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Richard-Devantoy S, Berlim MT, Garel N, Inja A, Turecki G. The impact of antidepressant treatment on the network structure of neurocognition and core emotional depressive symptoms among depressed individuals with a history of suicide attempt: An 8-week clinical study. J Affect Disord 2024; 361:425-433. [PMID: 38823590 DOI: 10.1016/j.jad.2024.05.111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 05/20/2024] [Accepted: 05/22/2024] [Indexed: 06/03/2024]
Abstract
BACKGROUND A more in-depth understanding of the relationship between depressive symptoms, neurocognition and suicidal behavior could provide insights into the prognosis and treatment of major depressive disorder (MDD) and suicide. We conducted a network analysis among depressed patients examining associations between history of suicide attempt (HSA), core emotional major depression disorder, and key neurocognitive domains. METHOD Depressed patients (n = 120) aged 18-65 years were recruited from a larger randomized clinical trial conducted at the Douglas Institute in Montreal, Canada. They were randomly assigned to receive one of two antidepressant treatments (i.e., escitalopram or desvenlafaxine) for 8 weeks. Core emotional MDD and key neurocognitive domains were assessed pre-post treatment. RESULTS At baseline, an association between history of suicide attempt (HSA) and phonemic verbal fluency (PVF) suggested that HSA patients reported lower levels of the latter. After 8 weeks of antidepressant treatment, HSA became conditionally independent from PVF. Similar results were found for both the HAM-D and the QIDS-SR core emotional MDD/neurocognitive networks. CONCLUSION Network analysis revealed a pre-treatment relationship between a HSA and decreased phonemic VF among depressed patients, which was no longer present after 8 weeks of antidepressant treatment.
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Affiliation(s)
- Stéphane Richard-Devantoy
- McGill University & Douglas Mental Health Research Institute, McGill Group for Suicide Studies, Montréal, Québec, Canada; CISSS des Laurentides, Department of Psychiatry, Saint-Jérôme, Canada.
| | - Marcelo T Berlim
- McGill University & Douglas Mental Health Research Institute, McGill Group for Suicide Studies, Montréal, Québec, Canada
| | - Nicolas Garel
- McGill University & Douglas Mental Health Research Institute, McGill Group for Suicide Studies, Montréal, Québec, Canada
| | - Ayla Inja
- McGill University & Douglas Mental Health Research Institute, McGill Group for Suicide Studies, Montréal, Québec, Canada
| | - Gustavo Turecki
- McGill University & Douglas Mental Health Research Institute, McGill Group for Suicide Studies, Montréal, Québec, Canada.
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Colombo F, Calesella F, Bravi B, Fortaner-Uyà L, Monopoli C, Tassi E, Carminati M, Zanardi R, Bollettini I, Poletti S, Lorenzi C, Spadini S, Brambilla P, Serretti A, Maggioni E, Fabbri C, Benedetti F, Vai B. Multimodal brain-derived subtypes of Major depressive disorder differentiate patients for anergic symptoms, immune-inflammatory markers, history of childhood trauma and treatment-resistance. Eur Neuropsychopharmacol 2024; 85:45-57. [PMID: 38936143 DOI: 10.1016/j.euroneuro.2024.05.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 05/20/2024] [Accepted: 05/27/2024] [Indexed: 06/29/2024]
Abstract
An estimated 30 % of Major Depressive Disorder (MDD) patients exhibit resistance to conventional antidepressant treatments. Identifying reliable biomarkers of treatment-resistant depression (TRD) represents a major goal of precision psychiatry, which is hampered by the clinical and biological heterogeneity. To uncover biologically-driven subtypes of MDD, we applied an unsupervised data-driven framework to stratify 102 MDD patients on their neuroimaging signature, including extracted measures of cortical thickness, grey matter volumes, and white matter fractional anisotropy. Our novel analytical pipeline integrated different machine learning algorithms to harmonize data, perform data dimensionality reduction, and provide a stability-based relative clustering validation. The obtained clusters were characterized for immune-inflammatory peripheral biomarkers, TRD, history of childhood trauma and depressive symptoms. Our results indicated two different clusters of patients, differentiable with 67 % of accuracy: one cluster (n = 59) was associated with a higher proportion of TRD, and higher scores of energy-related depressive symptoms, history of childhood abuse and emotional neglect; this cluster showed a widespread reduction in cortical thickness (d = 0.43-1.80) and volumes (d = 0.45-1.05), along with fractional anisotropy in the fronto-occipital fasciculus, stria terminalis, and corpus callosum (d = 0.46-0.52); the second cluster (n = 43) was associated with cognitive and affective depressive symptoms, thicker cortices and wider volumes. Multivariate analyses revealed distinct brain-inflammation relationships between the two clusters, with increase in pro-inflammatory markers being associated with decreased cortical thickness and volumes. Our stratification of MDD patients based on structural neuroimaging identified clinically-relevant subgroups of MDD with specific symptomatic and immune-inflammatory profiles, which can contribute to the development of tailored personalized interventions for MDD.
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Affiliation(s)
- Federica Colombo
- University Vita-Salute San Raffaele, Milano, Italy; Psychiatry and Clinical Psychobiology Unit, Division of Neuroscience, IRCCS San Raffaele Hospital, Milano, Italy.
| | - Federico Calesella
- Psychiatry and Clinical Psychobiology Unit, Division of Neuroscience, IRCCS San Raffaele Hospital, Milano, Italy
| | - Beatrice Bravi
- University Vita-Salute San Raffaele, Milano, Italy; Psychiatry and Clinical Psychobiology Unit, Division of Neuroscience, IRCCS San Raffaele Hospital, Milano, Italy
| | - Lidia Fortaner-Uyà
- University Vita-Salute San Raffaele, Milano, Italy; Psychiatry and Clinical Psychobiology Unit, Division of Neuroscience, IRCCS San Raffaele Hospital, Milano, Italy
| | - Camilla Monopoli
- Psychiatry and Clinical Psychobiology Unit, Division of Neuroscience, IRCCS San Raffaele Hospital, Milano, Italy
| | - Emma Tassi
- Department of Neurosciences and Mental Health, IRCCS Fondazione Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy; Politecnico di Milano, Department of Electronics, Information and Bioengineering, Milan, Italy
| | | | - Raffaella Zanardi
- University Vita-Salute San Raffaele, Milano, Italy; Mood Disorders Unit, Scientific Institute IRCCS San Raffaele Hospital, Milan, Italy
| | - Irene Bollettini
- Psychiatry and Clinical Psychobiology Unit, Division of Neuroscience, IRCCS San Raffaele Hospital, Milano, Italy
| | - Sara Poletti
- University Vita-Salute San Raffaele, Milano, Italy; Psychiatry and Clinical Psychobiology Unit, Division of Neuroscience, IRCCS San Raffaele Hospital, Milano, Italy
| | - Cristina Lorenzi
- Psychiatry and Clinical Psychobiology Unit, Division of Neuroscience, IRCCS San Raffaele Hospital, Milano, Italy
| | - Sara Spadini
- Psychiatry and Clinical Psychobiology Unit, Division of Neuroscience, IRCCS San Raffaele Hospital, Milano, Italy
| | - Paolo Brambilla
- Department of Neurosciences and Mental Health, IRCCS Fondazione Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | | | - Eleonora Maggioni
- Politecnico di Milano, Department of Electronics, Information and Bioengineering, Milan, Italy
| | - Chiara Fabbri
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Francesco Benedetti
- University Vita-Salute San Raffaele, Milano, Italy; Psychiatry and Clinical Psychobiology Unit, Division of Neuroscience, IRCCS San Raffaele Hospital, Milano, Italy
| | - Benedetta Vai
- University Vita-Salute San Raffaele, Milano, Italy; Psychiatry and Clinical Psychobiology Unit, Division of Neuroscience, IRCCS San Raffaele Hospital, Milano, Italy
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3
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Argyriou E, Gros D, Hernandez Tejada MA, Muzzy WA, Acierno R. A machine learning personalized treatment rule to optimize assignment to psychotherapies for grief among veterans. J Affect Disord 2024; 358:466-473. [PMID: 38718947 DOI: 10.1016/j.jad.2024.05.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 04/03/2024] [Accepted: 05/02/2024] [Indexed: 05/19/2024]
Abstract
BACKGROUND Complex grief patterns are associated with significant suffering, functional impairments, health and mental health problems, and increased healthcare use. This burden may be even more pronounced among veterans. Behavioral Activation and Therapeutic Exposure (BATE-G) and Cognitive Therapy for Grief (CT-G) are two evidence-based interventions for grief. The goal of this study was to use a precision medicine approach to develop a personalized treatment rule to optimize assignment among these psychotherapies. METHODS We analyzed data (N = 155) from a randomized clinical trial comparing BATE-G and CT-G. Outcome weighted learning was used to estimate an optimal personalized treatment rule. Baseline characteristics including demographics, social support, variables related to the death, and psychopathology dimensions were used as prescriptive factors of treatment assignment. RESULTS The estimated rule assigned 72 veterans to CT-G and 56 to BATE-G. Assigning participants according to this rule was estimated to lead to markedly lower mean grief level following 6 months from treatment compared to assigning everyone to either BATE-G (Vdopt - VBATE-G = -18.57 [95 % CI: -29.41, -7.72]) or CT-G (Vdopt - VBATE-G = -20.89 [95 % CI: -30.7, -11.07]) regardless of their characteristics. LIMITATIONS Participants were primarily male veterans, and identified with Black or White race. The estimated rule was not externally validated. CONCLUSION The estimated rule used relatively simple, easily accessible, client characteristics to personalize assignment to treatment using a precision medicine approach based on machine learning and causal inference. Upon further validation, such a rule can be easily implemented in clinical practice to prescriptively maximize treatment benefits.
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Affiliation(s)
- Evangelia Argyriou
- Department of Psychiatry & Behavioral Sciences, Medical University of South Carolina, United States; Department of Psychology, Indiana University Indianapolis, United States
| | - Daniel Gros
- Department of Psychiatry & Behavioral Sciences, Medical University of South Carolina, United States; Mental Health Service, Ralph H. Johnson VA Healthcare System, United States.
| | - Melba A Hernandez Tejada
- Faillace Department of Psychiatry, University of Texas Health Science Center at Houston, United States
| | - Wendy A Muzzy
- Department of Psychiatry & Behavioral Sciences, Medical University of South Carolina, United States; Mental Health Service, Ralph H. Johnson VA Healthcare System, United States
| | - Ronald Acierno
- Faillace Department of Psychiatry, University of Texas Health Science Center at Houston, United States
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Arnold PIM, Janzing JGE, Hommersom A. Machine learning for antidepressant treatment selection in depression. Drug Discov Today 2024; 29:104068. [PMID: 38925472 DOI: 10.1016/j.drudis.2024.104068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Revised: 06/07/2024] [Accepted: 06/19/2024] [Indexed: 06/28/2024]
Abstract
Finding the right antidepressant for the individual patient with major depressive disorder can be a difficult endeavor and is mostly based on trial-and-error. Machine learning (ML) is a promising tool to personalize antidepressant prescription. In this review, we summarize the current evidence of ML in the selection of antidepressants and conclude that its value for clinical practice is still limited. Apart from the current focus on effectiveness, several other factors should be taken into account to make ML-based prediction models useful for clinical application.
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Affiliation(s)
- Prehm I M Arnold
- Department of Psychiatry, Radboudumc, Nijmegen, the Netherlands.
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Lotfaliany M, Agustini B, Walker AJ, Turner A, Wrobel AL, Williams LJ, Dean OM, Miles S, Rossell SL, Berk M, Mohebbi M. Development of a harmonized sociodemographic and clinical questionnaire for mental health research: A Delphi-method-based consensus recommendation. Aust N Z J Psychiatry 2024; 58:656-667. [PMID: 38845137 DOI: 10.1177/00048674241253452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/07/2024]
Abstract
OBJECTIVE Harmonized tools are essential for reliable data sharing and accurate identification of relevant factors in mental health research. The primary objective of this study was to create a harmonized questionnaire to collect demographic, clinical and behavioral data in diverse clinical trials in adult psychiatry. METHODS We conducted a literature review and examined 24 questionnaires used in previously published randomized controlled trials in psychiatry, identifying a total of 27 domains previously explored. Using a Delphi-method process, a task force team comprising experts in psychiatry, epidemiology and statistics selected 15 essential domains for inclusion in the final questionnaire. RESULTS The final selection resulted in a concise set of 22 questions. These questions cover factors such as age, sex, gender, ancestry, education, living arrangement, employment status, home location, relationship status, and history of medical and mental illness. Behavioral factors like physical activity, diet, smoking, alcohol and illicit drug use were also included, along with one question addressing family history of mental illness. Income was excluded due to high confounding and redundancy, while language was included as a measure of migration status. CONCLUSION The recommendation and adoption of this harmonized tool for the assessment of demographic, clinical and behavioral data in mental health research can enhance data consistency and enable comparability across clinical trials.
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Affiliation(s)
- Mojtaba Lotfaliany
- Deakin University, School of Medicine, IMPACT, The Institute for Mental and Physical Health and Clinical Translation, Geelong, Australia
| | - Bruno Agustini
- Deakin University, School of Medicine, IMPACT, The Institute for Mental and Physical Health and Clinical Translation, Geelong, Australia
| | - Adam J Walker
- Deakin University, School of Medicine, IMPACT, The Institute for Mental and Physical Health and Clinical Translation, Geelong, Australia
| | - Alyna Turner
- Deakin University, School of Medicine, IMPACT, The Institute for Mental and Physical Health and Clinical Translation, Geelong, Australia
| | - Anna L Wrobel
- Deakin University, School of Medicine, IMPACT, The Institute for Mental and Physical Health and Clinical Translation, Geelong, Australia
- School of Psychology, Deakin University, Geelong, VIC, Australia
| | - Lana J Williams
- Deakin University, School of Medicine, IMPACT, The Institute for Mental and Physical Health and Clinical Translation, Geelong, Australia
| | - Olivia M Dean
- Deakin University, School of Medicine, IMPACT, The Institute for Mental and Physical Health and Clinical Translation, Geelong, Australia
- Florey Institute for Neuroscience & Mental Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Stephanie Miles
- Orygen, Parkville, VIC, Australia
- Department of Psychological Sciences, Swinburne University of Technology, Hawthorn, VIC, Australia
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Susan L Rossell
- Centre for Mental Health, Swinburne University of Technology, Melbourne, VIC, Australia
- Psychiatry, St Vincent's Hospital, Melbourne, VIC, Australia
| | - Michael Berk
- Deakin University, School of Medicine, IMPACT, The Institute for Mental and Physical Health and Clinical Translation, Geelong, Australia
- Florey Institute for Neuroscience & Mental Health, The University of Melbourne, Melbourne, VIC, Australia
- Orygen, Parkville, VIC, Australia
- Department of Psychiatry, The University of Melbourne, Melbourne, VIC, Australia
| | - Mohammadreza Mohebbi
- Biostatistics Unit, Faculty of Health, Deakin University, Burwood, VIC, Australia
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6
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Ghadiri P, Yaffe MJ, Adams AM, Abbasgholizadeh-Rahimi S. Primary care physicians' perceptions of artificial intelligence systems in the care of adolescents' mental health. BMC PRIMARY CARE 2024; 25:215. [PMID: 38872128 PMCID: PMC11170885 DOI: 10.1186/s12875-024-02417-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 05/06/2024] [Indexed: 06/15/2024]
Abstract
BACKGROUND Given that mental health problems in adolescence may have lifelong impacts, the role of primary care physicians (PCPs) in identifying and managing these issues is important. Artificial Intelligence (AI) may offer solutions to the current challenges involved in mental health care. We therefore explored PCPs' challenges in addressing adolescents' mental health, along with their attitudes towards using AI to assist them in their tasks. METHODS We used purposeful sampling to recruit PCPs for a virtual Focus Group (FG). The virtual FG lasted 75 minutes and was moderated by two facilitators. A life transcription was produced by an online meeting software. Transcribed data was cleaned, followed by a priori and inductive coding and thematic analysis. RESULTS We reached out to 35 potential participants via email. Seven agreed to participate, and ultimately four took part in the FG. PCPs perceived that AI systems have the potential to be cost-effective, credible, and useful in collecting large amounts of patients' data, and relatively credible. They envisioned AI assisting with tasks such as diagnoses and establishing treatment plans. However, they feared that reliance on AI might result in a loss of clinical competency. PCPs wanted AI systems to be user-friendly, and they were willing to assist in achieving this goal if it was within their scope of practice and they were compensated for their contribution. They stressed a need for regulatory bodies to deal with medicolegal and ethical aspects of AI and clear guidelines to reduce or eliminate the potential of patient harm. CONCLUSION This study provides the groundwork for assessing PCPs' perceptions of AI systems' features and characteristics, potential applications, possible negative aspects, and requirements for using them. A future study of adolescents' perspectives on integrating AI into mental healthcare might contribute a fuller understanding of the potential of AI for this population.
