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Lu J, Jin Y, Liang S, Wang Q, Li X, Li T. Risk factors and their association network for young adults' suicidality: a cross-sectional study. BMC Public Health 2024; 24:1378. [PMID: 38778312 PMCID: PMC11112863 DOI: 10.1186/s12889-024-18860-9] [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: 01/06/2024] [Accepted: 05/15/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND Understanding the intricate influences of risk factors contributing to suicide among young individuals remains a challenge. The current study employed interpretable machine learning and network analysis to unravel critical suicide-associated factors in Chinese university students. METHODS A total of 68,071 students were recruited between Sep 2016 and Sep 2020 in China. Students reported their lifetime experiences with suicidal thoughts and behaviors, categorized as suicide ideation (SI), suicide plan (SP), and suicide attempt (SA). We assessed 36 suicide-associated factors including psychopathology, family environment, life events, and stigma. Local interpretations were provided using Shapley additive explanation (SHAP) interaction values, while a mixed graphical model facilitated a global understanding of their interplay. RESULTS Local explanations based on SHAP interaction values suggested that psychoticism and depression severity emerged as pivotal factors for SI, while paranoid ideation strongly correlated with SP and SA. In addition, childhood neglect significantly predicted SA. Regarding the mixed graphical model, a hierarchical structure emerged, suggesting that family factors preceded proximal psychopathological factors, with abuse and neglect retaining unique effects. Centrality indices derived from the network highlighted the importance of subjective socioeconomic status and education in connecting various risk factors. CONCLUSIONS The proximity of psychopathological factors to suicidality underscores their significance. The global structures of the network suggested that co-occurring factors influence suicidal behavior in a hierarchical manner. Therefore, prospective prevention strategies should take into account the hierarchical structure and unique trajectories of factors.
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Affiliation(s)
- Junsong Lu
- Affiliated Mental Health Centre & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310013, China
- School of Humanities and Social Science, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518712, China
| | - Yan Jin
- Affiliated Mental Health Centre & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310013, China
- School of Humanities and Social Science, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518712, China
| | - Sugai Liang
- Affiliated Mental Health Centre & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310013, China
| | - Qiang Wang
- Mental Health Centre & Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Xiaojing Li
- Affiliated Mental Health Centre & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310013, China.
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou, 311121, China.
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, 310058, China.
| | - Tao Li
- Affiliated Mental Health Centre & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310013, China.
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou, 311121, China.
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, 310058, China.
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Hannon K, Bijsterbosch J. Challenges in Identifying Individualized Brain Biomarkers of Late Life Depression. ADVANCES IN GERIATRIC MEDICINE AND RESEARCH 2024; 5:e230010. [PMID: 38348374 PMCID: PMC10861244 DOI: 10.20900/agmr20230010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/15/2024]
Abstract
Research into neuroimaging biomarkers for Late Life Depression (LLD) has identified neural correlates of LLD including increased white matter hyperintensities and reduced hippocampal volume. However, studies into neuroimaging biomarkers for LLD largely fail to converge. This lack of replicability is potentially due to challenges linked to construct variability, etiological heterogeneity, and experimental rigor. We discuss suggestions to help address these challenges, including improved construct standardization, increased sample sizes, multimodal approaches to parse heterogeneity, and the use of individualized analytical models.
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Affiliation(s)
- Kayla Hannon
- Department of Radiology, Washington University in St Louis, St Louis MO, 63110, USA
| | - Janine Bijsterbosch
- Department of Radiology, Washington University in St Louis, St Louis MO, 63110, USA
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Zang JCS, Hohoff C, Van Assche E, Lange P, Kraft M, Sandmann S, Varghese J, Jörgens S, Knight MJ, Baune BT. Immune gene co-expression signatures implicated in occurence and persistence of cognitive dysfunction in depression. Prog Neuropsychopharmacol Biol Psychiatry 2023; 127:110826. [PMID: 37451594 DOI: 10.1016/j.pnpbp.2023.110826] [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: 04/30/2023] [Revised: 06/29/2023] [Accepted: 07/09/2023] [Indexed: 07/18/2023]
Abstract
Cognitive dysfunction contributes significantly to the burden caused by Major Depressive Disorder (MDD). Yet, while compelling evidence suggests that different biological processes play a part in both MDD aetiology and the development of cognitive decline more generally, we only begin to understand the molecular underpinnings of depression-related cognitive impairment. Developments in psychometric assessments, molecular high-throughput methods and systems biology derived analysis strategies advance this endeavour. Here, we aim to identify gene expression signatures associated with cognitive dysfunction and cognitive improvement following therapy using RNA sequencing to analyze the whole blood-derived transcriptome of altogether 101 MDD patients who enrolled in the CERT-D study. The mRNA(Nova)Seq based transcriptome was analyzed from whole blood taken at baseline assessment, and patients' cognitive performance was measured twice at baseline and following eight weeks of therapy by means of the THINC integrated tool. Thirty-six patients showed comparatively low cognitive performance at baseline assessment, and 32 patients showed comparatively strong cognitive improvement following therapy. Differential gene expression analysis was performed using limma to a significance threshold of 0.05 and a logFC cutoff of |1.2|. Although we observed some indications for expression differences related to low cognitive performance and cognitive therapy response, signals did not withstand adjustment for multiple testing. Applying WGCNA, we retrieved altogether 25 modules of co-expressed genes and we used a combination of correlational and linear analyses to identify modules related to baseline cognitive performance and cognitive improvement following therapy. Three immune modules reflected distinct but interrelated immune processes (the yellow module: neutrophil-mediated immunity, the darkorange module: interferon signaling, the tan module: platelet activation), and higher expression of the yellow (r = -0.21, p < .05), the dark orange (r = 0.2, p < .05), and the tan (r = -0.23, p < .05) module correlated significantly negatively with patients' cognitive baseline performance. Patients' cognitive baseline performance was a significant predictor of the darkorange module (b = -0.039, p < .05) and the tan module's expression (b = 0.02, p < .05) and was close to becoming a significant predictor of the yellow module's expression (b = -0.02, p = .05). Furthermore, patients characterized by comparatively low cognitive performance at baseline showed significantly higher expression of the tan module when compared to all other patients F(1,97) = 4.32, p < .05, η= 0.04. Following eight weeks of treatment, we observed altogether significant improvement in patients' cognitive performance (b = 0.30, p < .001), and patients with comparatively high cognitive gain showed noticeably lower, but not significantly lower F(1,98) = 3.76, p = .058, expression of a dark turquoise module, which reflects complement and B-cell-associated immune processes. Noteworthy, the relation between cognitive performance and module expression remained observable after controlling for symptom severity and BMI, which partly accounted for variance in module expression. As such, our findings provide further evidence for the involvement of immune processes in MDD related cognitive dysfunction and they suggest that different immune processes contribute to the development and long-term persistence of cognitive dysfunction in the context of depression.
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Affiliation(s)
- Johannes C S Zang
- Department of Psychiatry, University of Münster, 48149 Münster, Germany.
| | - Christa Hohoff
- Department of Psychiatry, University of Münster, 48149 Münster, Germany.
| | - Evelien Van Assche
- Department of Psychiatry, University of Münster, 48149 Münster, Germany.
| | - Pia Lange
- Institute of Medical Informatics, University of Münster, Münster, Germany.
| | - Manuel Kraft
- Department of Psychiatry, University of Münster, 48149 Münster, Germany.
| | - Sarah Sandmann
- Institute of Medical Informatics, University of Münster, Münster, Germany.
| | - Julian Varghese
- Institute of Medical Informatics, University of Münster, Münster, Germany.
| | - Silke Jörgens
- Department of Psychiatry, University of Münster, 48149 Münster, Germany.
| | - Matthew J Knight
- Discipline of Psychiatry, Adelaide Medical School, University of Adelaide, Adelaide, Australia
| | - Bernhard T Baune
- Department of Psychiatry, University of Münster, 48149 Münster, Germany; Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Parkville, VIC 3010, Australia; The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC 3010, Australia.
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Caldirola D, De Donatis D, Alciati A, Daccò S, Perna G. Pharmacological approaches to the management of panic disorder in older patients: a systematic review. Expert Rev Neurother 2023; 23:1013-1029. [PMID: 37676054 DOI: 10.1080/14737175.2023.2254938] [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: 06/14/2023] [Accepted: 08/30/2023] [Indexed: 09/08/2023]
Abstract
INTRODUCTION Recommendations for treating panic disorder (PD) in older patients are scarce. The authors have systematically reviewed whether several recommended medications are superior to others and their optimal doses in this age group. METHODS A database search of studies involving patients with PD with/without agoraphobia aged ≥ 60 years was carried out using PubMed, PsycINFO, Embase, and Clinical Trials.gov, from their inception dates to 1 March 2023. Only four (published from 2002 to 2010) of the 1292 records screened were included. A risk of bias assessment was provided. This systematic review was performed using The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA). RESULTS Two studies were randomized clinical trials, whereas two were open-label, including paroxetine, citalopram, escitalopram, and sertraline; three studies reported short-term evaluations, whereas one study included a 26-week follow-up. Medications provided benefits, with good tolerability. Preliminary results suggested greater benefits of paroxetine in reducing panic attacks vs. cognitive - behavioral therapy, and an earlier decrease in PAs with escitalopram vs. citalopram. Risk of bias was considerable. CONCLUSIONS The pharmacological management of PD in older patients has received no attention. Findings are scant, dated, and affected by methodological flaws; thus, they do not provide significant advances.
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Affiliation(s)
- Daniela Caldirola
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
- Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias, Albese con Cassano, Italy
- Humanitas San Pio X, Personalized Medicine Center for Anxiety and Panic Disorders, Milan, Italy
| | - Domenico De Donatis
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
- Humanitas San Pio X, Personalized Medicine Center for Anxiety and Panic Disorders, Milan, Italy
| | - Alessandra Alciati
- Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias, Albese con Cassano, Italy
- Humanitas Clinical and Research Center, IRCCS, Rozzano, Italy
| | - Silvia Daccò
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
- Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias, Albese con Cassano, Italy
- Humanitas San Pio X, Personalized Medicine Center for Anxiety and Panic Disorders, Milan, Italy
| | - Giampaolo Perna
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
- Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias, Albese con Cassano, Italy
- Humanitas San Pio X, Personalized Medicine Center for Anxiety and Panic Disorders, Milan, Italy
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Caldirola D, Carminati C, Daccò S, Grassi M, Perna G, Teggi R. Balance Rehabilitation with Peripheral Visual Stimulation in Patients with Panic Disorder and Agoraphobia: An Open-Pilot Intervention Study. Audiol Res 2023; 13:314-325. [PMID: 37218838 DOI: 10.3390/audiolres13030027] [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: 01/30/2023] [Revised: 03/31/2023] [Accepted: 04/25/2023] [Indexed: 05/24/2023] Open
Abstract
Given the involvement of balance system abnormalities in the pathophysiology of panic disorder and agoraphobia (PD-AG), we evaluated initial evidence for feasibility, acceptability, and potential clinical usefulness of 10 sessions of balance rehabilitation with peripheral visual stimulation (BR-PVS) in an open-pilot 5-week intervention study including six outpatients with PD-AG who presented residual agoraphobia after selective serotonin reuptake inhibitor (SSRI) treatment and cognitive-behavioral therapy, dizziness in daily life, and peripheral visual hypersensitivity measured by posturography. Before and after BR-PVS, patients underwent posturography, otovestibular examination (no patients presented peripheral vestibular abnormalities), and panic-agoraphobic symptom and dizziness evaluation with psychometric tools. After BR-PVS, four patients achieved postural control normalization measured by posturography, and one patient exhibited a favorable trend of improvement. Overall, panic-agoraphobic symptoms and dizziness decreased, even though to a lesser extent in one patient who had not completed the rehabilitation sessions. The study presented reasonable levels of feasibility and acceptability. These findings suggest that balance evaluation should be considered in patients with PD-AGO presenting residual agoraphobia and that BR-PVS might be an adjunctive therapeutic option worth being tested in larger randomized controlled studies.
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Affiliation(s)
- Daniela Caldirola
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090 Pieve Emanuele, Italy
- Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias, Via Roma 16, 22032 Albese con Cassano, Italy
- Humanitas San Pio X, Personalized Medicine Center for Anxiety and Panic Disorders, Via Francesco Nava 31, 20159 Milan, Italy
| | - Claudia Carminati
- Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias, Via Roma 16, 22032 Albese con Cassano, Italy
- Humanitas San Pio X, Personalized Medicine Center for Anxiety and Panic Disorders, Via Francesco Nava 31, 20159 Milan, Italy
| | - Silvia Daccò
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090 Pieve Emanuele, Italy
- Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias, Via Roma 16, 22032 Albese con Cassano, Italy
- Humanitas San Pio X, Personalized Medicine Center for Anxiety and Panic Disorders, Via Francesco Nava 31, 20159 Milan, Italy
| | - Massimiliano Grassi
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090 Pieve Emanuele, Italy
- Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias, Via Roma 16, 22032 Albese con Cassano, Italy
| | - Giampaolo Perna
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090 Pieve Emanuele, Italy
- Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias, Via Roma 16, 22032 Albese con Cassano, Italy
- Humanitas San Pio X, Personalized Medicine Center for Anxiety and Panic Disorders, Via Francesco Nava 31, 20159 Milan, Italy
| | - Roberto Teggi
- Department of Otolaryngology, San Raffaele Scientific Hospital, Via Olgettina 60, 20132 Milan, Italy
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Caldirola D, Daccò S, Grassi M, Alciati A, Sbabo WM, De Donatis D, Martinotti G, De Berardis D, Perna G. Cardiorespiratory Assessments in Panic Disorder Facilitated by Wearable Devices: A Systematic Review and Brief Comparison of the Wearable Zephyr BioPatch with the Quark-b2 Stationary Testing System. Brain Sci 2023; 13:brainsci13030502. [PMID: 36979312 PMCID: PMC10046237 DOI: 10.3390/brainsci13030502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Revised: 03/06/2023] [Accepted: 03/10/2023] [Indexed: 03/19/2023] Open
Abstract
Abnormalities in cardiorespiratory measurements have repeatedly been found in patients with panic disorder (PD) during laboratory-based assessments. However, recordings performed outside laboratory settings are required to test the ecological validity of these findings. Wearable devices, such as sensor-imbedded garments, biopatches, and smartwatches, are promising tools for this purpose. We systematically reviewed the evidence for wearables-based cardiorespiratory assessments in PD by searching for publications on the PubMed, PsycINFO, and Embase databases, from inception to 30 July 2022. After the screening of two-hundred and twenty records, eight studies were included. The limited number of available studies and critical aspects related to the uncertain reliability of wearables-based assessments, especially concerning respiration, prevented us from drawing conclusions about the cardiorespiratory function of patients with PD in daily life. We also present preliminary data on a pilot study conducted on volunteers at the Villa San Benedetto Menni Hospital for evaluating the accuracy of heart rate (HR) and breathing rate (BR) measurements by the wearable Zephyr BioPatch compared with the Quark-b2 stationary testing system. Our exploratory results suggested possible BR and HR misestimation by the wearable Zephyr BioPatch compared with the Quark-b2 system. Challenges of wearables-based cardiorespiratory assessment and possible solutions to improve their reliability and optimize their significant potential for the study of PD pathophysiology are presented.
