depressive disorder. Front Psychiatry 2023; 14:1173917. [PMID: 37588029 PMCID: PMC10426903 DOI: 10.3389/fpsyt.2023.1173917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 05/09/2023] [Indexed: 08/18/2023] Open
Abstract
Objective There are many studies on differences in the onset age of major depressive disorder (MDD) patients. However, study on differences in clinical correlates of suicide attempts between early- and late-onset MDD patients is limited. The aim of this study was to investigate the differences in the prevalence and clinical correlates of suicide attempts in patients with early- and late-onset MDD in China. Methods A total of 1718 adult outpatients with MDD were recruited. Demographic and clinical data were collected. The 17-item Hamilton Rating Scale for Depression (HAMD-17), Hamilton Anxiety Rating Scale (HAMA), Positive and Negative Syndrome Scale (PANSS) positive subscale, and Clinical Global Impression-Severity (CGI-S) Scales were used to assess their depressive, anxiety, psychotic symptoms, and the severity of the clinical symptoms, respectively. Results The prevalence of suicide attempts was higher in late-onset MDD patients (291/1369, 21.3%) than in early-onset MDD patients (55/349, 15.8%) (p = 0.023). However after Bonferroni correction no significant difference was found in the prevalence of suicide attempts in late-onset and late-onset MDD patients (p > 0.05). In both early- and late-onset groups, univariate analysis showed that the following characteristics were significantly associated with suicide attempts: HAMA, HAMD and PANSS positive subscale scores, thyroid stimulating hormone (TSH) levels, blood glucose levels, systolic blood pressure (SBP), and diastolic blood pressure (DBP). In both the early- and late-onset groups, the prevalence rates of severe anxiety disorder and psychotic symptoms were significantly higher in the suicide attempt group than in the non-suicide attempt group. In regression analysis, disease duration, TSH levels and HAMA score were independently associated with suicide attempts in the early-onset group, while TSH levels, HAMA and HAMD score were independently associated with suicide attempts in the late-onset group. Conclusion This study suggests that suicide attempts are not frequent in early-onset outpatients with MDD compared with late-onset, and some clinical correlates are associated with suicide attempt in early- and late-onset MDD.
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Affiliation(s)
- Xiao Huang
- Department of Anesthesiology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Yuan Sun
- Department of Pharmacy, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Anshi Wu
- Department of Anesthesiology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Xiangyang Zhang
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
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8
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Strege MV, Siegle GJ, Richey JA, Krawczak RA, Young K. Cingulate prediction of response to antidepressant and cognitive behavioral therapies for depression: Meta-analysis and empirical application. Brain Imaging Behav 2023; 17:450-460. [PMID: 36622532 PMCID: PMC10329727 DOI: 10.1007/s11682-022-00756-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 10/26/2022] [Accepted: 12/16/2022] [Indexed: 01/10/2023]
Abstract
We sought to identify baseline (pre-treatment) neural markers associated with treatment response in major depressive disorder (MDD), specific to treatment type, Cognitive Behavioral Therapy (CBT) or pharmacotherapy (selective serotonin reuptake inhibitors; SSRI). We conducted a meta-analysis of functional magnetic resonance imaging (fMRI) studies to identify neural prognostic indicators of response to CBT or SSRI. To verify the regions derived from literature, the meta-analytic regions were used to predict clinical change in a verification sample of participants with MDD who received either CBT (n = 60) or an SSRI (n = 19) as part of prior clinical trials. The meta-analysis consisted of 21 fMRI studies that used emotion-related tasks. It yielded prognostic regions of the perigenual (meta pgACC) and subgenual anterior cingulate cortex (meta sgACC), associated with SSRI and CBT response, respectively. When applying the meta-analytic regions to predict treatment response in the verification sample, reactivity of the meta pgACC was prognostic for SSRI response, yet the effect direction was opposite of most prior studies. Meta sgACC reactivity failed to be prognostic for CBT response. Results confirm the prognostic potential of neural reactivity of ACC subregions in MDD but further research is necessary for clinical translation.
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Affiliation(s)
- Marlene V Strege
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, United States.
| | - Greg J Siegle
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, United States
| | - John A Richey
- Department of Psychology, Virginia Polytechnic Institute and State University, Blacksburg, United States
| | | | - Kymberly Young
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, United States
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Lei D, Sun J, Xia J. Cuproptosis-related genes prediction feature and immune microenvironment in major depressive disorder. Heliyon 2023; 9:e18497. [PMID: 37576193 PMCID: PMC10415818 DOI: 10.1016/j.heliyon.2023.e18497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 07/17/2023] [Accepted: 07/19/2023] [Indexed: 08/15/2023] Open
Abstract
Background Major depressive disorder (MDD) is a severe, unpredictable, ill-cured, relapsing neuropsychiatric disorder. A recently identified type of death called cuproptosis has been linked to a number of illnesses. However, the influence of cuproptosis-related genes in MDD has not been comprehensively assessed in prior study. Aim This investigation intends to shed light on the predictive value of cuproptosis-related genes for MDD and the immunological microenvironment. Methods GSE38206, GSE76826, GSE9653 databases were used to analyze cuproptosis regulators and immune characteristics. To find the genes that were differently expressed, weighted gene co-expression network analysis was employed. We calculated the effectiveness of the random forest model, generalized linear model, and limit gradient lifting to arrive at the best machine prediction model. Nomogram, calibration curve, and decision curve analysis were used to show the anticipated MDD's accuracy. Results This study found that there were activated immune responses and cuproptosis-related genes that were dysregulated in people with MDD compared to healthy controls. Considering the test performance of the learned model and validation on subsequent datasets, the RF model (including OSBPL8, VBP1, MTM1, ELK3, and SLC39A6) was considered to have the best discriminative performance. (AUC = 0.875). Conclusion Our study constructed a prediction model to predict MDD risk and clarified the potential connection between cuproptosis and MDD.
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Affiliation(s)
- Daoyun Lei
- Department of Anesthesiology, Zhongda Hospital Southeast University (Jiangbei), Nanjing, 210048 Jiangsu, China
- Department of Anesthesiology, Zhongda Hospital Southeast University, Nanjing, 210009 Jiangsu, China
| | - Jie Sun
- Department of Anesthesiology, Zhongda Hospital Southeast University (Jiangbei), Nanjing, 210048 Jiangsu, China
- Department of Anesthesiology, Zhongda Hospital Southeast University, Nanjing, 210009 Jiangsu, China
| | - Jiangyan Xia
- Department of Anesthesiology, Zhongda Hospital Southeast University (Jiangbei), Nanjing, 210048 Jiangsu, China
- Department of Anesthesiology, Zhongda Hospital Southeast University, Nanjing, 210009 Jiangsu, China
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10
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Ping L, Sun S, Zhou C, Que J, You Z, Xu X, Cheng Y. Altered topology of individual brain structural covariance networks in major depressive disorder. Psychol Med 2023:1-12. [PMID: 37427670 DOI: 10.1017/s003329172300168x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
BACKGROUND The neurobiological pathogenesis of major depression disorder (MDD) remains largely controversial. Previous literatures with limited sample size utilizing group-level structural covariance networks (SCN) commonly generated mixed findings regarding the topology of brain networks. METHODS We analyzed T1 images from a high-powered multisite sample including 1173 patients with MDD and 1019 healthy controls (HCs). We used regional gray matter volume to construct individual SCN by utilizing a novel approach based on the interregional effect size difference. We further investigated MDD-related structural connectivity alterations using topological metrics. RESULTS Compared to HCs, the MDD patients showed a shift toward randomization characterized by increased integration. Further subgroup analysis of patients in different stages revealed this randomization pattern was also observed in patients with recurrent MDD, while the first-episode drug naïve patients exhibited decreased segregation. Altered nodal properties in several brain regions which have a key role in both emotion regulation and executive control were also found in MDD patients compared with HCs. The abnormalities in inferior temporal gyrus were not influenced by any specific site. Moreover, antidepressants increased nodal efficiency in the anterior ventromedial prefrontal cortex. CONCLUSIONS The MDD patients at different stages exhibit distinct patterns of randomization in their brain networks, with increased integration during illness progression. These findings provide valuable insights into the disruption in structural brain networks that occurs in patients with MDD and might be useful to guide future therapeutic interventions.
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Affiliation(s)
- Liangliang Ping
- Department of Psychiatry, Xiamen Xianyue Hospital, Xiamen, China
| | - Shan Sun
- Department of Psychiatry, Xiamen Xianyue Hospital, Xiamen, China
| | - Cong Zhou
- School of Mental Health, Jining Medical University, Jining, China
| | - Jianyu Que
- Department of Psychiatry, Xiamen Xianyue Hospital, Xiamen, China
| | - Zhiyi You
- Department of Psychiatry, Xiamen Xianyue Hospital, Xiamen, China
| | - Xiufeng Xu
- Department of Psychiatry, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yuqi Cheng
- Department of Psychiatry, The First Affiliated Hospital of Kunming Medical University, Kunming, China
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Dadkhah M, Jafarzadehgharehziaaddin M, Molaei S, Akbari M, Gholizadeh N, Fathi F. Major depressive disorder: biomarkers and biosensors. Clin Chim Acta 2023:117437. [PMID: 37315724 DOI: 10.1016/j.cca.2023.117437] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 06/01/2023] [Accepted: 06/07/2023] [Indexed: 06/16/2023]
Abstract
Depressive disorders belong to highly heterogeneous psychiatric diseases. Loss of in interest in previously enjoyed activities and a depressed mood are the main characteristics of major depressive disorder (MDD). Moreover, due to significant heterogeneity in clinical presentation and lack of applicable biomarkers, diagnosis and treatment remains challenging. Identification of relevant biomarkers would allow for improved disease classification and more personalized treatment strategies. Herein, we review the current state of these biomarkers and then discuss diagnostic techniques of aimed to specifically target these analytes using state of the art biosensor technology.