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Affiliation(s)
- Pooria Ghadiri
- Department of Family Medicine and Faculty of Dental Medicine and Oral Health Sciences, McGill University, 5858 Ch. de la Côte-des-Neiges, Montréal, QC, H3S 1Z1, Canada
- Mila-Quebec AI Institute, Montréal, QC, Canada
| | - Mark J Yaffe
- Department of Family Medicine and Faculty of Dental Medicine and Oral Health Sciences, McGill University, 5858 Ch. de la Côte-des-Neiges, Montréal, QC, H3S 1Z1, Canada
- St. Mary's Hospital Center of the Integrated University Centre for Health and Social Services of West Island of Montreal, Montréal, QC, Canada
| | - Alayne Mary Adams
- Department of Family Medicine and Faculty of Dental Medicine and Oral Health Sciences, McGill University, 5858 Ch. de la Côte-des-Neiges, Montréal, QC, H3S 1Z1, Canada
| | - Samira Abbasgholizadeh-Rahimi
- Department of Family Medicine and Faculty of Dental Medicine and Oral Health Sciences, McGill University, 5858 Ch. de la Côte-des-Neiges, Montréal, QC, H3S 1Z1, Canada.
- Mila-Quebec AI Institute, Montréal, QC, Canada.
- Lady Davis Institute for Medical Research (LDI), Jewish General Hospital, Montréal, QC, Canada.
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Mirjebreili SM, Shalbaf R, Shalbaf A. Prediction of treatment response in major depressive disorder using a hybrid of convolutional recurrent deep neural networks and effective connectivity based on EEG signal. Phys Eng Sci Med 2024; 47:633-642. [PMID: 38358619 DOI: 10.1007/s13246-024-01392-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 01/11/2024] [Indexed: 02/16/2024]
Abstract
In this study, we have developed a novel method based on deep learning and brain effective connectivity to classify responders and non-responders to selective serotonin reuptake inhibitors (SSRIs) antidepressants in major depressive disorder (MDD) patients prior to the treatment using EEG signal. The effective connectivity of 30 MDD patients was determined by analyzing their pretreatment EEG signals, which were then concatenated into delta, theta, alpha, and beta bands and transformed into images. Using these images, we then fine tuned a hybrid Convolutional Neural Network that is enhanced with bidirectional Long Short-Term Memory cells based on transfer learning. The Inception-v3, ResNet18, DenseNet121, and EfficientNet-B0 models are implemented as base models. Finally, the models are followed by BiLSTM and dense layers in order to classify responders and non-responders to SSRI treatment. Results showed that the EfficiencyNet-B0 has the highest accuracy of 98.33, followed by DensNet121, ResNet18 and Inception-v3. Therefore, a new method was proposed in this study that uses deep learning models to extract both spatial and temporal features automatically, which will improve classification results. The proposed method provides accurate identification of MDD patients who are responding, thereby reducing the cost of medical facilities and patient care.
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Affiliation(s)
| | - Reza Shalbaf
- Institute for Cognitive Science Studies, Tehran, Iran
| | - Ahmad Shalbaf
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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Tzeng NS, Chung JY, Lin CC, Cheng PY, Liu YP. Effects of Subchronic Buspirone Treatment on Depressive Profile in Socially Isolated Rats: Implication of Early Life Experience on 5-HT1A Receptor-Related Depression. Pharmaceuticals (Basel) 2024; 17:717. [PMID: 38931384 PMCID: PMC11206366 DOI: 10.3390/ph17060717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Revised: 05/15/2024] [Accepted: 05/18/2024] [Indexed: 06/28/2024] Open
Abstract
The heterogeneity of etiology may serve as a crucial factor in the challenges of treatment, including the low response rate and the delay in establishing therapeutic effect. In the present study, we examined whether social experience since early life is one of the etiologies, with the involvement of the 5-HT1A receptors, and explored the potentially therapeutic action of the subchronic administration of buspirone, a partial 5-HT1A agonist. Rats were isolation reared (IR) since their weaning, and the depressive profile indexed by the forced-swim test (FST) was examined in adulthood. Nonspecific locomotor activity was used for the IR validation. Buspirone administration (1 mg/kg/day) was introduced for 14 days (week 9-11). The immobility score of the FST was examined before and after the buspirone administration. Tissue levels of serotonin (5-HT) and its metabolite 5-HIAA were measured in the hippocampus, the amygdala, and the prefrontal cortex. Efflux levels of 5-HT, dopamine (DA), and norepinephrine (NE) were detected in the hippocampus by brain dialysis. Finally, the full 5-HT1A agonist 8-OH-DPAT (0.5 mg/kg) was acutely administered in both behavioral testing and the dialysis experiment. Our results showed (i) increased immobility time in the FST for the IR rats as compared to the social controls, which could not be reversed by the buspirone administration; (ii) IR-induced FST immobility in rats receiving buspirone was corrected by the 8-OH-DPAT; and (iii) IR-induced reduction in hippocampal 5-HT levels can be reversed by the buspirone administration. Our data indicated the 5-HT1A receptor-linked early life social experience as one of the mechanisms of later life depressive mood.
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Affiliation(s)
- Nian-Sheng Tzeng
- Department of Psychiatry, Tri-Service General Hospital, Taipei 114, Taiwan;
- Student Counseling Center, National Defense Medical Center, Taipei 114, Taiwan
| | - Jing-Yi Chung
- Laboratory of Cognitive Neuroscience, Department of Physiology and Biophysics, National Defense Medical Center, Taipei 114, Taiwan; (J.-Y.C.); (C.-C.L.); (P.-Y.C.)
| | - Chen-Cheng Lin
- Laboratory of Cognitive Neuroscience, Department of Physiology and Biophysics, National Defense Medical Center, Taipei 114, Taiwan; (J.-Y.C.); (C.-C.L.); (P.-Y.C.)
| | - Pao-Yun Cheng
- Laboratory of Cognitive Neuroscience, Department of Physiology and Biophysics, National Defense Medical Center, Taipei 114, Taiwan; (J.-Y.C.); (C.-C.L.); (P.-Y.C.)
| | - Yia-Ping Liu
- Laboratory of Cognitive Neuroscience, Department of Physiology and Biophysics, National Defense Medical Center, Taipei 114, Taiwan; (J.-Y.C.); (C.-C.L.); (P.-Y.C.)
- Department of Psychiatry, Cheng Hsin General Hospital, Taipei 112, Taiwan
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Miller EA, Afshar HT, Mishra J, McIntyre RS, Ramanathan D. Predicting non-response to ketamine for depression: An exploratory symptom-level analysis of real-world data among military veterans. Psychiatry Res 2024; 335:115858. [PMID: 38547599 DOI: 10.1016/j.psychres.2024.115858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 03/04/2024] [Accepted: 03/09/2024] [Indexed: 04/14/2024]
Abstract
Ketamine helps some patients with treatment resistant depression (TRD), but reliable methods for predicting which patients will, or will not, respond to treatment are lacking. Herein, we aim to inform prediction models of non-response to ketamine/esketamine in adults with TRD. This is a retrospective analysis of PHQ-9 item response data from 120 patients with TRD who received repeated doses of intravenous racemic ketamine or intranasal eskatamine in a real-world clinic. Regression models were fit to patients' symptom trajectories, showing that all symptoms improved on average, but depressed mood improved relatively faster than low energy. Principal component analysis revealed a first principal component (PC) representing overall treatment response, and a second PC that reflects variance across affective versus somatic symptom subdomains. We then trained logistic regression classifiers to predict overall response (improvement on PC1) better than chance using patients' baseline symptoms alone. Finally, by parametrically adjusting the classifier decision thresholds, we identified optimal models for predicting non-response with a negative predictive value of over 96 %, while retaining a specificity of 22 %. Thus, we could identify 22 % of patients who would not respond based purely on their baseline symptoms. This approach could inform rational treatment recommendations to avoid additional treatment failures.
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Affiliation(s)
- Eric A Miller
- Department of Mental Health, VA San Diego Medical Center, San Diego, CA 92161, USA; Department of Psychiatry, UC San Diego, La Jolla, CA 92093, USA
| | - Houtan Totonchi Afshar
- Department of Mental Health, VA San Diego Medical Center, San Diego, CA 92161, USA; Department of Psychiatry, UC San Diego, La Jolla, CA 92093, USA
| | - Jyoti Mishra
- Department of Psychiatry, UC San Diego, La Jolla, CA 92093, USA; Center of Excellence for Stress and Mental Health, VA San Diego Medical Center, USA
| | - Roger S McIntyre
- Department of Psychiatry, University of Toronto, Toronto, Canada; Department of Pharmacology, University of Toronto, Toronto, Canada; Brain and Cognition Discovery Foundation, Toronto, Canada
| | - Dhakshin Ramanathan
- Department of Mental Health, VA San Diego Medical Center, San Diego, CA 92161, USA; Department of Psychiatry, UC San Diego, La Jolla, CA 92093, USA; Center of Excellence for Stress and Mental Health, VA San Diego Medical Center, USA.
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10
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Shi Y, Lenze EJ, Mohr DC, Lee JM, Hu L, Metts CL, Fong MWM, Wong AWK. Post-stroke Depressive Symptoms and Cognitive Performances: A Network Analysis. Arch Phys Med Rehabil 2024; 105:892-900. [PMID: 37884084 PMCID: PMC11039566 DOI: 10.1016/j.apmr.2023.10.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 09/22/2023] [Accepted: 10/03/2023] [Indexed: 10/28/2023]
Abstract
OBJECTIVE To examine the relationships between post-stroke depression and cognition using network analysis. In particular, we identified central depressive symptoms, central cognitive performances, and bridge components that connect these 2 constructs. DESIGN An observational study. We applied network analysis to analyze baseline data to visualize and quantify the relationships between depression and cognition. SETTING Home and Community. PARTICIPANTS 202 participants with mild-to-moderate stroke (N=202; mean age: 59.7 years; 55% men; 55% Whites; 90% ischemic stroke). INTERVENTION Not applicable. MAIN OUTCOME MEASURES Patient Health Questionnaire (PHQ-8) for depressive symptoms and the NIH Toolbox Cognitive Battery for cognitive performances. RESULTS Depressive symptoms were positively intercorrelated with the network, with symptoms from similar domains clustered together. Mood (expected influence=1.58), concentration (expected influence=0.67), and guilt (expected influence=0.63) were the top 3 central depressive symptoms. Cognitive performances also showed similar network patterns, with executive function (expected influence=0.89), expressive language (expected influence=0.68), and processing speed (expected influence=0.48) identified as the top 3 central cognitive performances. Psychomotor functioning (bridge expected influence=2.49) and attention (bridge expected influence=1.10) were the components connecting depression and cognition. CONCLUSIONS The central and bridge components identified in this study might serve as targets for interventions against these deficits. Future trials are needed to compare the effectiveness of interventions targeting the central and bridge components vs general interventions treating depression and cognitive impairment as a homogenous clinical syndrome.
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Affiliation(s)
- Yun Shi
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, NYU Langone Health, New York, NY; Department of Population Health, New York University Grossman School of Medicine, New York, NY
| | - Eric J Lenze
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO
| | - David C Mohr
- Center for Behavioral Intervention Technologies, Northwestern University Feinberg School of Medicine, Chicago, IL; Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Jin-Moo Lee
- Department of Neurology, Washington University School of Medicine, St. Louis, MO
| | - Lu Hu
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, NYU Langone Health, New York, NY; Department of Population Health, New York University Grossman School of Medicine, New York, NY
| | - Christopher L Metts
- Department of Pathology and Laboratory Medicine, Medical University of South Carolina, Charleston, SC
| | - Mandy W M Fong
- Department of Physical Medicine and Rehabilitation, Northwestern University Feinberg School of Medicine, Chicago, IL; Michigan Avenue Neuropsychologists, Chicago, IL
| | - Alex W K Wong
- Department of Physical Medicine and Rehabilitation, Northwestern University Feinberg School of Medicine, Chicago, IL; Center for Rehabilitation Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL; Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL.
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11
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Ebrahimi OV, Borsboom D, Hoekstra RHA, Epskamp S, Ostinelli EG, Bastiaansen JA, Cipriani A. Towards precision in the diagnostic profiling of patients: leveraging symptom dynamics as a clinical characterisation dimension in the assessment of major depressive disorder. Br J Psychiatry 2024; 224:157-163. [PMID: 38584324 PMCID: PMC11039556 DOI: 10.1192/bjp.2024.19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 12/14/2023] [Accepted: 01/16/2024] [Indexed: 04/09/2024]
Abstract
BACKGROUND International guidelines present overall symptom severity as the key dimension for clinical characterisation of major depressive disorder (MDD). However, differences may reside within severity levels related to how symptoms interact in an individual patient, called symptom dynamics. AIMS To investigate these individual differences by estimating the proportion of patients that display differences in their symptom dynamics while sharing the same overall symptom severity. METHOD Participants with MDD (n = 73; mean age 34.6 years, s.d. = 13.1; 56.2% female) rated their baseline symptom severity using the Inventory for Depressive Symptomatology Self-Report (IDS-SR). Momentary indicators for depressive symptoms were then collected through ecological momentary assessments five times per day for 28 days; 8395 observations were conducted (average per person: 115; s.d. = 16.8). Each participant's symptom dynamics were estimated using person-specific dynamic network models. Individual differences in these symptom relationship patterns in groups of participants sharing the same symptom severity levels were estimated using individual network invariance tests. Subsequently, the overall proportion of participants that displayed differential symptom dynamics while sharing the same symptom severity was calculated. A supplementary simulation study was conducted to investigate the accuracy of our methodology against false-positive results. RESULTS Differential symptom dynamics were identified across 63.0% (95% bootstrapped CI 41.0-82.1) of participants within the same severity group. The average false detection of individual differences was 2.2%. CONCLUSIONS The majority of participants within the same depressive symptom severity group displayed differential symptom dynamics. Examining symptom dynamics provides information about person-specific psychopathological expression beyond severity levels by revealing how symptoms aggravate each other over time. These results suggest that symptom dynamics may be a promising new dimension for clinical characterisation, warranting replication in independent samples. To inform personalised treatment planning, a next step concerns linking different symptom relationship patterns to treatment response and clinical course, including patterns related to spontaneous recovery and forms of disorder progression.
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Affiliation(s)
- Omid V. Ebrahimi
- Department of Experimental Psychology, University of Oxford, Oxford, UK; and Department of Psychology , University of Oslo, Oslo, Norway
| | - Denny Borsboom
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Ria H. A. Hoekstra
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Sacha Epskamp
- Department of Psychology, National University of Singapore, Singapore, Singapore
| | - Edoardo G. Ostinelli
- Department of Psychiatry, University of Oxford, Oxford, UK; Oxford Precision Psychiatry Laboratory, NIHR Oxford Health Biomedical Research Centre, Oxford, UK; and Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, UK
| | - Jojanneke A. Bastiaansen
- Interdisciplinary Center Psychopathology and Emotion Regulation, Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands; and Friesland Mental Health Care Services, Leeuwarden, The Netherlands
| | - Andrea Cipriani
- Department of Psychiatry, University of Oxford, Oxford, UK; Oxford Precision Psychiatry Laboratory, NIHR Oxford Health Biomedical Research Centre, Oxford, UK; and Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, UK
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12
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Jeong HS, Kim HMS, Kim KM. Network Structure and Clustering Analysis Relating to Individual Symptoms of Problematic Internet Use in a Community Adolescent Population. Eur Addict Res 2024; 30:181-193. [PMID: 38615663 DOI: 10.1159/000535677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 12/01/2023] [Indexed: 04/16/2024]
Abstract
INTRODUCTION Problematic internet use (PIU) is a psychopathology that includes multiple symptoms and psychological constructs. Because no studies have considered both network structures and clusters among individual symptoms in the context of PIU in a Korean adolescent population, this study aimed to investigate network structures and clustering in relation to PIU symptoms in adolescents. METHODS Overall, 73,238 adolescents were included. PIU severity was assessed using a self-rating scale comprising 20 items and 6 subscales, namely, the Internet Addiction Proneness Scale for Youth-Short Form; KS scale. Network structures and clusters among symptoms were analyzed using a Gaussian graphical model and exploratory graph analysis, respectively. Centrality of strength, closeness, and betweenness scores was also calculated. RESULTS Our study identified four clusters: disturbance in adaptive functioning, virtual interpersonal relationships, withdrawal, and tolerance. The symptom of confidence served as a node bridging the cluster of virtual interpersonal relationships and other clusters of withdrawal and disturbances of adaptive function. The symptom of craving served as a bridge between the clusters of withdrawal and tolerance with high betweenness centrality. CONCLUSION This study identified network structures and clustering among PIU symptoms in adolescents and revealed that positive experiences derived from online interpersonal relationships were an important mechanism underlying PIU. These are novel insights concerning the interconnection among multiple symptoms and related clustering for the mechanism of adolescent PIU in terms of KS-scale PIU assessment.