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Affiliation(s)
- Daniela Caldirola
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090 Pieve Emanuele, Italy
- Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias, Via Roma 16, 22032 Albese con Cassano, Italy
- Humanitas San Pio X, Personalized Medicine Center for Anxiety and Panic Disorders, Via Francesco Nava 31, 20159 Milan, Italy
| | - Silvia Daccò
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090 Pieve Emanuele, Italy
- Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias, Via Roma 16, 22032 Albese con Cassano, Italy
| | - Massimiliano Grassi
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090 Pieve Emanuele, Italy
| | - Alessandra Alciati
- Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias, Via Roma 16, 22032 Albese con Cassano, Italy
- Humanitas Clinical and Research Center, IRCCS, Via Manzoni 56, 20089 Rozzano, Italy
| | - William M. Sbabo
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090 Pieve Emanuele, Italy
| | - Domenico De Donatis
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090 Pieve Emanuele, Italy
- Humanitas San Pio X, Personalized Medicine Center for Anxiety and Panic Disorders, Via Francesco Nava 31, 20159 Milan, Italy
| | - Giovanni Martinotti
- Department of Neuroscience, Imaging and Clinical Sciences, University “G. d’Annunzio”, 66100 Chieti, Italy
| | - Domenico De Berardis
- Department of Mental Health, NHS, ASL 4 Teramo, Contrada Casalena, 64100 Teramo, Italy
- Correspondence:
| | - Giampaolo Perna
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090 Pieve Emanuele, Italy
- Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias, Via Roma 16, 22032 Albese con Cassano, Italy
- Humanitas San Pio X, Personalized Medicine Center for Anxiety and Panic Disorders, Via Francesco Nava 31, 20159 Milan, Italy
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Sajno E, Bartolotta S, Tuena C, Cipresso P, Pedroli E, Riva G. Machine learning in biosignals processing for mental health: A narrative review. Front Psychol 2023; 13:1066317. [PMID: 36710855 PMCID: PMC9880193 DOI: 10.3389/fpsyg.2022.1066317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 12/16/2022] [Indexed: 01/15/2023] Open
Abstract
Machine Learning (ML) offers unique and powerful tools for mental health practitioners to improve evidence-based psychological interventions and diagnoses. Indeed, by detecting and analyzing different biosignals, it is possible to differentiate between typical and atypical functioning and to achieve a high level of personalization across all phases of mental health care. This narrative review is aimed at presenting a comprehensive overview of how ML algorithms can be used to infer the psychological states from biosignals. After that, key examples of how they can be used in mental health clinical activity and research are illustrated. A description of the biosignals typically used to infer cognitive and emotional correlates (e.g., EEG and ECG), will be provided, alongside their application in Diagnostic Precision Medicine, Affective Computing, and brain-computer Interfaces. The contents will then focus on challenges and research questions related to ML applied to mental health and biosignals analysis, pointing out the advantages and possible drawbacks connected to the widespread application of AI in the medical/mental health fields. The integration of mental health research and ML data science will facilitate the transition to personalized and effective medicine, and, to do so, it is important that researchers from psychological/ medical disciplines/health care professionals and data scientists all share a common background and vision of the current research.
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Affiliation(s)
- Elena Sajno
- Humane Technology Lab, Università Cattolica del Sacro Cuore, Milan, Italy,Department of Computer Science, University of Pisa, Pisa, Italy,*Correspondence: Elena Sajno, ✉
| | - Sabrina Bartolotta
- ExperienceLab, Università Cattolica del Sacro Cuore, Milan, Italy,Department of Psychology, Università Cattolica del Sacro Cuore, Milan, Italy
| | - Cosimo Tuena
- Applied Technology for Neuro-Psychology Lab, IRCCS Istituto Auxologico Italiano, Milan, Italy
| | - Pietro Cipresso
- Applied Technology for Neuro-Psychology Lab, IRCCS Istituto Auxologico Italiano, Milan, Italy,Department of Psychology, University of Turin, Turin, Italy
| | - Elisa Pedroli
- Department of Psychology, eCampus University, Novedrate, Italy
| | - Giuseppe Riva
- Humane Technology Lab, Università Cattolica del Sacro Cuore, Milan, Italy,Applied Technology for Neuro-Psychology Lab, IRCCS Istituto Auxologico Italiano, Milan, Italy
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8
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Kothgassner OD, Reichmann A, Bock MM. Virtual Reality Interventions for Mental Health. Curr Top Behav Neurosci 2023; 65:371-387. [PMID: 37106223 DOI: 10.1007/7854_2023_419] [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] [Indexed: 04/29/2023]
Abstract
Virtual Reality (VR) is a growing field in psychological research and therapy. While there is strong evidence for the efficacy of exposure therapy in VR (VRET) to treat anxiety disorders, new opportunities for using VR to treat mental health disorders are emerging. In this chapter, we first describe the value of VRET for the treatment of several anxiety disorders. Next, we introduce some recent developments in research using VR investigating schizophrenia, neurodevelopmental disorders, and eating disorders. This includes therapeutic strategies beyond VRET, including avatar-based therapies or those combining VR with biofeedback approaches. Although VR offers many convincing advantages, contraindications in treatment must be considered when implementing VR-supported therapy in clinical practice. Finally, we provide an outlook for future research, highlighting the integration of augmented reality and automation processes in VR environments to create more efficient and tailored therapeutic tools. Further, future treatments will benefit from the gamification approach, which integrates elements of computer games and narratives that promote patients' motivation and enables methods to reduce drop-outs during psychological therapy.
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Affiliation(s)
- Oswald D Kothgassner
- Department of Child and Adolescent Psychiatry, Medical University of Vienna, Vienna, Austria.
| | - Adelais Reichmann
- Department of Child and Adolescent Psychiatry, Medical University of Vienna, Vienna, Austria
| | - Mercedes M Bock
- Child and Adolescent Psychiatry, Social Psychiatric Services Vienna, Vienna, Austria
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Gyorda JA, Nemesure MD, Price G, Jacobson NC. Applying ensemble machine learning models to predict individual response to a digitally delivered worry postponement intervention. J Affect Disord 2023; 320:201-210. [PMID: 36167247 PMCID: PMC10037342 DOI: 10.1016/j.jad.2022.09.112] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 09/02/2022] [Accepted: 09/20/2022] [Indexed: 02/02/2023]
Abstract
OBJECTIVE Generalized anxiety disorder (GAD) is a prevalent mental health disorder that often goes untreated. A core aspect of GAD is worry, which is associated with negative health outcomes, accentuating a need for simple treatments for worry. The present study leveraged pretreatment individual differences to predict personalized treatment response to a digital intervention. METHODS Linear mixed-effect models were used to model changes in daytime and nighttime worry duration and frequency for 163 participants who completed a six-day worry postponement intervention. Ensemble-based machine learning regression and classification models were implemented to predict changes in worry across the intervention. Model feature importance was derived using SHapley Additive exPlanation (SHAP). RESULTS Moderate predictive performance was obtained for predicting changes in daytime worry duration (test r2 = 0.221, AUC = 0.77) and nighttime worry frequency (test r2 = 0.164, AUC = 0.72), while poor predictive performance was obtained for nighttime worry duration and daytime worry frequency. Baseline levels of worry and subjective health complaints were most important in driving model predictions. LIMITATIONS A complete-case analysis was leveraged to analyze the present data, which was collected from participants that were Dutch and majority female. CONCLUSIONS This study suggests that treatment response to a digital intervention for GAD can be accurately predicted using baseline characteristics. Particularly, this worry postponement intervention may be most beneficial for individuals with high baseline worry but fewer subjective health complaints. The present findings highlight the complexities of and need for further research into daily worry dynamics and the personalizable utility of digital interventions.
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Affiliation(s)
- Joseph A Gyorda
- Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States; Mathematical Data Science Program, Dartmouth College, Hanover, NH, United States.
| | - Matthew D Nemesure
- Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States; Quantitative Biomedical Sciences Program, Dartmouth College, Hanover, NH, United States
| | - George Price
- Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States; Quantitative Biomedical Sciences Program, Dartmouth College, Hanover, NH, United States
| | - Nicholas C Jacobson
- Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States; Quantitative Biomedical Sciences Program, Dartmouth College, Hanover, NH, United States; Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States; Department of Psychiatry, Geisel School of Medicine, Dartmouth College, Hanover, NH, United States
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10
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Badarnee M, Tirosh I, Kreitler S. Psychological tendencies of children with juvenile idiopathic arthritis. Scand J Psychol 2022; 63:624-633. [PMID: 35689406 PMCID: PMC9796744 DOI: 10.1111/sjop.12839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 05/13/2022] [Accepted: 05/19/2022] [Indexed: 01/07/2023]
Abstract
A bulk of studies showed an association between stressful events and juvenile idiopathic arthritis (JIA) but failed to identify specific psychological tendencies that contribute to the patients' vulnerability to stress. The purpose of this paper is to identify psychological tendencies specific to JIA that would unravel characteristic sources of stress. The study is based on the cognitive orientation model of health, which enables us to identify these kinds of tendencies in terms of four belief types (beliefs about self, general beliefs, beliefs about norms, and goals) that refer to specific themes. This is a case-control-cohort study that included a sample of 36 patients (mean age = 12.44 years, SD = 2.97, 21 females) and 41 matched controls (mean age = 13.15 years, SD = 2.01, 22 females). The JIA cognitive-orientation questionnaire was administered, and relevant medical parameters were recorded. The belief types differentiated between the two groups, and the patients were characterized using six themes. Examples of the themes are being over-sensitive, striving for success, and not fulfilling duties well. The themes differentiated between the participants' groups with an accuracy of 89.1%. The likelihood of the patients being characterized by the themes is 3.24-9.35 times more than the controls. The psychological tendencies of JIA were discussed as generators of stress (e.g., being over-sensitive) and cognitive conflicts (e.g., the contradiction between striving for success versus not fulfilling duties well). Also, the suggested reflections of these tendencies in the health workers' and patients' relationships, such as egalitarian interaction, and non-formal communication style, were described.
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Affiliation(s)
- Muhammad Badarnee
- School of Psychological SciencesTel‐Aviv UniversityTel AvivIsrael,The Psycho‐Oncology Research CenterThe Chaim Sheba Medical CenterRamat GanIsrael
| | - Irit Tirosh
- Sackler Faculty of MedicineTel‐Aviv UniversityTel AvivIsrael,The Edmond and Lily Safra Children's HospitalThe Chaim Sheba Medical CenterRamat GanIsrael
| | - Shulamith Kreitler
- School of Psychological SciencesTel‐Aviv UniversityTel AvivIsrael,The Psycho‐Oncology Research CenterThe Chaim Sheba Medical CenterRamat GanIsrael
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11
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Stein DJ, Shoptaw SJ, Vigo DV, Lund C, Cuijpers P, Bantjes J, Sartorius N, Maj M. Psychiatric diagnosis and treatment in the 21st century: paradigm shifts versus incremental integration. World Psychiatry 2022; 21:393-414. [PMID: 36073709 PMCID: PMC9453916 DOI: 10.1002/wps.20998] [Citation(s) in RCA: 51] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Psychiatry has always been characterized by a range of different models of and approaches to mental disorder, which have sometimes brought progress in clinical practice, but have often also been accompanied by critique from within and without the field. Psychiatric nosology has been a particular focus of debate in recent decades; successive editions of the DSM and ICD have strongly influenced both psychiatric practice and research, but have also led to assertions that psychiatry is in crisis, and to advocacy for entirely new paradigms for diagnosis and assessment. When thinking about etiology, many researchers currently refer to a biopsychosocial model, but this approach has received significant critique, being considered by some observers overly eclectic and vague. Despite the development of a range of evidence-based pharmacotherapies and psychotherapies, current evidence points to both a treatment gap and a research-practice gap in mental health. In this paper, after considering current clinical practice, we discuss some proposed novel perspectives that have recently achieved particular prominence and may significantly impact psychiatric practice and research in the future: clinical neuroscience and personalized pharmacotherapy; novel statistical approaches to psychiatric nosology, assessment and research; deinstitutionalization and community mental health care; the scale-up of evidence-based psychotherapy; digital phenotyping and digital therapies; and global mental health and task-sharing approaches. We consider the extent to which proposed transitions from current practices to novel approaches reflect hype or hope. Our review indicates that each of the novel perspectives contributes important insights that allow hope for the future, but also that each provides only a partial view, and that any promise of a paradigm shift for the field is not well grounded. We conclude that there have been crucial advances in psychiatric diagnosis and treatment in recent decades; that, despite this important progress, there is considerable need for further improvements in assessment and intervention; and that such improvements will likely not be achieved by any specific paradigm shifts in psychiatric practice and research, but rather by incremental progress and iterative integration.
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Affiliation(s)
- Dan J. Stein
- South African Medical Research Council Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape TownCape TownSouth Africa
| | - Steven J. Shoptaw
- Division of Family MedicineDavid Geffen School of Medicine, University of California Los AngelesLos AngelesCAUSA
| | - Daniel V. Vigo
- Department of PsychiatryUniversity of British ColumbiaVancouverBCCanada
| | - Crick Lund
- Centre for Global Mental Health, Health Service and Population Research DepartmentInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUK
| | - Pim Cuijpers
- Department of Clinical, Neuro and Developmental PsychologyAmsterdam Public Health Research Institute, Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Jason Bantjes
- Alcohol, Tobacco and Other Drug Research UnitSouth African Medical Research CouncilCape TownSouth Africa
| | - Norman Sartorius
- Association for the Improvement of Mental Health ProgrammesGenevaSwitzerland
| | - Mario Maj
- Department of PsychiatryUniversity of Campania “L. Vanvitelli”NaplesItaly
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12
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Marques-Costa C, Simões MR, Almiro PA, Prieto G, Salomé Pinho M. Integrating Technology in Neuropsychological Assessment. EUROPEAN PSYCHOLOGIST 2022. [DOI: 10.1027/1016-9040/a000484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Abstract. Although neuropsychological assessments include some measures that are administered, scored, or interpreted using new technologies, most researchers in this area advocate that more technology should be integrated. The current situation in neuropsychological assessment may be conceptualized as triggering a crisis leading to a paradigm shift, as there is some resistance to adopting more technology. In this paper, the context of the present crisis in neuropsychological assessment, the main obstacles, and new developments will be discussed. An example of a new computerized assessment tool, the NIH Toolbox, is highlighted. Also addressed are potential issues: in the assessment with tablets illustrating it with the older adult population and how to ensure the compatibility of data collected through these devices within the framework of the European General Data Protection Regulation (GDPR). Recommendations for research, test development, and clinical practice are also provided.
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Affiliation(s)
- Catarina Marques-Costa
- Faculty of Psychology and Educational Sciences, University of Coimbra, Portugal
- Center for Research in Neuropsychology and Cognitive and Behavioral Intervention (CINEICC), University of Coimbra, Portugal
- Psychological Assessment and Psychometrics Laboratory (PsyAssessmentLab), University of Coimbra, Portugal
| | - Mário R. Simões
- Faculty of Psychology and Educational Sciences, University of Coimbra, Portugal
- Center for Research in Neuropsychology and Cognitive and Behavioral Intervention (CINEICC), University of Coimbra, Portugal
- Psychological Assessment and Psychometrics Laboratory (PsyAssessmentLab), University of Coimbra, Portugal
| | - Pedro A. Almiro
- Center for Research in Neuropsychology and Cognitive and Behavioral Intervention (CINEICC), University of Coimbra, Portugal
- Psychological Assessment and Psychometrics Laboratory (PsyAssessmentLab), University of Coimbra, Portugal
- Research Centre for Psychology (CIP), Autonomous University Lisbon, Portugal
| | - Gerardo Prieto
- Psychological Assessment and Psychometrics Laboratory (PsyAssessmentLab), University of Coimbra, Portugal
- Faculty of Psychology, University of Salamanca, Spain
| | - Maria Salomé Pinho
- Faculty of Psychology and Educational Sciences, University of Coimbra, Portugal
- Center for Research in Neuropsychology and Cognitive and Behavioral Intervention (CINEICC), University of Coimbra, Portugal
- Memory, Language, and Executive Functions Laboratory, University of Coimbra, Portugal
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13
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Merone M, D'Addario SL, Mirino P, Bertino F, Guariglia C, Ventura R, Capirchio A, Baldassarre G, Silvetti M, Caligiore D. A multi-expert ensemble system for predicting Alzheimer transition using clinical features. Brain Inform 2022; 9:20. [PMID: 36056985 PMCID: PMC9440971 DOI: 10.1186/s40708-022-00168-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 05/16/2022] [Indexed: 11/18/2022] Open
Abstract
Alzheimer’s disease (AD) diagnosis often requires invasive examinations (e.g., liquor analyses), expensive tools (e.g., brain imaging) and highly specialized personnel. The diagnosis commonly is established when the disorder has already caused severe brain damage, and the clinical signs begin to be apparent. Instead, accessible and low-cost approaches for early identification of subjects at high risk for developing AD years before they show overt symptoms are fundamental to provide a critical time window for more effective clinical management, treatment, and care planning. This article proposes an ensemble-based machine learning algorithm for predicting AD development within 9 years from first overt signs and using just five clinical features that are easily detectable with neuropsychological tests. The validation of the system involved both healthy individuals and mild cognitive impairment (MCI) patients drawn from the ADNI open dataset, at variance with previous studies that considered only MCI. The system shows higher levels of balanced accuracy, negative predictive value, and specificity than other similar solutions. These results represent a further important step to build a preventive fast-screening machine-learning-based tool to be used as a part of routine healthcare screenings.