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Affiliation(s)
- Masoomeh Dadkhah
- Pharmaceutical Sciences Research Center, Ardabil University of Medical Sciences, Ardabil, Iran
| | | | - Soheila Molaei
- Zoonoses Research Center, Ardabil University of Medical Sciences, Ardabil, Iran
| | - Morteza Akbari
- Immunology Research Center (IRC), Tabriz University of Medical Sciences, Tabriz, Iran
| | - Neghin Gholizadeh
- Students Research Committee, Health School, Ardabil University of Medical Sciences, Ardabil, Iran
| | - Farzaneh Fathi
- Pharmaceutical Sciences Research Center, Ardabil University of Medical Sciences, Ardabil, Iran; Biosensor Sciences and Technologies Research Center, Ardabil University of Medical Sciences, Ardabil, Iran.
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12
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Liu Q, Ostinelli EG, De Crescenzo F, Li Z, Tomlinson A, Salanti G, Cipriani A, Efthimiou O. Predicting outcomes at the individual patient level: what is the best method? BMJ Ment Health 2023; 26:e300701. [PMID: 37316257 PMCID: PMC10277128 DOI: 10.1136/bmjment-2023-300701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 04/26/2023] [Indexed: 06/16/2023]
Abstract
OBJECTIVE When developing prediction models, researchers commonly employ a single model which uses all the available data (end-to-end approach). Alternatively, a similarity-based approach has been previously proposed, in which patients with similar clinical characteristics are first grouped into clusters, then prediction models are developed within each cluster. The potential advantage of the similarity-based approach is that it may better address heterogeneity in patient characteristics. However, it remains unclear whether it improves the overall predictive performance. We illustrate the similarity-based approach using data from people with depression and empirically compare its performance with the end-to-end approach. METHODS We used primary care data collected in general practices in the UK. Using 31 predefined baseline variables, we aimed to predict the severity of depressive symptoms, measured by Patient Health Questionnaire-9, 60 days after initiation of antidepressant treatment. Following the similarity-based approach, we used k-means to cluster patients based on their baseline characteristics. We derived the optimal number of clusters using the Silhouette coefficient. We used ridge regression to build prediction models in both approaches. To compare the models' performance, we calculated the mean absolute error (MAE) and the coefficient of determination (R2) using bootstrapping. RESULTS We analysed data from 16 384 patients. The end-to-end approach resulted in an MAE of 4.64 and R2 of 0.20. The best-performing similarity-based model was for four clusters, with MAE of 4.65 and R2 of 0.19. CONCLUSIONS The end-to-end and the similarity-based model yielded comparable performance. Due to its simplicity, the end-to-end approach can be favoured when using demographic and clinical data to build prediction models on pharmacological treatments for depression.
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Affiliation(s)
- Qiang Liu
- Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Precision Psychiatry Lab, NIHR Oxford Health Biomedical Research Centre, Oxford, UK
- Department of Engineering Mathematics, University of Bristol, Bristol, UK
| | - Edoardo Giuseppe Ostinelli
- Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Precision Psychiatry Lab, NIHR Oxford Health Biomedical Research Centre, Oxford, UK
- Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, UK
| | - Franco De Crescenzo
- Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Precision Psychiatry Lab, NIHR Oxford Health Biomedical Research Centre, Oxford, UK
| | - Zhenpeng Li
- Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Precision Psychiatry Lab, NIHR Oxford Health Biomedical Research Centre, Oxford, UK
| | - Anneka Tomlinson
- Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Precision Psychiatry Lab, NIHR Oxford Health Biomedical Research Centre, Oxford, UK
| | - Georgia Salanti
- Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland
| | - Andrea Cipriani
- Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Precision Psychiatry Lab, NIHR Oxford Health Biomedical Research Centre, Oxford, UK
- Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, UK
| | - Orestis Efthimiou
- Oxford Precision Psychiatry Lab, NIHR Oxford Health Biomedical Research Centre, Oxford, UK
- Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland
- Institute of Primary Health Care (BIHAM), University of Bern, Bern, Switzerland
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Wu X, Tu M, Chen N, Yang J, Jin J, Qu S, Xiong S, Cao Z, Xu M, Pei S, Hu H, Ge Y, Fang J, Shao X. The efficacy and cerebral mechanism of intradermal acupuncture for major depressive disorder: a study protocol for a randomized controlled trial. Front Psychiatry 2023; 14:1181947. [PMID: 37255689 PMCID: PMC10226652 DOI: 10.3389/fpsyt.2023.1181947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 04/27/2023] [Indexed: 06/01/2023] Open
Abstract
Background Major depressive disorder (MDD) has emerged as the fifth leading cause of years lived with disability, with a high prevalent, affecting nearly 4% of the global population. While available evidence suggests that intradermal acupuncture may enhance the effectiveness of antidepressants, whether its efficacy is a specific therapeutic effect or a placebo effect has not been reported. Moreover, the cerebral mechanism of intradermal acupuncture as a superficial acupuncture (usually subcutaneous needling to a depth of 1-2 mm) for MDD remains unclear. Methods A total of 120 participants with MDD will be enrolled and randomized to the waiting list group, sham intradermal acupuncture group and active intradermal acupuncture group. All 3 groups will receive a 6-week intervention and a 4-week follow-up. The primary outcome will be measured by the Hamilton Depression Rating Scale-17 and the secondary outcome measures will be the Self-Rating depression scale and Pittsburgh sleep quality index. Assessments will be conducted at baseline, 3 weeks, 6 weeks, and during the follow-up period. In addition, 20 eligible participants in each group will be randomly selected to undergo head magnetic resonance imaging before and after the intervention to explore the effects of intradermal acupuncture on brain activity in MDD patients. Discussion If the intradermal acupuncture is beneficial, it is promising to be included in the routine treatment of MDD. Clinical Trial Registration Clinicaltrials.gov, NCT05720637.
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Affiliation(s)
- Xiaoting Wu
- Key Laboratory for Research of Acupuncture Treatment and Transformation of Emotional Diseases, The Third Clinical Medical College, Zhejiang Chinese Medical University,, Hangzhou, China
- The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Mingqi Tu
- Key Laboratory for Research of Acupuncture Treatment and Transformation of Emotional Diseases, The Third Clinical Medical College, Zhejiang Chinese Medical University,, Hangzhou, China
- The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Nisang Chen
- Key Laboratory for Research of Acupuncture Treatment and Transformation of Emotional Diseases, The Third Clinical Medical College, Zhejiang Chinese Medical University,, Hangzhou, China
- The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Jiajia Yang
- The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Junyan Jin
- Key Laboratory for Research of Acupuncture Treatment and Transformation of Emotional Diseases, The Third Clinical Medical College, Zhejiang Chinese Medical University,, Hangzhou, China
- The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Siying Qu
- Key Laboratory for Research of Acupuncture Treatment and Transformation of Emotional Diseases, The Third Clinical Medical College, Zhejiang Chinese Medical University,, Hangzhou, China
- The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Sangsang Xiong
- Key Laboratory for Research of Acupuncture Treatment and Transformation of Emotional Diseases, The Third Clinical Medical College, Zhejiang Chinese Medical University,, Hangzhou, China
- The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Zhijian Cao
- The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Maosheng Xu
- The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Shuangyi Pei
- The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Hantong Hu
- The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Yinyan Ge
- The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Jianqiao Fang
- The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Xiaomei Shao
- Key Laboratory for Research of Acupuncture Treatment and Transformation of Emotional Diseases, The Third Clinical Medical College, Zhejiang Chinese Medical University,, Hangzhou, China
- The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
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14
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Guo ZP, Chen L, Tang LR, Gao Y, Chand T, Sen ZD, Li M, Walter M, Wang L, Liu CH. Association between decreased interhemispheric functional connectivity of the insula and duration of illness in recurrent depression. J Affect Disord 2023; 329:88-95. [PMID: 36841304 DOI: 10.1016/j.jad.2023.02.083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 02/14/2023] [Accepted: 02/19/2023] [Indexed: 02/27/2023]
Abstract
OBJECTIVE To investigate the altered interhemispheric functional connectivity in the resting state in patients with recurrent major depressive disorder (MDD). METHODS Voxel-mirrored homotopic connectivity (VMHC), a measure of the functional connectivity between any pair of symmetrical interhemispheric voxels, and pattern classification were examined in 41 recurrent MDD patients (22 during the depressive state and 19 during the remitted state) and 60 age, sex, and education level-matched healthy controls (HC) using resting-state functional magnetic resonance imaging (fMRI). RESULTS Compared with HC, the recurrent MDD patients exhibited decreased VMHC values in the bilateral fusiform, inferior occipital gyrus, posterior insula, precentral gyrus, precuneus, superior temporal gyrus, and thalamus. A significant negative correlation between the VMHC value of the bilateral posterior insula and illness duration in recurrent MDD was identified. Support vector machine (SVM) analysis showed that VMHC in the fusiform and posterior insula could be used to distinguish recurrent MDD patients from HC with a sensitivity and accuracy >0.6. CONCLUSION Our findings revealed a reduction in the resting-state brain activity across several neural networks in patients with recurrent MDD, including within the posterior insula. Lower VMHC values in the posterior insula were associated with longer illness duration, suggesting that impairment in interhemispheric synchronization within the salience network may be due to the accumulated pathology of depression and may contribute to future depression relapse. VMHC changes in the posterior insula may serve as a potential imaging marker to discriminate recurrent MDD patients from HC.
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Affiliation(s)
- Zhi-Peng Guo
- Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing 100010, China
| | - Lei Chen
- Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing 100010, China
| | - Li-Rong Tang
- Beijing Hospital of Anding, Capital Medical University, Beijing 100088, China
| | - Yue Gao
- Beijing Hospital of Anding, Capital Medical University, Beijing 100088, China
| | - Tara Chand
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena 07743, Germany; Clinical Affective Neuroimaging Laboratory (CANLAB), Magdeburg 39120, Germany; Department of Clinical Psychology, Friedrich Schiller University, Jena, Germany
| | - Zümrüt Duygu Sen
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena 07743, Germany; Center for Intervention and Research on adaptive and maladaptive brain Circuits underlying mental health (C-I-R-C), Jena-Magdeburg-Halle, Germany
| | - Meng Li
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena 07743, Germany; Clinical Affective Neuroimaging Laboratory (CANLAB), Magdeburg 39120, Germany; Center for Intervention and Research on adaptive and maladaptive brain Circuits underlying mental health (C-I-R-C), Jena-Magdeburg-Halle, Germany
| | - Martin Walter
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena 07743, Germany; Clinical Affective Neuroimaging Laboratory (CANLAB), Magdeburg 39120, Germany; Center for Intervention and Research on adaptive and maladaptive brain Circuits underlying mental health (C-I-R-C), Jena-Magdeburg-Halle, Germany; German Center for Mental Health (DZPG), Site Halle-Jena-Magdeburg, Germany; Department of Psychiatry and Psychotherapy, University of Tuebingen, Tuebingen 72074, Germany; Department Systems Physiology of Learning, Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Lihong Wang
- Department of Psychiatry, University of Connecticut Health Center, Farmington, CT 06030, USA.
| | - Chun-Hong Liu
- Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing 100010, China.