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Affiliation(s)
- Hyu Seok Jeong
- Department of Psychiatry, Dankook University Hospital, Cheonan, Republic of Korea
- Department of Psychiatry, College of Medicine, Dankook University, Cheonan, Republic of Korea
- Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Hillary Mi-Sung Kim
- Department of Child Psychology and Education, Sungkyunkwan Univeristy, Seoul, Republic of Korea
| | - Kyoung Min Kim
- Department of Psychiatry, Dankook University Hospital, Cheonan, Republic of Korea
- Department of Psychiatry, College of Medicine, Dankook University, Cheonan, Republic of Korea
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13
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Kim KM, Lee KH, Kim H, Kim O, Kim JW. Symptom clusters in adolescent depression and differential responses of clusters to pharmacologic treatment. J Psychiatr Res 2024; 172:59-65. [PMID: 38364553 DOI: 10.1016/j.jpsychires.2024.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 11/20/2023] [Accepted: 02/01/2024] [Indexed: 02/18/2024]
Abstract
OBJECTIVE Symptoms of depression in adolescents are widely variable, but they are often interactive and clustered. The analysis of interactions and clusters among individual symptoms may help predict treatment outcomes. We aimed to determine clusters of individual symptoms in adolescent depression and their changes in the response to pharmacological treatment. METHOD A total of 95 adolescents, aged 12-17 years, with major depressive disorder were included. Participants were treated with escitalopram, and depressive symptoms were assessed at baseline (V1) and 1, 2, 4, 6, and 8 weeks (V6). The severity of depression was assessed using the Children's Depression Rating Scale-Revised. To construct network and clustering structures among symptoms, the Gaussian graphical model and Exploratory Graph Analysis with the tuning parameter to minimize the extended Bayesian information criterion were adopted. RESULTS Exploratory Graph Analysis revealed that symptoms of depression comprised four clusters: impaired activity, somatic concerns, subjective mood, and observed affect. The main effect of visit with decreased symptom severity was significant in all four clusters; however, the degree of symptom improvement differed among the four clusters. The effect size of score differences from V1 to V6 was the highest in the subjective mood (Cohen's d = 1.075), and lowest in impaired activity (d = 0.501) clusters. CONCLUSION The present study identified four symptom clusters associated with adolescent depression and their differential changes related to antidepressant treatment. This finding suggests that escitalopram was the most effective at improving subjective mood among different clusters. However, other therapeutic modalities may be needed to improve other clusters of symptoms, consequently leading to increased overall improvement of depression in adolescents.
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Affiliation(s)
- Kyoung Min Kim
- Department of Psychiatry, College of Medicine, Dankook University, Cheonan, Republic of Korea; Department of Psychiatry, Dankook University Hospital, Cheonan, Republic of Korea
| | - Kyung Hwa Lee
- Division of Child and Adolescent Psychiatry, Department of Psychiatry, Seoul National University Hospital, Seoul, Republic of Korea
| | - Haebin Kim
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Ok Kim
- Department of Psychology, Graduate School of Dankook University, Cheonan, Republic of Korea
| | - Jae-Won Kim
- Division of Child and Adolescent Psychiatry, Department of Psychiatry, Seoul National University Hospital, Seoul, Republic of Korea; Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea.
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14
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Anmella G, Primé-Tous M, Segú X, Solanes A, Ruíz V, Martín-Villalba I, Morilla I, Also-Fontanet A, Sant E, Murgui S, Sans-Corrales M, Murru A, Zahn R, Young AH, Vicens V, Viñas-Bardolet C, Martínez-Cerdá JF, Blanch J, Radua J, Fullana MÀ, Cavero M, Vieta E, Hidalgo-Mazzei D. PRimary carE digital Support ToOl in mental health (PRESTO): Design, development and study protocols. SPANISH JOURNAL OF PSYCHIATRY AND MENTAL HEALTH 2024; 17:114-125. [PMID: 33933665 DOI: 10.1016/j.rpsm.2021.04.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 03/22/2021] [Accepted: 04/19/2021] [Indexed: 12/24/2022]
Abstract
BACKGROUND About 30-50% of Primary Care (PC) users in Spain suffer mental health problems, mostly mild to moderate anxious and depressive symptoms, which account for 2% of Spain's total Gross domestic product and 50% of the costs associated to all mental disorders. Mobile health tools have demonstrated to cost-effectively reduce anxious and depressive symptoms while machine learning (ML) techniques have shown to accurately detect severe cases. The main aim of this project is to develop a comprehensive ML digital support platform (PRESTO) to cost-effectively screen, assess, triage, and provide personalized treatments for anxious and depressive symptoms in PC. METHODS The project will be carried out in 3 complementary phases: First, a ML predictive severity model will be built based on all the cases referred to the PC mental health support programme during the last 5 years in Catalonia. Simultaneously, a smartphone app to monitor and deliver psychological interventions for anxiety and depressive symptoms will be developed and tested in a clinical trial. Finally, the ML models and the app will be integrated in a comprehensive decision-support platform (PRESTO) which will triage and assign to each patient a specific intervention based on individual personal and clinical characteristics. The effectiveness of PRESTO to reduce waiting times in receiving mental healthcare will be tested in a stepped-wedge cluster randomized controlled trial in 5 PC centres. DISCUSSION PRESTO will offer timely and personalized cost-effective mental health treatment to people with mild to moderate anxious and depressive symptoms. This will result in a reduction of the burden of mental health problems in PC and on society as a whole. TRIAL REGISTRATION The project and their clinical trials were registered in Clinical Trials.gov: NCT04559360 (September 2020).
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Affiliation(s)
- Gerard Anmella
- Department of Psychiatry and Psychology, Institute of Neuroscience, Hospital Clínic de Barcelona, 170 Villarroel st, 12-0, 08036 Barcelona, Catalonia, Spain; Digital Innovation Group, Bipolar and Depressive Disorders Unit, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Catalonia, Spain; University of Barcelona, Barcelona, Catalonia, Spain; Mental Health Research Networking Center (CIBERSAM), Madrid, Spain
| | - Mireia Primé-Tous
- Department of Psychiatry and Psychology, Institute of Neuroscience, Hospital Clínic de Barcelona, 170 Villarroel st, 12-0, 08036 Barcelona, Catalonia, Spain
| | - Xavier Segú
- Department of Psychiatry and Psychology, Institute of Neuroscience, Hospital Clínic de Barcelona, 170 Villarroel st, 12-0, 08036 Barcelona, Catalonia, Spain
| | - Aleix Solanes
- Mental Health Research Networking Center (CIBERSAM), Madrid, Spain; Imaging of Mood- and Anxiety-Related Disorders (IMARD) group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Victoria Ruíz
- Department of Psychiatry and Psychology, Institute of Neuroscience, Hospital Clínic de Barcelona, 170 Villarroel st, 12-0, 08036 Barcelona, Catalonia, Spain
| | - Inés Martín-Villalba
- Department of Psychiatry and Psychology, Institute of Neuroscience, Hospital Clínic de Barcelona, 170 Villarroel st, 12-0, 08036 Barcelona, Catalonia, Spain
| | - Ivette Morilla
- Department of Psychiatry and Psychology, Institute of Neuroscience, Hospital Clínic de Barcelona, 170 Villarroel st, 12-0, 08036 Barcelona, Catalonia, Spain; University of Barcelona, Barcelona, Catalonia, Spain
| | - Antonieta Also-Fontanet
- CAP Casanova, Consorci d'Atenció Primaria de Salut Barcelona Esquerra (CAPSBE), Barcelona, Catalonia, Spain
| | - Elisenda Sant
- CAP Casanova, Consorci d'Atenció Primaria de Salut Barcelona Esquerra (CAPSBE), Barcelona, Catalonia, Spain
| | - Sandra Murgui
- CAP Borrell, Consorci d'Atenció Primaria de Salut Barcelona Esquerra (CAPSBE), Barcelona, Catalonia, Spain
| | - Mireia Sans-Corrales
- CAP Borrell, Consorci d'Atenció Primaria de Salut Barcelona Esquerra (CAPSBE), Barcelona, Catalonia, Spain
| | - Andrea Murru
- Department of Psychiatry and Psychology, Institute of Neuroscience, Hospital Clínic de Barcelona, 170 Villarroel st, 12-0, 08036 Barcelona, Catalonia, Spain; Digital Innovation Group, Bipolar and Depressive Disorders Unit, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Catalonia, Spain; University of Barcelona, Barcelona, Catalonia, Spain; Mental Health Research Networking Center (CIBERSAM), Madrid, Spain
| | - Roland Zahn
- Centre for Affective Disorders, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Allan H Young
- Centre for Affective Disorders, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Victor Vicens
- Chief Medical Officer and co-founder of Abi Global Health, Spain
| | - Clara Viñas-Bardolet
- Data Analytics Programme for Health Research and Innovation (PADRIS) from the Catalan Agency for Health Quality and Evaluation (AQuAS), Catalonia, Spain
| | - Juan Francisco Martínez-Cerdá
- Data Analytics Programme for Health Research and Innovation (PADRIS) from the Catalan Agency for Health Quality and Evaluation (AQuAS), Catalonia, Spain
| | - Jordi Blanch
- Department of Psychiatry and Psychology, Institute of Neuroscience, Hospital Clínic de Barcelona, 170 Villarroel st, 12-0, 08036 Barcelona, Catalonia, Spain; University of Barcelona, Barcelona, Catalonia, Spain; Chief Medical Officer and co-founder of Abi Global Health, Spain; Director of the Mental Health and Addiction Programme, Department of Health, Generalitat de Catalunya, Spain; President of the European Association of Psychosomatic Medicine, Spain
| | - Joaquim Radua
- Mental Health Research Networking Center (CIBERSAM), Madrid, Spain; Imaging of Mood- and Anxiety-Related Disorders (IMARD) group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Early Psychosis: Interventions and Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom; Centre for Psychiatric Research and Education, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Miquel-Àngel Fullana
- Department of Psychiatry and Psychology, Institute of Neuroscience, Hospital Clínic de Barcelona, 170 Villarroel st, 12-0, 08036 Barcelona, Catalonia, Spain; Mental Health Research Networking Center (CIBERSAM), Madrid, Spain; Imaging of Mood- and Anxiety-Related Disorders (IMARD) group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Myriam Cavero
- Department of Psychiatry and Psychology, Institute of Neuroscience, Hospital Clínic de Barcelona, 170 Villarroel st, 12-0, 08036 Barcelona, Catalonia, Spain; University of Barcelona, Barcelona, Catalonia, Spain; Mental Health Research Networking Center (CIBERSAM), Madrid, Spain
| | - Eduard Vieta
- Department of Psychiatry and Psychology, Institute of Neuroscience, Hospital Clínic de Barcelona, 170 Villarroel st, 12-0, 08036 Barcelona, Catalonia, Spain; Digital Innovation Group, Bipolar and Depressive Disorders Unit, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Catalonia, Spain; University of Barcelona, Barcelona, Catalonia, Spain; Mental Health Research Networking Center (CIBERSAM), Madrid, Spain
| | - Diego Hidalgo-Mazzei
- Department of Psychiatry and Psychology, Institute of Neuroscience, Hospital Clínic de Barcelona, 170 Villarroel st, 12-0, 08036 Barcelona, Catalonia, Spain; Digital Innovation Group, Bipolar and Depressive Disorders Unit, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Catalonia, Spain; University of Barcelona, Barcelona, Catalonia, Spain; Mental Health Research Networking Center (CIBERSAM), Madrid, Spain; Centre for Affective Disorders, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.
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15
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Meeker KL, Luckett PH, Barthélemy NR, Hobbs DA, Chen C, Bollinger J, Ovod V, Flores S, Keefe S, Henson RL, Herries EM, McDade E, Hassenstab JJ, Xiong C, Cruchaga C, Benzinger TLS, Holtzman DM, Schindler SE, Bateman RJ, Morris JC, Gordon BA, Ances BM. Comparison of cerebrospinal fluid, plasma and neuroimaging biomarker utility in Alzheimer's disease. Brain Commun 2024; 6:fcae081. [PMID: 38505230 PMCID: PMC10950051 DOI: 10.1093/braincomms/fcae081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 02/01/2024] [Accepted: 03/14/2024] [Indexed: 03/21/2024] Open
Abstract
Alzheimer's disease biomarkers are crucial to understanding disease pathophysiology, aiding accurate diagnosis and identifying target treatments. Although the number of biomarkers continues to grow, the relative utility and uniqueness of each is poorly understood as prior work has typically calculated serial pairwise relationships on only a handful of markers at a time. The present study assessed the cross-sectional relationships among 27 Alzheimer's disease biomarkers simultaneously and determined their ability to predict meaningful clinical outcomes using machine learning. Data were obtained from 527 community-dwelling volunteers enrolled in studies at the Charles F. and Joanne Knight Alzheimer Disease Research Center at Washington University in St Louis. We used hierarchical clustering to group 27 imaging, CSF and plasma measures of amyloid beta, tau [phosphorylated tau (p-tau), total tau t-tau)], neuronal injury and inflammation drawn from MRI, PET, mass-spectrometry assays and immunoassays. Neuropsychological and genetic measures were also included. Random forest-based feature selection identified the strongest predictors of amyloid PET positivity across the entire cohort. Models also predicted cognitive impairment across the entire cohort and in amyloid PET-positive individuals. Four clusters emerged reflecting: core Alzheimer's disease pathology (amyloid and tau), neurodegeneration, AT8 antibody-associated phosphorylated tau sites and neuronal dysfunction. In the entire cohort, CSF p-tau181/Aβ40lumi and Aβ42/Aβ40lumi and mass spectrometry measurements for CSF pT217/T217, pT111/T111, pT231/T231 were the strongest predictors of amyloid PET status. Given their ability to denote individuals on an Alzheimer's disease pathological trajectory, these same markers (CSF pT217/T217, pT111/T111, p-tau/Aβ40lumi and t-tau/Aβ40lumi) were largely the best predictors of worse cognition in the entire cohort. When restricting analyses to amyloid-positive individuals, the strongest predictors of impaired cognition were tau PET, CSF t-tau/Aβ40lumi, p-tau181/Aβ40lumi, CSF pT217/217 and pT205/T205. Non-specific CSF measures of neuronal dysfunction and inflammation were poor predictors of amyloid PET and cognitive status. The current work utilized machine learning to understand the interrelationship structure and utility of a large number of biomarkers. The results demonstrate that, although the number of biomarkers has rapidly expanded, many are interrelated and few strongly predict clinical outcomes. Examining the entire corpus of available biomarkers simultaneously provides a meaningful framework to understand Alzheimer's disease pathobiological change as well as insight into which biomarkers may be most useful in Alzheimer's disease clinical practice and trials.