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Affiliation(s)
- Mario Merone
- Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128, Rome, Italy
| | - Sebastian Luca D'Addario
- Department of Psychology, Sapienza University, Piazzale Aldo Moro 5, 00185, Rome, Italy.,Computational and Translational Neuroscience Laboratory, Institute of Cognitive Sciences and Technologies, National Research Council (CTNLab-ISTC-CNR), Via San Martino della Battaglia 44, 00185, Rome, Italy.,IRCCS Fondazione Santa Lucia, Via Ardeatina, 306 and Via Del Fosso di Fiorano, 64, 00143, Rome, Italy
| | - Pierandrea Mirino
- Department of Psychology, Sapienza University, Piazzale Aldo Moro 5, 00185, Rome, Italy.,Computational and Translational Neuroscience Laboratory, Institute of Cognitive Sciences and Technologies, National Research Council (CTNLab-ISTC-CNR), Via San Martino della Battaglia 44, 00185, Rome, Italy.,AI2Life s.r.l., Innovative Start-Up, ISTC-CNR Spin-Off, Via Sebino 32, 00199, Rome, Italy
| | - Francesca Bertino
- Computational and Translational Neuroscience Laboratory, Institute of Cognitive Sciences and Technologies, National Research Council (CTNLab-ISTC-CNR), Via San Martino della Battaglia 44, 00185, Rome, Italy
| | - Cecilia Guariglia
- Department of Psychology, Sapienza University, Piazzale Aldo Moro 5, 00185, Rome, Italy.,IRCCS Fondazione Santa Lucia, Via Ardeatina, 306 and Via Del Fosso di Fiorano, 64, 00143, Rome, Italy
| | - Rossella Ventura
- Department of Psychology, Sapienza University, Piazzale Aldo Moro 5, 00185, Rome, Italy.,IRCCS Fondazione Santa Lucia, Via Ardeatina, 306 and Via Del Fosso di Fiorano, 64, 00143, Rome, Italy
| | - Adriano Capirchio
- AI2Life s.r.l., Innovative Start-Up, ISTC-CNR Spin-Off, Via Sebino 32, 00199, Rome, Italy
| | - Gianluca Baldassarre
- AI2Life s.r.l., Innovative Start-Up, ISTC-CNR Spin-Off, Via Sebino 32, 00199, Rome, Italy.,Laboratory of Embodied Natural and Artificial Intelligence, Institute of Cognitive Sciences and Technologies, National Research Council (LENAI-ISTC-CNR), Via San Martino della Battaglia 44, 00185, Rome, Italy
| | - Massimo Silvetti
- Computational and Translational Neuroscience Laboratory, Institute of Cognitive Sciences and Technologies, National Research Council (CTNLab-ISTC-CNR), Via San Martino della Battaglia 44, 00185, Rome, Italy
| | - Daniele Caligiore
- Computational and Translational Neuroscience Laboratory, Institute of Cognitive Sciences and Technologies, National Research Council (CTNLab-ISTC-CNR), Via San Martino della Battaglia 44, 00185, Rome, Italy. .,AI2Life s.r.l., Innovative Start-Up, ISTC-CNR Spin-Off, Via Sebino 32, 00199, Rome, Italy.
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14
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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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15
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Caldirola D, Cuniberti F, Daccò S, Grassi M, Torti T, Perna G. Predicting New-Onset Psychiatric Disorders Throughout the COVID-19 Pandemic: A Machine Learning Approach. J Neuropsychiatry Clin Neurosci 2022; 34:233-246. [PMID: 35306830 DOI: 10.1176/appi.neuropsych.21060148] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
OBJECTIVE The investigators estimated new-onset psychiatric disorders (PsyDs) throughout the COVID-19 pandemic in Italian adults without preexisting PsyDs and developed a machine learning (ML) model predictive of at least one new-onset PsyD in subsequent independent samples. METHODS Data were from the first (May 18-June 20, 2020) and second (September 15-October 20, 2020) waves of an ongoing longitudinal study, based on a self-reported online survey. Provisional diagnoses of PsyDs (PPsyDs) were assessed via DSM-based screening tools to maximize assessment specificity. Gradient-boosted decision trees as an ML modeling technique and the SHapley Additive exPlanations technique were applied to identify each variable's contribution to the model. RESULTS From the original sample of 3,532 participants, the final sample included 500 participants in the first wave and 236 in the second. Some 16.0% of first-wave participants and 18.6% of second-wave participants met criteria for at least one new-onset PPsyD. The final best ML predictive model, trained on the first wave, displayed a sensitivity of 70% and a specificity of 73% when tested on the second wave. The following variables made the largest contributions: low resilience, being an undergraduate student, and being stressed by pandemic-related conditions. Living alone and having ceased physical activity contributed to a lesser extent. CONCLUSIONS Substantial rates of new-onset PPsyDs emerged among Italians throughout the pandemic, and the ML model exhibited moderate predictive performance. Results highlight modifiable vulnerability factors that are suitable for targeting by public campaigns or interventions to mitigate the pandemic's detrimental effects on mental health.
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Affiliation(s)
- Daniela Caldirola
- Department of Biomedical Sciences, Humanitas University, Milan, Italy (Caldirola, Cuniberti, Daccò, Grassi, Torti, Perna); Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias, Como, Italy (Caldirola, Cuniberti, Daccò, Grassi, Perna); ASIPSE School of Cognitive-Behavioral Therapy, Milan, Italy (Torti); Humanitas San Pio X, Personalized Medicine Center for Anxiety and Panic Disorders, Milan, Italy (Caldirola, Cuniberti, Perna)
| | - Francesco Cuniberti
- Department of Biomedical Sciences, Humanitas University, Milan, Italy (Caldirola, Cuniberti, Daccò, Grassi, Torti, Perna); Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias, Como, Italy (Caldirola, Cuniberti, Daccò, Grassi, Perna); ASIPSE School of Cognitive-Behavioral Therapy, Milan, Italy (Torti); Humanitas San Pio X, Personalized Medicine Center for Anxiety and Panic Disorders, Milan, Italy (Caldirola, Cuniberti, Perna)
| | - Silvia Daccò
- Department of Biomedical Sciences, Humanitas University, Milan, Italy (Caldirola, Cuniberti, Daccò, Grassi, Torti, Perna); Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias, Como, Italy (Caldirola, Cuniberti, Daccò, Grassi, Perna); ASIPSE School of Cognitive-Behavioral Therapy, Milan, Italy (Torti); Humanitas San Pio X, Personalized Medicine Center for Anxiety and Panic Disorders, Milan, Italy (Caldirola, Cuniberti, Perna)
| | - Massimiliano Grassi
- Department of Biomedical Sciences, Humanitas University, Milan, Italy (Caldirola, Cuniberti, Daccò, Grassi, Torti, Perna); Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias, Como, Italy (Caldirola, Cuniberti, Daccò, Grassi, Perna); ASIPSE School of Cognitive-Behavioral Therapy, Milan, Italy (Torti); Humanitas San Pio X, Personalized Medicine Center for Anxiety and Panic Disorders, Milan, Italy (Caldirola, Cuniberti, Perna)
| | - Tatiana Torti
- Department of Biomedical Sciences, Humanitas University, Milan, Italy (Caldirola, Cuniberti, Daccò, Grassi, Torti, Perna); Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias, Como, Italy (Caldirola, Cuniberti, Daccò, Grassi, Perna); ASIPSE School of Cognitive-Behavioral Therapy, Milan, Italy (Torti); Humanitas San Pio X, Personalized Medicine Center for Anxiety and Panic Disorders, Milan, Italy (Caldirola, Cuniberti, Perna)
| | - Giampaolo Perna
- Department of Biomedical Sciences, Humanitas University, Milan, Italy (Caldirola, Cuniberti, Daccò, Grassi, Torti, Perna); Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias, Como, Italy (Caldirola, Cuniberti, Daccò, Grassi, Perna); ASIPSE School of Cognitive-Behavioral Therapy, Milan, Italy (Torti); Humanitas San Pio X, Personalized Medicine Center for Anxiety and Panic Disorders, Milan, Italy (Caldirola, Cuniberti, Perna)
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16
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Caldirola D, Daccò S, Cuniberti F, Grassi M, Alciati A, Torti T, Perna G. First-onset major depression during the COVID-19 pandemic: A predictive machine learning model. J Affect Disord 2022; 310:75-86. [PMID: 35489559 PMCID: PMC9044654 DOI: 10.1016/j.jad.2022.04.145] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 04/20/2022] [Accepted: 04/24/2022] [Indexed: 01/17/2023]
Abstract
BACKGROUND This study longitudinally evaluated first-onset major depression rates during the pandemic in Italian adults without any current clinician-diagnosed psychiatric disorder and created a predictive machine learning model (MLM) to evaluate subsequent independent samples. METHODS An online, self-reported survey was released during two pandemic periods (May to June and September to October 2020). Provisional diagnoses of major depressive disorder (PMDD) were determined using a diagnostic algorithm based on the DSM criteria of the Patient Health Questionnaire-9 to maximize specificity. Gradient-boosted decision trees and the SHapley Additive exPlanations technique created the MLM and estimated each variable's predictive contribution. RESULTS There were 3532 participants in the study. The final sample included 633 participants in the first wave (FW) survey and 290 in the second (SW). First-onset PMDD was found in 7.4% of FW participants and 7.2% of the SW. The final MLM, trained on the FW, displayed a sensitivity of 76.5% and a specificity of 77.8% when tested on the SW. The main factors identified in the MLM were low resilience, being an undergraduate student, being stressed by pandemic-related conditions, and low satisfaction with usual sleep before the pandemic and support from relatives. Current smoking and taking medication for medical conditions also contributed, albeit to a lesser extent. LIMITATIONS Small sample size; self-report assessment; data covering 2020 only. CONCLUSIONS Rates of first-onset PMDD among Italians during the first phases of the pandemic were considerable. Our MLM displayed a good predictive performance, suggesting potential goals for depression-preventive interventions during public health crises.
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Affiliation(s)
- Daniela Caldirola
- Humanitas University, Department of Biomedical Sciences, Via Rita Levi Montalcini 4, 20090 Pieve Emanuele, Milan, Italy; Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias, Via Roma 16, 22032 Albese con Cassano, Como, Italy; Humanitas San Pio X, Personalized Medicine Center for Anxiety and Panic Disorders, Via Francesco Nava 31, 20159 Milan, Italy.
| | - Silvia Daccò
- Humanitas University, Department of Biomedical Sciences, Via Rita Levi Montalcini 4, 20090 Pieve Emanuele, Milan, Italy,Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias, Via Roma 16, 22032 Albese con Cassano, Como, Italy
| | - Francesco Cuniberti
- Humanitas University, Department of Biomedical Sciences, Via Rita Levi Montalcini 4, 20090 Pieve Emanuele, Milan, Italy,Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias, Via Roma 16, 22032 Albese con Cassano, Como, Italy,Humanitas San Pio X, Personalized Medicine Center for Anxiety and Panic Disorders, Via Francesco Nava 31, 20159 Milan, Italy
| | - Massimiliano Grassi
- Humanitas University, Department of Biomedical Sciences, Via Rita Levi Montalcini 4, 20090 Pieve Emanuele, Milan, Italy,Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias, Via Roma 16, 22032 Albese con Cassano, Como, Italy
| | - Alessandra Alciati
- Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias, Via Roma 16, 22032 Albese con Cassano, Como, Italy,Humanitas Clinical and Research Center, IRCCS, Via Manzoni 56, 20089 Rozzano, Milan, Italy
| | - Tatiana Torti
- Humanitas University, Department of Biomedical Sciences, Via Rita Levi Montalcini 4, 20090 Pieve Emanuele, Milan, Italy,ASIPSE School of Cognitive-Behavioral-Therapy, Milan, Italy
| | - Giampaolo Perna
- Humanitas University, Department of Biomedical Sciences, Via Rita Levi Montalcini 4, 20090 Pieve Emanuele, Milan, Italy,Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias, Via Roma 16, 22032 Albese con Cassano, Como, Italy,Humanitas San Pio X, Personalized Medicine Center for Anxiety and Panic Disorders, Via Francesco Nava 31, 20159 Milan, Italy
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17
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Buitelaar J, Bölte S, Brandeis D, Caye A, Christmann N, Cortese S, Coghill D, Faraone SV, Franke B, Gleitz M, Greven CU, Kooij S, Leffa DT, Rommelse N, Newcorn JH, Polanczyk GV, Rohde LA, Simonoff E, Stein M, Vitiello B, Yazgan Y, Roesler M, Doepfner M, Banaschewski T. Toward Precision Medicine in ADHD. Front Behav Neurosci 2022; 16:900981. [PMID: 35874653 PMCID: PMC9299434 DOI: 10.3389/fnbeh.2022.900981] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 05/16/2022] [Indexed: 11/17/2022] Open
Abstract
Attention-Deficit Hyperactivity Disorder (ADHD) is a complex and heterogeneous neurodevelopmental condition for which curative treatments are lacking. Whilst pharmacological treatments are generally effective and safe, there is considerable inter-individual variability among patients regarding treatment response, required dose, and tolerability. Many of the non-pharmacological treatments, which are preferred to drug-treatment by some patients, either lack efficacy for core symptoms or are associated with small effect sizes. No evidence-based decision tools are currently available to allocate pharmacological or psychosocial treatments based on the patient's clinical, environmental, cognitive, genetic, or biological characteristics. We systematically reviewed potential biomarkers that may help in diagnosing ADHD and/or stratifying ADHD into more homogeneous subgroups and/or predict clinical course, treatment response, and long-term outcome across the lifespan. Most work involved exploratory studies with cognitive, actigraphic and EEG diagnostic markers to predict ADHD, along with relatively few studies exploring markers to subtype ADHD and predict response to treatment. There is a critical need for multisite prospective carefully designed experimentally controlled or observational studies to identify biomarkers that index inter-individual variability and/or predict treatment response.