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15
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Rost N, Dwyer DB, Gaffron S, Rechberger S, Maier D, Binder EB, Brückl TM. Multimodal predictions of treatment outcome in major depression: A comparison of data-driven predictors with importance ratings by clinicians. J Affect Disord 2023; 327:330-339. [PMID: 36750160 DOI: 10.1016/j.jad.2023.02.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 01/23/2023] [Accepted: 02/01/2023] [Indexed: 02/08/2023]
Abstract
BACKGROUND Reliable prediction models of treatment outcome in Major Depressive Disorder (MDD) are currently lacking in clinical practice. Data-driven outcome definitions, combining data from multiple modalities and incorporating clinician expertise might improve predictions. METHODS We used unsupervised machine learning to identify treatment outcome classes in 1060 MDD inpatients. Subsequently, classification models were created on clinical and biological baseline information to predict treatment outcome classes and compared to the performance of two widely used classical outcome definitions. We also related the findings to results from an online survey that assessed which information clinicians use for outcome prognosis. RESULTS Three and four outcome classes were identified by unsupervised learning. However, data-driven outcome classes did not result in more accurate prediction models. The best prediction model was targeting treatment response in its standard definition and reached accuracies of 63.9 % in the test sample, and 59.5 % and 56.9 % in the validation samples. Top predictors included sociodemographic and clinical characteristics, while biological parameters did not improve prediction accuracies. Treatment history, personality factors, prior course of the disorder, and patient attitude towards treatment were ranked as most important indicators by clinicians. LIMITATIONS Missing data limited the power to identify biological predictors of treatment outcome from certain modalities. CONCLUSIONS So far, the inclusion of available biological measures in addition to psychometric and clinical information did not improve predictive value of the models, which was overall low. Optimized biomarkers, stratified predictions and the inclusion of clinical expertise may improve future prediction models.
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Affiliation(s)
- Nicolas Rost
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany; International Max Planck Research School for Translational Psychiatry, Munich, Germany.
| | - Dominic B Dwyer
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University, Munich, Germany; Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia
| | | | | | | | - Elisabeth B Binder
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Tanja M Brückl
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
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16
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Li Y, Gui Y, Zhao M, Chen X, Li H, Tian C, Zhao H, Jiang C, Xu P, Zhang S, Ye S, Huang M. The roles of extracellular vesicles in major depressive disorder. Front Psychiatry 2023; 14:1138110. [PMID: 36970289 PMCID: PMC10033661 DOI: 10.3389/fpsyt.2023.1138110] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 02/01/2023] [Indexed: 03/11/2023] Open
Abstract
Major depressive disorder (MDD) is a serious mental disease characterized by depressed mood, loss of interest and suicidal ideation. Its rising prevalence has rendered MDD one of the largest contributors to the global disease burden. However, its pathophysiological mechanism is still unclear, and reliable biomarkers are lacking. Extracellular vesicles (EVs) are widely considered important mediators of intercellular communication, playing an important role in many physiological and pathological processes. Most preclinical studies focus on the related proteins and microRNAs in EVs, which can regulate energy metabolism, neurogenesis, neuro-inflammation and other pathophysiological processes in the development of MDD. The purpose of this review is to describe the current research progress of EVs in MDD and highlight their potential roles as biomarkers, therapeutic indicators and drug delivery carriers for the treatment of MDD.
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Affiliation(s)
- Ying Li
- Department of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou, China
- Brain Research Institute, Zhejiang University, Hangzhou, China
- Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, China
| | - Yan Gui
- Department of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou, China
- Brain Research Institute, Zhejiang University, Hangzhou, China
- Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, China
- Department of Psychiatry, Tongde Hospital of Zhejiang Province, Mental Health Center of Zhejiang Province, Hangzhou, China
| | - Miaomiao Zhao
- Department of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou, China
- Brain Research Institute, Zhejiang University, Hangzhou, China
- Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, China
| | - Xuanqiang Chen
- Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
| | - Haimei Li
- Department of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou, China
- Brain Research Institute, Zhejiang University, Hangzhou, China
- Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, China
| | - Chen Tian
- Department of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou, China
- Brain Research Institute, Zhejiang University, Hangzhou, China
- Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, China
| | - Haoyang Zhao
- Department of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou, China
- Brain Research Institute, Zhejiang University, Hangzhou, China
- Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, China
| | - Chaonan Jiang
- Department of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou, China
- Brain Research Institute, Zhejiang University, Hangzhou, China
- Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, China
| | - Pengfeng Xu
- Department of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou, China
- Brain Research Institute, Zhejiang University, Hangzhou, China
- Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, China
| | - Shiyi Zhang
- Department of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou, China
- Brain Research Institute, Zhejiang University, Hangzhou, China
- Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, China
| | - Shaoyong Ye
- Henan University School of Medicine, Henan University, Kaifeng, China
| | - Manli Huang
- Department of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou, China
- Brain Research Institute, Zhejiang University, Hangzhou, China
- Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, China
- *Correspondence: Manli Huang,
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Haddad E, Pizzagalli F, Zhu AH, Bhatt RR, Islam T, Ba Gari I, Dixon D, Thomopoulos SI, Thompson PM, Jahanshad N. Multisite test-retest reliability and compatibility of brain metrics derived from FreeSurfer versions 7.1, 6.0, and 5.3. Hum Brain Mapp 2023; 44:1515-1532. [PMID: 36437735 PMCID: PMC9921222 DOI: 10.1002/hbm.26147] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 10/19/2022] [Accepted: 10/19/2022] [Indexed: 11/29/2022] Open
Abstract
Automatic neuroimaging processing tools provide convenient and systematic methods for extracting features from brain magnetic resonance imaging scans. One tool, FreeSurfer, provides an easy-to-use pipeline to extract cortical and subcortical morphometric measures. There have been over 25 stable releases of FreeSurfer, with different versions used across published works. The reliability and compatibility of regional morphometric metrics derived from the most recent version releases have yet to be empirically assessed. Here, we used test-retest data from three public data sets to determine within-version reliability and between-version compatibility across 42 regional outputs from FreeSurfer versions 7.1, 6.0, and 5.3. Cortical thickness from v7.1 was less compatible with that of older versions, particularly along the cingulate gyrus, where the lowest version compatibility was observed (intraclass correlation coefficient 0.37-0.61). Surface area of the temporal pole, frontal pole, and medial orbitofrontal cortex, also showed low to moderate version compatibility. We confirm low compatibility between v6.0 and v5.3 of pallidum and putamen volumes, while those from v7.1 were compatible with v6.0. Replication in an independent sample showed largely similar results for measures of surface area and subcortical volumes, but had lower overall regional thickness reliability and compatibility. Batch effect correction may adjust for some inter-version effects when most sites are run with one version, but results vary when more sites are run with different versions. Age associations in a quality controlled independent sample (N = 106) revealed version differences in results of downstream statistical analysis. We provide a reference to highlight the regional metrics that may yield recent version-related inconsistencies in published findings. An interactive viewer is provided at http://data.brainescience.org/Freesurfer_Reliability/.
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Affiliation(s)
- Elizabeth Haddad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Fabrizio Pizzagalli
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA.,Department of Neurosciences, University of Turin, Turin, Italy
| | - Alyssa H Zhu
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Ravi R Bhatt
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Tasfiya Islam
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Iyad Ba Gari
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Daniel Dixon
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
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Rost N, Binder EB, Brückl TM. Predicting treatment outcome in depression: an introduction into current concepts and challenges. Eur Arch Psychiatry Clin Neurosci 2023; 273:113-127. [PMID: 35587279 PMCID: PMC9957888 DOI: 10.1007/s00406-022-01418-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Accepted: 04/11/2022] [Indexed: 12/19/2022]
Abstract
Improving response and remission rates in major depressive disorder (MDD) remains an important challenge. Matching patients to the treatment they will most likely respond to should be the ultimate goal. Even though numerous studies have investigated patient-specific indicators of treatment efficacy, no (bio)markers or empirical tests for use in clinical practice have resulted as of now. Therefore, clinical decisions regarding the treatment of MDD still have to be made on the basis of questionnaire- or interview-based assessments and general guidelines without the support of a (laboratory) test. We conducted a narrative review of current approaches to characterize and predict outcome to pharmacological treatments in MDD. We particularly focused on findings from newer computational studies using machine learning and on the resulting implementation into clinical decision support systems. The main issues seem to rest upon the unavailability of robust predictive variables and the lacking application of empirical findings and predictive models in clinical practice. We outline several challenges that need to be tackled on different stages of the translational process, from current concepts and definitions to generalizable prediction models and their successful implementation into digital support systems. By bridging the addressed gaps in translational psychiatric research, advances in data quantity and new technologies may enable the next steps toward precision psychiatry.
