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Kalisch R, Russo SJ, Müller MB. Neurobiology and systems biology of stress resilience. Physiol Rev 2024; 104:1205-1263. [PMID: 38483288 DOI: 10.1152/physrev.00042.2023] [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: 11/01/2023] [Revised: 03/06/2024] [Accepted: 03/12/2024] [Indexed: 05/16/2024] Open
Abstract
Stress resilience is the phenomenon that some people maintain their mental health despite exposure to adversity or show only temporary impairments followed by quick recovery. Resilience research attempts to unravel the factors and mechanisms that make resilience possible and to harness its insights for the development of preventative interventions in individuals at risk for acquiring stress-related dysfunctions. Biological resilience research has been lagging behind the psychological and social sciences but has seen a massive surge in recent years. At the same time, progress in this field has been hampered by methodological challenges related to finding suitable operationalizations and study designs, replicating findings, and modeling resilience in animals. We embed a review of behavioral, neuroimaging, neurobiological, and systems biological findings in adults in a critical methods discussion. We find preliminary evidence that hippocampus-based pattern separation and prefrontal-based cognitive control functions protect against the development of pathological fears in the aftermath of singular, event-type stressors [as found in fear-related disorders, including simpler forms of posttraumatic stress disorder (PTSD)] by facilitating the perception of safety. Reward system-based pursuit and savoring of positive reinforcers appear to protect against the development of more generalized dysfunctions of the anxious-depressive spectrum resulting from more severe or longer-lasting stressors (as in depression, generalized or comorbid anxiety, or severe PTSD). Links between preserved functioning of these neural systems under stress and neuroplasticity, immunoregulation, gut microbiome composition, and integrity of the gut barrier and the blood-brain barrier are beginning to emerge. On this basis, avenues for biological interventions are pointed out.
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Affiliation(s)
- Raffael Kalisch
- Leibniz Institute for Resilience Research (LIR), Mainz, Germany
- Neuroimaging Center (NIC), Focus Program Translational Neuroscience (FTN), Johannes Gutenberg University Medical Center, Mainz, Germany
| | - Scott J Russo
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, United States
- Brain and Body Research Center, Icahn School of Medicine at Mount Sinai, New York, New York, United States
| | - Marianne B Müller
- Leibniz Institute for Resilience Research (LIR), Mainz, Germany
- Translational Psychiatry, Department of Psychiatry and Psychotherapy, Johannes Gutenberg University Medical Center, Mainz, Germany
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Liu SR, Moore TM, Gur RC, Nievergelt C, Baker DG, Risbrough V, Acheson DT. High executive functioning is associated with reduced posttraumatic stress after trauma exposure among male U.S. military personnel. Front Psychol 2023; 14:1181055. [PMID: 37818418 PMCID: PMC10560729 DOI: 10.3389/fpsyg.2023.1181055] [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: 03/06/2023] [Accepted: 08/31/2023] [Indexed: 10/12/2023] Open
Abstract
Introduction Evidence suggests that executive function (EF) may play a key role in development of PTSD, possibly influenced by factors such as trauma type and timing. Since EF can be improved through intervention, it may be an important target for promoting resilience to trauma exposure. However, more research is needed to understand the relation between trauma exposure, EF, and PTSD. The goal of this study was to improve understanding of EF as a potential antecedent or protective factor for the development of PTSD among military personnel. Method In a cohort of U.S. Marines and Navy personnel (N = 1,373), the current study tested the association between exposure to traumatic events (pre-deployment and during deployment) and PTSD severity, and whether EF moderated these associations. Three types of pre-deployment trauma exposure were examined: cumulative exposure, which included total number of events participants endorsed as having happened to them, witnessed, or learned about; direct exposure, which included total number of events participants endorsed as having happened to them; and interpersonal exposure, which included total number of interpersonally traumatic events participants' endorsed. EF was measured using the Penn Computerized Neurocognitive Battery. Results EF was associated with less PTSD symptom severity at pre-deployment, even when adjusting for trauma exposure, alcohol use, traumatic brain injury, and number of years in the military. EF also moderated the relation between cumulative trauma exposure and interpersonal trauma exposure and PTSD, with higher EF linked to a 20 and 33% reduction in expected point increase in PTSD symptoms with cumulative and interpersonal trauma exposure, respectively. Finally, higher pre-deployment EF was associated with reduced PTSD symptom severity at post-deployment, independent of deployment-related trauma exposure and adjusting for pre-deployment PTSD. Conclusion Our results suggest that EF plays a significant, if small role in the development of PTSD symptoms after trauma exposure among military personnel. These findings provide important considerations for future research and intervention and prevention, specifically, incorporating a focus on improving EF in PTSD treatment.