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Affiliation(s)
- Jan Buitelaar
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, Netherlands.,Karakter Child and Adolescent Psychiatry University Center, Nijmegen, Netherlands
| | - Sven Bölte
- Center of Neurodevelopmental Disorders (KIND), Centre for Psychiatry Research, Department of Women's and Children's Health, Karolinska Institutet, Solna, Sweden.,Child and Adolescent Psychiatry, Stockholm Health Care Services, Stockholm, Sweden.,Curtin Autism Research Group, School of Occupational Therapy, Social Work and Speech Pathology, Curtin University, Perth, WA, Australia
| | - Daniel Brandeis
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany.,Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric Hospital, University of Zurich, Zurich, Switzerland
| | - Arthur Caye
- Department of Psychiatry, Hospital de Clinicas de Porto Alegre, Federal University of Rio Grande do Sul, Porto Alegre, Brazil.,National Institute of Developmental Psychiatry for Children and Adolescents, São Paulo, Brazil
| | - Nina Christmann
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany
| | - Samuele Cortese
- Centre for Innovation in Mental Health, Academic Unit of Psychology, Faculty of Environmental and Life Sciences, University of Southampton, Southampton, United Kingdom.,Clinical and Experimental Sciences (CNS and Psychiatry), Faculty of Medicine, University of Southampton, Southampton, United Kingdom.,Solent National Health System Trust, Southampton, United Kingdom.,Hassenfeld Children's Hospital at NYU Langone, New York University Child Study Center, New York, NY, United States.,Division of Psychiatry and Applied Psychology, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - David Coghill
- Departments of Paediatrics and Psychiatry, Royal Children's Hospital, University of Melbourne, Melbourne, VIC, Australia
| | - Stephen V Faraone
- Departments of Psychiatry, Neuroscience and Physiology, SUNY Upstate Medical University, Syracuse, New York, NY, United States
| | - Barbara Franke
- Departments of Human Genetics and Psychiatry, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands
| | - Markus Gleitz
- Medice Arzneimittel Pütter GmbH & Co. KG, Iserlohn, Germany
| | - Corina U Greven
- Karakter Child and Adolescent Psychiatry University Center, Nijmegen, Netherlands.,Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands.,King's College London, Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, London, United Kingdom
| | - Sandra Kooij
- Amsterdam University Medical Center, Location VUMc, Amsterdam, Netherlands.,PsyQ, Expertise Center Adult ADHD, The Hague, Netherlands
| | - Douglas Teixeira Leffa
- Department of Psychiatry, Hospital de Clinicas de Porto Alegre, Federal University of Rio Grande do Sul, Porto Alegre, Brazil.,National Institute of Developmental Psychiatry for Children and Adolescents, São Paulo, Brazil
| | - Nanda Rommelse
- Karakter Child and Adolescent Psychiatry University Center, Nijmegen, Netherlands.,Department of Psychiatry, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands
| | - Jeffrey H Newcorn
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Guilherme V Polanczyk
- Department of Psychiatry, Faculdade de Medicina FMUSP, Universidade de São Paulo, São Paulo, Brazil
| | - Luis Augusto Rohde
- National Institute of Developmental Psychiatry for Children and Adolescents, São Paulo, Brazil.,ADHD Outpatient Program and Developmental Psychiatry Program, Hospital de Clinica de Porto Alegre, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
| | - Emily Simonoff
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom
| | - Mark Stein
- Department of Psychiatry and Behavioral Sciences, Seattle, WA, United States
| | - Benedetto Vitiello
- Department of Public Health and Pediatric Sciences, Section of Child and Adolescent Neuropsychiatry, University of Turin, Turin, Italy.,Department of Public Health, Johns Hopkins University, Baltimore, MA, United States
| | - Yanki Yazgan
- GuzelGunler Clinic, Istanbul, Turkey.,Yale Child Study Center, New Haven, CT, United States
| | - Michael Roesler
- Institute for Forensic Psychology and Psychiatry, Neurocenter, Saarland, Germany
| | - Manfred Doepfner
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Medical Faculty of the University of Cologne, Cologne, Germany.,School for Child and Adolescent Cognitive Behavioural Therapy, University Hospital of Cologne, Cologne, Germany
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany
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18
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Real-World Implementation of Precision Psychiatry: A Systematic Review of Barriers and Facilitators. Brain Sci 2022; 12:brainsci12070934. [PMID: 35884740 PMCID: PMC9313345 DOI: 10.3390/brainsci12070934] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 07/08/2022] [Accepted: 07/12/2022] [Indexed: 01/27/2023] Open
Abstract
Background: Despite significant research progress surrounding precision medicine in psychiatry, there has been little tangible impact upon real-world clinical care. Objective: To identify barriers and facilitators affecting the real-world implementation of precision psychiatry. Method: A PRISMA-compliant systematic literature search of primary research studies, conducted in the Web of Science, Cochrane Central Register of Controlled Trials, PsycINFO and OpenGrey databases. We included a qualitative data synthesis structured according to the ‘Consolidated Framework for Implementation Research’ (CFIR) key constructs. Results: Of 93,886 records screened, 28 studies were suitable for inclusion. The included studies reported 38 barriers and facilitators attributed to the CFIR constructs. Commonly reported barriers included: potential psychological harm to the service user (n = 11), cost and time investments (n = 9), potential economic and occupational harm to the service user (n = 8), poor accuracy and utility of the model (n = 8), and poor perceived competence in precision medicine amongst staff (n = 7). The most highly reported facilitator was the availability of adequate competence and skills training for staff (n = 7). Conclusions: Psychiatry faces widespread challenges in the implementation of precision medicine methods. Innovative solutions are required at the level of the individual and the wider system to fulfil the translational gap and impact real-world care.
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19
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Biagianti B. What Can Mobile Sensing and Assessment Strategies Capture About Human Subjectivity? Front Digit Health 2022; 4:871133. [PMID: 35493531 PMCID: PMC9051043 DOI: 10.3389/fdgth.2022.871133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 03/28/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Bruno Biagianti
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
- *Correspondence: Bruno Biagianti
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20
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Jalal B, Chamberlain SR, Robbins TW, Sahakian BJ. Obsessive-compulsive disorder-contamination fears, features, and treatment: novel smartphone therapies in light of global mental health and pandemics (COVID-19). CNS Spectr 2022; 27:136-144. [PMID: 33081864 PMCID: PMC7691644 DOI: 10.1017/s1092852920001947] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Accepted: 10/04/2020] [Indexed: 02/06/2023]
Abstract
This review aims to shed light on the symptoms of obsessive-compulsive disorder (OCD) with a focus on contamination fears. In addition, we will briefly review the current therapies for OCD and detail what their limitations are. A key focus will be on discussing how smartphone solutions may provide approaches to novel treatments, especially when considering global mental health and the challenges imposed by rural environments and limited resources; as well as restrictions imposed by world-wide pandemics such as COVID-19. In brief, research that questions this review will seek to address include: (1) What are the symptoms of contamination-related OCD? (2) How effective are current OCD therapies and what are their limitations? (3) How can novel technologies help mitigate challenges imposed by global mental health and pandemics/COVID-19.
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Affiliation(s)
- Baland Jalal
- Department of Psychiatry, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, United Kingdom
| | - Samuel R. Chamberlain
- Department of Psychiatry, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
- Department of Psychiatry, Faculty of Medicine, University of Southampton; and Southern Health NHS Foundation Trust, Cambridgeshire & Peterborough NHS Foundation Trust, United Kingdom
| | - Trevor W. Robbins
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, United Kingdom
- Department of Psychology, University of Cambridge, Cambridge, United Kingdom
| | - Barbara J. Sahakian
- Department of Psychiatry, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, United Kingdom
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21
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Xie Q, Torous J, Goldberg SB. E-Mental Health for People with Personality Disorders: A Systematic Review. Curr Psychiatry Rep 2022; 24:541-552. [PMID: 35972718 PMCID: PMC9379895 DOI: 10.1007/s11920-022-01360-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/18/2022] [Indexed: 01/29/2023]
Abstract
PURPOSE OF REVIEW Provision of mental health services through digital technologies (e-mental health) can potentially expand access to treatments for personality disorders (PDs). We evaluated studies on e-mental health for PDs published over the last 3 years (2019-2022). RECENT FINDINGS Studies published in English that used e-mental health to treat people with PDs or PD-related symptoms were identified. We identified 19 studies, including four randomized controlled trials and one meta-analysis. Most interventions were based on Dialectical Behavior Therapy and delivered through smartphone applications for adults with Borderline Personality Disorder [BPD] or related symptoms. User experiences of the interventions were generally positive. Evidence for efficacy was limited. The current literature on e-mental health for PDs is limited in scope. Research in understudied populations and randomized controlled trials designed to establish efficacy are warranted. It is not yet clear whether e-mental health may be helpful for the treatment of PDs.
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Affiliation(s)
- Qiang Xie
- Department of Counseling Psychology, University of Wisconsin-Madison, 335 Education Building, 1000 Bascom Mall, Madison, WI, 53706, USA
- Center for Healthy Minds, University of Wisconsin-Madison, Madison, WI, USA
| | - John Torous
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Simon B Goldberg
- Department of Counseling Psychology, University of Wisconsin-Madison, 335 Education Building, 1000 Bascom Mall, Madison, WI, 53706, USA.
- Center for Healthy Minds, University of Wisconsin-Madison, Madison, WI, USA.
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22
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Goodsmith N, Cruz M. Mental Health Services Research and Community Psychiatry. TEXTBOOK OF COMMUNITY PSYCHIATRY 2022:411-425. [DOI: 10.1007/978-3-031-10239-4_30] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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23
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Jadhakhan F, Blake H, Hett D, Marwaha S. Efficacy of digital technologies aimed at enhancing emotion regulation skills: Literature review. Front Psychiatry 2022; 13:809332. [PMID: 36159937 PMCID: PMC9489858 DOI: 10.3389/fpsyt.2022.809332] [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] [Received: 11/04/2021] [Accepted: 08/19/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The impact of emotion regulation (ER) interventions on mental health and wellbeing has been extensively documented in the literature, although only recently have digital technologies been incorporated in intervention design. The aim of this review is to explore available published literature relating to the efficacy, barriers and facilitators of digital technologies in enhancing emotion/mood regulation skills. METHODS A review of the literature was performed to explore the effectiveness of digital technology in enhancing ER skills. MEDLINE, CINAHL, PsycINFO and Web of Science databases were searched from inception to 31st August 2020. In addition, the first 10 pages of Google Scholar were examined for relevant articles. The following MeSH term and key words were used to identify relevant articles: "emotion regulation OR mood regulation" AND "intervention OR treatment OR program$ OR therap$" AND "digital technologies OR web-based OR mobile application OR App." Reference lists of retrieved papers were hand searched to identify additional publications. Findings were summarized narratively. RESULTS Titles and abstracts were reviewed by one reviewer in two phases, and confirmed by a second reviewer; discrepancies were resolved through discussion. First, the retrieved titles and abstracts were reviewed to identify relevant studies. Full texts of retrieved studies were then read to determine eligibility. The search resulted in 209 studies of which 191 citations were identified as potentially meeting the inclusion criteria. After reviewing the title and abstract of the 191 papers, 33 studies were identified as potentially meeting the inclusion criteria. Following full-text review, 10 studies met the inclusion criteria. Findings indicated the potential effectiveness of online, text-messaging, and smartphone interventions for enhancing ER skills. CONCLUSION There is encouraging evidence that digital technologies may be beneficial for enhancing ER skills and providing personalized care remotely. Digital technologies, particularly the use of smartphones, were instrumental in facilitating assessments and delivering online self-help interventions such as cognitive behavioral therapy. Continued research is required to rigorously evaluate the effectiveness of digital technologies in ER skills and carefully consider risks/benefits while determining how emerging technologies might support the scale-up of ER skills and mental health treatment.
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Affiliation(s)
- Ferozkhan Jadhakhan
- Institute for Mental Health, School of Psychology, College of Life and Environmental Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Holly Blake
- School of Health Sciences, University of Nottingham, Nottingham, United Kingdom.,NIHR Nottingham Biomedical Research Centre, Nottingham, United Kingdom
| | - Danielle Hett
- Institute for Mental Health, School of Psychology, College of Life and Environmental Sciences, University of Birmingham, Birmingham, United Kingdom.,Birmingham and Solihull Mental Health NHS Foundation Trust, Birmingham, United Kingdom
| | - Steven Marwaha
- Institute for Mental Health, School of Psychology, College of Life and Environmental Sciences, University of Birmingham, Birmingham, United Kingdom.,Birmingham and Solihull Mental Health NHS Foundation Trust, Birmingham, United Kingdom
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24
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Fernández-Alvarez J, Grassi M, Colombo D, Botella C, Cipresso P, Perna G, Riva G. Efficacy of bio- and neurofeedback for depression: a meta-analysis. Psychol Med 2022; 52:201-216. [PMID: 34776024 PMCID: PMC8842225 DOI: 10.1017/s0033291721004396] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Revised: 09/29/2021] [Accepted: 10/07/2021] [Indexed: 12/30/2022]
Abstract
BACKGROUND For many years, biofeedback and neurofeedback have been implemented in the treatment of depression. However, the effectiveness of these techniques on depressive symptomatology is still controversial. Hence, we conducted a meta-analysis of studies extracted from PubMed, Scopus, Web of Science and Embase. METHODS Two different strings were considered for each of the two objectives of the study: A first group comprising studies patients with major depressive disorder (MDD) and a second group including studies targeting depressive symptomatology reduction in other mental or medical conditions. RESULTS In the first group of studies including patients with MDD, the within-group analyses yielded an effect size of Hedges' g = 0.717, while the between-group analysis an effect size of Hedges' g = 1.050. Moderator analyses indicate that treatment efficacy is only significant when accounting for experimental design, in favor of randomized controlled trials (RCTs) in comparison to non RCTs, whereas the type of neurofeedback, trial design, year of publication, number of sessions, age, sex and quality of study did not influence treatment efficacy. In the second group of studies, a small but significant effect between groups was found (Hedges' g = 0.303) in favor of bio- and neurofeedback against control groups. Moderator analyses revealed that treatment efficacy was not moderated by any of the sociodemographic and clinical variables. CONCLUSIONS Heart rate variability (HRV) biofeedback and neurofeedback are associated with a reduction in self-reported depression. Despite the fact that the field has still a large room for improvement in terms of research quality, the results presented in this study suggests that both modalities may become relevant complementary strategies for the treatment of MDD and depressive symptomatology in the coming years.
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Affiliation(s)
- J. Fernández-Alvarez
- Department of Psychology, Catholic University of the Sacred Heart, Milan, Italy
- Department of Basic Psychology, Clinic and Psychobiology, Universitat Jaume I, Castellón, Spain
| | - M. Grassi
- Department of Clinical Neurosciences, Hermanas Hospitalarias, Villa San Benedetto Menni Hospital, FoRiPsi, Albese con Cassano, Como, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy
| | - D. Colombo
- Department of Basic Psychology, Clinic and Psychobiology, Universitat Jaume I, Castellón, Spain
| | - C. Botella
- Ciber Fisiopatología Obesidad y Nutrición, CB06/03 Instituto Salud Carlos III, Madrid, Spain
| | - P. Cipresso
- Applied Technology for Neuro-Psychology Lab, IRCCS Istituto Auxologico Italiano, Milan, Italy
- Department of Psychology, University of Turin, Turin, Italy
| | - G. Perna
- Department of Clinical Neurosciences, Hermanas Hospitalarias, Villa San Benedetto Menni Hospital, FoRiPsi, Albese con Cassano, Como, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy
- Department of Psychiatry and Behavioral Sciences, Miller School of Medicine, University of Miami, Miami, FL, USA
- Research Institute of Mental Health and Neuroscience and Department of Psychiatry and Neuropsychology, Faculty of Health, Medicine and Life Sciences, University of Maastricht, Maastricht, the Netherlands
| | - G. Riva
- Department of Psychology, Catholic University of the Sacred Heart, Milan, Italy
- Applied Technology for Neuro-Psychology Lab, IRCCS Istituto Auxologico Italiano, Milan, Italy
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25
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Starke G, De Clercq E, Borgwardt S, Elger BS. Computing schizophrenia: ethical challenges for machine learning in psychiatry. Psychol Med 2021; 51:2515-2521. [PMID: 32536358 DOI: 10.1017/s0033291720001683] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Recent advances in machine learning (ML) promise far-reaching improvements across medical care, not least within psychiatry. While to date no psychiatric application of ML constitutes standard clinical practice, it seems crucial to get ahead of these developments and address their ethical challenges early on. Following a short general introduction concerning ML in psychiatry, we do so by focusing on schizophrenia as a paradigmatic case. Based on recent research employing ML to further the diagnosis, treatment, and prediction of schizophrenia, we discuss three hypothetical case studies of ML applications with view to their ethical dimensions. Throughout this discussion, we follow the principlist framework by Tom Beauchamp and James Childress to analyse potential problems in detail. In particular, we structure our analysis around their principles of beneficence, non-maleficence, respect for autonomy, and justice. We conclude with a call for cautious optimism concerning the implementation of ML in psychiatry if close attention is paid to the particular intricacies of psychiatric disorders and its success evaluated based on tangible clinical benefit for patients.