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Affiliation(s)
- Karin L Meeker
- Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA
| | - Patrick H Luckett
- Department of Neurosurgery, Washington University in St Louis, St Louis, MO 63110, USA
| | - Nicolas R Barthélemy
- Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA
| | - Diana A Hobbs
- Department of Radiology, Washington University in St Louis, St Louis, MO 63110, USA
| | - Charles Chen
- Department of Radiology, Washington University in St Louis, St Louis, MO 63110, USA
| | - James Bollinger
- Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA
| | - Vitaliy Ovod
- Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA
| | - Shaney Flores
- Department of Radiology, Washington University in St Louis, St Louis, MO 63110, USA
| | - Sarah Keefe
- Department of Radiology, Washington University in St Louis, St Louis, MO 63110, USA
| | - Rachel L Henson
- Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA
| | - Elizabeth M Herries
- Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA
| | - Eric McDade
- Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA
| | - Jason J Hassenstab
- Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, MO 63110, USA
| | - Chengjie Xiong
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, MO 63110, USA
- Division of Biostatistics, Washington University in St Louis, St Louis, MO 63110, USA
| | - Carlos Cruchaga
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, MO 63110, USA
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO 63110, USA
| | - Tammie L S Benzinger
- Department of Radiology, Washington University in St Louis, St Louis, MO 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, MO 63110, USA
| | - David M Holtzman
- Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, MO 63110, USA
| | - Suzanne E Schindler
- Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, MO 63110, USA
| | - Randall J Bateman
- Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA
| | - John C Morris
- Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, MO 63110, USA
| | - Brian A Gordon
- Department of Radiology, Washington University in St Louis, St Louis, MO 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, MO 63110, USA
| | - Beau M Ances
- Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA
- Department of Radiology, Washington University in St Louis, St Louis, MO 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, MO 63110, USA
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16
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Ter Hark SE, Coenen MJH, Vos CF, Aarnoutse RE, Nolen WA, Birkenhager TK, van den Broek WW, Schellekens AFA, Verkes RJ, Janzing JGE. A genetic risk score to predict treatment nonresponse in psychotic depression. Transl Psychiatry 2024; 14:132. [PMID: 38431658 PMCID: PMC10908776 DOI: 10.1038/s41398-024-02842-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 02/09/2024] [Accepted: 02/19/2024] [Indexed: 03/05/2024] Open
Abstract
Psychotic depression is a severe and difficult-to-treat subtype of major depressive disorder for which higher rates of treatment-resistant depression were found. Studies have been performed aiming to predict treatment-resistant depression or treatment nonresponse. However, most of these studies excluded patients with psychotic depression. We created a genetic risk score (GRS) based on a large treatment-resistant depression genome-wide association study. We tested whether this GRS was associated with nonresponse, nonremission and the number of prior adequate antidepressant trials in patients with a psychotic depression. Using data from a randomized clinical trial with patients with a psychotic depression (n = 122), we created GRS deciles and calculated positive prediction values (PPV), negative predictive values (NPV) and odds ratios (OR). Nonresponse and nonremission were assessed after 7 weeks of treatment with venlafaxine, imipramine or venlafaxine plus quetiapine. The GRS was negatively correlated with treatment response (r = -0.32, p = 0.0023, n = 88) and remission (r = -0.31, p = 0.0037, n = 88), but was not correlated with the number of prior adequate antidepressant trials. For patients with a GRS in the top 10%, we observed a PPV of 100%, a NPV of 73.7% and an OR of 52.4 (p = 0.00072, n = 88) for nonresponse. For nonremission, a PPV of 100%, a NPV of 51.9% and an OR of 21.3 (p = 0.036, n = 88) was observed for patients with a GRS in the top 10%. Overall, an increased risk for nonresponse and nonremission was seen in patients with GRSs in the top 40%. Our results suggest that a treatment-resistant depression GRS is predictive of treatment nonresponse and nonremission in psychotic depression.
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Affiliation(s)
- Sophie E Ter Hark
- Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands.
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands.
| | - Marieke J H Coenen
- Department of Clinical Chemistry, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Cornelis F Vos
- Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Rob E Aarnoutse
- Department of Pharmacy, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Willem A Nolen
- Department of Psychiatry, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Tom K Birkenhager
- Department of Psychiatry, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | | | - Arnt F A Schellekens
- Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands
- Nijmegen Institute for Scientist Practitioners in Addiction (NISPA), Radboud University, Nijmegen, The Netherlands
| | - Robbert-Jan Verkes
- Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Joost G E Janzing
- Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands
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17
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Liu X, Radojčić MR, Huang Z, Shi B, Li G, Chen L. Antidepressants for chronic pain management: considerations from predictive modeling and personalized medicine perspectives. FRONTIERS IN PAIN RESEARCH 2024; 5:1359024. [PMID: 38385140 PMCID: PMC10879562 DOI: 10.3389/fpain.2024.1359024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 01/26/2024] [Indexed: 02/23/2024] Open
Affiliation(s)
- Xinyue Liu
- Department of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Maja R. Radojčić
- Division of Psychology and Mental Health, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
| | - Ziye Huang
- Department of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Baoyi Shi
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, United States
| | - Ge Li
- Department of Public Health, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Lingxiao Chen
- Department of Orthopaedics, Shandong University Centre for Orthopaedics, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Sydney Musculoskeletal Health, School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
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18
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Chekroud AM, Hawrilenko M, Loho H, Bondar J, Gueorguieva R, Hasan A, Kambeitz J, Corlett PR, Koutsouleris N, Krumholz HM, Krystal JH, Paulus M. Illusory generalizability of clinical prediction models. Science 2024; 383:164-167. [PMID: 38207039 DOI: 10.1126/science.adg8538] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 11/10/2023] [Indexed: 01/13/2024]
Abstract
It is widely hoped that statistical models can improve decision-making related to medical treatments. Because of the cost and scarcity of medical outcomes data, this hope is typically based on investigators observing a model's success in one or two datasets or clinical contexts. We scrutinized this optimism by examining how well a machine learning model performed across several independent clinical trials of antipsychotic medication for schizophrenia. Models predicted patient outcomes with high accuracy within the trial in which the model was developed but performed no better than chance when applied out-of-sample. Pooling data across trials to predict outcomes in the trial left out did not improve predictions. These results suggest that models predicting treatment outcomes in schizophrenia are highly context-dependent and may have limited generalizability.
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Affiliation(s)
- Adam M Chekroud
- Spring Health, New York City, NY 10010, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06520, USA
| | | | - Hieronimus Loho
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06520, USA
| | | | | | - Alkomiet Hasan
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Augsburg, 86159 Augsburg, Germany
| | - Joseph Kambeitz
- Department of Psychiatry and Psychotherapy, University of Cologne, Faculty of Medicine and University Hospital of Cologne, Cologne, Germany
| | - Philip R Corlett
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University, Munich, Germany
| | - Harlan M Krumholz
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT 06520, USA
| | - John H Krystal
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Martin Paulus
- Laureate Institute for Brain Research, Tulsa, OK 74136, USA
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19
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Ebrahimzadeh E, Dehghani A, Asgarinejad M, Soltanian-Zadeh H. Non-linear processing and reinforcement learning to predict rTMS treatment response in depression. Psychiatry Res Neuroimaging 2024; 337:111764. [PMID: 38043370 DOI: 10.1016/j.pscychresns.2023.111764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 11/05/2023] [Accepted: 11/09/2023] [Indexed: 12/05/2023]
Abstract
BACKGROUND Forecasting the efficacy of repetitive transcranial magnetic stimulation (rTMS) therapy can lead to substantial time and cost savings by preventing futile treatments. To achieve this objective, we've formulated a machine learning approach aimed at categorizing patients with major depressive disorder (MDD) into two groups: individuals who respond (R) positively to rTMS treatment and those who do not respond (NR). METHODS Preceding the commencement of treatment, we obtained resting-state EEG data from 106 patients diagnosed with MDD, employing 32 electrodes for data collection. These patients then underwent a 7-week course of rTMS therapy, and 54 of them exhibited positive responses to the treatment. Employing Independent Component Analysis (ICA) on the EEG data, we successfully pinpointed relevant brain sources that could potentially serve as markers of neural activity within the dorsolateral prefrontal cortex (DLPFC). These identified sources were further scrutinized to estimate the sources of activity within the sensor domain. Then, we integrated supplementary physiological data and implemented specific criteria to yield more realistic estimations when compared to conventional EEG analysis. In the end, we selected components corresponding to the DLPFC region within the sensor domain. Features were derived from the time-series data of these relevant independent components. To identify the most significant features, we used Reinforcement Learning (RL). In categorizing patients into two groups - R and NR to rTMS treatment - we utilized three distinct classification algorithms including K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Multilayer Perceptron (MLP). We assessed the performance of these classifiers through a ten-fold cross-validation method. Additionally, we conducted a statistical test to evaluate the discriminative capacity of these features between responders and non-responders, opening the door for further exploration in this field. RESULTS We identified EEG features that can anticipate the response to rTMS treatment. The most robust discriminators included EEG beta power, the sum of bispectrum diagonal elements in the delta and beta frequency bands. When these features were combined into a single vector, the classification of responders and non-responders achieved impressive performance, with an accuracy of 95.28 %, specificity at 94.23 %, sensitivity reaching 96.29 %, and precision standing at 94.54 %, all achieved using SVM. CONCLUSIONS The results of this study suggest that the proposed approach, utilizing power, non-linear, and bispectral features extracted from relevant independent component time-series, has the capability to forecast the treatment outcome of rTMS for MDD patients based solely on a single pre-treatment EEG recording session. The achieved findings demonstrate the superior performance of our method compared to previous techniques.
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Affiliation(s)
- Elias Ebrahimzadeh
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran; School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.
| | - Amin Dehghani
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | | | - Hamid Soltanian-Zadeh
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran; School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
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20
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Li CT, Chen CS, Cheng CM, Chen CP, Chen JP, Chen MH, Bai YM, Tsai SJ. Prediction of antidepressant responses to non-invasive brain stimulation using frontal electroencephalogram signals: Cross-dataset comparisons and validation. J Affect Disord 2023; 343:86-95. [PMID: 37579885 DOI: 10.1016/j.jad.2023.08.059] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 08/10/2023] [Accepted: 08/11/2023] [Indexed: 08/16/2023]
Abstract
BACKGROUND 10-Hz repetitive transcranial magnetic stimulation(rTMS) and intermittent theta-burst stimulation(iTBS) over left prefrontal cortex are FDA-approved, effective options for treatment-resistant depression (TRD). Optimal prediction models for iTBS and rTMS remain elusive. Therefore, our primary objective was to compare prediction accuracy between classification by frontal theta activity alone and machine learning(ML) models by linear and non-linear frontal signals. The second objective was to study an optimal ML model for predicting responses to rTMS and iTBS. METHODS Two rTMS and iTBS datasets (n = 163) were used: one randomized controlled trial dataset (RCTD; n = 96) and one outpatient dataset (OPD; n = 67). Frontal theta and non-linear EEG features that reflect trend, stability, and complexity were extracted. Pretreatment frontal EEG and ML algorithms, including classical support vector machine(SVM), random forest(RF), XGBoost, and CatBoost, were analyzed. Responses were defined as ≥50 % depression improvement after treatment. Response rates between those with and without pretreatment prediction in another independent outpatient cohort (n = 208) were compared. RESULTS Prediction accuracy using combined EEG features by SVM was better than frontal theta by logistic regression. The accuracy for OPD patients significantly dropped using the RCTD-trained SVM model. Modern ML models, especially RF (rTMS = 83.3 %, iTBS = 88.9 %, p-value(ACC > NIR) < 0.05 for iTBS), performed significantly above chance and had higher accuracy than SVM using both selected features (p < 0.05, FDR corrected for multiple comparisons) or all EEG features. Response rates among those receiving prediction before treatment were significantly higher than those without prediction (p = 0.035). CONCLUSION The first study combining linear and non-linear EEG features could accurately predict responses to left PFC iTBS. The bootstraps-based ML model (i.e., RF) had the best predictive accuracy for rTMS and iTBS.
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Affiliation(s)
- Cheng-Ta Li
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan; Division of Psychiatry, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan; Institute of Brain Science and Brain Research Center, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan; Institute of Cognitive Neuroscience, National Central University, Jhongli, Taiwan.
| | - Chi-Sheng Chen
- Graduate Institute of Biomedical Electronics and Bioinformatics College of Electrical Engineering and Computer Science, National Taiwan University, Taipei, Taiwan
| | - Chih-Ming Cheng
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Chung-Ping Chen
- Graduate Institute of Biomedical Electronics and Bioinformatics College of Electrical Engineering and Computer Science, National Taiwan University, Taipei, Taiwan
| | - Jen-Ping Chen
- Division of Psychiatry, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
| | - Mu-Hong Chen
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan; Division of Psychiatry, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan; Institute of Brain Science and Brain Research Center, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
| | - Ya-Mei Bai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan; Division of Psychiatry, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan; Institute of Brain Science and Brain Research Center, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
| | - Shih-Jen Tsai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan; Division of Psychiatry, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan; Institute of Brain Science and Brain Research Center, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
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21
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Ng E, Nestor SM, Rabin JS, Hamani C, Lipsman N, Giacobbe P. Seasonal pattern and depression outcomes from repetitive transcranial magnetic stimulation. Psychiatry Res 2023; 329:115525. [PMID: 37820574 DOI: 10.1016/j.psychres.2023.115525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 09/29/2023] [Accepted: 10/02/2023] [Indexed: 10/13/2023]
Abstract
Individuals with major depressive disorder (MDD) may exhibit a seasonal pattern. The impact of a seasonal pattern in depressive symptoms on rTMS outcomes is unexplored. A retrospective analysis was performed on patients with MDD receiving open-label high frequency rTMS to the left dorsolateral prefrontal cortex. Having a seasonal pattern was defined as scoring ≥ 12 on the Personal Inventory for Depression and Seasonal Affective Disorder (PIDS). Primary outcomes included improvement in the Hamilton Depression Rating Scale (HAMD) and remission. Secondary analyses included the use of the self-rated Quick Inventory of Depressive Symptomatology (QIDS) to assess for changes in atypical neurovegetative symptoms. Multiple linear regression, multiple logistic regression, and linear mixed effects analyses were performed. 46 % (58/127) of the sample had a seasonal pattern. Seasonal pattern did not significantly influence improvement in HAMD (PIDS < 12, 7.8, SD 5.9; PIDS ≥ 12, 10.4, SD 4.9 or remission (PIDS < 12, 30 %; PIDS ≥ 12, 34 %). There were equivalent degrees of improvement in atypical neurovegetative symptoms over time as assessed using the QIDS. Depression with seasonal pattern was found to respond to rTMS treatment similarly to depression without seasonal pattern, suggesting that this may be a viable treatment for this group.
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Affiliation(s)
- Enoch Ng
- Department of Psychiatry, University of Toronto, 250 College Street, Toronto, ON M5T 1R8, Canada
| | - Sean M Nestor
- Department of Psychiatry, University of Toronto, 250 College Street, Toronto, ON M5T 1R8, Canada; Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, Toronto, ON M4N 3M5, Canada; Harquail Centre for Neuromodulation, 2075 Bayview Avenue, Toronto, ON M4N 3M5, Canada
| | - Jennifer S Rabin
- Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, Toronto, ON M4N 3M5, Canada; Harquail Centre for Neuromodulation, 2075 Bayview Avenue, Toronto, ON M4N 3M5, Canada; Division of Neurology, Department of Medicine, University of Toronto, 6 Queen's Park Crescent West, Toronto, ON M5S 3Hs, Canada; Rehabilitation Sciences Institute, University of Toronto, Toronto, ON M5G 1V7, Canada
| | - Clement Hamani
- Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, Toronto, ON M4N 3M5, Canada; Harquail Centre for Neuromodulation, 2075 Bayview Avenue, Toronto, ON M4N 3M5, Canada; Department of Surgery, University of Toronto, 149 College Street, Toronto, ON M5T 1P5, Canada
| | - Nir Lipsman
- Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, Toronto, ON M4N 3M5, Canada; Harquail Centre for Neuromodulation, 2075 Bayview Avenue, Toronto, ON M4N 3M5, Canada; Department of Surgery, University of Toronto, 149 College Street, Toronto, ON M5T 1P5, Canada
| | - Peter Giacobbe
- Department of Psychiatry, University of Toronto, 250 College Street, Toronto, ON M5T 1R8, Canada; Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, Toronto, ON M4N 3M5, Canada; Harquail Centre for Neuromodulation, 2075 Bayview Avenue, Toronto, ON M4N 3M5, Canada.
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22
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De Schuyteneer E, Giltay E, Vansteelandt K, Obbels J, Van den Eynde L, Verspecht S, Verledens C, Hebbrecht K, Sienaert P. Electroconvulsive therapy improves somatic symptoms before mood in patients with depression: A directed network analysis. Brain Stimul 2023; 16:1677-1683. [PMID: 37952571 DOI: 10.1016/j.brs.2023.11.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 10/24/2023] [Accepted: 11/07/2023] [Indexed: 11/14/2023] Open
Abstract
BACKGROUND The recent network perspective of depression conceptualizes depression as a dynamic network of causally related symptoms, that contrasts with the traditional view of depression as a discrete latent entity that causes all symptoms. Electroconvulsive therapy (ECT) is an effective treatment for severe depression, but little is known about the temporal trajectories of symptom improvement during a course of ECT. OBJECTIVE To gain insight into the dynamics of depressive symptoms in individuals treated with ECT. METHODS The Quick Inventory of Depressive Symptomatology (QIDS) was used to assess symptoms twice a week in 68 participants with a unipolar or bipolar depression treated with ECT, with an average of 12 assessments per participant. Dynamic time warping (DTW) was used to analyze individual time series data, which were subsequently aggregated to calculate a directed symptom network and the in- and out-strength for each symptom. RESULTS Participants had a mean age of 49.6 (SD = 12.8) and 60% were female. Somatic symptoms (e.g., decreased weight) and suicidal ideation showed the highest out-strength values, indicating that their improvement tended to precede improvements in mood symptoms, which showed high in-strength. Sad mood had the highest in-strength, and thus appeared to be the last symptom to improve during ECT treatment (p < 0.001). CONCLUSION This study addresses a gap in the existing literature on ECT, by first analysing the temporal trajectories of symptoms within individual patients and subsequently aggregating them to the group level. The results show that somatic symptoms tend to improve before mood symptoms during ECT.