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Affiliation(s)
- Nicolas Rost
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstraße 2-10, 80804, Munich, Germany. .,International Max Planck Research School for Translational Psychiatry, Munich, Germany.
| | - Elisabeth B. Binder
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstraße 2-10, 80804 Munich, Germany
| | - Tanja M. Brückl
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstraße 2-10, 80804 Munich, Germany
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Ali FZ, Wengler K, He X, Nguyen MH, Parsey RV, DeLorenzo C. Gradient boosting decision-tree-based algorithm with neuroimaging for personalized treatment in depression. Neurosci Inform 2022; 2:100110. [PMID: 36699194 PMCID: PMC9873411 DOI: 10.1016/j.neuri.2022.100110] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Introduction Pretreatment positron emission tomography (PET) with 2-deoxy-2-[18F]fluoro-D-glucose (FDG) and magnetic resonance spectroscopy (MRS) may identify biomarkers for predicting remission (absence of depression). Yet, no such image-based biomarkers have achieved clinical validity. The purpose of this study was to identify biomarkers of remission using machine learning (ML) with pretreatment FDG-PET/MRS neuroimaging, to reduce patient suffering and economic burden from ineffective trials. Methods This study used simultaneous PET/MRS neuroimaging from a double-blind, placebo-controlled, randomized antidepressant trial on 60 participants with major depressive disorder (MDD) before initiating treatment. After eight weeks of treatment, those with ≤ 7 on 17-item Hamilton Depression Rating Scale were designated a priori as remitters (free of depression, 37%). Metabolic rate of glucose uptake (metabolism) from 22 brain regions were acquired from PET. Concentrations (mM) of glutamine and glutamate and gamma-aminobutyric acid (GABA) in anterior cingulate cortex were quantified from MRS. The data were randomly split into 67% train and cross-validation (n = 40), and 33% test (n = 20) sets. The imaging features, along with age, sex, handedness, and treatment assignment (selective serotonin reuptake inhibitor or SSRI vs. placebo) were entered into the eXtreme Gradient Boosting (XGBoost) classifier for training. Results In test data, the model showed 62% sensitivity, 92% specificity, and 77% weighted accuracy. Pretreatment metabolism of left hippocampus from PET was the most predictive of remission. Conclusions The pretreatment neuroimaging takes around 60 minutes but has potential to prevent weeks of failed treatment trials. This study effectively addresses common issues for neuroimaging analysis, such as small sample size, high dimensionality, and class imbalance.
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Affiliation(s)
- Farzana Z. Ali
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
| | - Kenneth Wengler
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
- Department of Psychiatry, Columbia University and New York State Psychiatric Institute, New York, NY, USA
| | - Xiang He
- Department of Radiology, Stony Brook Medicine, Stony Brook, NY, USA
- Department of Radiology, Northshore University Hospital, Manhasset, NY, USA
| | - Minh Hoai Nguyen
- Department of Computer Science, Stony Brook University, Stony Brook, NY, USA
| | - Ramin V. Parsey
- Department of Psychiatry, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Christine DeLorenzo
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
- Department of Psychiatry, Columbia University and New York State Psychiatric Institute, New York, NY, USA
- Department of Psychiatry, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
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20
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Ma X, Liu P, Law S, Ravindran N, Xu B, Fan T, Feng K. Characteristics of psychomotor retardation distinguishes patients with depression using multichannel near-infrared spectroscopy and finger tapping task. J Affect Disord 2022; 318:255-262. [PMID: 36087791 DOI: 10.1016/j.jad.2022.09.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 08/26/2022] [Accepted: 09/05/2022] [Indexed: 11/29/2022]
Abstract
BACKGROUND Psychomotor retardation (PMR) is frequently noted as a characteristic feature of major depressive disorder (MDD). In patients with depression, it is characterized by retardation of speech, emotion, thinking, and cognition. This study explored the activation pattern of the prefrontal cortex (PFC) during the finger-tapping task (FTT) in subjects with MDD, aiming to provide additional understanding on the connection between PMR and PFC activation pattern in depression through the use of near-Infrared Spectroscopy (NIRS). We hypothesized that, through use of NIRS during the FTT, motor retardation in depression would generate a distinct PFC activation pattern, allowing for differentiation between patients with MDD and healthy controls (HCs). METHODS Thirty-five patients with MDD and thirty-nine HCs underwent NIRS evaluation during performance of the FTT. The FTT included both left-finger tapping and right-finger tapping performed by a computer screen. Each participant was assessed using a 45-channel NIRS and various clinical scales. FINDINGS During the left-FTT, the left orbitofrontal cortex (OFC) showed higher oxy-hemoglobin (Oxy-Hb) activation in the MDD group when compared to the HCs. During the right-FTT, the right dorsolateral prefrontal cortex (DLPFC) demonstrated lower Oxy-Hb activation, and the dorsomedial prefrontal cortex (DMPFC) showed higher Oxy-Hb activation in the MDD group versus the HC group. CONCLUSION Our results demonstrated different activation patterns of the PFC between the MDD and HC groups, using FTT as a motor performance task. In particular, the OFC, the DLPFC and the DMPFC areas hold promise as new useful sites for such differentiation in future investigations.
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Affiliation(s)
- Xiangyun Ma
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
| | - Pozi Liu
- YuQuan Hospital, Tsinghua University, Beijing 10000, China
| | - Samuel Law
- Department of Psychiatry, University of Toronto, Canada
| | | | - Bo Xu
- YuQuan Hospital, Tsinghua University, Beijing 10000, China
| | - Tengteng Fan
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China.
| | - Kun Feng
- YuQuan Hospital, Tsinghua University, Beijing 10000, China.
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Huang CC, Huang HC, Lin CJ, Hsu CC, Lee CS, Hsu YH, Chen TL, Liao WH, Wu YH, Yang FPG, Liu SI. Subclinical alterations of resting state functional brain network for adjunctive bright light therapy in nonseasonal major depressive disorder: A double blind randomized controlled trial. Front Neurol 2022; 13:979500. [DOI: 10.3389/fneur.2022.979500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 10/27/2022] [Indexed: 11/11/2022] Open
Abstract
IntroductionThe treatment effect of bright light therapy (BLT) on major depressive disorder (MDD) has been proven, but the underlying mechanism remains unclear. Neuroimaging biomarkers regarding disease alterations in MDD and treatment response are rarely focused on BLT. This study aimed to identify the modulatory mechanism of BLT in MDD using resting-state functional magnetic resonance imaging (rfMRI).Materials and methodsThis double-blind, randomized controlled clinical trial included a dim red light (dRL) control group and a BLT experimental group. All participants received light therapy for 30 min every morning for 4 weeks. The assessment of the Hamilton Depression Rating Scale-24 (HAMD-24) and brain MRI exam were performed at the baseline and the 4-week endpoint. The four networks in interest, including the default mode network (DMN), frontoparietal network (FPN), salience network (SN), and sensorimotor network (SMN), were analyzed. Between-group differences of the change in these four networks were evaluated.ResultsThere were 22 and 21 participants in the BLT and dRL groups, respectively. Age, sex, years of education, baseline severity, and improvement in depressive symptoms were not significantly different between the two groups. The baseline rfMRI data did not show any significant functional connectivity differences within the DMN, FPN, SN, and SMN between the two groups. Compared with the dRL group, the BTL group showed significantly increased functional connectivity after treatment within the DMN, FPN, SN, and SMN. Graph analysis of the BLT group demonstrated an enhancement of betweenness centrality and global efficiency.ConclusionBLT can enhance intra-network functional connectivity in the DMN, FPN, SN, and SMN for MDD patients. Furthermore, BLT improves the information processing of the whole brain.Clinical trial registrationThe ClinicalTrials.gov identifier was NCT03941301.
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Brenner AM, Claudino FCDA, Burin LM, Scheibe VM, Padilha BL, de Souza GR, Duarte JA, da Rocha NS. Structural magnetic resonance imaging findings in severe mental disorders adult inpatients: A systematic review. Psychiatry Res Neuroimaging 2022; 326:111529. [PMID: 36058133 DOI: 10.1016/j.pscychresns.2022.111529] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 07/13/2022] [Accepted: 08/04/2022] [Indexed: 11/21/2022]
Abstract
In severe presentations, major depressive disorder (MDD), schizophrenia (SZ), and bipolar disorder (BD) can be categorized as severe mental disorders (SMD). Our aim is to evaluate structural magnetic resonance imaging and computed tomography findings in adult inpatients diagnosed with SMD and hospitalized at psychiatric wards. PubMed, Embase, PsycInfo, Cochrane Library, and Web of Science were searched up to May 27th, 2021. Articles were screened and extracted by two independent groups, with third-party raters for discrepancies. Quality of evidence was evaluated with the Newcastle-Ottawa Scale. Synthesis was made by qualitative analysis. This study was registered on PROSPERO (CRD42020171718) and followed the PRISMA protocol. 35 studies were included, of which none was considered to likely introduce bias in our analyses. Overlapping areas in MDD, SZ, and Affective Psychosis (AP) patients, that include BD and MDD with psychotic features, are presented in the inferior temporal and cingulate gyri. MDD and SZ had commonly affected areas in the inferior and middle frontal gyri, transverse temporal gyrus, insula, and hippocampus. SZ and AP had commonly affected areas in the temporal pole. Overlapping affected areas among SMD patients are reported, but the heterogeneity of studies' designs and findings are still a limitation for clinically relevant guidelines.