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Affiliation(s)
- Georg Starke
- Institute for Biomedical Ethics, University of Basel, Basel, Switzerland
| | - Eva De Clercq
- Institute for Biomedical Ethics, University of Basel, Basel, Switzerland
| | - Stefan Borgwardt
- Department of Psychiatry, University of Basel, Basel, Switzerland
- Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
| | - Bernice Simone Elger
- Institute for Biomedical Ethics, University of Basel, Basel, Switzerland
- University Center of Legal Medicine, University of Geneva, Geneva, Switzerland
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26
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Perna G, Nemeroff CB. Can personalized medicine mitigate confirmation bias in mental health? BRAZILIAN JOURNAL OF PSYCHIATRY 2021; 44:121-123. [PMID: 34669842 PMCID: PMC9041965 DOI: 10.1590/1516-4446-2021-0032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 07/20/2021] [Indexed: 12/03/2022]
Affiliation(s)
- Giampaolo Perna
- Department of Biological Sciences, Humanitas University, Pieve Emanuele, Milan, Italy.,Department of Clinical Neurosciences, Villa San Benedetto Menni, Hermanas Hospitalarias, Albese con Cassano, Como, Italy
| | - Charles B Nemeroff
- Department of Psychiatry and Behavioral Sciences, Dell Medical School, University of Texas at Austin, Austin, TX, USA
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27
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Positive psychology interventions in the United Arab Emirates: boosting wellbeing - and changing culture? CURRENT PSYCHOLOGY 2021; 42:7475-7488. [PMID: 34305364 PMCID: PMC8284689 DOI: 10.1007/s12144-021-02080-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/05/2021] [Indexed: 01/07/2023]
Abstract
As the science of wellbeing has grown, universities have adopted the challenge of prioritizing the wellbeing of students. Positive psychology interventions (PPIs), activities designed to increase the frequency of positive emotions and experiences, which help to facilitate the use of actions and thoughts that lead to human flourishing, are being increasingly used worldwide. Known to boost wellbeing and a number of other variables, it nonetheless remains unknown whether their use can influence other variables in non-Western cultures. In this study, we determined the impact of PPIs on a variety of wellbeing outcomes. The 6-week PPI program was conducted in the United Arab Emirates on Emirati university students (n = 120) who reported more positive emotion and overall balance of feelings that favored positivity over time relative to a control group. Yet, there was no effect found on negative emotions, life satisfaction, perceived stress, fear of happiness, locus of control, or somatic symptoms, and no effect on levels of collectivism or individualism. Our findings nonetheless support the use of PPIs in higher education as they show an increase in the experience of positive emotion, with this in itself bringing positive life outcomes, and no negative impact on culture. Our findings serve to build a foundation for understanding for whom PPIs work best - and least - around the world.
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28
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Ribera C, Vidal-Rubio SL, Romeu-Climent JE, Vila-Francés J, Van Rheenen TE, Balanzá-Martínez V. Cognitive impairment and consumption of mental healthcare resources in outpatients with bipolar disorder. J Psychiatr Res 2021; 138:535-540. [PMID: 33990024 DOI: 10.1016/j.jpsychires.2021.05.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 04/27/2021] [Accepted: 05/01/2021] [Indexed: 11/16/2022]
Abstract
Cognitive dysfunction is a major predictor of functional outcomes, and loss of occupational functioning is usually linked with a higher cost of illness. However, the association between cognitive impairment and consumption of health resources has not been studied in bipolar disorder to date. This study aims to examine this relationship. This is an observational, retrospective study of a representative sample of euthymic outpatients between 18 and 55 years, fulfilling DSM 5 criteria for bipolar disorder and recruited at a catchment area in Spain. Cognitive performance was screened with the Spanish version of the Screen for Cognitive Impairment in Psychiatry (SCIP-S), and several variables of health resources consumption during the previous year were registered. A total of 72 patients were assessed. Cognitive impairment according to the SCIP-S was significantly associated with the number of scheduled clinical appointments (p < 0.005) and hospital admissions (p < 0.04) but not with other health resources consumption variables. These results need to be interpreted with caution given that neither a control group nor a comprehensive, objective neuropsychological battery were used. However, despite these limitations, this study shows that in euthymic outpatients with bipolar disorder, those with suspected cognitive impairment had consumed a higher number of health resources over the previous year. These preliminary results may foster similar studies on the relationship between mental healthcare resource use and cognitive dysfunction in bipolar disorder and other psychiatric disorders.
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Affiliation(s)
- Carlos Ribera
- Department of Mental Health, La Ribera University Hospital, Carretera Corbera Km 1 s/n 46600, Alzira, Valencia, Spain
| | - Sonia Ll Vidal-Rubio
- Department of Mental Health, La Ribera University Hospital, Carretera Corbera Km 1 s/n 46600, Alzira, Valencia, Spain
| | - Jose E Romeu-Climent
- Department of Mental Health, La Ribera University Hospital, Carretera Corbera Km 1 s/n 46600, Alzira, Valencia, Spain
| | - Joan Vila-Francés
- Intelligent Data Analysis Laboratory (IDAL) University of Valencia, Avenida Universitat s/n 46100, Burjassot, Valencia, Spain
| | - Tamsyn E Van Rheenen
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Level 3, Alan Gilbert Building, 161 Barry St, Carlton, VIC, 3053, Australia; Faculty of Health, Arts and Design, School of Health Sciences, Center for Mental Health, Swinburne University, Level 3, Alan Gilbert Building, 161 Barry St, Carlton, VIC, 3053, Australia
| | - Vicent Balanzá-Martínez
- Teaching Unit of Psychiatry, Department of Medicine, University of Valencia, Valencia, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), ISCIII., Avenida Blasco Ibáñez 15, 46010, Valencia, Spain.
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29
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Squarcina L, Villa FM, Nobile M, Grisan E, Brambilla P. Deep learning for the prediction of treatment response in depression. J Affect Disord 2021; 281:618-622. [PMID: 33248809 DOI: 10.1016/j.jad.2020.11.104] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 11/08/2020] [Accepted: 11/13/2020] [Indexed: 12/30/2022]
Abstract
BACKGROUND Mood disorders are characterized by heterogeneity in severity, symptoms and treatment response. The possibility of selecting the correct therapy on the basis of patient-specific biomarker may be a considerable step towards personalized psychiatry. Machine learning methods are gaining increasing popularity in the medical field. Once trained, the possibility to consider single patients in the analyses instead of whole groups makes them particularly appealing to investigate treatment response. Deep learning, a branch of machine learning, lately gained attention, due to its effectiveness in dealing with large neuroimaging data and to integrate them with clinical, molecular or -omics biomarkers. METHODS In this mini-review, we summarize studies that use deep learning methods to predict response to treatment in depression. We performed a bibliographic search on PUBMED, Google Scholar and Web of Science using the terms "psychiatry", "mood disorder", "depression", "treatment", "deep learning", "neural networks". Only studies considering patients' datasets are considered. RESULTS Eight studies met the inclusion criteria. Accuracies in prediction of response to therapy were considerably high in all studies, but results may be not easy to interpret. LIMITATIONS The major limitation for the current studies is the small sample size, which constitutes an issue for machine learning methods. CONCLUSIONS Deep learning shows promising results in terms of prediction of treatment response, often outperforming regression methods and reaching accuracies of around 80%. This could be of great help towards personalized medicine. However, more efforts are needed in terms of increasing datasets size and improved interpretability of results.
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Affiliation(s)
- Letizia Squarcina
- Department of Pathophysiology and Transplantation and Department of Neurosciences and Mental Health, University of Milan, Milan, Italy.
| | - Filippo Maria Villa
- Scientific Institute, IRCCS E. Medea, Developmental Psychopathology Unit, Bosisio Parini, Lecco, Italy
| | - Maria Nobile
- Scientific Institute, IRCCS E. Medea, Developmental Psychopathology Unit, Bosisio Parini, Lecco, Italy
| | - Enrico Grisan
- Department of Information Engineering, University of Padova, Padova, Italy; School of Engineering, London South Bank University, London, UK
| | - Paolo Brambilla
- Department of Pathophysiology and Transplantation and Department of Neurosciences and Mental Health, University of Milan, Milan, Italy; Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
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30
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Korda AI, Andreou C, Borgwardt S. Pattern classification as decision support tool in antipsychotic treatment algorithms. Exp Neurol 2021; 339:113635. [PMID: 33548218 DOI: 10.1016/j.expneurol.2021.113635] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 01/20/2021] [Accepted: 02/01/2021] [Indexed: 10/22/2022]
Abstract
Pattern classification aims to establish a new approach in personalized treatment. The scope is to tailor treatment on individual characteristics during all phases of care including prevention, diagnosis, treatment, and clinical outcome. In psychotic disorders, this need results from the fact that a third of patients with psychotic symptoms do not respond to antipsychotic treatment and are described as having treatment-resistant disorders. This, in addition to the high variability of treatment responses among patients, enhances the need of applying advanced classification algorithms to identify antipsychotic treatment patterns. This review comprehensively summarizes advancements and challenges of pattern classification in antipsychotic treatment response to date and aims to introduce clinicians and researchers to the challenges of including pattern classification into antipsychotic treatment decision algorithms.
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Affiliation(s)
- Alexandra I Korda
- Department of Psychiatry and Psychotherapy, University Hospital Lübeck (UKSH), Ratzeburger Allee 160, 23538 Lübeck, Germany
| | - Christina Andreou
- Department of Psychiatry and Psychotherapy, University Hospital Lübeck (UKSH), Ratzeburger Allee 160, 23538 Lübeck, Germany
| | - Stefan Borgwardt
- Department of Psychiatry and Psychotherapy, University Hospital Lübeck (UKSH), Ratzeburger Allee 160, 23538 Lübeck, Germany.
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31
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van Bronswijk SC, Lemmens LHJM, Huibers MJH, Peeters FPML. Selecting the optimal treatment for a depressed individual: Clinical judgment or statistical prediction? J Affect Disord 2021; 279:149-157. [PMID: 33049433 DOI: 10.1016/j.jad.2020.09.135] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 07/25/2020] [Accepted: 09/27/2020] [Indexed: 12/28/2022]
Abstract
BACKGROUND Optimizing treatment selection is a way to enhance treatment success in major depressive disorder (MDD). In clinical practice, treatment selection heavily depends on clinical judgment. However, research has consistently shown that statistical prediction is as accurate - or more accurate - than predictions based on clinical judgment. In the context of new technological developments, the current aim was to compare the accuracy of clinical judgment versus statistical predictions in selecting cognitive therapy (CT) or interpersonal psychotherapy (IPT) for MDD. METHODS Data came from a randomized trial comparing CT (n=76) with IPT (n=75) for MDD. Prior to randomization, therapists' recommendations were formulated during multidisciplinary staff meetings. Statistical predictions were based on Personalized Advantage Index models. Primary outcomes were post-treatment and 17-month follow-up depression severity. Secondary outcome was treatment dropout. RESULTS Individuals receiving treatment according to their statistical prediction were less depressed at post-treatment and follow-up compared to those receiving their predicted non-indicated treatment. This difference was not found for recommended versus non-recommended treatments based on clinical judgment. Moreover, for individuals with an IPT recommendation by therapists, higher post-treatment and follow-up depression severity was found for those that actually received IPT compared to those that received CT. Recommendations based on statistical prediction and clinical judgment were not associated with differences in treatment dropout. LIMITATIONS Information on the clinical reasoning behind therapist recommendations was not collected, and statistical predictions were not externally validated. CONCLUSIONS Statistical prediction outperforms clinical judgment in treatment selection for MDD and has the potential to personalize treatment strategies.
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Affiliation(s)
- Suzanne C van Bronswijk
- Department of Clinical Psychological Science, Faculty of Psychology and Neuroscience, Maastricht University, P.O. Box 616, 6200, MD Maastricht, the Netherlands.
| | - Lotte H J M Lemmens
- Department of Clinical Psychological Science, Faculty of Psychology and Neuroscience, Maastricht University, P.O. Box 616, 6200, MD Maastricht, the Netherlands
| | - Marcus J H Huibers
- Department of Clinical Psychology, VU University Amsterdam, Van der Boechorststraat 1, 1081 BT Amsterdam, the Netherlands; Department of Psychology, University of Pennsylvania, 3720 Walnut Street, Philadelphia, PA 19104-6241, USA
| | - Frenk P M L Peeters
- Department of Clinical Psychological Science, Faculty of Psychology and Neuroscience, Maastricht University, P.O. Box 616, 6200, MD Maastricht, the Netherlands
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Češková E, Šilhán P. From Personalized Medicine to Precision Psychiatry? Neuropsychiatr Dis Treat 2021; 17:3663-3668. [PMID: 34934319 PMCID: PMC8684413 DOI: 10.2147/ndt.s337814] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 11/16/2021] [Indexed: 12/28/2022] Open
Abstract
Personalised medicine aims to find an individualized approach for each particular patient. Most factors used in current psychiatry, however, depend on the assessment made by the individual clinician and lack a higher degree of reliability. Precision medicine bases decisions on quantifiable indicators available thanks to the tremendous progress in science and technology facilitating the acquisition, processing and analysis of huge amounts of data. So far, psychiatry has not been benefiting enough from the advanced diagnostic technologies; nevertheless, we are witnessing the dawn of the era of precision psychiatry, starting with the gathering of sufficient amounts of data and its analysis by the means of artificial intelligence and machine learning. First results of this approach in psychiatry are available, which facilitate diagnosis assessment, course prediction, and appropriate treatment choice. These processes are often so complex and difficult to understand that they may resemble a "black box", which can slow down the acceptance of the results of this approach in clinical practice. Still, bringing precision medicine including psychiatry to standard clinical practice is a big challenge that can result in a completely new and transformative concept of health care. Such extensive changes naturally have both their supporters and opponents. This paper aims to familiarize clinically oriented physicians with precision psychiatry and to attract their attention to its recent developments. We cover the theoretical basis of precision medicine, its specifics in psychiatry, and provide examples of its use in the field of diagnostic assessment, course prediction, and appropriate treatment planning.
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Affiliation(s)
- Eva Češková
- Department of Psychiatry, University Hospital Ostrava, Ostrava, Czech Republic.,Department of Clinical Neurosciences, Faculty of Medicine, University of Ostrava, Ostrava, Czech Republic.,Department of Psychiatry, University Hospital Brno, Brno, Czech Republic.,Department of Psychiatry, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Petr Šilhán
- Department of Psychiatry, University Hospital Ostrava, Ostrava, Czech Republic.,Department of Clinical Neurosciences, Faculty of Medicine, University of Ostrava, Ostrava, Czech Republic
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Precision Psychiatry: Biomarker-Guided Tailored Therapy for Effective Treatment and Prevention in Major Depression. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1305:535-563. [PMID: 33834417 DOI: 10.1007/978-981-33-6044-0_27] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Depression contributes greatly to global disability and is a leading cause of suicide. It has multiple etiologies and therefore response to treatment can vary significantly. By applying the concepts of personalized medicine, precision psychiatry attempts to optimize psychiatric patient care by better predicting which individuals will develop an illness, by giving a more accurate biologically based diagnosis, and by utilizing more effective treatments based on an individual's biological characteristics (biomarkers). In this chapter, we discuss the basic principles underlying the role of biomarkers in psychiatric pathology and then explore multiple biomarkers that are specific to depression. These include endophenotypes, gene variants/polymorphisms, epigenetic factors such as methylation, biochemical measures, circadian rhythm dysregulation, and neuroimaging findings. We also examine the role of early childhood trauma in the development of, and treatment response to, depression. In addition, we review how new developments in technology may play a greater role in the determination of new biomarkers for depression.