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Affiliation(s)
- Emma De Schuyteneer
- Department of Neurosciences, Research Group Psychiatry, Neuropsychiatry, Academic Center for ECT and Neuromodulation (AcCENT), University Psychiatric Center KU Leuven, Kortenberg, Belgium; Department of Neurosciences, Mind Body Research, KU Leuven, Leuven, Belgium
| | - Erik Giltay
- Department of Psychiatry, Leiden University Medical Center, Leiden, the Netherlands; Department of Public Health and Primary Care, Health Campus the Hague, Leiden University Medical Center, The Hague, the Netherlands.
| | - Kristof Vansteelandt
- Department of Neurosciences, Research Group Psychiatry, Neuropsychiatry, Academic Center for ECT and Neuromodulation (AcCENT), University Psychiatric Center KU Leuven, Kortenberg, Belgium
| | - Jasmien Obbels
- Department of Neurosciences, Research Group Psychiatry, Neuropsychiatry, Academic Center for ECT and Neuromodulation (AcCENT), University Psychiatric Center KU Leuven, Kortenberg, Belgium
| | - Liese Van den Eynde
- Department of Neurosciences, Research Group Psychiatry, Neuropsychiatry, Academic Center for ECT and Neuromodulation (AcCENT), University Psychiatric Center KU Leuven, Kortenberg, Belgium
| | - Shauni Verspecht
- Department of Neurosciences, Research Group Psychiatry, Neuropsychiatry, Academic Center for ECT and Neuromodulation (AcCENT), University Psychiatric Center KU Leuven, Kortenberg, Belgium
| | - Chelsea Verledens
- Department of Neurosciences, Research Group Psychiatry, Neuropsychiatry, Academic Center for ECT and Neuromodulation (AcCENT), University Psychiatric Center KU Leuven, Kortenberg, Belgium
| | - Kaat Hebbrecht
- Department of Neurosciences, Research Group Psychiatry, Neuropsychiatry, Academic Center for ECT and Neuromodulation (AcCENT), University Psychiatric Center KU Leuven, Kortenberg, Belgium
| | - Pascal Sienaert
- Department of Neurosciences, Research Group Psychiatry, Neuropsychiatry, Academic Center for ECT and Neuromodulation (AcCENT), University Psychiatric Center KU Leuven, Kortenberg, Belgium
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23
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Maj M. Understanding depression beyond the "mind-body" dichotomy. World Psychiatry 2023; 22:349-350. [PMID: 37713548 PMCID: PMC10503906 DOI: 10.1002/wps.21142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/17/2023] Open
Affiliation(s)
- Mario Maj
- Department of Psychiatry, University of Campania "L. Vanvitelli", Naples, Italy
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24
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Belanger HG, Lee C, Winsberg M. Symptom clustering of major depression in a national telehealth sample. J Affect Disord 2023; 338:129-134. [PMID: 37245550 DOI: 10.1016/j.jad.2023.05.026] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 03/30/2023] [Accepted: 05/11/2023] [Indexed: 05/30/2023]
Abstract
BACKGROUND Major depressive disorder (MDD) is a heterogeneous disorder whose possible symptom combinations have not been well delineated. The aim of this study was to explore the heterogeneity of symptoms experienced by those with MDD to characterize phenotypic presentations. METHODS Cross-sectional data (N = 10,158) from a large telemental health platform were used to identify subtypes of MDD. Symptom data, gathered from both clinically-validated surveys and intake questions, were analyzed via polychoric correlations, principal component analysis, and cluster analysis. RESULTS Principal components analysis (PCA) of baseline symptom data revealed 5 components, including anxious distress, core emotional, agitation/irritability, insomnia, and anergic/apathy components. PCA-based cluster analysis resulted in four MDD phenotypes, the largest of which was characterized by a prominent elevation on the anergic/apathy component, but also core emotional. The four clusters differed on demographic and clinical characteristics. LIMITATIONS The primary limitation of this study is that the phenotypes uncovered are limited by the questions asked. These phenotypes will need to be cross validated with other samples, potentially expanded to include biological/genetic variables, and followed longitudinally. CONCLUSIONS The heterogeneity in MDD, as illustrated by the phenotypes in this sample, may explain the heterogeneity of treatment response in large-scale treatment trials. These phenotypes can be used to study varying rates of recovery following treatment and to develop clinical decision support tools and artificial intelligence algorithms. Strengths of this study include its size, breadth of included symptoms, and novel use of a telehealth platform.
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Affiliation(s)
- Heather G Belanger
- Brightside Health Inc., 5241F Diamond Heights Blvd #3422, San Francisco CA 94131, United States of America; University of South Florida, Department of Psychiatry and Behavioral Neurosciences, 3515 E Fletcher Ave, Tampa, FL 33613, United States of America.
| | - Christine Lee
- Brightside Health Inc., 5241F Diamond Heights Blvd #3422, San Francisco CA 94131, United States of America
| | - Mirène Winsberg
- Brightside Health Inc., 5241F Diamond Heights Blvd #3422, San Francisco CA 94131, United States of America
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25
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Sharpley CF, Bitsika V, Shadli SM, Jesulola E, Agnew LL. Alpha wave asymmetry is associated with only one component of melancholia, and in different directions across brain regions. Psychiatry Res Neuroimaging 2023; 334:111687. [PMID: 37480706 DOI: 10.1016/j.pscychresns.2023.111687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 06/06/2023] [Accepted: 07/17/2023] [Indexed: 07/24/2023]
Abstract
Alpha wave asymmetry inconsistently correlates with Major Depressive Disorder (MDD). One possible reason for this inconsistency is the heterogeneity of MDD, leading to study of depressive 'subtypes', one of which is Melancholia. To investigate the correlation between Melancholia and alpha-wave asymmetry, 100 community participants (44 males, 56 females; aged at least 18 yr) completed the Zung self-rated Depression Scale, and underwent 3 min of eyes closed EEG recording from 24 scalp sites. There was no significant correlation between EEG data and Melancholia total score for the entire sample, but there was for those participants who had clinically significant depression (n = 33). When examined at the level of individual Melancholia scale items, significant EEG data correlations were found for some of the items but not for others. Factor analysis revealed a two-factor structure for the Melancholia scale, only one of which exhibited significant correlations with EEG AA data. Further exploration of those data identified two subcomponents of that Melancholia factor, one which was inversely correlated with frontal alpha asymmetry, and another which was directly correlated with parietal-occipital alpha wave asymmetry. These findings suggest that Melancholia may itself be heterogeneous, similarly to MDD, and rely upon different aspects of cognitive function.
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Affiliation(s)
- Christopher F Sharpley
- Brain-Behaviour Research Group, University of New England, Armidale, New South Wales, 2350, Australia; School of Science & Technology, University of New England, Queen Elizabeth Drive, Armidale, New South Wales, 2351, Australia.
| | - Vicki Bitsika
- Brain-Behaviour Research Group, University of New England, Armidale, New South Wales, 2350, Australia
| | - Shabah M Shadli
- Brain-Behaviour Research Group, University of New England, Armidale, New South Wales, 2350, Australia
| | - Emmanuel Jesulola
- Brain-Behaviour Research Group, University of New England, Armidale, New South Wales, 2350, Australia; Emmanuel Jesulola is now at Department of Neurosurgery, The Alfred Hospital, Melbourne, Australia
| | - Linda L Agnew
- Brain-Behaviour Research Group, University of New England, Armidale, New South Wales, 2350, Australia; Linda Agnew is now at Griffith University, Qld, Australia
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26
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Nunez JJ, Liu YS, Cao B, Frey BN, Ho K, Milev R, Müller DJ, Rotzinger S, Soares CN, Taylor VH, Uher R, Kennedy SH, Lam RW. Response trajectories during escitalopram treatment of patients with major depressive disorder. Psychiatry Res 2023; 327:115361. [PMID: 37523890 DOI: 10.1016/j.psychres.2023.115361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 07/17/2023] [Accepted: 07/21/2023] [Indexed: 08/02/2023]
Abstract
Depression is a leading global cause of disability, yet about half of patients do not respond to initial antidepressant treatment. This treatment difficulty may be in part due to the heterogeneity of depression and corresponding response to treatment. Unsupervised machine learning allows underlying patterns to be uncovered, and can be used to understand this heterogeneity by finding groups of patients with similar response trajectories. Prior studies attempting this have clustered patients using a narrow range of data primarily from depression scales. In this work, we used unsupervised machine learning to cluster patients receiving escitalopram therapy using a wide variety of subjective and objective clinical features from the first eight weeks of the Canadian Biomarker Integration Network in Depression-1 trial. We investigated how these clusters responded to treatment by comparing changes in symptoms and symptom categories, and by using Principal Component Analysis (PCA). Our algorithm found three clusters, which broadly represented non-responders, responders, and remitters. Most categories of features followed this response pattern except for objective cognitive features. Using PCA with our clusters, we found that subjective mood state/anhedonia is the core feature of response with escitalopram, but there exists other distinct patterns of response around neurovegetative symptoms, activation, and cognition.
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Affiliation(s)
- John-Jose Nunez
- Department of Psychiatry, University of British Columbia, Vancouver, Canada.
| | - Yang S Liu
- Department of Psychiatry, University of Alberta, Edmonton, Canada
| | - Bo Cao
- Department of Psychiatry, University of Alberta, Edmonton, Canada; Department of Computing Science, University of Alberta, Edmonton, Canada
| | - Benicio N Frey
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Canada
| | - Keith Ho
- Department of Psychiatry, University of Toronto, Toronto, Canada
| | - Roumen Milev
- Department of Psychiatry, Queen's University, Providence Care, Kingston, Canada
| | - Daniel J Müller
- Department of Psychiatry, University of Toronto, Toronto, Canada; Centre for Addiction and Mental Health, Toronto, Canada
| | - Susan Rotzinger
- Department of Psychiatry, University of Toronto, Toronto, Canada
| | - Claudio N Soares
- Department of Psychiatry, Queen's University, Providence Care, Kingston, Canada
| | - Valerie H Taylor
- Department of Psychiatry, University of Calgary, Calgary, Canada
| | - Rudolf Uher
- Department of Psychiatry, Dalhousie University, Halifax, Canada
| | - Sidney H Kennedy
- Department of Psychiatry, University of Toronto, Toronto, Canada
| | - Raymond W Lam
- Department of Psychiatry, University of British Columbia, Vancouver, Canada
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27
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Fagiolini A, Cardoner N, Pirildar S, Ittsakul P, Ng B, Duailibi K, El Hindy N. Moving from serotonin to serotonin-norepinephrine enhancement with increasing venlafaxine dose: clinical implications and strategies for a successful outcome in major depressive disorder. Expert Opin Pharmacother 2023; 24:1715-1723. [PMID: 37501324 DOI: 10.1080/14656566.2023.2242264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 07/25/2023] [Indexed: 07/29/2023]
Abstract
INTRODUCTION Mental health disorders, especially depressive and anxiety disorders, are associated with substantial health-related burden. While the second-generation antidepressants are widely accepted as first-line pharmacological treatment for major depressive disorder (MDD), patient response to such treatment is variable, with more than half failing to achieve complete remission, and residual symptoms are frequently present. AREAS COVERED Here, the pharmacodynamics of venlafaxine XR are reviewed in relation to its role as both a selective serotonin reuptake inhibitor (SSRI) and a serotonin-norepinephrine-reuptake inhibitor (SNRI), and we look at how these pharmacodynamic properties can be harnessed to guide clinical practice, asking the question 'is it possible to develop a symptom-cluster-based approach to the treatment of MDD with comorbid anxiety utilizing venlafaxine XR?.' Additionally, three illustrative clinical cases provide practical examples of the utility of venlafaxine-XR in real-world clinical practice. The place of venlafaxine XR in managing fatigue/low energy, a frequent residual symptom in MDD, is explored using pooled data from clinical trials of venlafaxine XR. EXPERT OPINION Venlafaxine XR should be considered as a first-line treatment for MDD with or without comorbid anxiety, and there are clear pharmacodynamic signals supporting a symptom cluster-based treatment paradigm for venlafaxine XR.
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Affiliation(s)
- Andrea Fagiolini
- Department of Molecular Medicine, University of Siena School of Medicine, Siena, Italy
| | - Narcis Cardoner
- Department of Psychiatry and Legal Medicine, Hospital de la Santa Creu I Sant Pau, Biomedical Research Institute Sant Pau (IIB-Sant Pau), Universitat Autònoma de Barcelona (UAB), CIBERSAM, Carlos III Health Institute, Madrid, Spain
| | - Sebnem Pirildar
- Department of Mental Health and Diseases, Ege University Medical School, Izmir, Turkey
| | - Pichai Ittsakul
- Department of Psychiatry, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Bernardo Ng
- Mexican Consortium of Neuropsychopharmacology, Mexico, Sun Valley Research Center, Imperial, California, USA
| | - Kalil Duailibi
- Department of Psychiatry, Santo Amaro University, São Paulo, Brazil
| | - Nasser El Hindy
- American Center Neurology and Psychiatry, Dubai, United Arab Emirates
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28
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Hicks PB, Sevilimedu V, Johnson GR, Tal IR, Chen P, Davis LL, Vertrees JE, Zisook S, Mohamed S. Factors Affecting Antidepressant Response Trajectories: A Veterans Affairs Augmentation and Switching Treatments for Improving Depression Outcomes Trial Report. PSYCHIATRIC RESEARCH AND CLINICAL PRACTICE 2023; 5:131-143. [PMID: 38077276 PMCID: PMC10698706 DOI: 10.1176/appi.prcp.20230017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 06/12/2023] [Accepted: 06/29/2023] [Indexed: 02/12/2024] Open
Abstract
Background In this secondary analysis of the VA Augmentation and Switching Treatments for Improving Depression Outcomes (VAST-D) study we used antidepressant response trajectories to assess the association of treatment and multiple clinical/demographic factors with the probability of response. Methods Using data from VAST-D, a multi-site, randomized, single-blind trial with parallel-assignment to one of three treatment interventions in 1522 Veterans whose major depressive disorder was unresponsive to at least one antidepressant trial, we evaluated response patterns using group-based trajectory modeling (GBTM). A weighted multinomial logistic regression analysis with backward elimination and additional exploratory analyses were performed to evaluate the association of multiple clinical/demographic factors with the probability of inclusion into specific trajectories. Additional exploratory analyses were used to identify factors associated with trajectory group membership that could have been missed in the primary analysis. Results GBTM showed the best fit for depression symptom change was comprised of six trajectories, with some trajectories demonstrating minimal improvement and others showing a high probability of remission. High baseline depression and anxiety severity scores decreased, and early improvement increased, the likelihood of inclusion into the most responsive trajectory in both the GBTM and exploratory analyses. Conclusion While multiple factors influence responsiveness, the probability of inclusion into a specific depression symptom trajectory is most strongly influenced by three factors: baseline depression, baseline anxiety, and the presence of early improvement.