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Affiliation(s)
- Augusto Mädke Brenner
- Center for Clinical Research, Hospital de Clínicas de Porto Alegre, Porto Alegre, Rio Grande do Sul, Brazil; School of Medicine, Universidade Federal de Ciências da Saúde de Porto Alegre, Porto Alegre, Rio Grande do Sul, Brazil; Post-graduation Program in Psychiatry and Behavioral Sciences, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil.
| | - Felipe Cesar de Almeida Claudino
- Center for Clinical Research, Hospital de Clínicas de Porto Alegre, Porto Alegre, Rio Grande do Sul, Brazil; Post-graduation Program in Psychiatry and Behavioral Sciences, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil
| | - Luísa Monteiro Burin
- Center for Clinical Research, Hospital de Clínicas de Porto Alegre, Porto Alegre, Rio Grande do Sul, Brazil; Post-graduation Program in Psychiatry and Behavioral Sciences, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil
| | - Victória Machado Scheibe
- Center for Clinical Research, Hospital de Clínicas de Porto Alegre, Porto Alegre, Rio Grande do Sul, Brazil; Post-graduation Program in Psychiatry and Behavioral Sciences, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil; School of Medicine, Universidade Luterana do Brasil, Canoas, Rio Grande do Sul, Brazil
| | - Barbara Larissa Padilha
- Center for Clinical Research, Hospital de Clínicas de Porto Alegre, Porto Alegre, Rio Grande do Sul, Brazil; Post-graduation Program in Psychiatry and Behavioral Sciences, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil; School of Medicine, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil
| | - Gianfranco Rizzotto de Souza
- Center for Clinical Research, Hospital de Clínicas de Porto Alegre, Porto Alegre, Rio Grande do Sul, Brazil; School of Medicine, Universidade Federal de Ciências da Saúde de Porto Alegre, Porto Alegre, Rio Grande do Sul, Brazil; Post-graduation Program in Psychiatry and Behavioral Sciences, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil
| | - Juliana Avila Duarte
- Center for Clinical Research, Hospital de Clínicas de Porto Alegre, Porto Alegre, Rio Grande do Sul, Brazil; Post-graduation Program in Medical Sciences, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil
| | - Neusa Sica da Rocha
- Center for Clinical Research, Hospital de Clínicas de Porto Alegre, Porto Alegre, Rio Grande do Sul, Brazil; Post-graduation Program in Psychiatry and Behavioral Sciences, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil; Department of Psychiatry, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil
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Ya Y, Ji L, Jia Y, Zou N, Jiang Z, Yin H, Mao C, Luo W, Wang E, Fan G. Machine Learning Models for Diagnosis of Parkinson's Disease Using Multiple Structural Magnetic Resonance Imaging Features. Front Aging Neurosci 2022; 14:808520. [PMID: 35493923 PMCID: PMC9043762 DOI: 10.3389/fnagi.2022.808520] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 03/08/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose This study aimed to develop machine learning models for the diagnosis of Parkinson's disease (PD) using multiple structural magnetic resonance imaging (MRI) features and validate their performance. Methods Brain structural MRI scans of 60 patients with PD and 56 normal controls (NCs) were enrolled as development dataset and 69 patients with PD and 71 NCs from Parkinson's Progression Markers Initiative (PPMI) dataset as independent test dataset. First, multiple structural MRI features were extracted from cerebellar, subcortical, and cortical regions of the brain. Then, the Pearson's correlation test and least absolute shrinkage and selection operator (LASSO) regression were used to select the most discriminating features. Finally, using logistic regression (LR) classifier with the 5-fold cross-validation scheme in the development dataset, the cerebellar, subcortical, cortical, and a combined model based on all features were constructed separately. The diagnostic performance and clinical net benefit of each model were evaluated with the receiver operating characteristic (ROC) analysis and the decision curve analysis (DCA) in both datasets. Results After feature selection, 5 cerebellar (absolute value of left lobule crus II cortical thickness (CT) and right lobule IV volume, relative value of right lobule VIIIA CT and lobule VI/VIIIA gray matter volume), 3 subcortical (asymmetry index of caudate volume, relative value of left caudate volume, and absolute value of right lateral ventricle), and 4 cortical features (local gyrification index of right anterior circular insular sulcus and anterior agranular insula complex, local fractal dimension of right middle insular area, and CT of left supplementary and cingulate eye field) were selected as the most distinguishing features. The area under the curve (AUC) values of the cerebellar, subcortical, cortical, and combined models were 0.679, 0.555, 0.767, and 0.781, respectively, for the development dataset and 0.646, 0.632, 0.690, and 0.756, respectively, for the independent test dataset, respectively. The combined model showed higher performance than the other models (Delong's test, all p-values < 0.05). All models showed good calibration, and the DCA demonstrated that the combined model has a higher net benefit than other models. Conclusion The combined model showed favorable diagnostic performance and clinical net benefit and had the potential to be used as a non-invasive method for the diagnosis of PD.
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Affiliation(s)
- Yang Ya
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Lirong Ji
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Yujing Jia
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Nan Zou
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Zhen Jiang
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Hongkun Yin
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China
| | - Chengjie Mao
- Department of Neurology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Weifeng Luo
- Department of Neurology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Erlei Wang
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Guohua Fan
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
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Mosabbir AA, Braun Janzen T, Al Shirawi M, Rotzinger S, Kennedy SH, Farzan F, Meltzer J, Bartel L. Investigating the Effects of Auditory and Vibrotactile Rhythmic Sensory Stimulation on Depression: An EEG Pilot Study. Cureus 2022; 14:e22557. [PMID: 35371676 PMCID: PMC8958118 DOI: 10.7759/cureus.22557] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/23/2022] [Indexed: 12/18/2022] Open
Abstract
Background Major depressive disorder (MDD) is a persistent psychiatric condition and one of the leading causes of global disease burden. In a previous study, we investigated the effects of a five-week intervention consisting of rhythmic gamma frequency (30-70 Hz) vibroacoustic stimulation in 20 patients formally diagnosed with MDD. In that study, the findings suggested a significant clinical improvement in depression symptoms as measured using the Montgomery-Asberg Depression Rating Scale (MADRS), with 37% of participants meeting the criteria for clinical response. The goal of the present research was to examine possible changes from baseline to posttreatment in resting-state electroencephalography (EEG) recordings using the same treatment protocol and to characterize basic changes in EEG related to treatment response. Materials and methods The study sample consisted of 19 individuals aged 18-70 years with a clinical diagnosis of MDD. The participants were assessed before and after a five-week treatment period, which consisted of listening to an instrumental musical track on a vibroacoustic device, delivering auditory and vibrotactile stimulus in the gamma-band range (30-70 Hz, with particular emphasis on 40 Hz). The primary outcome measure was the change in Montgomery-Asberg Depression Rating Scale (MADRS) from baseline to posttreatment and resting-state EEG. Results Analysis comparing MADRS score at baseline and post-intervention indicated a significant change in the severity of depression symptoms after five weeks (t = 3.9923, df = 18, p = 0.0009). The clinical response rate was 36.85%. Resting-state EEG power analysis revealed a significant increase in occipital alpha power (t = -2.149, df = 18, p = 0.04548), as well as an increase in the prefrontal gamma power of the responders (t = 2.8079, df = 13.431, p = 0.01442). Conclusions The results indicate that improvements in MADRS scores after rhythmic sensory stimulation (RSS) were accompanied by an increase in alpha power in the occipital region and an increase in gamma in the prefrontal region, thus suggesting treatment effects on cortical activity in depression. The results of this pilot study will help inform subsequent controlled studies evaluating whether treatment response to vibroacoustic stimulation constitutes a real and replicable reduction of depressive symptoms and to characterize the underlying mechanisms.
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Affiliation(s)
| | | | | | - Susan Rotzinger
- Department of Psychiatry, University Health Network, Toronto, CAN
| | - Sidney H Kennedy
- Centre for Depression and Suicide Studies, St. Michael's Hospital, Toronto, CAN
| | - Faranak Farzan
- School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, CAN
| | - Jed Meltzer
- Rotman Research Institute, Baycrest Health Sciences, Toronto, CAN
| | - Lee Bartel
- Faculty of Music, University of Toronto, Toronto, CAN
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Guo XJ, Wu P, Jia X, Dong YM, Zhao CM, Chen NN, Zhang ZY, Miao YT, Yun KM, Gao CR, Ren Y. Mapping the structure of depression biomarker research: A bibliometric analysis. Front Psychiatry 2022; 13:943996. [PMID: 36186850 PMCID: PMC9523516 DOI: 10.3389/fpsyt.2022.943996] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Accepted: 08/29/2022] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Depression is a common mental disorder and the diagnosis is still based on the descriptions of symptoms. Biomarkers can reveal disease characteristics for diagnosis, prognosis, and treatment. In recent years, many biomarkers relevant to the mechanisms of depression have been identified. This study uses bibliometric methods and visualization tools to analyse the literature on depression biomarkers and its hot topics, and research frontiers to provide references for future research. METHODS Scientific publications related to depression biomarkers published between 2009 and 2022 were obtained from the Web of Science database. The BICOMB software was used to extract high-frequency keywords and to construct binary word-document and co-word matrices. gCLUTO was used for bicluster and visual analyses of high-frequency keywords. Further graphical visualizations were generated using R, CiteSpace and VOSviewer software. RESULTS A total of 14,403 articles related to depression biomarkers were identified. The United States (34.81%) and China (15.68%), which together account for more than half of all publications, can be considered the research base for the field. Among institutions, the University of California, University of London, and Harvard University are among the top in terms of publication number. Three authors (Maes M, Penninx B.W.J.H., and Berk M) emerged as eminent researchers in the field. Finally, eight research hotspots for depression biomarkers were identified using reference co-citation analysis. CONCLUSION This study used bibliometric methods to characterize the body of literature and subject knowledge in the field of depression biomarker research. Among the core biomarkers of depression, functional magnetic resonance imaging (fMRI), cytokines, and oxidative stress are relatively well established; however, research on machine learning, metabolomics, and microRNAs holds potential for future development. We found "microRNAs" and "gut microbiota" to be the most recent burst terms in the study of depression biomarkers and the likely frontiers of future research.