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Affiliation(s)
- Sabrina R. Liu
- Department of Human Development, California State University San Marcos, San Marcos, CA, United States
| | - Tyler M. Moore
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, United States
| | - Ruben C. Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, United States
| | - Caroline Nievergelt
- Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, La Jolla, CA, United States
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States
| | - Dewleen G. Baker
- Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, La Jolla, CA, United States
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States
| | - Victoria Risbrough
- Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, La Jolla, CA, United States
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States
| | - Dean T. Acheson
- Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, La Jolla, CA, United States
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States
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Mentis AFA, Lee D, Roussos P. Applications of artificial intelligence-machine learning for detection of stress: a critical overview. Mol Psychiatry 2023:10.1038/s41380-023-02047-6. [PMID: 37020048 DOI: 10.1038/s41380-023-02047-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 03/17/2023] [Accepted: 03/20/2023] [Indexed: 04/07/2023]
Abstract
Psychological distress is a major contributor to human physiology and pathophysiology, and it has been linked to several conditions, such as auto-immune diseases, metabolic syndrome, sleep disorders, and suicidal thoughts and inclination. Therefore, early detection and management of chronic stress is crucial for the prevention of several diseases. Artificial intelligence (AI) and Machine Learning (ML) have promoted a paradigm shift in several areas of biomedicine including diagnosis, monitoring, and prognosis of disease. Here, our review aims to present some of the AI and ML applications for solving biomedical issues related to psychological stress. We provide several lines of evidence from previous studies highlighting that AI and ML have been able to predict stress and detect the brain normal states vs. abnormal states (notably, in post-traumatic stress disorder (PTSD)) with accuracy around 90%. Of note, AI/ML-driven technology applied to identify ubiquitously present stress exposure may not reach its full potential, unless future analytics focus on detecting prolonged distress through such technology instead of merely assessing stress exposure. Moving forward, we propose that a new subcategory of AI methods called Swarm Intelligence (SI) can be used towards detecting stress and PTSD. SI involves ensemble learning techniques to efficiently solve a complex problem, such as stress detection, and it offers particular strength in clinical settings, such as privacy preservation. We posit that AI and ML approaches will be beneficial for the medical and patient community when applied to predict and assess stress levels. Last, we encourage additional research to bring AI and ML into the standard clinical practice for diagnostics in the not-too-distant future.
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Affiliation(s)
- Alexios-Fotios A Mentis
- University Research Institute of Maternal and Child Health & Precision Medicine, Athens, Greece.
- UNESCO Chair on Adolescent Health Care, National and Kapodistrian University of Athens, "Aghia Sophia" Children's Hospital, Athens, Greece.
| | - Donghoon Lee
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science and Institute for Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Panos Roussos
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science and Institute for Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY, USA
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Jibb LA, Nanos SM, Alexander S, Malfitano C, Rydall A, Gupta S, Schimmer AD, Zimmermann C, Hales S, Nissim R, Marmar C, Schultebraucks K, Mah K, Rodin G. Traumatic stress symptoms in family caregivers of patients with acute leukaemia: protocol for a multisite mixed methods, longitudinal, observational study. BMJ Open 2022; 12:e065422. [PMID: 36332954 PMCID: PMC9639100 DOI: 10.1136/bmjopen-2022-065422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
INTRODUCTION The diagnosis, progression or recurrence of cancer is often highly traumatic for family caregivers (FCs), but systematic assessments of distress and approaches for its prevention and treatment are lacking. Acute leukaemia (AL) is a life-threatening cancer of the blood, which most often presents acutely, requires intensive treatment and is associated with severe physical symptoms. Consequently, traumatic stress may be common in the FCs of patients with AL. We aim to determine the prevalence, severity, longitudinal course and predictors of traumatic stress symptoms in FCs of patients with AL in the first year after diagnosis, and to understand their lived experience of traumatic stress and perceived support needs. METHODS AND ANALYSIS This two-site longitudinal, observational, mixed methods study will recruit 223 adult FCs of paediatric or adult patients newly diagnosed with AL from two tertiary care centres. Quantitative data will be collected from self-report questionnaires at enrolment, and 1, 3, 6, 9 and 12 months after admission to hospital for initial treatment. Quantitative data will be analysed using descriptive and machine learning approaches and a multilevel modelling (MLM) approach will be used to confirm machine learning findings. Semi-structured qualitative interviews will be conducted at 3, 6 and 12 months and analysed using a grounded theory approach. ETHICS AND DISSEMINATION This study is funded by the Canadian Institutes of Health Research (CIHR number PJT 173255) and has received ethical approval from the Ontario Cancer Research Ethics Board (CTO Project ID: 2104). The data generated have the potential to inform the development of targeted psychosocial interventions for traumatic stress, which is a public health priority for high-risk populations such as FCs of patients with haematological malignancies. An integrated and end-of-study knowledge translation strategy that involves FCs and other stakeholders will be used to interpret and disseminate study results.