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Affiliation(s)
- Paul B. Hicks
- Department of PsychiatryBaylor Scott & White HealthTempleTexas
- Texas A&M College of MedicineTempleTexas
| | - Varadan Sevilimedu
- Biostatistics ServiceDepartment of Epidemiology and BiostatisticsMemorial Sloan Kettering Cancer CenterNew YorkNew York
- Yale University School of Public HealthNew HavenConnecticut
- Cooperative Studies Program Coordinating CenterVA Connecticut Healthcare SystemWest HavenConnecticut
| | - Gary R. Johnson
- Cooperative Studies Program Coordinating CenterVA Connecticut Healthcare SystemWest HavenConnecticut
| | | | - Peijun Chen
- Department of PsychiatryVISN10 Geriatric Research, Education and Clinical CenterVA Northeast Ohio Healthcare SystemClevelandOhio
- Case Western Reserve UniversityClevelandOhio
| | - Lori L. Davis
- Tuscaloosa VA Medical CenterTuscaloosaAlabama
- University of Alabama School of MedicineBirminghamAlabama
| | - Julia E. Vertrees
- Cooperative Studies Program Clinical Research Pharmacy Coordinating CenterAlbuquerqueNew Mexico
| | - Sidney Zisook
- VA San Diego Healthcare SystemSan DiegoCalifornia
- University of CaliforniaSan DiegoCalifornia
| | - Somaia Mohamed
- Veterans Affairs (VA) New England Mental Illness Research, Education and Clinical CenterVA Connecticut Healthcare SystemWest HavenConnecticut
- Yale University School of MedicineNew HavenConnecticut
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29
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Flint J. The genetic basis of major depressive disorder. Mol Psychiatry 2023; 28:2254-2265. [PMID: 36702864 PMCID: PMC10611584 DOI: 10.1038/s41380-023-01957-9] [Citation(s) in RCA: 28] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 12/30/2022] [Accepted: 01/11/2023] [Indexed: 01/27/2023]
Abstract
The genetic dissection of major depressive disorder (MDD) ranks as one of the success stories of psychiatric genetics, with genome-wide association studies (GWAS) identifying 178 genetic risk loci and proposing more than 200 candidate genes. However, the GWAS results derive from the analysis of cohorts in which most cases are diagnosed by minimal phenotyping, a method that has low specificity. I review data indicating that there is a large genetic component unique to MDD that remains inaccessible to minimal phenotyping strategies and that the majority of genetic risk loci identified with minimal phenotyping approaches are unlikely to be MDD risk loci. I show that inventive uses of biobank data, novel imputation methods, combined with more interviewer diagnosed cases, can identify loci that contribute to the episodic severe shifts of mood, and neurovegetative and cognitive changes that are central to MDD. Furthermore, new theories about the nature and causes of MDD, drawing upon advances in neuroscience and psychology, can provide handles on how best to interpret and exploit genetic mapping results.
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Affiliation(s)
- Jonathan Flint
- Department of Psychiatry and Biobehavioral Sciences, Billy and Audrey Wilder Endowed Chair in Psychiatry and Neuroscience, Center for Neurobehavioral Genetics, 695 Charles E. Young Drive South, 3357B Gonda, Box 951761, Los Angeles, CA, 90095-1761, USA.
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30
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Zhou KN, Wang Y, Xie Y, Yang SH, Liu SY, Fang YH, Zhang Y. Symptom burden survey and symptom clusters in patients with cervical cancer: a cross-sectional survey. Support Care Cancer 2023; 31:338. [PMID: 37191783 DOI: 10.1007/s00520-023-07802-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 05/05/2023] [Indexed: 05/17/2023]
Abstract
PURPOSE The purpose of this study is to determine the incidence and severity of symptoms of patients with cervical cancer within 6 months after radiotherapy and chemotherapy, form a symptom burden report, evaluate the distribution characteristics of symptoms, identify symptom clusters, and provide a basis for clinical doctors and nurses to improve the symptom management of patients with cervical cancer after radiotherapy and chemotherapy. METHODS The patients with cervical cancer within 6 months after radiotherapy and chemotherapy were recruited to investigate their symptom burden. Exploratory factor analysis was used to identify symptom clusters. RESULTS A total of 250 patients participated in the study. The study found that the most common symptom among the 40 symptoms was fatigue, and the most serious symptom was nocturia. Based on the occurrence rate and severity of symptoms, nine symptom clusters were identified, including psycho-emotion-related symptom cluster, pain-disturbed sleep-related symptom cluster, menopausal symptom cluster, tinnitus-dizziness-related symptom cluster, urinary-related symptom cluster, dry mouth-bitter taste-related symptom cluster, intestinal-related symptom cluster, memory loss-numbness-related symptom cluster, and emaciation-related symptom cluster. The three most serious symptom clusters are pain-disturbed sleep-related symptom cluster, urinary-related symptom cluster, and memory loss-numbness-related symptom cluster. CONCLUSION The symptoms of patients with cervical cancer within 6 months after radiotherapy and chemotherapy are complex, and nine symptom clusters can be identified according to the incidence and severity of symptoms. We can find the potential biological mechanism of each symptom cluster through the discussion of previous mechanism research and clinical research. The number of symptom clusters and the number of symptoms within the symptom cluster are closely related to the symptom evaluation scale selected for the study. Therefore, the symptom cluster study urgently needs a targeted symptom evaluation scale that can comprehensively reflect the patient's condition.
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Affiliation(s)
- Kai-Nan Zhou
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, No. 5, Beixian Ge Street, Xicheng District, Beijing, 100053, China
| | - Yan Wang
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, No. 5, Beixian Ge Street, Xicheng District, Beijing, 100053, China
| | - Yi Xie
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, No. 5, Beixian Ge Street, Xicheng District, Beijing, 100053, China
- Graduate School, Beijing University of Chinese Medicine, No. 11, Beisanhuan Dong Road, Chaoyang District, Beijing, 100029, China
| | - Shu-Han Yang
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, No. 5, Beixian Ge Street, Xicheng District, Beijing, 100053, China
- Graduate School, Beijing University of Chinese Medicine, No. 11, Beisanhuan Dong Road, Chaoyang District, Beijing, 100029, China
| | - Su-Ying Liu
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, No. 5, Beixian Ge Street, Xicheng District, Beijing, 100053, China
| | - Yu-Hang Fang
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, No. 5, Beixian Ge Street, Xicheng District, Beijing, 100053, China
- Graduate School, Beijing University of Chinese Medicine, No. 11, Beisanhuan Dong Road, Chaoyang District, Beijing, 100029, China
| | - Ying Zhang
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, No. 5, Beixian Ge Street, Xicheng District, Beijing, 100053, China.
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Gibbs-Dean T, Katthagen T, Tsenkova I, Ali R, Liang X, Spencer T, Diederen K. Belief updating in psychosis, depression and anxiety disorders: A systematic review across computational modelling approaches. Neurosci Biobehav Rev 2023; 147:105087. [PMID: 36791933 DOI: 10.1016/j.neubiorev.2023.105087] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 01/31/2023] [Accepted: 02/09/2023] [Indexed: 02/15/2023]
Abstract
Alterations in belief updating are proposed to underpin symptoms of psychiatric illness, including psychosis, depression, and anxiety. Key parameters underlying belief updating can be captured using computational modelling techniques, aiding the identification of unique and shared deficits, and improving diagnosis and treatment. We systematically reviewed research that applied computational modelling to probabilistic tasks measuring belief updating in stable and volatile (changing) environments, across clinical and subclinical psychosis (n = 17), anxiety (n = 9), depression (n = 9) and transdiagnostic samples (n = 9). Depression disorders related to abnormal belief updating in response to the valence of rewards, evidenced in both stable and volatile environments. Whereas psychosis and anxiety disorders were associated with difficulties adapting to changing contingencies specifically, indicating an inflexibility and/or insensitivity to environmental volatility. Higher-order learning models revealed additional difficulties in the estimation of overall environmental volatility across psychosis disorders, showing increased updating to irrelevant information. These findings stress the importance of investigating belief updating in transdiagnostic samples, using homogeneous experimental and computational modelling approaches.
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Affiliation(s)
- Toni Gibbs-Dean
- Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.
| | - Teresa Katthagen
- Department of Psychiatry and Neuroscience CCM, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Germany
| | - Iveta Tsenkova
- Psychological Medicine, Institute of Psychiatry, Psychology and neuroscience, King's College London, UK
| | - Rubbia Ali
- Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Xinyi Liang
- Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Thomas Spencer
- Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Kelly Diederen
- Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
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32
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Ebrahimzadeh E, Fayaz F, Rajabion L, Seraji M, Aflaki F, Hammoud A, Taghizadeh Z, Asgarinejad M, Soltanian-Zadeh H. Machine learning approaches and non-linear processing of extracted components in frontal region to predict rTMS treatment response in major depressive disorder. Front Syst Neurosci 2023; 17:919977. [PMID: 36968455 PMCID: PMC10034109 DOI: 10.3389/fnsys.2023.919977] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 02/13/2023] [Indexed: 03/12/2023] Open
Abstract
Predicting the therapeutic result of repetitive transcranial magnetic stimulation (rTMS) treatment could save time and costs as ineffective treatment can be avoided. To this end, we presented a machine-learning-based strategy for classifying patients with major depression disorder (MDD) into responders (R) and nonresponders (NR) to rTMS treatment. Resting state EEG data were recorded using 32 electrodes from 88 MDD patients before treatment. Then, patients underwent 7 weeks of rTMS, and 46 of them responded to treatment. By applying Independent Component Analysis (ICA) on EEG, we identified the relevant brain sources as possible indicators of neural activity in the dorsolateral prefrontal cortex (DLPFC). This was served through estimating the generators of activity in the sensor domain. Subsequently, we added physiological information and placed certain terms and conditions to offer a far more realistic estimation than the classic EEG. Ultimately, those components mapped in accordance with the region of the DLPFC in the sensor domain were chosen. Features extracted from the relevant ICs time series included permutation entropy (PE), fractal dimension (FD), Lempel-Ziv Complexity (LZC), power spectral density, correlation dimension (CD), features based on bispectrum, frontal and prefrontal cordance, and a combination of them. The most relevant features were selected by a Genetic Algorithm (GA). For classifying two groups of R and NR, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Multilayer Perceptron (MLP) were applied to predict rTMS treatment response. To evaluate the performance of classifiers, a 10-fold cross-validation method was employed. A statistical test was used to assess the capability of features in differentiating R and NR for further research. EEG characteristics that can predict rTMS treatment response were discovered. The strongest discriminative indicators were EEG beta power, the sum of bispectrum diagonal elements in delta and beta bands, and CD. The Combined feature vector classified R and NR with a high performance of 94.31% accuracy, 92.85% specificity, 95.65% sensitivity, and 92.85% precision using SVM. This result indicates that our proposed method with power and nonlinear and bispectral features from relevant ICs time-series can predict the treatment outcome of rTMS for MDD patients only by one session pretreatment EEG recording. The obtained results show that the proposed method outperforms previous methods.
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Affiliation(s)
- Elias Ebrahimzadeh
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
- *Correspondence: Elias Ebrahimzadeh
| | - Farahnaz Fayaz
- Biomedical Engineering Department, School of Electrical Engineering, Payame Noor University of North Tehran, Tehran, Iran
| | - Lila Rajabion
- School of Graduate Studies, SUNY Empire State College, Manhattan, NY, United States
| | - Masoud Seraji
- Department of Psychology, University of Texas at Austin, Austin, TX, United States
| | - Fatemeh Aflaki
- Department of Biomedical Engineering, Islamic Azad University Central Tehran Branch, Tehran, Iran
| | - Ahmad Hammoud
- Department of Medical and Technical Information Technology, Bauman Moscow State Technical University, Moscow, Russia
| | - Zahra Taghizadeh
- Department of Bioengineering, George Mason University, Fairfax, VA, United States
| | - Mostafa Asgarinejad
- Department of Cognitive Neuroscience, Institute for Cognitive Sciences Studies, Tehran, Iran
| | - Hamid Soltanian-Zadeh
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
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33
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Zangen A, Zibman S, Tendler A, Barnea-Ygael N, Alyagon U, Blumberger DM, Grammer G, Shalev H, Gulevski T, Vapnik T, Bystritsky A, Filipčić I, Feifel D, Stein A, Deutsch F, Roth Y, George MS. Pursuing personalized medicine for depression by targeting the lateral or medial prefrontal cortex with Deep TMS. JCI Insight 2023; 8:165271. [PMID: 36692954 PMCID: PMC9977507 DOI: 10.1172/jci.insight.165271] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 01/05/2023] [Indexed: 01/25/2023] Open
Abstract
BACKGROUNDMajor depressive disorder (MDD) can benefit from novel interventions and personalization. Deep transcranial magnetic stimulation (Deep TMS) targeting the lateral prefrontal cortex (LPFC) using the H1 coil was FDA cleared for treatment of MDD. However, recent preliminary data indicate that targeting the medial prefrontal cortex (MPFC) using the H7 coil might induce outcomes that are as good or even better. Here, we explored whether Deep TMS targeting the MPFC is noninferior to targeting the LPFC and whether electrophysiological or clinical markers for patient selection can be identified.METHODSThe present prospective, multicenter, randomized study enrolled 169 patients with MDD for whom antidepressants failed in the current episode. Patients were randomized to receive 24 Deep TMS sessions over 6 weeks, using either the H1 coil or the H7 coil. The primary efficacy endpoint was the change from baseline to week 6 in Hamilton Depression Rating Scale scores.RESULTSClinical efficacy and safety profiles were similar and not significantly different between groups, with response rates of 60.9% for the H1 coil and 64.2% for the H7 coil. Moreover, brain activity measured by EEG during the first treatment session correlated with clinical outcomes in a coil-specific manner, and a cluster of baseline clinical symptoms was found to potentially distinguish between patients who can benefit from each Deep TMS target.CONCLUSIONThis study provides a treatment option for MDD, using the H7 coil, and initial guidance to differentiate between patients likely to respond to LPFC versus MPFC stimulation targets, which require further validation studies.TRIAL REGISTRATIONClinicalTrials.gov NCT03012724.FUNDINGBrainsWay Ltd.
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Affiliation(s)
| | - Samuel Zibman
- Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Aron Tendler
- Advanced Mental Health Care Inc., Royal Palm Beach, Florida, USA
| | | | - Uri Alyagon
- Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Daniel M Blumberger
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, and Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | | | - Hadar Shalev
- Ben-Gurion University of the Negev, Beer-Sheva, Israel.,Department of Psychiatry, Soroka Medical Center, Beer-Sheva, Israel
| | | | - Tanya Vapnik
- Pacific Institute of Medical Research, Los Angeles, California, USA
| | | | - Igor Filipčić
- Psychiatric Hospital Sveti Ivan and School of Medicine, University of Zagreb, Zagreb, Croatia
| | - David Feifel
- Kadima Neuropsychiatry Institute, La Jolla, California, USA
| | - Ahava Stein
- A. Stein - Regulatory Affairs Consulting Ltd, Kfar Saba, Israel
| | | | - Yiftach Roth
- Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Mark S George
- Medical University of South Carolina, Columbia, South Carolina, USA.,Ralph H. Johnson VA Medical Center, Charleston, South Carolina, USA
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34
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Boschloo L, Hieronymus F, Lisinski A, Cuijpers P, Eriksson E. The complex clinical response to selective serotonin reuptake inhibitors in depression: a network perspective. Transl Psychiatry 2023; 13:19. [PMID: 36681669 PMCID: PMC9867733 DOI: 10.1038/s41398-022-02285-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 11/29/2022] [Accepted: 12/08/2022] [Indexed: 01/22/2023] Open
Abstract
The clinical response to selective serotonin reuptake inhibitors (SSRIs) in depression takes weeks to be fully developed and is still not entirely understood. This study aimed to determine the direct and indirect effects of SSRIs relative to a placebo control condition on clinical symptoms of depression. We included data of 8262 adult patients with major depression participating in 28 industry-sponsored US Food and Drug Administration (FDA) registered trials on the efficacy of SSRIs. Clinical symptoms of depression were assessed by the 17 separate items of the Hamilton Depression Rating Scale (HDRS) after 1, 2, 3, 4 and 6 weeks of treatment. Network estimation techniques showed that SSRIs had quick and strong direct effects on the two affective symptoms, i.e., depressed mood and psychic anxiety; direct effects on other symptoms were weak or absent. Substantial indirect effects were found for all four cognitive symptoms, which showed larger reductions in the SSRI condition but mainly in patients reporting larger reductions in depressed mood. Smaller indirect effects were found for two arousal/somatic symptoms via the direct effect on psychic anxiety. Both direct and indirect effects on sleep problems and most arousal/somatic symptoms were weak or absent. In conclusion, our study revealed that SSRIs primarily caused reductions in affective symptoms, which were related to reductions in mainly cognitive symptoms and some specific arousal/somatic symptoms. The results can contribute to disclosing the mechanisms of action of SSRIs, and has the potential to facilitate early detection of responders and non-responders in clinical practice.