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Affiliation(s)
- Xiang-Jie Guo
- Department of Forensic Medicine, Shanxi Medical University, Taiyuan, China
| | - Peng Wu
- Department of Forensic Medicine, Shanxi Medical University, Taiyuan, China
| | - Xiao Jia
- College of Pharmacy, Nankai University, Tianjin, China
| | - Yi-Ming Dong
- Department of Forensic Medicine, Shanxi Medical University, Taiyuan, China
| | - Chun-Mei Zhao
- Department of Forensic Medicine, Shanxi Medical University, Taiyuan, China
| | - Nian-Nian Chen
- Department of Forensic Medicine, Shanxi Medical University, Taiyuan, China
| | - Zhi-Yong Zhang
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Yu-Ting Miao
- Department of Psychology, School of Humanities and Social Sciences, Shanxi Medical University, Taiyuan, China
| | - Ke-Ming Yun
- Department of Forensic Medicine, Shanxi Medical University, Taiyuan, China
| | - Cai-Rong Gao
- Department of Forensic Medicine, Shanxi Medical University, Taiyuan, China
| | - Yan Ren
- Department of Psychiatry, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China.,Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Wang K, Tan F, Zhu Z, Kong L. Exploring changes in depression and radiology-related publications research focus: A bibliometrics and content analysis based on natural language processing. Front Psychiatry 2022; 13:978763. [PMID: 36532194 PMCID: PMC9748702 DOI: 10.3389/fpsyt.2022.978763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 11/14/2022] [Indexed: 12/02/2022] Open
Abstract
OBJECTIVE This study aims to construct and use natural language processing and other methods to analyze major depressive disorder (MDD) and radiology studies' publications in the PubMed database to understand the historical growth, current state, and potential expansion trend. METHODS All MDD radiology studies publications from January 2002 to January 2022 were downloaded from PubMed using R, a statistical computing language. R and the interpretive general-purpose programming language Python were used to extract publication dates, geographic information, and abstracts from each publication's metadata for bibliometric analysis. The generative statistical algorithm "Latent Dirichlet allocation" (LDA) was applied to identify specific research focus and trends. The unsupervised Leuven algorithm was used to build a network to identify relationships between research focus. RESULTS A total of 5,566 publications on MDD and radiology research were identified, and there is a rapid upward trend. The top-cited publications were 11,042, and the highly-cited publications focused on improving diagnostic performance and establishing imaging standards. Publications came from 76 countries, with the most from research institutions in the United States and China. Hospitals and radiology departments take the lead in research and have an advantage. The extensive field of study contains 12,058 Medical Subject Heading (MeSH) terms. Based on the LDA algorithm, three areas were identified that have become the focus of research in recent years, "Symptoms and treatment," "Brain structure and imaging," and "Comorbidities research." CONCLUSION Latent Dirichlet allocation analysis methods can be well used to analyze many texts and discover recent research trends and focus. In the past 20 years, the research on MDD and radiology has focused on exploring MDD mechanisms, establishing standards, and constructing imaging methods. Recent research focuses are "Symptoms and sleep," "Brain structure study," and "functional connectivity." New progress may be made in studies on MDD complications and the combination of brain structure and metabolism.
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Affiliation(s)
- Kangtao Wang
- Department of General Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Fengbo Tan
- Department of General Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Zhiming Zhu
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Lingyu Kong
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, Hunan, China
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Abstract
PURPOSE OF REVIEW This comprehensive review of mood disorders brings together the past and current literature on the diagnosis, evaluation, and treatment of the depressive and bipolar disorders. It highlights the primary mood disorders and secondary neurologic causes of mood disorders that are commonly encountered in a clinical setting. As the literature and our understanding evolve, recent additions to the current literature are important to bring forth to the readers. RECENT FINDINGS Advancements in clinical medicine have strengthened our understanding of the associations of neurologic and psychiatric diseases. This article highlights the medications frequently used with newly identified mood disorders and the common side effects of these medications. A paradigm shift has moved toward newer treatment modalities, such as the use of ketamine, repetitive transcranial magnetic stimulation, and complementary and alternative medicine. The risks and benefits of such therapies, along with medications, are reviewed in this article. SUMMARY Mood disorders are extraordinarily complex disorders with significant association with many neurologic disorders. Early identification of these mood disorders can prevent significant morbidity and mortality associated with them. With further expansion of pharmacologic options, more targeted therapy is possible in improving quality of life for patients.
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Lin E, Lin CH, Lane HY. Machine Learning and Deep Learning for the Pharmacogenomics of Antidepressant Treatments. Clin Psychopharmacol Neurosci 2021; 19:577-588. [PMID: 34690113 PMCID: PMC8553527 DOI: 10.9758/cpn.2021.19.4.577] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 04/10/2021] [Indexed: 12/31/2022]
Abstract
A growing body of evidence now proposes that machine learning and deep learning techniques can serve as a vital foundation for the pharmacogenomics of antidepressant treatments in patients with major depressive disorder (MDD). In this review, we focus on the latest developments for pharmacogenomics research using machine learning and deep learning approaches together with neuroimaging and multi-omics data. First, we review relevant pharmacogenomics studies that leverage numerous machine learning and deep learning techniques to determine treatment prediction and potential biomarkers for antidepressant treatments in MDD. In addition, we depict some neuroimaging pharmacogenomics studies that utilize various machine learning approaches to predict antidepressant treatment outcomes in MDD based on the integration of research on pharmacogenomics and neuroimaging. Moreover, we summarize the limitations in regard to the past pharmacogenomics studies of antidepressant treatments in MDD. Finally, we outline a discussion of challenges and directions for future research. In light of latest advancements in neuroimaging and multi-omics, various genomic variants and biomarkers associated with antidepressant treatments in MDD are being identified in pharmacogenomics research by employing machine learning and deep learning algorithms.
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Affiliation(s)
- Eugene Lin
- Department of Biostatistics, University of Washington, Seattle, WA, USA
- Department of Electrical & Computer Engineering, University of Washington, Seattle, WA, USA
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan
| | - Chieh-Hsin Lin
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan
- Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan
- School of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Hsien-Yuan Lane
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan
- Department of Psychiatry, China Medical University Hospital, Taichung, Taiwan
- Department of Brain Disease Research Center, China Medical University Hospital, Taichung, Taiwan
- Department of Psychology, College of Medical and Health Sciences, Asia University, Taichung, Taiwan
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Ballester PL, Suh JS, Nogovitsyn N, Hassel S, Strother SC, Arnott SR, Minuzzi L, Sassi RB, Lam RW, Milev R, Müller DJ, Taylor VH, Kennedy SH, Frey BN. Accelerated brain aging in major depressive disorder and antidepressant treatment response: A CAN-BIND report. Neuroimage Clin 2021; 32:102864. [PMID: 34710675 PMCID: PMC8556529 DOI: 10.1016/j.nicl.2021.102864] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 10/08/2021] [Accepted: 10/18/2021] [Indexed: 10/24/2022]
Abstract
OBJECTIVES Previous studies suggest that major depressive disorder (MDD) may be associated with volumetric indications of accelerated brain aging. This study investigated neuroanatomical signs of accelerated aging in MDD and evaluated whether a brain age gap is associated with antidepressant response. METHODS Individuals in a major depressive episode received escitalopram treatment (10-20 mg/d) for 8 weeks. Depression severity was assessed at baseline and at weeks 8 and 16 using the Montgomery-Asberg Depression Rating Scale (MADRS). Response to treatment was characterized by a significant reduction in the MADRS (≥50%). Nonresponders received adjunctive aripiprazole treatment (2-10 mg/d) for a further 8 weeks. The brain-predicted age difference (brain-PAD) at baseline was determined using machine learning methods trained on 3377 healthy individuals from seven publicly available datasets. The model used features from all brain regions extracted from structural magnetic resonance imaging data. RESULTS Brain-PAD was significantly higher in older MDD participants compared to younger MDD participants [t(147.35) = -2.35, p < 0.03]. BMI was significantly associated with brain-PAD in the MDD group [r(155) = 0.19, p < 0.03]. Response to treatment was not significantly associated with brain-PAD. CONCLUSION We found an elevated brain age gap in older individuals with MDD. Brain-PAD was not associated with overall treatment response to escitalopram monotherapy or escitalopram plus adjunctive aripiprazole.
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Affiliation(s)
- Pedro L Ballester
- Neuroscience Graduate Program, McMaster University, Hamilton, ON, Canada
| | - Jee Su Suh
- Neuroscience Graduate Program, McMaster University, Hamilton, ON, Canada
| | - Nikita Nogovitsyn
- Mood Disorders Program, Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
| | - Stefanie Hassel
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Stephen C Strother
- Rotman Research Institute, Baycrest, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Department of Medical Biophysics, University of Toronto, ON, Canada
| | | | - Luciano Minuzzi
- Mood Disorders Program, Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada; Women's Health Concerns Clinic, St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada
| | - Roberto B Sassi
- Mood Disorders Program, Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
| | - Raymond W Lam
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Roumen Milev
- Departments of Psychiatry and Psychology, Queen's University, and Providence Care, Kingston, ON, Canada
| | - Daniel J Müller
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Valerie H Taylor
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Sidney H Kennedy
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Centre for Mental Health, University Health Network, Toronto, ON, Canada; Krembil Research Institute, University Health Network, Toronto, ON, Canada; Centre for Depression and Suicide Studies, and Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, ON, Canada
| | - Benicio N Frey
- Mood Disorders Program, Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada; Women's Health Concerns Clinic, St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada.
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Yang Z, Jian L, Qiu H, Zhang C, Cheng S, Ji J, Li T, Wang Y, Li J, Li K. Understanding complex functional wiring patterns in major depressive disorder through brain functional connectome. Transl Psychiatry 2021; 11:526. [PMID: 34645783 DOI: 10.1038/s41398-021-01646-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 09/20/2021] [Accepted: 09/29/2021] [Indexed: 02/06/2023] Open
Abstract
Brain function relies on efficient communications between distinct brain systems. The pathology of major depressive disorder (MDD) damages functional brain networks, resulting in cognitive impairment. Here, we reviewed the associations between brain functional connectome changes and MDD pathogenesis. We also highlighted the utility of brain functional connectome for differentiating MDD from other similar psychiatric disorders, predicting recurrence and suicide attempts in MDD, and evaluating treatment responses. Converging evidence has now linked aberrant brain functional network organization in MDD to the dysregulation of neurotransmitter signaling and neuroplasticity, providing insights into the neurobiological mechanisms of the disease and antidepressant efficacy. Widespread connectome dysfunctions in MDD patients include multiple, large-scale brain networks as well as local disturbances in brain circuits associated with negative and positive valence systems and cognitive functions. Although the clinical utility of the brain functional connectome remains to be realized, recent findings provide further promise that research in this area may lead to improved diagnosis, treatments, and clinical outcomes of MDD.