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Tretiakov A, Malakhova A, Naumova E, Rudko O, Klimov E. Genetic Biomarkers of Panic Disorder: A Systematic Review. Genes (Basel) 2020; 11:genes11111310. [PMID: 33158196 PMCID: PMC7694264 DOI: 10.3390/genes11111310] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 10/29/2020] [Accepted: 11/02/2020] [Indexed: 12/30/2022] Open
Abstract
(1) Background: Although panic disorder (PD) is one of the most common anxiety disorders severely impacting quality of life, no effective genetic testing exists; known data on possible genetic biomarkers is often scattered and unsystematic which complicates further studies. (2) Methods: We used PathwayStudio 12.3 (Elsevier, The Netherlands) to acquire literature data for further manual review and analysis. 229 articles were extracted, 55 articles reporting associations, and 32 articles reporting no associations were finally selected. (3) Results: We provide exhaustive information on genetic biomarkers associated with PD known in the scientific literature. Data is presented in two tables. Genes COMT and SLC6A4 may be considered the most promising for PD diagnostic to date. (4) Conclusions: This review illustrates current progress in association studies of PD and may indicate possible molecular mechanisms of its pathogenesis. This is a possible basis for data analysis, novel experimental studies, or developing test systems and personalized treatment approaches.
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Affiliation(s)
- Artemii Tretiakov
- Faculty of Biology, Lomonosov Moscow State University, 119991 Moscow, Russia; (A.T.); (A.M.); (E.N.); (O.R.)
- Center of Genetics and Life Sciences, Sirius University of Science and Technology, 354340 Sochi, Russia
| | - Alena Malakhova
- Faculty of Biology, Lomonosov Moscow State University, 119991 Moscow, Russia; (A.T.); (A.M.); (E.N.); (O.R.)
| | - Elena Naumova
- Faculty of Biology, Lomonosov Moscow State University, 119991 Moscow, Russia; (A.T.); (A.M.); (E.N.); (O.R.)
- Center of Genetics and Life Sciences, Sirius University of Science and Technology, 354340 Sochi, Russia
| | - Olga Rudko
- Faculty of Biology, Lomonosov Moscow State University, 119991 Moscow, Russia; (A.T.); (A.M.); (E.N.); (O.R.)
- Center of Genetics and Life Sciences, Sirius University of Science and Technology, 354340 Sochi, Russia
| | - Eugene Klimov
- Faculty of Biology, Lomonosov Moscow State University, 119991 Moscow, Russia; (A.T.); (A.M.); (E.N.); (O.R.)
- Center of Genetics and Life Sciences, Sirius University of Science and Technology, 354340 Sochi, Russia
- Correspondence:
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Gaebel W, Falkai P. [Not Available]. FORTSCHRITTE DER NEUROLOGIE-PSYCHIATRIE 2020; 88:756-758. [PMID: 33307560 DOI: 10.1055/a-1130-8059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
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Orsolini L, Fiorani M, Volpe U. Digital Phenotyping in Bipolar Disorder: Which Integration with Clinical Endophenotypes and Biomarkers? Int J Mol Sci 2020; 21:ijms21207684. [PMID: 33081393 PMCID: PMC7589576 DOI: 10.3390/ijms21207684] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 10/08/2020] [Accepted: 10/08/2020] [Indexed: 01/05/2023] Open
Abstract
Bipolar disorder (BD) is a complex neurobiological disorder characterized by a pathologic mood swing. Digital phenotyping, defined as the 'moment-by-moment quantification of the individual-level human phenotype in its own environment', represents a new approach aimed at measuring the human behavior and may theoretically enhance clinicians' capability in early identification, diagnosis, and management of any mental health conditions, including BD. Moreover, a digital phenotyping approach may easily introduce and allow clinicians to perform a more personalized and patient-tailored diagnostic and therapeutic approach, in line with the framework of precision psychiatry. The aim of the present paper is to investigate the role of digital phenotyping in BD. Despite scarce literature published so far, extremely heterogeneous methodological strategies, and limitations, digital phenotyping may represent a grounding research and clinical field in BD, by owning the potentialities to quickly identify, diagnose, longitudinally monitor, and evaluating clinical response and remission to psychotropic drugs. Finally, digital phenotyping might potentially constitute a possible predictive marker for mood disorders.
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Gärtner M, Ghisu E, Herrera-Melendez AL, Koslowski M, Aust S, Asbach P, Otte C, Regen F, Heuser I, Borgwardt K, Grimm S, Bajbouj M. Using routine MRI data of depressed patients to predict individual responses to electroconvulsive therapy. Exp Neurol 2020; 335:113505. [PMID: 33068570 DOI: 10.1016/j.expneurol.2020.113505] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 10/07/2020] [Accepted: 10/07/2020] [Indexed: 12/30/2022]
Abstract
Electroconvulsive therapy (ECT) is one of the most effective treatments in cases of severe and treatment resistant major depression. 60-80% of patients respond to ECT, but the procedure is demanding and robust prediction of ECT responses would be of great clinical value. Predictions based on neuroimaging data have recently come into focus, but still face methodological and practical limitations that are hampering the translation into clinical practice. In this retrospective study, we investigated the feasibility of ECT response prediction using structural magnetic resonance imaging (sMRI) data that was collected during ECT routine examinations. We applied machine learning techniques to predict individual treatment outcomes in a cohort of N = 71 ECT patients, N = 39 of which responded to the treatment. SMRI-based classification of ECT responders and non-responders reached an accuracy of 69% (sensitivity: 67%; specificity: 72%). Classification on additionally investigated clinical variables had no predictive power. Since dichotomisation of patients into ECT responders and non-responders is debatable due to many patients only showing a partial response, we additionally performed a post-hoc regression-based prediction analysis on continuous symptom improvements. This analysis yielded a significant relationship between true and predicted treatment outcomes and might be a promising alternative to dichotomization of patients. Based on our results, we argue that the prediction of individual ECT responses based on routine sMRI holds promise to overcome important limitations that are currently hampering the translation of such treatment biomarkers into everyday clinical practice. Finally, we discuss how the results of such predictive data analysis could best support the clinician's decision on whether a patient should be treated with ECT.
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Affiliation(s)
- Matti Gärtner
- Charité - Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Campus Benjamin Franklin, Hindenburgdamm 30, 12203 Berlin, Germany; MSB - Medical School Berlin, Rüdesheimer Str. 50, 14197 Berlin.
| | - Elisabetta Ghisu
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, Basel, 4058, Switzerland; SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Ana Lucia Herrera-Melendez
- Charité - Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Campus Benjamin Franklin, Hindenburgdamm 30, 12203 Berlin, Germany
| | - Michael Koslowski
- Charité - Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Campus Mitte, Charitéplatz 1, 10117 Berlin, Germany
| | - Sabine Aust
- Charité - Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Campus Benjamin Franklin, Hindenburgdamm 30, 12203 Berlin, Germany
| | - Patrick Asbach
- Charité - Universitätsmedizin Berlin, Department of Radiology, Campus Benjamin Franklin, Hindenburgdamm 30, 12203 Berlin, Germany
| | - Christian Otte
- Charité - Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Campus Benjamin Franklin, Hindenburgdamm 30, 12203 Berlin, Germany
| | - Francesca Regen
- Charité - Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Campus Benjamin Franklin, Hindenburgdamm 30, 12203 Berlin, Germany
| | - Isabella Heuser
- Charité - Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Campus Benjamin Franklin, Hindenburgdamm 30, 12203 Berlin, Germany
| | - Karsten Borgwardt
- Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, Basel, 4058, Switzerland; SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Simone Grimm
- Charité - Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Campus Benjamin Franklin, Hindenburgdamm 30, 12203 Berlin, Germany; MSB - Medical School Berlin, Rüdesheimer Str. 50, 14197 Berlin
| | - Malek Bajbouj
- Charité - Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Campus Benjamin Franklin, Hindenburgdamm 30, 12203 Berlin, Germany
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Deep neural networks detect suicide risk from textual facebook posts. Sci Rep 2020; 10:16685. [PMID: 33028921 PMCID: PMC7542168 DOI: 10.1038/s41598-020-73917-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 09/23/2020] [Indexed: 01/07/2023] Open
Abstract
Detection of suicide risk is a highly prioritized, yet complicated task. Five decades of research have produced predictions slightly better than chance (AUCs = 0.56–0.58). In this study, Artificial Neural Network (ANN) models were constructed to predict suicide risk from everyday language of social media users. The dataset included 83,292 postings authored by 1002 authenticated Facebook users, alongside valid psychosocial information about the users. Using Deep Contextualized Word Embeddings for text representation, two models were constructed: A Single Task Model (STM), to predict suicide risk from Facebook postings directly (Facebook texts → suicide) and a Multi-Task Model (MTM), which included hierarchical, multilayered sets of theory-driven risk factors (Facebook texts → personality traits → psychosocial risks → psychiatric disorders → suicide). Compared with the STM predictions (0.621 ≤ AUC ≤ 0.629), the MTM produced significantly improved prediction accuracy (0.697 ≤ AUC ≤ 0.746), with substantially larger effect sizes (0.729 ≤ d ≤ 0.936). Subsequent content analyses suggested that predictions did not rely on explicit suicide-related themes, but on a range of text features. The findings suggest that machine learning based analyses of everyday social media activity can improve suicide risk predictions and contribute to the development of practical detection tools.
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Giordano GM, Pezzella P, Perrottelli A, Galderisi S. Die "Präzisionspsychiatrie" muss Teil der "personalisierten Psychiatrie" werden. FORTSCHRITTE DER NEUROLOGIE-PSYCHIATRIE 2020; 88:767-772. [PMID: 32869236 DOI: 10.1055/a-1211-2826] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
'Precision medicine' is defined as 'an emerging approach for treatment and prevention that takes into account each person's variability in genes, environment, and lifestyle'. Sometimes the term 'personalized medicine' is also used, either as a synonym or in a broader sense. In psychiatry, the term 'personalized' applies to different levels of health-care provision, such as the service organization and the choice of treatment plans based on the characterization of the individual patient. This approach is already feasible but, currently, it is often hampered by the shortage of human and financial resources. Recently, the terminology of 'precision medicine' has been extended to psychiatry: the term 'precision psychiatry' refers to the full exploitation of recent scientific and technological advances to achieve a close match between individual biosignature and prevention / treatment strategies. This article provides an overview of recent advances in neuroimaging, multi-omics and computational neuroscience, which have contributed to foster our understanding of the neurobiology of major mental disorders, and led to the implementation of a precision medicine-oriented approach in psychiatry.We argue that, while 'precision psychiatry' represents an important step to further advance the effectiveness of the 'personalized psychiatry', the distinction between the two terms is important to avoid dangerous neglect of the current potential of personalized care in psychiatry and to underscore the need for disseminating good existing practices aimed at organizing mental health services and providing care according to person's psychopathological characteristics, illness trajectory, needs, environment and preferences.In conclusion, 'precision psychiatry' will contribute to advance 'personalized psychiatry', but for the time being keeping the distinction between the two terms will contribute to fully exploit the current potential of personalized care.
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Furukawa TA, Debray TPA, Akechi T, Yamada M, Kato T, Seo M, Efthimiou O. Can personalized treatment prediction improve the outcomes, compared with the group average approach, in a randomized trial? Developing and validating a multivariable prediction model in a pragmatic megatrial of acute treatment for major depression. J Affect Disord 2020; 274:690-697. [PMID: 32664003 DOI: 10.1016/j.jad.2020.05.141] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Revised: 03/25/2020] [Accepted: 05/26/2020] [Indexed: 02/09/2023]
Abstract
BACKGROUND Clinical trials have traditionally been analysed at the aggregate level, assuming that the group average would be applicable to all eligible and similar patients. We re-analyzed a mega-trial of antidepressant therapy for major depression to explore whether a multivariable prediction model may lead to different treatment recommendations for individual participants. METHODS The trial compared the second-line treatment strategies of continuing sertraline, combining it with mirtazapine or switching to mirtazapine after initial failure to remit on sertraline among 1,544 patients with major depression. The outcome was the Personal Health Questionnaire-9 (PHQ-9) at week 9: the original analyses showed that both combining and switching resulted in greater reduction in PHQ-9 by 1.0 point than continuing. We considered several models of penalized regression or machine learning. RESULTS Models using support vector machines (SVMs) provided the best performance. Using SVMs, continuing sertraline was predicted to be the best treatment for 123 patients, combining for 696 patients, and switching for 725 patients. In the last two subgroups, both combining and switching were equally superior to continuing by 1.2 to 1.4 points, resulting in the same treatment recommendations as with the original aggregate data level analyses; in the first subgroup, however, switching was substantively inferior to combining (-3.1, 95%CI: -5.4 to -0.5). LIMITATIONS Stronger predictors are needed to make more precise predictions. CONCLUSIONS The multivariable prediction models led to improved recommendations for a minority of participants than the group average approach in a megatrial.
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Affiliation(s)
- Toshi A Furukawa
- Departments of Health Promotion and Human Behavior and of Clinical Epidemiology, Kyoto University Graduate School of Medicine / School of Public Health, Kyoto, Japan.
| | - Thomas P A Debray
- Julius Center for Health Sciences and Primary Care, UMC Utrecht, Utrecht University, The Netherlands.
| | - Tatsuo Akechi
- Department of Psychiatry and Cognitive-Behavioral Medicine, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan.
| | - Mitsuhiko Yamada
- Department of Neuropsychopharmacology, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan.
| | | | - Michael Seo
- Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland.
| | - Orestis Efthimiou
- Institute of Social and Preventive Medicine, University of Bern, Switzerland.
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Perna G, Cuniberti F, Daccò S, Grassi M, Caldirola D. 'Precision' or 'personalized' psychiatry: different terms - same content? FORTSCHRITTE DER NEUROLOGIE-PSYCHIATRIE 2020; 88:759-766. [PMID: 32838431 DOI: 10.1055/a-1211-2722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
Due to the increased lifetime prevalence and personal, social, and economic burden of mental disorders, psychiatry is in need of a significant change in several aspects of its clinical and research approaches. Over the last few decades, the development of personalized / precision medicine in psychiatry focusing on tailored therapies that fit each patient's unique individual, physiological, and genetic profile has not achieved the same results as those obtained in other branches, such as oncology. The long-awaited revolution has not yet surfaced. There are various explanations for this including imprecise diagnostic criteria, incomplete understanding of the molecular pathology involved, absence of available clinical tools and, finally, the characteristics of the patient. Since then, the co-existence of the two terms has sparked a great deal of discussion around the definition and differentiation between the two types of psychiatry, as they often seem similar or even superimposable. Generally, the two terminologies are used indiscriminately, alternatively, and / or separately, within the same scientific works. In this paper, an overview is provided on the overlap between the application and meaning of the terms 'precision psychiatry' and 'personalized psychiatry'.