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Affiliation(s)
- Lynn Boschloo
- Department of Clinical Psychology, Utrecht University, Utrecht, The Netherlands. .,Department of Clinical, Neuro, and Developmental Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
| | - Fredrik Hieronymus
- grid.8761.80000 0000 9919 9582Department of Pharmacology, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Alexander Lisinski
- grid.8761.80000 0000 9919 9582Department of Pharmacology, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Pim Cuijpers
- grid.12380.380000 0004 1754 9227Department of Clinical, Neuro, and Developmental Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands ,grid.7399.40000 0004 1937 1397International Institute for Psychotherapy, Babeș-Bolyai University, Cluj-Napoca, Romania
| | - Elias Eriksson
- grid.8761.80000 0000 9919 9582Department of Pharmacology, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
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35
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Kim B, Niu X, Zhang F. Functional connectivity strength and topology differences in social phobia adolescents with and without ADHD comorbidity. Neuropsychologia 2023; 178:108418. [PMID: 36403658 DOI: 10.1016/j.neuropsychologia.2022.108418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 11/04/2022] [Accepted: 11/06/2022] [Indexed: 11/18/2022]
Abstract
Social phobia (SP) is associated with changes in functional connectivity strength and topology. However, reported changes have been heterogeneous due to small sample sizes, inconsistent methodologies, and comorbidities, such as attention-deficit/hyperactivity disorder (ADHD), which has a high comorbidity rate with SP. Furthermore, there are few studies looking at SP in an adolescent population, a critical period for the development of the social brain. This project focuses on functional connectivity strength and topological differences in social phobia patients with and without ADHD comorbidity. We examined resting-state functional MRI images from 158 subjects, including 36 SP participants without ADHD comorbidity, 60 SP participants with ADHD comorbidity, and 62 healthy controls, with an overall average age of 14.16. We used a data-driven approach to examine impaired functional connectivity in a whole-brain analysis and higher-order topological differences in functional brain networks. We identified changes in the cerebellum and default mode network in social phobia patients as a whole, with the presence of ADHD comorbidity affecting various subsystems of the default mode network. Social phobia functional connectivity networks resembled random graphs, and local connectivity patterns in the superior occipital gyrus were different due to ADHD comorbidity. These alterations may indicate impairments in self-related processing, imagery, mentalizing, and predictive processes. We then used these changes in a linear support vector machine to distinguish between each pair of groups and achieved prediction accuracy significantly above chance rates. Our study extends prior research by showing that functional connectivity changes exist at adolescence, which are affected by ADHD comorbidity. As such, these results offer a new perspective in examining neurobiological changes in SP patients.
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Affiliation(s)
- Brian Kim
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, PA, USA.
| | - Xin Niu
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, PA, USA
| | - Fengqing Zhang
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, PA, USA.
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36
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Kaster TS, Downar J, Vila-Rodriguez F, Baribeau DA, Thorpe KE, Daskalakis ZJ, Blumberger DM. Differential symptom cluster responses to repetitive transcranial magnetic stimulation treatment in depression. EClinicalMedicine 2023; 55:101765. [PMID: 36483268 PMCID: PMC9722479 DOI: 10.1016/j.eclinm.2022.101765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 11/16/2022] [Accepted: 11/16/2022] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND Repetitive transcranial magnetic stimulation (rTMS) can target specific neural circuits, which may allow for personalized treatment of depression. Treatment outcome is typically determined using sum scores from validated measurement scales; however, this may obscure differential improvements within distinct symptom domains. The objectives for this work were to determine: (1) whether a standard depression measure can be represented using a four symptom cluster model and (2) whether these symptom clusters had a differential response to rTMS treatment. METHODS Data were obtained from two multi-centre randomized controlled trials of rTMS delivered to the left dorsolateral prefrontal cortex (DLPFC) for participants with treatment-resistant depression (TRD) conducted in Canada (THREE-D [Conducted between Sept 2013, and Oct 2016] and CARTBIND [Conducted between Apr 2016 and Feb 2018]). The first objective used confirmatory factor analytic techniques, and the second objective used a linear mixed effects model. Trial Registration: NCT01887782, NCT02729792. FINDINGS In the total sample of 596 participants with TRD, we found a model consisting of four symptom clusters adequately fit the data. The primary analysis using the THREE-D treatment trial found that symptom clusters demonstrated a differential response to rTMS treatment (F(3,5984) = 31.92, p < 0.001). The anxiety symptom cluster was significantly less responsive to treatment than other symptom clusters (t(6001) = -8.02, p < 0.001). These findings were replicated using data from the CARTBIND trial. INTERPRETATION There are distinct symptom clusters experienced by individuals with TRD that have a differential response to rTMS. Future work will determine whether differing rTMS treatment targets have distinct patterns of symptom cluster responses with the eventual goal of personalizing rTMS protocols based on an individual's clinical presentation. FUNDING Canadian Institutes of Health Research, Brain Canada.
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Key Words
- CFA, Confirmatory factor analysis
- CFI, Comparative fit index
- Cluster analysis
- DLPFC, Dorsolateral prefrontal cortex
- Depressive disorders
- HDRS-17, 17-item Hamilton Depression Rating Scale
- HFL, High-frequency left stimulation
- MDD, Major depressive disorder
- MINI, Mini International Neuropsychiatric Interview
- RMSEA, Root mean square error of approximation
- Repetitive transcranial magnetic stimulation
- SRMR, Standardized root mean squared residual
- TRD, Treatment-resistant depression
- Treatment outcomes
- iTBS, Intermittent theta-burst stimulation
- rTMS, Repetitive transcranial magnetic stimulation
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Affiliation(s)
- Tyler S. Kaster
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, Canada
- Department of Psychiatry, University of Toronto, Toronto, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada
- Corresponding author. 1025 Queen St. W., Toronto, ON, M6J 1H4, Canada.
| | - Jonathan Downar
- Department of Psychiatry, University of Toronto, Toronto, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Fidel Vila-Rodriguez
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
- Non-Invasive Neurostimulation Therapies (NINET) Laboratory, University of British Columbia, Vancouver, BC, Canada
| | - Danielle A. Baribeau
- Department of Psychiatry, University of Toronto, Toronto, Canada
- Holland Bloorview Kids Rehabilitation Hospital, Bloorview Research Institute, Toronto, ON, Canada
| | - Kevin E. Thorpe
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Zafiris J. Daskalakis
- Department of Psychiatry, University of California, San Diego Health, CA, United States
| | - Daniel M. Blumberger
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, Canada
- Department of Psychiatry, University of Toronto, Toronto, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada
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37
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Chen Y, Stewart JW, Ge J, Cheng B, Chekroud A, Hellerstein DJ. Personalized Symptom Clusters that Predict Depression Treatment Outcomes: A Replication of Machine Learning Methods. JOURNAL OF AFFECTIVE DISORDERS REPORTS 2023. [DOI: 10.1016/j.jadr.2023.100470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
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Cohen ZD, DeRubeis RJ, Hayes R, Watkins ER, Lewis G, Byng R, Byford S, Crane C, Kuyken W, Dalgleish T, Schweizer S. The development and internal evaluation of a predictive model to identify for whom Mindfulness-Based Cognitive Therapy (MBCT) offers superior relapse prevention for recurrent depression versus maintenance antidepressant medication. Clin Psychol Sci 2023; 11:59-76. [PMID: 36698442 PMCID: PMC7614103 DOI: 10.1177/21677026221076832] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Depression is highly recurrent, even following successful pharmacological and/or psychological intervention. We aimed to develop clinical prediction models to inform adults with recurrent depression choosing between antidepressant medication (ADM) maintenance or switching to Mindfulness-Based Cognitive Therapy (MBCT). Using data from the PREVENT trial (N=424), we constructed prognostic models using elastic net regression that combined demographic, clinical and psychological factors to predict relapse at 24 months under ADM or MBCT. Only the ADM model (discrimination performance: AUC=.68) predicted relapse better than baseline depression severity (AUC=.54; one-tailed DeLong's test: z=2.8, p=.003). Individuals with the poorest ADM prognoses who switched to MBCT had better outcomes compared to those who maintained ADM (48% vs. 70% relapse, respectively; superior survival times [z=-2.7, p=.008]). For individuals with moderate-to-good ADM prognosis, both treatments resulted in similar likelihood of relapse. If replicated, the results suggest that predictive modeling can inform clinical decision-making around relapse prevention in recurrent depression.
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Affiliation(s)
| | | | - Rachel Hayes
- National Institute for Health Research (NIHR) Applied Research Collaboration (ARC) South West Peninsula, University of Exeter
| | | | - Glyn Lewis
- Division of Psychiatry, Faulty of Brain Sciences, University College London
- Community Primary Care Research Group, University of Plymouth
| | - Richard Byng
- Community Primary Care Research Group, University of Plymouth
- National Institute of Health Research Collaboration for Leadership in Applied Health Research and Care, South West Peninsula, England
| | - Sarah Byford
- Health Service and Population Research Department, Institute of Psychiatry, Psychology and Neuroscience, King’s College London
| | - Catherine Crane
- Department of Psychiatry, Medical Sciences Division, University of Oxford
| | - Willem Kuyken
- Department of Psychiatry, Medical Sciences Division, University of Oxford
| | - Tim Dalgleish
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, England
| | - Susanne Schweizer
- Department of Psychology, University of Cambridge
- School of Psychology, University of New South Wales
- Susanne Schweizer, Department of Psychology, University of Cambridge
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Athreya AP, Vande Voort JL, Shekunov J, Rackley SJ, Leffler JM, McKean AJ, Romanowicz M, Kennard BD, Emslie GJ, Mayes T, Trivedi M, Wang L, Weinshilboum RM, Bobo WV, Croarkin PE. Evidence for machine learning guided early prediction of acute outcomes in the treatment of depressed children and adolescents with antidepressants. J Child Psychol Psychiatry 2022; 63:1347-1358. [PMID: 35288932 PMCID: PMC9475486 DOI: 10.1111/jcpp.13580] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/17/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND The treatment of depression in children and adolescents is a substantial public health challenge. This study examined artificial intelligence tools for the prediction of early outcomes in depressed children and adolescents treated with fluoxetine, duloxetine, or placebo. METHODS The study samples included training datasets (N = 271) from patients with major depressive disorder (MDD) treated with fluoxetine and testing datasets from patients with MDD treated with duloxetine (N = 255) or placebo (N = 265). Treatment trajectories were generated using probabilistic graphical models (PGMs). Unsupervised machine learning identified specific depressive symptom profiles and related thresholds of improvement during acute treatment. RESULTS Variation in six depressive symptoms (difficulty having fun, social withdrawal, excessive fatigue, irritability, low self-esteem, and depressed feelings) assessed with the Children's Depression Rating Scale-Revised at 4-6 weeks predicted treatment outcomes with fluoxetine at 10-12 weeks with an average accuracy of 73% in the training dataset. The same six symptoms predicted 10-12 week outcomes at 4-6 weeks in (a) duloxetine testing datasets with an average accuracy of 76% and (b) placebo-treated patients with accuracies of 67%. In placebo-treated patients, the accuracies of predicting response and remission were similar to antidepressants. Accuracies for predicting nonresponse to placebo treatment were significantly lower than antidepressants. CONCLUSIONS PGMs provided clinically meaningful predictions in samples of depressed children and adolescents treated with fluoxetine or duloxetine. Future work should augment PGMs with biological data for refined predictions to guide the selection of pharmacological and psychotherapeutic treatment in children and adolescents with depression.
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Affiliation(s)
- Arjun P. Athreya
- Department of Molecular Pharmacology and Experimental TherapeuticsMayo ClinicRochesterMNUSA
| | | | - Julia Shekunov
- Department of Psychiatry and PsychologyMayo ClinicRochesterMNUSA
| | | | | | | | | | - Betsy D. Kennard
- Peter O’Donnell Jr. Brain Institute and the Department of PsychiatryUniversity of Texas Southwestern Medical CenterDallasTXUSA
| | - Graham J. Emslie
- Peter O’Donnell Jr. Brain Institute and the Department of PsychiatryUniversity of Texas Southwestern Medical CenterDallasTXUSA,Children’s HealthChildren’s Medical CenterDallasTXUSA
| | - Taryn Mayes
- Peter O’Donnell Jr. Brain Institute and the Department of PsychiatryUniversity of Texas Southwestern Medical CenterDallasTXUSA
| | - Madhukar Trivedi
- Peter O’Donnell Jr. Brain Institute and the Department of PsychiatryUniversity of Texas Southwestern Medical CenterDallasTXUSA
| | - Liewei Wang
- Department of Molecular Pharmacology and Experimental TherapeuticsMayo ClinicRochesterMNUSA
| | | | - William V. Bobo
- Department of Psychiatry and PsychologyMayo ClinicJacksonvilleFLUSA
| | - Paul E. Croarkin
- Department of Psychiatry and PsychologyMayo ClinicRochesterMNUSA
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Efficacy of adjunctive brexpiprazole on symptom clusters of major depressive disorder: A post hoc analysis of four clinical studies. J Affect Disord 2022; 316:201-208. [PMID: 35970327 DOI: 10.1016/j.jad.2022.08.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 07/04/2022] [Accepted: 08/10/2022] [Indexed: 10/31/2022]
Abstract
BACKGROUND Major depressive disorder (MDD) is a clinically heterogenous condition and its treatment should be individualized according to the presence of particular symptom clusters. The aim of this pooled analysis was to investigate the effects of adjunctive brexpiprazole on different symptom clusters in MDD. METHODS Data were included from four similarly designed, short-term, randomized, double-blind, placebo-controlled studies of adjunctive brexpiprazole in adults with MDD and inadequate response to 2-4 antidepressant treatments (ADTs), including 1 administered by investigators. Mean changes from baseline and Cohen's d effect sizes (ES) versus placebo were determined for the following Montgomery-Åsberg Depression Rating Scale symptom clusters: core, anhedonia, dysphoria, retardation, vegetative, loss of interest, and lassitude. RESULTS Over 6 weeks, ADT + brexpiprazole 2 mg (n = 486) showed greater improvement than ADT + placebo (n = 585) for all symptom clusters: core (ES = 0.36; p < 0.0001), anhedonia (ES = 0.43; p < 0.0001), dysphoria (ES = 0.27; p < 0.0001), retardation (ES = 0.32; p < 0.0001), vegetative (ES = 0.29; p < 0.0001), loss of interest (ES = 0.30; p < 0.0001), and lassitude (ES = 0.33; p < 0.0001). Improvements of similar magnitude were observed for ADT + brexpiprazole 2-3 mg (n = 770) versus ADT + placebo (n = 788) (ES = 0.24-0.38; all clusters p < 0.0001). In most cases, improvement over ADT + placebo was observed from Week 1 onwards. LIMITATIONS Post hoc analysis with no adjunctive active comparator. CONCLUSIONS Patients receiving adjunctive brexpiprazole versus adjunctive placebo showed improvements across a range of MDD symptom clusters. Improvements appeared early (generally from Week 1) and were maintained over 6 weeks. These data indicate that adjunctive brexpiprazole may benefit multiple subtypes of patient with MDD and inadequate response to ADTs.
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Advances in the treatment of depression. Int Clin Psychopharmacol 2022; 37:183-184. [PMID: 35916263 DOI: 10.1097/yic.0000000000000424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Bobo WV, Van Ommeren B, Athreya AP. Machine learning, pharmacogenomics, and clinical psychiatry: predicting antidepressant response in patients with major depressive disorder. Expert Rev Clin Pharmacol 2022; 15:927-944. [DOI: 10.1080/17512433.2022.2112949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Affiliation(s)
- William V. Bobo
- Department of Psychiatry & Psychology, Mayo Clinic Florida, Jacksonville, FL, USA
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN & Jacksonville, FL, USA
| | | | - Arjun P. Athreya
- Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
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Ohnishi T, Wakamatsu A, Kobayashi H. Different symptomatic improvement pattern revealed by factor analysis between placebo response and response to Esketamine in treatment resistant depression. Psychiatry Clin Neurosci 2022; 76:377-383. [PMID: 35596932 DOI: 10.1111/pcn.13379] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 04/14/2022] [Accepted: 05/08/2022] [Indexed: 11/28/2022]
Abstract
AIMS The aim of this study is to determine whether there is difference in the change in each symptom of depression and in symptomatic improvement pattern between placebo and antidepressant responses. METHODS Using data from a randomized, double-blind (DB), placebo-controlled trial of esketamine (ESK) in patients with treatment-resistant depression (TRD), we conducted exploratory analyses. To determine differences in the change in each depressive symptom on the MADRS subscale between placebo and antidepressant responses, a two-way factorial analysis was conducted using the amount of change on Day 2 and 28 of treatment. In addition, exploratory and confirmatory factor analyses were conducted on the MADRS subtotal variables on Day 2 and 28 of treatment to determine symptomatic improvement pattern between placebo response and antidepressant responses. RESULTS We found that as well as MADRS total score, each subscale of MADRS score did not significantly differ between esketamine and placebo at Day 2 and 28. On the other hand, factor analysis revealed that the factor structure of the response was different between esketamine and placebo at the 2nd day. There was no difference in the factor structure between esketamine and placebo in response on Day 28 of treatment. CONCLUSION Factor analysis revealed different patterns of symptom improvement in the early phase of the intervention between esketamine and placebo. This finding suggests that a data driven approach may provide detailed efficacy information in clinical trials for antidepressants. CLINICAL TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT02918318. Registered: 28 September 2016.