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31
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Suh JS, Fiori LM, Ali M, Harkness KL, Ramonas M, Minuzzi L, Hassel S, Strother SC, Zamyadi M, Arnott SR, Farzan F, Foster JA, Lam RW, MacQueen GM, Milev R, Müller DJ, Parikh SV, Rotzinger S, Sassi RB, Soares CN, Uher R, Kennedy SH, Turecki G, Frey BN. Hypothalamus volume and DNA methylation of stress axis genes in major depressive disorder: A CAN-BIND study report. Psychoneuroendocrinology 2021; 132:105348. [PMID: 34229186 DOI: 10.1016/j.psyneuen.2021.105348] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 06/14/2021] [Accepted: 06/25/2021] [Indexed: 11/28/2022]
Abstract
Dysfunction of the hypothalamic-pituitary-adrenal (HPA) axis is considered one of the mechanisms underlying the development of major depressive disorder (MDD), but the exact nature of this dysfunction is unknown. We investigated the relationship between hypothalamus volume (HV) and blood-derived DNA methylation in MDD. We obtained brain MRI, clinical and molecular data from 181 unmedicated MDD and 90 healthy control (HC) participants. MDD participants received a 16-week standardized antidepressant treatment protocol, as part of the first Canadian Biomarker Integration Network in Depression (CAN-BIND) study. We collected bilateral HV measures via manual segmentation by two independent raters. DNA methylation and RNA sequencing were performed for three key HPA axis-regulating genes coding for the corticotropin-binding protein (CRHBP), glucocorticoid receptor (NR3C1) and FK506 binding protein 5 (FKBP5). We used elastic net regression to perform variable selection and assess predictive ability of methylation variables on HV. Left HV was negatively associated with duration of current episode (ρ = -0.17, p = 0.035). We did not observe significant differences in HV between MDD and HC or any associations between HV and treatment response at weeks 8 or 16, overall depression severity, illness duration or childhood maltreatment. We also did not observe any differentially methylated CpG sites between MDD and HC groups. After assessing functionality by correlating methylation levels with RNA expression of the respective genes, we observed that the number of functionally relevant CpG sites differed between MDD and HC groups in FKBP5 (χ2 = 77.25, p < 0.0001) and NR3C1 (χ2 = 7.29, p = 0.007). Cross-referencing functionally relevant CpG sites to those that were highly ranked in predicting HV in elastic net modeling identified one site from FKBP5 (cg03591753) and one from NR3C1 (cg20728768) within the MDD group. Stronger associations between DNA methylation, gene expression and HV in MDD suggest a novel putative molecular pathway of stress-related sensitivity in depression. Future studies should consider utilizing the epigenome and ultra-high field MR data which would allow the investigation of HV sub-fields.
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Affiliation(s)
- Jee Su Suh
- Neuroscience Graduate Program, McMaster University, Hamilton, ON, Canada; Mood Disorders Program and Women's Health Concerns Clinic, St. Joseph's Healthcare Hamilton, ON, Canada
| | - Laura M Fiori
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Mohammad Ali
- Neuroscience Graduate Program, McMaster University, Hamilton, ON, Canada; Mood Disorders Program and Women's Health Concerns Clinic, St. Joseph's Healthcare Hamilton, ON, Canada
| | - Kate L Harkness
- Department of Psychology, Queen's University, Kingston, ON, Canada
| | - Milita Ramonas
- Juravinski Hospital and Cancer Centre, Hamilton Health Sciences, Hamilton, ON, Canada
| | - Luciano Minuzzi
- Neuroscience Graduate Program, McMaster University, Hamilton, ON, Canada; Mood Disorders Program and Women's Health Concerns Clinic, St. Joseph's Healthcare Hamilton, ON, Canada; Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
| | - Stefanie Hassel
- Department of Psychiatry, University of Calgary, Calgary, AB, Canada
| | | | - Mojdeh Zamyadi
- Rotman Research Institute, Baycrest, Toronto, ON, Canada
| | | | - Faranak Farzan
- eBrain Lab, School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC, Canada
| | - Jane A Foster
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
| | - Raymond W Lam
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Glenda M MacQueen
- Department of Psychiatry, University of Calgary, Calgary, AB, Canada
| | - Roumen Milev
- Departments of Psychiatry and Psychology, Queen's University, and Providence Care Hospital, Kingston, ON, Canada
| | - Daniel J Müller
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Centre for Addiction and Mental Health, Pharmacogenetics Research Clinic, Toronto, ON, Canada
| | - Sagar V Parikh
- University of Michigan Depression Center, Ann Arbor, MI, United States
| | - Susan Rotzinger
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Roberto B Sassi
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
| | - Claudio N Soares
- Departments of Psychiatry and Psychology, Queen's University, and Providence Care Hospital, Kingston, ON, Canada
| | - Rudolf Uher
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Sidney H Kennedy
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Centre for Depression and Suicide Studies, St. Michael's Hospital, Toronto, ON, Canada
| | - Gustavo Turecki
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Benicio N Frey
- Neuroscience Graduate Program, McMaster University, Hamilton, ON, Canada; Mood Disorders Program and Women's Health Concerns Clinic, St. Joseph's Healthcare Hamilton, ON, Canada; Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada.
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Bassa A, Sagués T, Porta-Casteràs D, Serra P, Martínez-Amorós E, Palao DJ, Cano M, Cardoner N. The Neurobiological Basis of Cognitive Side Effects of Electroconvulsive Therapy: A Systematic Review. Brain Sci 2021; 11:1273. [PMID: 34679338 DOI: 10.3390/brainsci11101273] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Revised: 09/20/2021] [Accepted: 09/22/2021] [Indexed: 11/26/2022] Open
Abstract
Decades of research have consistently demonstrated the efficacy of electroconvulsive therapy (ECT) for the treatment of major depressive disorder (MDD), but its clinical use remains somewhat restricted because of its cognitive side effects. The aim of this systematic review is to comprehensively summarize current evidence assessing potential biomarkers of ECT-related cognitive side effects. Based on our systematic search of human studies indexed in PubMed, Scopus, and Web of Knowledge, a total of 29 studies evaluating patients with MDD undergoing ECT were reviewed. Molecular biomarkers studies did not consistently identify concentration changes in plasma S-100 protein, neuron-specific enolase (NSE), or Aβ peptides significantly associated with cognitive performance after ECT. Importantly, these findings suggest that ECT-related cognitive side effects cannot be explained by mechanisms of neural cell damage. Notwithstanding, S-100b protein and Aβ40 peptide concentrations, as well as brain-derived neurotrophic factor (BDNF) polymorphisms, have been suggested as potential predictive biomarkers of cognitive dysfunction after ECT. In addition, recent advances in brain imaging have allowed us to identify ECT-induced volumetric and functional changes in several brain structures closely related to memory performance such as the hippocampus. We provide a preliminary framework to further evaluate neurobiological cognitive vulnerability profiles of patients with MDD treated with ECT.
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Kelley ME, Choi KS, Rajendra JK, Craighead WE, Rakofsky JJ, Dunlop BW, Mayberg HS. Establishing Evidence for Clinical Utility of a Neuroimaging Biomarker in Major Depressive Disorder: Prospective Testing and Implementation Challenges. Biol Psychiatry 2021; 90:236-242. [PMID: 33896622 PMCID: PMC8324510 DOI: 10.1016/j.biopsych.2021.02.966] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 01/25/2021] [Accepted: 02/12/2021] [Indexed: 12/21/2022]
Abstract
BACKGROUND Although a number of neuroimaging biomarkers for response have been proposed, none have been tested prospectively for direct effects on treatment outcomes. To the best of our knowledge, this is the first prospective test of the clinical utility of the use of an imaging biomarker to select treatment for patients with major depressive disorder. METHODS Eligible participants (n = 60) had a primary diagnosis of major depressive disorder and were assigned to either escitalopram or cognitive behavioral therapy based on fluorodeoxyglucose positron emission tomography activity in the right anterior insula. The overall study remission rate after 12 weeks of treatment, based on the end point Hamilton Depression Rating Scale score, was then examined for futility and benefit of the strategy. RESULTS Remission rates demonstrated lack of futility at the end of stage 1 (37%, 10/27), and the study proceeded to stage 2. After adjustment for the change in stage 2 sample size, the complete remission rate did not demonstrate evidence of benefit (37.7%, 95% confidence interval, 26.3%-51.4%, p = .38). However, total remission rates (complete and partial remission) did reach significance in post hoc analysis (49.1%, 95% confidence interval, 37.6%-60.7%, p = .020). CONCLUSIONS The study shows some evidence for a role of the right anterior insula in the clinical choice of major depressive disorder monotherapy. The effect size, however, is insufficient for the use of insula activity as a sole predictive biomarker of remission. The study also demonstrates the logistical difficulties in establishing clinical utility of biomarkers.
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Affiliation(s)
- Mary E. Kelley
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Ki Sueng Choi
- Center for Advanced Circuit Therapeutics , Icahn School of Medicine at Mount Sinai, New York, NY, USA,Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Justin K. Rajendra
- Scientific and Statistical Computing Core, NIMH/NIH/DHHS, Bethesda, Maryland, USA
| | - W. Edward Craighead
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA.,Department of Psychology, Emory University, Atlanta, GA, USA
| | - Jeffrey J. Rakofsky
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Boadie W. Dunlop
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Helen S. Mayberg
- Center for Advanced Circuit Therapeutics , Icahn School of Medicine at Mount Sinai, New York, NY, USA,Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Cho SE, Park CA, Na KS, Chung C, Ma HJ, Kang CK, Kang SG. Left-right asymmetric and smaller right habenula volume in major depressive disorder on high-resolution 7-T magnetic resonance imaging. PLoS One 2021; 16:e0255459. [PMID: 34343199 DOI: 10.1371/journal.pone.0255459] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2020] [Accepted: 07/18/2021] [Indexed: 02/08/2023] Open
Abstract
The habenula (Hb) has been hypothesized to play an essential role in major depressive disorder (MDD) as it is considered to be an important node between fronto-limbic areas and midbrain monoaminergic structures based on animal studies. In this study, we aimed to investigate the differences in volume and T1 value of the Hb between patients with MDD and healthy control (HC) subjects. Analysis for the Hb volumes was performed using high-resolution 7-T magnetic resonance (MR) image data from 33 MDD patients and 36 healthy subjects. Two researchers blinded to the clinical data manually delineated the habenular nuclei and Hb volume, and T1 values were calculated based on overlapping voxels. We compared the Hb volume and T1 value between the MDD and HC groups and compared the volume and T1 values between the left and right Hbs in each group. Compared to HC subjects, MDD patients had a smaller right Hb volume; however, there was no significant volume difference in the left Hb between groups. In the MDD group, the right Hb was smaller in volume and lower in T1 value than the left Hb. The present findings suggest a smaller right Hb volume and left-right asymmetry of Hb volume in MDD. Future high-resolution 7-T MR imaging studies with larger sample sizes will be needed to derive a more definitive conclusion.