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Affiliation(s)
- Lindsay A Jibb
- Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Ontario, Canada
- Lawrence S. Bloomberg Faculty of Nursing, University of Toronto, Toronto, Ontario, Canada
| | - Stephanie M Nanos
- Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Supportive Care, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Sarah Alexander
- Division of Haematology/Oncology, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Pediatrics, University of Toronto, Toronto, Ontario, Canada
| | - Carmine Malfitano
- Department of Supportive Care, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Anne Rydall
- Department of Supportive Care, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Sumit Gupta
- Division of Haematology/Oncology, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Pediatrics, University of Toronto, Toronto, Ontario, Canada
| | - Aaron D Schimmer
- Department of Medical Oncology/Hematology, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Camilla Zimmermann
- Department of Supportive Care, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Sarah Hales
- Department of Supportive Care, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Rinat Nissim
- Department of Supportive Care, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Charles Marmar
- Department of Psychiatry, New York University, New York, New York, USA
| | - Katharina Schultebraucks
- Department of Emergency Medicine, Columbia University Irving Medical Center, New York, New York, USA
- Department of Psychiatry, Columbia University, New York, New York, USA
| | - Kenneth Mah
- Department of Supportive Care, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Gary Rodin
- Department of Supportive Care, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
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Rubega M, Ciringione L, Bertuccelli M, Paramento M, Sparacino G, Vianello A, Masiero S, Vallesi A, Formaggio E, Del Felice A. High-density EEG sleep correlates of cognitive and affective impairment at 12-month follow-up after COVID-19. Clin Neurophysiol 2022; 140:126-135. [PMID: 35763985 PMCID: PMC9292469 DOI: 10.1016/j.clinph.2022.05.017] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 05/17/2022] [Accepted: 05/31/2022] [Indexed: 11/29/2022]
Abstract
Objective To disentangle the pathophysiology of cognitive/affective impairment in Coronavirus Disease-2019 (COVID-19), we studied long-term cognitive and affective sequelae and sleep high-density electroencephalography (EEG) at 12-month follow-up in people with a previous hospital admission for acute COVID-19. Methods People discharged from an intensive care unit (ICU) and a sub-intensive ward (nonICU) between March and May 2020 were contacted between March and June 2021. Participants underwent cognitive, psychological, and sleep assessment. High-density EEG recording was acquired during a nap. Slow and fast spindles density/amplitude/frequency and source reconstruction in brain gray matter were extracted. The relationship between psychological and cognitive findings was explored with Pearson correlation. Results We enrolled 33 participants ( 17 nonICU) and 12 controls. We observed a lower Physical Quality of Life index, higher post-traumatic stress disorder (PTSD) score, and a worse executive function performance in nonICU participants. Higher PTSD and Beck Depression Inventory scores correlated with lower executive performance. The same group showed a reorganization of spindle cortical generators. Conclusions Our results show executive and psycho-affective deficits and spindle alterations in COVID-19 survivors – especially in nonICU participants – after 12 months from discharge. Significance These findings may be suggestive of a crucial contribution of stress experienced during hospital admission on long-term cognitive functioning.
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Affiliation(s)
- Maria Rubega
- Department of Neuroscience, Section of Rehabilitation, University of Padova, via Giustiniani, 3, Padova 35128, Italy.
| | - Luciana Ciringione
- Department of Neuroscience, Section of Rehabilitation, University of Padova, via Giustiniani, 3, Padova 35128, Italy.
| | - Margherita Bertuccelli
- Department of Neuroscience, Section of Rehabilitation, University of Padova, via Giustiniani, 3, Padova 35128, Italy; Padova Neuroscience Center, University of Padova, via Orus 2/B, Padova 35129, Italy.
| | - Matilde Paramento
- Department of Information Engineering, University of Padova, via Gradenigo 6/B, Padova 35131, Italy.
| | - Giovanni Sparacino
- Department of Information Engineering, University of Padova, via Gradenigo 6/B, Padova 35131, Italy.
| | - Andrea Vianello
- Department of Cardio-Thoraco-Vascular Sciences and Public Health, University of Padova, via Giustiniani, 2, Padova 35128, Italy.
| | - Stefano Masiero
- Department of Neuroscience, Section of Rehabilitation, University of Padova, via Giustiniani, 3, Padova 35128, Italy; Padova Neuroscience Center, University of Padova, via Orus 2/B, Padova 35129, Italy.
| | - Antonino Vallesi
- Department of Neuroscience, Section of Rehabilitation, University of Padova, via Giustiniani, 3, Padova 35128, Italy; Padova Neuroscience Center, University of Padova, via Orus 2/B, Padova 35129, Italy.
| | - Emanuela Formaggio
- Department of Neuroscience, Section of Rehabilitation, University of Padova, via Giustiniani, 3, Padova 35128, Italy; Padova Neuroscience Center, University of Padova, via Orus 2/B, Padova 35129, Italy.
| | - Alessandra Del Felice
- Department of Neuroscience, Section of Rehabilitation, University of Padova, via Giustiniani, 3, Padova 35128, Italy; Padova Neuroscience Center, University of Padova, via Orus 2/B, Padova 35129, Italy.
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