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Affiliation(s)
- Takashi Ohnishi
- Medical Affairs Division, Janssen Pharmaceutical K.K., Tokyo, Japan
| | | | - Hisanori Kobayashi
- Research and Development, Clinical Science Division, Janssen Pharmaceutical K.K., Tokyo, Japan
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Digital tools for the assessment of pharmacological treatment for depressive disorder: State of the art. Eur Neuropsychopharmacol 2022; 60:100-116. [PMID: 35671641 DOI: 10.1016/j.euroneuro.2022.05.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 05/13/2022] [Accepted: 05/17/2022] [Indexed: 12/23/2022]
Abstract
Depression is an invalidating disorder, marked by phenotypic heterogeneity. Clinical assessments for treatment adjustments and data-collection for pharmacological research often rely on subjective representations of functioning. Better phenotyping through digital applications may add unseen information and facilitate disentangling the clinical characteristics and impact of depression and its pharmacological treatment in everyday life. Researchers, physicians, and patients benefit from well-understood digital phenotyping approaches to assess the treatment efficacy and side-effects. This review discusses the current possibilities and pitfalls of wearables and technology for the assessment of the pharmacological treatment of depression. Their applications in the whole spectrum of treatment for depression, including diagnosis, treatment of an episode, and monitoring of relapse risk and prevention are discussed. Multiple aspects are to be considered, including concerns that come with collecting sensitive data and health recordings. Also, privacy and trust are addressed. Available applications range from questionnaire-like apps to objective assessment of behavioural patterns and promises in handling suicidality. Nonetheless, interpretation and integration of this high-resolution information with other phenotyping levels, remains challenging. This review provides a state-of-the-art description of wearables and technology in digital phenotyping for monitoring pharmacological treatment in depression, focusing on the challenges and opportunities of its application in clinical trials and research.
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Waqas A, Sikander S, Malik A, Atif N, Karyotaki E, Rahman A. Predicting Remission among Perinatal Women with Depression in Rural Pakistan: A Prognostic Model for Task-Shared Interventions in Primary Care Settings. J Pers Med 2022; 12:jpm12071046. [PMID: 35887543 PMCID: PMC9320748 DOI: 10.3390/jpm12071046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 06/14/2022] [Accepted: 06/15/2022] [Indexed: 12/03/2022] Open
Abstract
Perinatal depression is highly prevalent in low- and middle-income countries (LMICs) and is associated with adverse maternal and child health consequences. Task-shared psychological and psychosocial interventions for perinatal depression have demonstrated clinical and cost-effectiveness when delivered on a large scale. However, task-sharing approaches, especially in LMICs, require an effective mechanism, whereby clients who are not likely to benefit from such interventions are identified from the outset so that they can benefit from higher intensity treatments. Such a stratified approach can ensure that limited resources are utilized appropriately and effectively. The use of standardized and easy-to-implement algorithmic devices (e.g., nomograms) could help with such targeted dissemination of interventions. The present investigation posits a prognostic model and a nomogram to predict the prognosis of perinatal depression among women in rural Pakistan. The nomogram was developed to deliver stratified model of care in primary care settings by identifying those women who respond well to a non-specialist delivered intervention and those requiring specialist care. This secondary analysis utilized data from 903 pregnant women with depression who participated in a cluster randomized, controlled trial that tested the effectiveness of the Thinking Healthy Program in rural Rawalpindi, Pakistan. The participants were recruited from 40 union councils in two sub-districts of Rawalpindi and randomly assigned to intervention and enhanced usual care. Sixteen sessions of the THP intervention were delivered by trained community health workers to women with depression over pregnancy and the postnatal period. A trained assessment team used the Structured Clinical Interview for DSM-IV current major depressive episode module to diagnose major depressive disorder at baseline and post-intervention. The intervention received by the participants emerged as the most significant predictor in the prognostic model. Among clinical factors, baseline severity of core-emotional symptoms emerged as an essential predictor, followed by atypical symptoms and insomnia. Higher severity of these symptoms was associated with a poorer prognosis. Other important predictors of a favorable prognosis included support from one’s mother or mother-in-law, financial empowerment, higher socioeconomic class, and living in a joint family system. This prognostic model yielded acceptable discrimination (c-statistic = 0.75) and calibration to aid in personalized delivery of the intervention.
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Affiliation(s)
- Ahmed Waqas
- Department of Primary Care & Mental Health, Institute of Population Health, University of Liverpool, Liverpool L69 7ZA, UK; (S.S.); (A.R.)
- Correspondence: ; Tel.: +44-794-767-3943
| | - Siham Sikander
- Department of Primary Care & Mental Health, Institute of Population Health, University of Liverpool, Liverpool L69 7ZA, UK; (S.S.); (A.R.)
- Global Institute of Human Development, Shifa Tameer-e-Millat University, Rawalpindi 46000, Pakistan
| | - Abid Malik
- Department of Public Mental Health, Health Services Academy, Chak Shahzad, Islamabad 44000, Pakistan;
- Rawalpindi Medical University, Rawalpindi 46000, Pakistan
| | - Najia Atif
- Human Development Research Foundation, Islamabad, Pakistan;
| | - Eirini Karyotaki
- Department of Clinical Neuro- and Developmental Psychology, Vrije Universiteit Amsterdam, 1081 BT Amsterdam, The Netherlands;
| | - Atif Rahman
- Department of Primary Care & Mental Health, Institute of Population Health, University of Liverpool, Liverpool L69 7ZA, UK; (S.S.); (A.R.)
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Beaver JN, Weber BL, Ford MT, Anello AE, Kassis SK, Gilman TL. Uncovering Functional Contributions of PMAT ( Slc29a4) to Monoamine Clearance Using Pharmacobehavioral Tools. Cells 2022; 11:cells11121874. [PMID: 35741002 PMCID: PMC9220966 DOI: 10.3390/cells11121874] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 06/01/2022] [Accepted: 06/07/2022] [Indexed: 11/16/2022] Open
Abstract
Plasma membrane monoamine transporter (PMAT, Slc29a4) transports monoamine neurotransmitters, including dopamine and serotonin, faster than more studied monoamine transporters, e.g., dopamine transporter (DAT), or serotonin transporter (SERT), but with ~400–600-fold less affinity. A considerable challenge in understanding PMAT’s monoamine clearance contributions is that no current drugs selectively inhibit PMAT. To advance knowledge about PMAT’s monoamine uptake role, and to circumvent this present challenge, we investigated how drugs that selectively block DAT/SERT influence behavioral readouts in PMAT wildtype, heterozygote, and knockout mice of both sexes. Drugs typically used as antidepressants (escitalopram, bupropion) were administered acutely for readouts in tail suspension and locomotor tests. Drugs with psychostimulant properties (cocaine, D-amphetamine) were administered repeatedly to assess initial locomotor responses plus psychostimulant-induced locomotor sensitization. Though we hypothesized that PMAT-deficient mice would exhibit augmented responses to antidepressant and psychostimulant drugs due to constitutively attenuated monoamine uptake, we instead observed sex-selective responses to antidepressant drugs in opposing directions, and subtle sex-specific reductions in psychostimulant-induced locomotor sensitization. These results suggest that PMAT functions differently across sexes, and support hypotheses that PMAT’s monoamine clearance contribution emerges when frontline transporters (e.g., DAT, SERT) are absent, saturated, and/or blocked. Thus, known human polymorphisms that reduce PMAT function could be worth investigating as contributors to varied antidepressant and psychostimulant responses.
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Tutun S, Johnson ME, Ahmed A, Albizri A, Irgil S, Yesilkaya I, Ucar EN, Sengun T, Harfouche A. An AI-based Decision Support System for Predicting Mental Health Disorders. INFORMATION SYSTEMS FRONTIERS : A JOURNAL OF RESEARCH AND INNOVATION 2022; 25:1261-1276. [PMID: 35669335 PMCID: PMC9142346 DOI: 10.1007/s10796-022-10282-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/21/2022] [Indexed: 05/27/2023]
Abstract
Approximately one billion individuals suffer from mental health disorders, such as depression, bipolar disorder, schizophrenia, and anxiety. Mental health professionals use various assessment tools to detect and diagnose these disorders. However, these tools are complex, contain an excessive number of questions, and require a significant amount of time to administer, leading to low participation and completion rates. Additionally, the results obtained from these tools must be analyzed and interpreted manually by mental health professionals, which may yield inaccurate diagnoses. To this extent, this research utilizes advanced analytics and artificial intelligence to develop a decision support system (DSS) that can efficiently detect and diagnose various mental disorders. As part of the DSS development process, the Network Pattern Recognition (NEPAR) algorithm is first utilized to build the assessment tool and identify the questions that participants need to answer. Then, various machine learning models are trained using participants' answers to these questions and other historical data as inputs to predict the existence and the type of their mental disorder. The results show that the proposed DSS can automatically diagnose mental disorders using only 28 questions without any human input, to an accuracy level of 89%. Furthermore, the proposed mental disorder diagnostic tool has significantly fewer questions than its counterparts; hence, it provides higher participation and completion rates. Therefore, mental health professionals can use this proposed DSS and its accompanying assessment tool for improved clinical decision-making and diagnostic accuracy.
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Affiliation(s)
- Salih Tutun
- Washington University in St. Louis, St. Louis, MO USA
| | | | | | | | - Sedat Irgil
- Guven Private Health Laboratory, Guven, Turkey
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K Blais R, K Zalta A, S Livingston W. Interpersonal Trauma and Sexual Function and Satisfaction: The Mediating Role of Negative Affect Among Survivors of Military Sexual Trauma. JOURNAL OF INTERPERSONAL VIOLENCE 2022; 37:NP5517-NP5537. [PMID: 32990170 DOI: 10.1177/0886260520957693] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Healthy sexual function among women service members/veterans (SM/Vs) is associated with higher quality of life, lower incidence and severity of mental health diagnoses, higher relationship satisfaction, and less frequent suicidal ideation. Although trauma exposure has been established as a predictor of poor sexual function and satisfaction in women SM/Vs, no study to date has examined whether specific trauma types, such as military sexual trauma (MST), increase risk for sexual issues. Moreover, the possible mechanisms of this association have not been explored. The current study examined whether posttraumatic stress disorder (PTSD) and depression symptom clusters mediated the association of trauma type and sexual function and satisfaction in 426 trauma-exposed women SM/Vs. Two hundred seventy participants (63.4%) identified MST as their index trauma. Path analyses demonstrated that MST was related to poorer sexual function and lower satisfaction relative to the other traumas (χ2[28, N = 426] = 43.3, p = 0.03, CFI = 1.00, TLI = 0.99, and RMSEA = 0.04), and this association was mediated by higher non-somatic depressive symptoms and PTSD symptom clusters of anhedonia and negative alterations in cognition and mood (NACM). Causality cannot be inferred due to the cross-sectional nature of the data. However, our findings suggest that interventions aimed at decreasing sexual issues among female SM/Vs with MST should target depressogenic symptoms, whether the origin is depression or PTSD. Longitudinal research exploring the etiological processes that contribute to sexual dysfunction among those with MST is needed.
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Soleimani L, Schnaider Beeri M, Grossman H, Sano M, Zhu CW. Specific depression dimensions are associated with a faster rate of cognitive decline in older adults. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2022; 14:e12268. [PMID: 35317432 PMCID: PMC8923346 DOI: 10.1002/dad2.12268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 01/02/2022] [Accepted: 10/22/2021] [Indexed: 06/14/2023]
Abstract
Introduction Understanding the relationship between different depression presentations and cognitive outcome may elucidate high-risk sub-groups for cognitive decline. Methods In this study we utilized longitudinal data from the National Alzheimer's Coordinating Center (NACC) on 16,743 initially not demented older adults followed every 12 months for an average of 5 years. Depression dimensions were defined based on the 15-item Geriatric Depression Scale (GDS-15), that is, dysphoric mood, Withdrawal-Apathy-Vigor (WAV), anxiety, hopelessness, and subjective memory complaint (SMC). Results After adjustment for sociodemographic and clinical covariates, SMC and hopelessness were associated with faster decline in global cognition and all cognitive domains and WAV with decline executive function. Dysphoric mood and anxiety were not associated with a faster cognitive decline in any of the cognitive domains. Discussion Different depression dimensions had different associations with the rate of cognitive decline, suggesting distinct pathophysiology and the need for more targeted interventions.
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Affiliation(s)
- Laili Soleimani
- Department of PsychiatryThe Icahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Michal Schnaider Beeri
- Department of PsychiatryThe Icahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- The Joseph Sagol Neuroscience CenterSheba Medical CenterTel‐HashomerIsrael
| | - Hillel Grossman
- Department of PsychiatryThe Icahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- James J Peters VAMCBronxNew YorkUSA
| | - Mary Sano
- Department of PsychiatryThe Icahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- James J Peters VAMCBronxNew YorkUSA
| | - Carolyn W. Zhu
- Department of PsychiatryThe Icahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- James J Peters VAMCBronxNew YorkUSA
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Ricci A, Calhoun SL, He F, Fang J, Vgontzas AN, Liao D, Bixler EO, Younes M, Fernandez-Mendoza J. Association of a novel EEG metric of sleep depth/intensity with attention-deficit/hyperactivity, learning, and internalizing disorders and their pharmacotherapy in adolescence. Sleep 2022; 45:zsab287. [PMID: 34888687 PMCID: PMC8919202 DOI: 10.1093/sleep/zsab287] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 11/17/2021] [Indexed: 01/08/2023] Open
Abstract
STUDY OBJECTIVES Psychiatric/learning disorders are associated with sleep disturbances, including those arising from abnormal cortical activity. The odds ratio product (ORP) is a standardized electroencephalogram metric of sleep depth/intensity validated in adults, while ORP data in youth are lacking. We tested ORP as a measure of sleep depth/intensity in adolescents with and without psychiatric/learning disorders. METHODS Four hundred eighteen adolescents (median 16 years) underwent a 9-hour, in-lab polysomnography. Of them, 263 were typically developing (TD), 89 were unmedicated, and 66 were medicated for disorders including attention-deficit/hyperactivity (ADHD), learning (LD), and internalizing (ID). Central ORP during non-rapid eye movement (NREM) sleep was the primary outcome. Secondary/exploratory outcomes included central and frontal ORP during NREM stages, in the 9-seconds following arousals (ORP-9), in the first and second halves of the night, during REM sleep and wakefulness. RESULTS Unmedicated youth with ADHD/LD had greater central ORP than TD during stage 3 and in central and frontal regions during stage 2 and the second half of the sleep period, while ORP in youth with ADHD/LD on stimulants did not significantly differ from TD. Unmedicated youth with ID did not significantly differ from TD in ORP, while youth with ID on antidepressants had greater central and frontal ORP than TD during NREM and REM sleep, and higher ORP-9. CONCLUSIONS The greater ORP in unmedicated youth with ADHD/LD, and normalized levels in those on stimulants, suggests ORP is a useful metric of decreased NREM sleep depth/intensity in ADHD/LD. Antidepressants are associated with greater ORP/ORP-9, suggesting these medications induce cortical arousability.
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Affiliation(s)
- Anna Ricci
- Sleep Research and Treatment Center, Department of Psychiatry and Behavioral Health, Penn State College of Medicine, Hershey, PA,USA
| | - Susan L Calhoun
- Sleep Research and Treatment Center, Department of Psychiatry and Behavioral Health, Penn State College of Medicine, Hershey, PA,USA
| | - Fan He
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA
| | - Jidong Fang
- Sleep Research and Treatment Center, Department of Psychiatry and Behavioral Health, Penn State College of Medicine, Hershey, PA,USA
| | - Alexandros N Vgontzas
- Sleep Research and Treatment Center, Department of Psychiatry and Behavioral Health, Penn State College of Medicine, Hershey, PA,USA
| | - Duanping Liao
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA
| | - Edward O Bixler
- Sleep Research and Treatment Center, Department of Psychiatry and Behavioral Health, Penn State College of Medicine, Hershey, PA,USA
| | - Magdy Younes
- Sleep Disorders Centre, Department of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Julio Fernandez-Mendoza
- Sleep Research and Treatment Center, Department of Psychiatry and Behavioral Health, Penn State College of Medicine, Hershey, PA,USA
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