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Abstract
A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer's disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous deep learning models are being exploited to distinguish AD from normal controls and/or to distinguish AD from mild cognitive impairment in recent research studies. In this review, we focus on the latest developments for AD prediction using deep learning techniques in cooperation with the principles of neuroimaging and genomics. First, we narrate various investigations that make use of deep learning algorithms to establish AD prediction using genomics or neuroimaging data. Particularly, we delineate relevant integrative neuroimaging genomics investigations that leverage deep learning methods to forecast AD on the basis of incorporating both neuroimaging and genomics data. Moreover, we outline the limitations as regards to the recent AD investigations of deep learning with neuroimaging and genomics. Finally, we depict a discussion of challenges and directions for future research. The main novelty of this work is that we summarize the major points of these investigations and scrutinize the similarities and differences among these investigations.
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Affiliation(s)
- Eugene Lin
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA;
- Department of Electrical & Computer Engineering, University of Washington, Seattle, WA 98195, USA
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
| | - Chieh-Hsin Lin
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
- Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 83301, Taiwan
- School of Medicine, Chang Gung University, Taoyuan 33302, Taiwan
| | - Hsien-Yuan Lane
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
- Department of Psychiatry, China Medical University Hospital, Taichung 40447, Taiwan
- Brain Disease Research Center, China Medical University Hospital, Taichung 40447, Taiwan
- Department of Psychology, College of Medical and Health Sciences, Asia University, Taichung 41354, Taiwan
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Koyanagi M, Yamada M, Higashi T, Mitsunaga W, Moriuchi T, Tsujihata M. The Usefulness of Functional Near-Infrared Spectroscopy for the Assessment of Post-Stroke Depression. Front Hum Neurosci 2021; 15:680847. [PMID: 34239431 PMCID: PMC8258375 DOI: 10.3389/fnhum.2021.680847] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 05/14/2021] [Indexed: 01/10/2023] Open
Abstract
Background: Post-stroke depression (PSD) is the most common mood disorder following stroke and is also the main factor that limits the recovery and rehabilitation of patients with stroke. The prevalence of PSD is ~30%. Since there is no gold standard for the diagnosis and evaluation of PSD, it is important to raise awareness of PSD and to establish methods for its evaluation, early diagnosis, and treatment. In the field of psychiatry, functional near-infrared spectroscopy (fNIRS) has been used as a diagnostic tool for the measurement of oxygenated hemoglobin (oxy-Hb). This study aimed to assess whether fNIRS could be applied in the diagnosis and evaluation of PSD. Methods: We recruited 45 patients with stroke, who were admitted to Nagasaki Kita Hospital between May 2015 and April 2019. The 17-item Hamilton Rating Scale for Depression (HAMD17), which is considered to be a useful screening and evaluation tool for PSD, was used for the assessment of patients after stroke; moreover, oxy-Hb was measured in the pre-frontal cortex. The subjects were divided into two groups: the depressed group (n = 13) and the non-depressed group (n = 32). We evaluated the correlation between the oxy-Hb integral values and HAMD17 scores. Results: We investigated the relationship between the oxy-Hb integral values and HAMD17 total scores, and found a negative correlation between them (ρ = −0.331, P < 0.005). There was a significant difference in the oxy-Hb integral values during the activation task period between the depressed and non-depressed groups (3.16 ± 2.7 and 1.71 ± 2.4, respectively; P = 0.040). The results indicated that the patients of the depressed group showed lower oxy-Hb integral values and lower activation in the frontal lobe in comparison with the patients of the non-depressed group. Conclusion: The present study highlights that the measurement of oxy-Hb by using fNIRS is a useful methodology for the diagnosis of PSD in patients after stroke.
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Affiliation(s)
- Masahiko Koyanagi
- Department of Health Sciences, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan.,Department of Rehabilitation, Nagasaki Kita Hospital, Nagasaki, Japan
| | - Mai Yamada
- Department of Health Sciences, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan.,Department of Rehabilitation, Nagasaki Kita Hospital, Nagasaki, Japan
| | - Toshio Higashi
- Department of Health Sciences, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Wataru Mitsunaga
- Department of Health Sciences, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Takefumi Moriuchi
- Department of Health Sciences, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
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Zhao S, Bao Z, Zhao X, Xu M, Li MD, Yang Z. Identification of Diagnostic Markers for Major Depressive Disorder Using Machine Learning Methods. Front Neurosci 2021; 15:645998. [PMID: 34220416 PMCID: PMC8249859 DOI: 10.3389/fnins.2021.645998] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Accepted: 05/25/2021] [Indexed: 12/12/2022] Open
Abstract
Background Major depressive disorder (MDD) is a global health challenge that impacts the quality of patients’ lives severely. The disorder can manifest in many forms with different combinations of symptoms, which makes its clinical diagnosis difficult. Robust biomarkers are greatly needed to improve diagnosis and to understand the etiology of the disease. The main purpose of this study was to create a predictive model for MDD diagnosis based on peripheral blood transcriptomes. Materials and Methods We collected nine RNA expression datasets for MDD patients and healthy samples from the Gene Expression Omnibus database. After a series of quality control and heterogeneity tests, 302 samples from six studies were deemed suitable for the study. R package “MetaOmics” was applied for systematic meta-analysis of genome-wide expression data. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic effectiveness of individual genes. To obtain a better diagnostic model, we also adopted the support vector machine (SVM), random forest (RF), k-nearest neighbors (kNN), and naive Bayesian (NB) tools for modeling, with the RF method being used for feature selection. Results Our analysis revealed six differentially expressed genes (AKR1C3, ARG1, KLRB1, MAFG, TPST1, and WWC3) with a false discovery rate (FDR) < 0.05 between MDD patients and control subjects. We then evaluated the diagnostic ability of these genes individually. With single gene prediction, we achieved a corresponding area under the curve (AUC) value of 0.63 ± 0.04, 0.67 ± 0.07, 0.70 ± 0.11, 0.64 ± 0.08, 0.68 ± 0.07, and 0.62 ± 0.09, respectively, for these genes. Next, we constructed the classifiers of SVM, RF, kNN, and NB with an AUC of 0.84 ± 0.09, 0.81 ± 0.10, 0.73 ± 0.11, and 0.83 ± 0.09, respectively, in validation datasets, suggesting that the SVM classifier might be superior for constructing an MDD diagnostic model. The final SVM classifier including 70 feature genes was capable of distinguishing MDD samples from healthy controls and yielded an AUC of 0.78 in an independent dataset. Conclusion This study provides new insights into potential biomarkers through meta-analysis of GEO data. Constructing different machine learning models based on these biomarkers could be a valuable approach for diagnosing MDD in clinical practice.
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Affiliation(s)
- Shu Zhao
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhiwei Bao
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xinyi Zhao
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Mengxiang Xu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Ming D Li
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Research Center for Air Pollution and Health, Zhejiang University, Hangzhou, China
| | - Zhongli Yang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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Khadka B, Lee JY, Park EK, Kim KT, Bae JS. Impacts of Drug Interactions on Pharmacokinetics and the Brain Transporters: A Recent Review of Natural Compound-Drug Interactions in Brain Disorders. Int J Mol Sci 2021; 22:1809. [PMID: 33670407 DOI: 10.3390/ijms22041809] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 01/29/2021] [Accepted: 02/09/2021] [Indexed: 12/15/2022] Open
Abstract
Natural compounds such as herbal medicines and/or phyto-compounds from foods, have frequently been used to exert synergistic therapeutic effects with anti-brain disorder drugs, supplement the effects of nutrients, and boost the immune system. However, co-administration of natural compounds with the drugs can cause synergistic toxicity or impeditive drug interactions due to changes in pharmacokinetic properties (e.g., absorption, metabolism, and excretion) and various drug transporters, particularly brain transporters. In this review, natural compound–drug interactions (NDIs), which can occur during the treatment of brain disorders, are emphasized from the perspective of pharmacokinetics and cellular transport. In addition, the challenges emanating from NDIs and recent approaches are discussed.
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Korda AI, Andreou C, Borgwardt S. Pattern classification as decision support tool in antipsychotic treatment algorithms. Exp Neurol 2021; 339:113635. [PMID: 33548218 DOI: 10.1016/j.expneurol.2021.113635] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 01/20/2021] [Accepted: 02/01/2021] [Indexed: 10/22/2022]
Abstract
Pattern classification aims to establish a new approach in personalized treatment. The scope is to tailor treatment on individual characteristics during all phases of care including prevention, diagnosis, treatment, and clinical outcome. In psychotic disorders, this need results from the fact that a third of patients with psychotic symptoms do not respond to antipsychotic treatment and are described as having treatment-resistant disorders. This, in addition to the high variability of treatment responses among patients, enhances the need of applying advanced classification algorithms to identify antipsychotic treatment patterns. This review comprehensively summarizes advancements and challenges of pattern classification in antipsychotic treatment response to date and aims to introduce clinicians and researchers to the challenges of including pattern classification into antipsychotic treatment decision algorithms.
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Affiliation(s)
- Alexandra I Korda
- Department of Psychiatry and Psychotherapy, University Hospital Lübeck (UKSH), Ratzeburger Allee 160, 23538 Lübeck, Germany
| | - Christina Andreou
- Department of Psychiatry and Psychotherapy, University Hospital Lübeck (UKSH), Ratzeburger Allee 160, 23538 Lübeck, Germany
| | - Stefan Borgwardt
- Department of Psychiatry and Psychotherapy, University Hospital Lübeck (UKSH), Ratzeburger Allee 160, 23538 Lübeck, Germany.
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