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Affiliation(s)
- Giampaolo Perna
- Department of Biomedical Sciences, Humanitas University, San Benedetto Menni Hospital, Department of Clinical Neurosciences; Maastricht University Faculty of Health Medicine and Life Sciences, Department of Psychiatry and Neuropsychology; Leonard M Miller School of Medicine, Department of Psychiatry and Behavioral Sciences
| | - Francesco Cuniberti
- Humanitas University, Department of Biomedical Sciences; San Benedetto Menni Hospital, Department of Clinical Neurosciences
| | - Silvia Daccò
- Humanitas University, Department of Biomedical Sciences; San Benedetto Menni Hospital, Department of Clinical Neurosciences
| | - Massimiliano Grassi
- Humanitas University, Department of Biomedical Sciences; San Benedetto Menni Hospital, Department of Clinical Neurosciences
| | - Daniela Caldirola
- Humanitas University, Department of Biomedical Sciences; San Benedetto Menni Hospital, Department of Clinical Neurosciences
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Newson JJ, Thiagarajan TC. Assessment of Population Well-Being With the Mental Health Quotient (MHQ): Development and Usability Study. JMIR Ment Health 2020; 7:e17935. [PMID: 32706730 PMCID: PMC7400040 DOI: 10.2196/17935] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 05/06/2020] [Accepted: 05/23/2020] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Existing mental health assessment tools provide an incomplete picture of symptom experience and create ambiguity, bias, and inconsistency in mental health outcomes. Furthermore, by focusing on disorders and dysfunction, they do not allow a view of mental health and well-being across a general population. OBJECTIVE This study aims to demonstrate the outcomes and validity of a new web-based assessment tool called the Mental Health Quotient (MHQ), which is designed for the general population. The MHQ covers the complete breadth of clinical mental health symptoms and also captures healthy mental functioning to provide a complete profile of an individual's mental health from clinical to thriving. METHODS The MHQ was developed based on the coding of symptoms assessed in 126 existing Diagnostic and Statistical Manual of Mental Disorders (DSM)-based psychiatric assessment tools as well as neuroscientific criteria laid out by Research Domain Criteria to arrive at a comprehensive set of semantically distinct mental health symptoms and attributes. These were formulated into questions on a 9-point scale with both positive and negative dimensions and developed into a web-based tool that takes approximately 14 min to complete. As its output, the assessment provides overall MHQ scores as well as subscores for 6 categories of mental health that distinguish clinical and at-risk groups from healthy populations based on a nonlinear scoring algorithm. MHQ items were also mapped to the DSM fifth edition (DSM-5), and clinical diagnostic criteria for 10 disorders were applied to the MHQ outcomes to cross-validate scores labeled at-risk and clinical. Initial data were collected from 1665 adult respondents to test the tool. RESULTS Scores in the normal healthy range spanned from 0 to 200 for the overall MHQ, with an average score of approximately 100 (SD 45), and from 0 to 100 with average scores between 48 (SD 21) and 55 (SD 22) for subscores in each of the 6 mental health subcategories. Overall, 2.46% (41/1665) and 13.09% (218/1665) of respondents were classified as clinical and at-risk, respectively, with negative scores. Validation against DSM-5 diagnostic criteria showed that 95% (39/41) of those designated clinical were positive for at least one DSM-5-based disorder, whereas only 1.14% (16/1406) of those with a positive MHQ score met the diagnostic criteria for a mental health disorder. CONCLUSIONS The MHQ provides a fast, easy, and comprehensive way to assess population mental health and well-being; identify at-risk individuals and subgroups; and provide diagnosis-relevant information across 10 disorders.
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Zhou G, Lee MC, Atieli HE, Githure JI, Githeko AK, Kazura JW, Yan G. Adaptive interventions for optimizing malaria control: an implementation study protocol for a block-cluster randomized, sequential multiple assignment trial. Trials 2020; 21:665. [PMID: 32690063 PMCID: PMC7372887 DOI: 10.1186/s13063-020-04573-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Accepted: 07/02/2020] [Indexed: 02/08/2023] Open
Abstract
Background In the past two decades, the massive scale-up of long-lasting insecticidal nets (LLINs) and indoor residual spraying (IRS) has led to significant reductions in malaria mortality and morbidity. Nonetheless, the malaria burden remains high, and a dozen countries in Africa show a trend of increasing malaria incidence over the past several years. This underscores the need to improve the effectiveness of interventions by optimizing first-line intervention tools and integrating newly approved products into control programs. Because transmission settings and vector ecologies vary from place to place, malaria interventions should be adapted and readapted over time in response to evolving malaria risks. An adaptive approach based on local malaria epidemiology and vector ecology may lead to significant reductions in malaria incidence and transmission risk. Methods/design This study will use a longitudinal block-cluster sequential multiple assignment randomized trial (SMART) design with longitudinal outcome measures for a period of 3 years to develop an adaptive intervention for malaria control in western Kenya, the first adaptive trial for malaria control. The primary outcome is clinical malaria incidence rate. This will be a two-stage trial with 36 clusters for the initial trial. At the beginning of stage 1, all clusters will be randomized with equal probability to either LLIN, piperonyl butoxide-treated LLIN (PBO Nets), or LLIN + IRS by block randomization based on their respective malaria risks. Intervention effectiveness will be evaluated with 12 months of follow-up monitoring. At the end of the 12-month follow-up, clusters will be assessed for “response” versus “non-response” to PBO Nets or LLIN + IRS based on the change in clinical malaria incidence rate and a pre-defined threshold value of cost-effectiveness set by the Ministry of Health. At the beginning of stage 2, if an intervention was effective in stage 1, then the intervention will be continued. Non-responders to stage 1 PBO Net treatment will be randomized equally to either PBO Nets + LSM (larval source management) or an intervention determined by an enhanced reinforcement learning method. Similarly, non-responders to stage 1 LLIN + IRS treatment will be randomized equally to either LLIN + IRS + LSM or PBO Nets + IRS. There will be an 18-month evaluation follow-up period for stage 2 interventions. We will monitor indoor and outdoor vector abundance using light traps. Clinical malaria will be monitored through active case surveillance. Cost-effectiveness of the interventions will be assessed using Q-learning. Discussion This novel adaptive intervention strategy will optimize existing malaria vector control tools while allowing for the integration of new control products and approaches in the future to find the most cost-effective malaria control strategies in different settings. Given the urgent global need for optimization of malaria control tools, this study can have far-reaching implications for malaria control and elimination. Trial registration US National Institutes of Health, study ID NCT04182126. Registered on 26 November 2019.
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Affiliation(s)
- Guofa Zhou
- Program in Public Health, University of California, Irvine, CA, USA
| | - Ming-Chieh Lee
- Program in Public Health, University of California, Irvine, CA, USA
| | | | - John I Githure
- Department of Public Health, Maseno University, Kisumu, Kenya
| | | | - James W Kazura
- Center for Global Health and Diseases, Case Western Reserve University, Cleveland, OH, USA
| | - Guiyun Yan
- Program in Public Health, University of California, Irvine, CA, USA.
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Akhter-Khan SC, Au R. Why Loneliness Interventions Are Unsuccessful: A Call for Precision Health. ADVANCES IN GERIATRIC MEDICINE AND RESEARCH 2020; 2:e200016. [PMID: 36037052 PMCID: PMC9410567 DOI: 10.20900/agmr20200016] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/11/2023]
Abstract
Background Loneliness has drawn increasing attention over the past few decades due to rising recognition of its close connection with serious health issues, like dementia. Yet, researchers are failing to find solutions to alleviate the globally experienced burden of loneliness. Purpose This review aims to shed light on possible reasons for why interventions have been ineffective. We suggest new directions for research on loneliness as it relates to precision health, emerging technologies, digital phenotyping, and machine learning. Results Current loneliness interventions are unsuccessful due to (i) their inconsideration of loneliness as a heterogeneous construct and (ii) not being targeted at individuals' needs and contexts. We propose a model for how loneliness interventions can move towards finding the right solution for the right person at the right time. Taking a precision health approach, we explore how transdisciplinary research can contribute to creating a more holistic picture of loneliness and shift interventions from treatment to prevention. Conclusions We urge the field to rethink metrics to account for diverse intra-individual experiences and trajectories of loneliness. Big data sharing and evolving technologies that emphasize human connection raise hope for realizing our model of precision health applied to loneliness. There is an urgent need for precise, integrated, and theory-driven interventions that focus on individuals' needs and the subjective burden of loneliness in the ageing context.
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Affiliation(s)
- Samia C. Akhter-Khan
- Department of Psychology, Humboldt University of Berlin, 10117 Berlin, Germany
- Department of Psychology & Neuroscience, Duke University Graduate School, NC 27705, USA
| | - Rhoda Au
- Departments of Anatomy & Neurobiology and Neurology, Boston University Alzheimer’s Disease Center, Framingham Heart Study, Boston University School of Medicine, Boston, MA 02118, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA 02118, USA
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Caldirola D, Alciati A, Daccò S, Micieli W, Perna G. Relapse prevention in panic disorder with pharmacotherapy: where are we now? Expert Opin Pharmacother 2020; 21:1699-1711. [PMID: 32543949 DOI: 10.1080/14656566.2020.1779220] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Introduction: A substantial number of patients with PD experience relapse after the discontinuation of effective pharmacotherapy, leading to detrimental effects on the individuals and considerable societal costs. This suggests the need to optimize pharmacotherapy to minimize relapse risk. Area covered: The present systematic review examines randomized, double-blind, placebo-controlled relapse prevention studies published over the last 20 years involving recommended medications. The authors aim to provide an overview of this topic and evaluate whether recent advances were achieved. Only seven studies were included, providing limited results. One-year maintenance pharmacotherapy with constant doses had protective effects against relapse in patients who had previously exhibited satisfactory responses to the same medication at the same doses. The duration of maintenance treatment did not influence relapse risk. No data were available concerning the use of lower doses or the predictors of relapse. Expert opinion: Relapse prevention in PD has received limited attention. Recent progress and conclusive indications are lacking. Rethinking pharmacological research in PD may be productive. Collecting a wide range of clinical and individual features/biomarkers in large-scale, multicenter long-term naturalistic studies, and implementing recent technological innovations (e.g., electronic medical records/'big data' platforms, wearable devices, and machine learning techniques) may help identify reliable predictive models.
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Affiliation(s)
- Daniela Caldirola
- Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias , Albese Con Cassano (Como), Italy.,Department of Biomedical Sciences, Humanitas University , Pieve Emanuele (Milan), Italy
| | - Alessandra Alciati
- Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias , Albese Con Cassano (Como), Italy.,Humanitas Clinical and Research Center, IRCCS , Rozzano (Milan), Italy
| | - Silvia Daccò
- Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias , Albese Con Cassano (Como), Italy.,Department of Biomedical Sciences, Humanitas University , Pieve Emanuele (Milan), Italy
| | - Wilma Micieli
- Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias , Albese Con Cassano (Como), Italy
| | - Giampaolo Perna
- Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias , Albese Con Cassano (Como), Italy.,Department of Biomedical Sciences, Humanitas University , Pieve Emanuele (Milan), Italy.,Department of Psychiatry and Neuropsychology, Faculty of Health, Medicine and Life Sciences, Maastricht University , Maastricht, The Netherlands.,Department of Psychiatry and Behavioral Sciences, Leonard Miller School of Medicine, Miami University , Miami, FL, USA
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46
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Perna G, Alciati A, Daccò S, Grassi M, Caldirola D. Personalized Psychiatry and Depression: The Role of Sociodemographic and Clinical Variables. Psychiatry Investig 2020; 17:193-206. [PMID: 32160691 PMCID: PMC7113177 DOI: 10.30773/pi.2019.0289] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Accepted: 01/14/2020] [Indexed: 02/06/2023] Open
Abstract
Despite several pharmacological options, the clinical outcomes of major depressive disorder (MDD) are often unsatisfactory. Personalized psychiatry attempts to tailor therapeutic interventions according to each patient's unique profile and characteristics. This approach can be a crucial strategy in improving pharmacological outcomes in MDD and overcoming trial-and-error treatment choices. In this narrative review, we evaluate whether sociodemographic (i.e., gender, age, race/ethnicity, and socioeconomic status) and clinical [i.e., body mass index (BMI), severity of depressive symptoms, and symptom profiles] variables that are easily assessable in clinical practice may help clinicians to optimize the selection of antidepressant treatment for each patient with MDD at the early stages of the disorder. We found that several variables were associated with poorer outcomes for all antidepressants. However, only preliminary associations were found between some clinical variables (i.e., BMI, anhedonia, and MDD with melancholic/atypical features) and possible benefits with some specific antidepressants. Finally, in clinical practice, the assessment of sociodemographic and clinical variables considered in our review can be valuable for early identification of depressed individuals at high risk for poor responses to antidepressants, but there are not enough data on which to ground any reliable selection of specific antidepressant class or compounds. Recent advances in computational resources, such as machine learning techniques, which are able to integrate multiple potential predictors, such as individual/ clinical variables, biomarkers, and genetic factors, may offer future reliable tools to guide personalized antidepressant choice for each patient with MDD.
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Affiliation(s)
- Giampaolo Perna
- Humanitas University Department of Biomedical Sciences, Milan, Italy.,Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias, Como, Italy.,Department of Psychiatry and Neuropsychology, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands.,Department of Psychiatry and Behavioral Sciences, Leonard Miller School of Medicine, Miami University, Miami, USA
| | - Alessandra Alciati
- Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias, Como, Italy.,Humanitas Clinical and Research Center, IRCCS, Milan, Italy
| | - Silvia Daccò
- Humanitas University Department of Biomedical Sciences, Milan, Italy.,Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias, Como, Italy
| | - Massimiliano Grassi
- Humanitas University Department of Biomedical Sciences, Milan, Italy.,Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias, Como, Italy
| | - Daniela Caldirola
- Humanitas University Department of Biomedical Sciences, Milan, Italy.,Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias, Como, Italy
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47
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Ceskova E. Pharmacological strategies for the management of comorbid depression and schizophrenia. Expert Opin Pharmacother 2020; 21:459-465. [DOI: 10.1080/14656566.2020.1717466] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Affiliation(s)
- Eva Ceskova
- Central European Institute of Technology (CEITEC), Masaryk University, Brno, Czech Republic
- Department of Psychiatry, University Hospital Brno, Brno, Czech Republic
- Department of Psychiatry, University Hospital Ostrava, Ostrava, Czech Republic
- Department of Neurology and Psychiatry, Faculty of Medicine, University of Ostrava, Ostrava, Czech Republic
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48
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Mehltretter J, Rollins C, Benrimoh D, Fratila R, Perlman K, Israel S, Miresco M, Wakid M, Turecki G. Analysis of Features Selected by a Deep Learning Model for Differential Treatment Selection in Depression. Front Artif Intell 2020; 2:31. [PMID: 33733120 PMCID: PMC7861264 DOI: 10.3389/frai.2019.00031] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Accepted: 12/06/2019] [Indexed: 12/13/2022] Open
Abstract
Background: Deep learning has utility in predicting differential antidepressant treatment response among patients with major depressive disorder, yet there remains a paucity of research describing how to interpret deep learning models in a clinically or etiologically meaningful way. In this paper, we describe methods for analyzing deep learning models of clinical and demographic psychiatric data, using our recent work on a deep learning model of STAR*D and CO-MED remission prediction. Methods: Our deep learning analysis with STAR*D and CO-MED yielded four models that predicted response to the four treatments used across the two datasets. Here, we use classical statistics and simple data representations to improve interpretability of the features output by our deep learning model and provide finer grained understanding of their clinical and etiological significance. Specifically, we use representations derived from our model to yield features predicting both treatment non-response and differential treatment response to four standard antidepressants, and use linear regression and t-tests to address questions about the contribution of trauma, education, and somatic symptoms to our models. Results: Traditional statistics were able to probe the input features of our deep learning models, reproducing results from previous research, while providing novel insights into depression causes and treatments. We found that specific features were predictive of treatment response, and were able to break these down by treatment and non-response categories; that specific trauma indices were differentially predictive of baseline depression severity; that somatic symptoms were significantly different between males and females, and that education and low income proved important psycho-social stressors associated with depression. Conclusion: Traditional statistics can augment interpretation of deep learning models. Such interpretation can lend us new hypotheses about depression and contribute to building causal models of etiology and prognosis. We discuss dataset-specific effects and ideal clinical samples for machine learning analysis aimed at improving tools to assist in optimizing treatment.
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Affiliation(s)
- Joseph Mehltretter
- Department of Computer Science, University of Southern California, Los Angeles, CA, United States
| | - Colleen Rollins
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - David Benrimoh
- Department of Psychiatry, McGill University, Montreal, QC, Canada.,Faculty of Medicine, McGill University, Montreal, QC, Canada.,Douglas Mental Health University Institute, Montreal, QC, Canada.,Aifred Health, Montreal, QC, Canada
| | | | - Kelly Perlman
- Douglas Mental Health University Institute, Montreal, QC, Canada.,Aifred Health, Montreal, QC, Canada
| | - Sonia Israel
- Douglas Mental Health University Institute, Montreal, QC, Canada.,Aifred Health, Montreal, QC, Canada
| | - Marc Miresco
- Aifred Health, Montreal, QC, Canada.,Department of Psychiatry, Jewish General Hospital, Montreal, QC, Canada
| | - Marina Wakid
- Douglas Mental Health University Institute, Montreal, QC, Canada
| | - Gustavo Turecki
- Department of Psychiatry, McGill University, Montreal, QC, Canada.,Douglas Mental Health University Institute, Montreal, QC, Canada
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49
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Daniëls NEM, Hochstenbach LMJ, van Bokhoven MA, Beurskens AJHM, Delespaul PAEG. Implementing Experience Sampling Technology for Functional Analysis in Family Medicine - A Design Thinking Approach. Front Psychol 2019; 10:2782. [PMID: 31920830 PMCID: PMC6917593 DOI: 10.3389/fpsyg.2019.02782] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Accepted: 11/26/2019] [Indexed: 12/30/2022] Open
Abstract
Background A paradigm shift in health care from illness to wellbeing requires new assessment technologies and intervention strategies. Self-monitoring tools based on the Experience Sampling Method (ESM) might provide a solution. They enable patients to monitor both vulnerability and resilience in daily life. Although ESM solutions are extensively used in research, a translation from science into daily clinical practice is needed. Objective To investigate the redesign process of an existing platform for ESM data collection for detailed functional analysis and disease management used by psychological assistants to the general practitioner (PAGPs) in family medicine. Methods The experience-sampling platform was reconceptualized according to the design thinking framework in three phases. PAGPs were closely involved in co-creation sessions. In the ‘understand’ phase, knowledge about end-users’ characteristics and current eHealth use was collected (nominal group technique – 2 sessions with N = 15). In the ‘explore’ phase, the key needs concerning the platform content and functionalities were evaluated and prioritized (empathy mapping – 1 session with N = 5, moderated user testing – 1 session with N = 4). In the ‘materialize’ phase, the adjusted version of the platform was tested in daily clinical practice (4 months with N = 4). The whole process was extensively logged, analyzed using content analysis, and discussed with an interprofessional project group. Results In the ‘understand’ phase, PAGPs emphasized the variability in symptoms reported by patients. Therefore, moment-to-moment assessment of mood and behavior in a daily life context could be valuable. In the ‘explore’ phase, (motivational) functionalities, technological performance and instructions turned out to be important user requirements and could be improved. In the ‘materialize’ phase, PAGPs encountered barriers to implement the experience-sampling platform. They were insufficiently facilitated by the regional primary care group and general practitioners. Conclusion The redesign process in co-creation yielded meaningful insights into the needs, desires and daily routines in family medicine. Severe barriers were encountered related to the use and uptake of the experience-sampling platform in settings where health care professionals lack the time, knowledge and skills. Future research should focus on the applicability of this platform in family medicine and incorporate patient experiences.
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Affiliation(s)
- Naomi E M Daniëls
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands.,Department of Family Medicine, Care and Public Health Research Institute, Maastricht University, Maastricht, Netherlands
| | - Laura M J Hochstenbach
- Research Centre for Remote Health Care, Zuyd University of Applied Sciences, Heerlen, Netherlands
| | - Marloes A van Bokhoven
- Department of Family Medicine, Care and Public Health Research Institute, Maastricht University, Maastricht, Netherlands
| | - Anna J H M Beurskens
- Research Centre for Autonomy and Participation for Persons with a Chronic Illness, Zuyd University of Applied Sciences, Heerlen, Netherlands
| | - Philippe A E G Delespaul
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands.,Mondriaan Mental Health Trust, Heerlen, Netherlands
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50
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Scott J, Hidalgo-Mazzei D, Strawbridge R, Young A, Resche-Rigon M, Etain B, Andreassen OA, Bauer M, Bennabi D, Blamire AM, Boumezbeur F, Brambilla P, Cattane N, Cattaneo A, Chupin M, Coello K, Cointepas Y, Colom F, Cousins DA, Dubertret C, Duchesnay E, Ferro A, Garcia-Estela A, Goikolea J, Grigis A, Haffen E, Høegh MC, Jakobsen P, Kalman JL, Kessing LV, Klohn-Saghatolislam F, Lagerberg TV, Landén M, Lewitzka U, Lutticke A, Mazer N, Mazzelli M, Mora C, Muller T, Mur-Mila E, Oedegaard KJ, Oltedal L, Pålsson E, Papadopoulos Orfanos D, Papiol S, Perez-Sola V, Reif A, Ritter P, Rossi R, Schulze T, Senner F, Smith FE, Squarcina L, Steen NE, Thelwall PE, Varo C, Vieta E, Vinberg M, Wessa M, Westlye LT, Bellivier F. Prospective cohort study of early biosignatures of response to lithium in bipolar-I-disorders: overview of the H2020-funded R-LiNK initiative. Int J Bipolar Disord 2019; 7:20. [PMID: 31552554 PMCID: PMC6760458 DOI: 10.1186/s40345-019-0156-x] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Accepted: 07/24/2019] [Indexed: 01/01/2023] Open
Abstract
Background Lithium is recommended as a first line treatment for bipolar disorders. However, only 30% of patients show an optimal outcome and variability in lithium response and tolerability is poorly understood. It remains difficult for clinicians to reliably predict which patients will benefit without recourse to a lengthy treatment trial. Greater precision in the early identification of individuals who are likely to respond to lithium is a significant unmet clinical need. Structure The H2020-funded Response to Lithium Network (R-LiNK; http://www.r-link.eu.com/) will undertake a prospective cohort study of over 300 individuals with bipolar-I-disorder who have agreed to commence a trial of lithium treatment following a recommendation by their treating clinician. The study aims to examine the early prediction of lithium response, non-response and tolerability by combining systematic clinical syndrome subtyping with examination of multi-modal biomarkers (or biosignatures), including omics, neuroimaging, and actigraphy, etc. Individuals will be followed up for 24 months and an independent panel will assess and classify each participants’ response to lithium according to predefined criteria that consider evidence of relapse, recurrence, remission, changes in illness activity or treatment failure (e.g. stopping lithium; new prescriptions of other mood stabilizers) and exposure to lithium. Novel elements of this study include the recruitment of a large, multinational, clinically representative sample specifically for the purpose of studying candidate biomarkers and biosignatures; the application of lithium-7 magnetic resonance imaging to explore the distribution of lithium in the brain; development of a digital phenotype (using actigraphy and ecological momentary assessment) to monitor daily variability in symptoms; and economic modelling of the cost-effectiveness of introducing biomarker tests for the customisation of lithium treatment into clinical practice. Also, study participants with sub-optimal medication adherence will be offered brief interventions (which can be delivered via a clinician or smartphone app) to enhance treatment engagement and to minimize confounding of lithium non-response with non-adherence. Conclusions The paper outlines the rationale, design and methodology of the first study being undertaken by the newly established R-LiNK collaboration and describes how the project may help to refine the clinical response phenotype and could translate into the personalization of lithium treatment. Electronic supplementary material The online version of this article (10.1186/s40345-019-0156-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jan Scott
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK.,Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.,Université Paris Diderot, 75013, Paris, France
| | - Diego Hidalgo-Mazzei
- Bipolar and Depressive Disorders Unit, Department of Psychiatry and Psychology, Institute of Neurosciences, Hospital Clinic de Barcelona, University of Barcelona, IDIBAPS, CIBERSAM, Villaroel 170, 08036, Barcelona, Catalonia, Spain
| | - Rebecca Strawbridge
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Allan Young
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Matthieu Resche-Rigon
- Université Paris Diderot, 75013, Paris, France.,Service de Biostatistique et Information Médicale, Hôpital Saint-Louis, AP-HP, Paris, France.,Inserm, UMR 1153, Equipe ECSTRA, Paris, France
| | - Bruno Etain
- Université Paris Diderot, 75013, Paris, France.,Département de Psychiatrie et de Médecine Addictologique, AP-HP, GH Saint-Louis - Lariboisière - F. Widal, 75475, Paris, France.,Inserm, U1144, Team 1, 75006, Paris, France
| | - Ole A Andreassen
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Michael Bauer
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Djamila Bennabi
- Department of Clinical Psychiatry, Inserm CIC 1431, CHU Besançon, 25000, Besançon, France.,Laboratoire de Neurosciences, Université Bourgogne Franche-Comté, 25000, Besançon, France
| | - Andrew M Blamire
- Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK.,Newcastle Magnetic Resonance Centre, Campus for Ageing and Vitality, Newcastle University, Newcastle upon Tyne, NE4 5PL, UK
| | - Fawzi Boumezbeur
- NeuroSpin, CEA, Université Paris-Saclay, 91191, Gif-sur-Yvette, France
| | - Paolo Brambilla
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, University of Milan, Milan, Italy.,Department of Psychiatry and Behavioural Neurosciences, University of Texas at Houston, Houston, TX, USA
| | - Nadia Cattane
- IRCCS Istituto Centro San Giovanni di Dio - Fatebenefratelli, Brescia, Italy
| | - Annamaria Cattaneo
- IRCCS Istituto Centro San Giovanni di Dio - Fatebenefratelli, Brescia, Italy
| | - Marie Chupin
- CATI Neuroimaging Platform, ICM, Pitié Salpétrière Hospital, 75013, Paris, France.,Institut du Cerveau et de la Moelle épinière, ICM, 75013, Paris, France.,Inserm, U1127, 75013, Paris, France.,CNRS, UMR 7225, 75013, Paris, France.,Sorbonne Université, 75013, Paris, France
| | - Klara Coello
- Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, University Hospital of Copenhagen, Copenhagen, Denmark
| | - Yann Cointepas
- NeuroSpin, CEA, Université Paris-Saclay, 91191, Gif-sur-Yvette, France.,CATI Neuroimaging Platform, ICM, Pitié Salpétrière Hospital, 75013, Paris, France
| | - Francesc Colom
- Mental Health Research Program, IMIM, Hospital del Mar, CIBERSAM, Barcelona, Catalonia, Spain
| | - David A Cousins
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK.,Northumberland Tyne and Wear NHS Foundation Trust, Newcastle upon Tyne, NE3 3XT, UK
| | - Caroline Dubertret
- Université Paris Diderot, 75013, Paris, France.,APHP; Psychiatry Department, University Hospital Louis Mourier, Colombes, France.,INSERM U894, Institute of Psychiatry and Neurosciences of Paris, Paris, France
| | - Edouard Duchesnay
- NeuroSpin, CEA, Université Paris-Saclay, 91191, Gif-sur-Yvette, France
| | - Adele Ferro
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, University of Milan, Milan, Italy
| | - Aitana Garcia-Estela
- Mental Health Research Program, IMIM, Hospital del Mar, CIBERSAM, Barcelona, Catalonia, Spain
| | - Jose Goikolea
- Bipolar and Depressive Disorders Unit, Department of Psychiatry and Psychology, Institute of Neurosciences, Hospital Clinic de Barcelona, University of Barcelona, IDIBAPS, CIBERSAM, Villaroel 170, 08036, Barcelona, Catalonia, Spain
| | - Antoine Grigis
- NeuroSpin, CEA, Université Paris-Saclay, 91191, Gif-sur-Yvette, France
| | - Emmanuel Haffen
- Department of Clinical Psychiatry, Inserm CIC 1431, CHU Besançon, 25000, Besançon, France.,Laboratoire de Neurosciences, Université Bourgogne Franche-Comté, 25000, Besançon, France
| | - Margrethe C Høegh
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Petter Jakobsen
- NORMENT, Division of Psychiatry, Haukeland University Hospital, Bergen, Norway.,Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Janos L Kalman
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany.,Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany.,International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany
| | - Lars V Kessing
- Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, University Hospital of Copenhagen, Copenhagen, Denmark
| | - Farah Klohn-Saghatolislam
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany.,Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Trine V Lagerberg
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Mikael Landén
- Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Ute Lewitzka
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Ashley Lutticke
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany.,Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Nicolas Mazer
- APHP; Psychiatry Department, University Hospital Louis Mourier, Colombes, France.,INSERM U894, Institute of Psychiatry and Neurosciences of Paris, Paris, France
| | - Monica Mazzelli
- IRCCS Istituto Centro San Giovanni di Dio - Fatebenefratelli, Brescia, Italy
| | - Cristina Mora
- IRCCS Istituto Centro San Giovanni di Dio - Fatebenefratelli, Brescia, Italy
| | - Thorsten Muller
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany
| | - Estanislao Mur-Mila
- Mental Health Research Program, IMIM, Hospital del Mar, CIBERSAM, Barcelona, Catalonia, Spain
| | - Ketil Joachim Oedegaard
- NORMENT, Division of Psychiatry, Haukeland University Hospital, Bergen, Norway.,Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Leif Oltedal
- Department of Clinical Medicine, University of Bergen, Bergen, Norway.,Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Erik Pålsson
- Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | | | - Sergi Papiol
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany.,Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Victor Perez-Sola
- Mental Health Research Program, IMIM, Hospital del Mar, CIBERSAM, Barcelona, Catalonia, Spain
| | - Andreas Reif
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Philipp Ritter
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Roberto Rossi
- Unit of Psychiatry, IRCCS Istituto Centro San Giovanni di Dio - Fatebenefratelli, Brescia, Italy
| | - Thomas Schulze
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany
| | - Fanny Senner
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany.,Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Fiona E Smith
- Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK.,Newcastle Magnetic Resonance Centre, Campus for Ageing and Vitality, Newcastle University, Newcastle upon Tyne, NE4 5PL, UK
| | - Letizia Squarcina
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, University of Milan, Milan, Italy
| | - Nils Eiel Steen
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Pete E Thelwall
- Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK.,Newcastle Magnetic Resonance Centre, Campus for Ageing and Vitality, Newcastle University, Newcastle upon Tyne, NE4 5PL, UK
| | - Cristina Varo
- Bipolar and Depressive Disorders Unit, Department of Psychiatry and Psychology, Institute of Neurosciences, Hospital Clinic de Barcelona, University of Barcelona, IDIBAPS, CIBERSAM, Villaroel 170, 08036, Barcelona, Catalonia, Spain
| | - Eduard Vieta
- Bipolar and Depressive Disorders Unit, Department of Psychiatry and Psychology, Institute of Neurosciences, Hospital Clinic de Barcelona, University of Barcelona, IDIBAPS, CIBERSAM, Villaroel 170, 08036, Barcelona, Catalonia, Spain
| | - Maj Vinberg
- Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, University Hospital of Copenhagen, Copenhagen, Denmark
| | - Michele Wessa
- Department of Clinical Psychology and Neuropsychology, Institute for Psychology, Johannes Gutenberg-University Mainz, Wallstraße 3, 55122, Mainz, Germany
| | - Lars T Westlye
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.,Department of Psychology, University of Oslo, Oslo, Norway
| | - Frank Bellivier
- Université Paris Diderot, 75013, Paris, France. .,Département de Psychiatrie et de Médecine Addictologique, AP-HP, GH Saint-Louis - Lariboisière - F. Widal, 75475, Paris, France. .,Inserm, U1144, Team 1, 75006, Paris, France.
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