Societies need to be better prepared for rapid changes in economics, politics, and climate. This will depend on our ability to cope with complexity. We propose a new framework based on neuroscience for understanding resilience at multiple levels. This framework considers adaptation, transformation, and the hierarchical structure from brain cells and physiological stress systems to organizations, communities, cultures, and countries. We also discuss how resilience can be managed through transdisciplinary and collaborative approaches, creating a resilient neuroscience system to withstand potentially catastrophic changes to social-ecological systems.
The COVID-19 pandemic has clarified that resilience is a complex and multi-tiered phenomenon. Medical specialties have commonly focused on the individual brain systems for resilience, but attention to subcellular, epigenetic, brain connectivity, and organ system factors has been growing. These are embedded in social, environmental, and developmental contexts that play a determinative role in the ability of humans to navigate major challenges successfully. Our proposed multi-level systems neuroscience of resilience framework is based on an ecological conceptualization of resilience.1
A collaborative approach using concepts of transdisciplinary (known as convergence) science is needed to prepare individuals and societies for the complex social-ecological challenges that lay ahead.2 Increasing evidence shows how perturbations negatively affect mental health and sustainable development.3-5 However, the potential direct and indirect effects of disturbances on mental health, such as those arising from climate change, likely add more complexity by exacerbating the effects evident in a single shock such as COVID-19.4,6 Siloed approaches studying mental health disorders or resilience across the lifespan are inadequate for assessing this complexity. Although this work is meritorious, it needs to be integrated with a holistic perspective. Thus, our framework emphasizes that cognition cannot be properly understood or supported without attention to multiple factors and levels of influence.7
We envision a multi-level neuroscience system as an emergent property or phenomenon that arises from dynamic, nonstationary, multi-scale, hierarchical interaction (for a definition of terms in italics see Table 18).
This covers internal factors such as genetics, stress, neurobiology, plasticity, and external forces such as close and extended social supports, positive and adaptive organization, economics, and individual environments. These are the life history factors and trajectories that influence our reactions and capacity to act (adaptive capacity) in the present moment (Table 18).
Complex Adaptive Systems
This complexity is accounted for in ecological resilience, which we use as the framework scaffold. Ecological resilience emphasizes the emergent property of all complex adaptive systems (such as ecosystems and the human brain), which extends beyond models used in other domains like resilience in health care and disaster resilience.1,9 These disciplines focus on adaptation, recovery, and coping with stressful events. As such, features are individual manifestations of patterns and processes integrated and subsumed within adaptive capacity as in the allosteric model.10
At an individual level, adaptive capacity is often predicated on prior adaptation and mastery of challenges. However, at a systemic level, the function is to maintain equilibrium and contribute to the stationary dynamics of specific system regimes as outlined in Figure 1.8
The adaptive capacity, therefore, allows the complex system (individual or ecosystem) to return to the predisturbance conditions after disruptions. In a practical setting, this can be boosted by additional management interventions such as psychotherapy and preventative fitness interventions. Adaptive capacity is thus a system component that is nested in ecological resilience.11
An important caveat is that complex adaptive systems cannot infinitely cope with disturbances. They operate within defined bounds of system dynamics that are at least partly learned from prior appropriate adaptations to environmental demands.12,13 Once adaptive capacity is exhausted, a critical disturbance threshold is passed, and the system flips into a novel, alternative regime with different system dynamics (Figure 1). Such fundamental, often abrupt, nonlinear system change is inherent in ecological resilience. It highlights the ability of complex systems to undergo a nonstationary transformation and to exist in regimes with each of their structures, processes, feedbacks, and adaptive capacity.
Consider a democracy shifting to an authoritarian regime, rainforests turning into deserts and healthy individuals and populations developing chronic diseases. Once complex systems are “degraded,” such as individuals and communities living with persistent health problems, resilience-based management allows for purposefully eroding the adaptive capacity of an impaired, suboptimal regime to make it prone to transformation into a more “desired” regime that would allow fostering of improved health.14 Alternative regimes may lose critical functions, such as food rationing, energy, and communication, relative to previous social-ecological systems regimes. Degraded regimes often need constant costly management to force desired functions artificially. In such coerced regimes functions can no longer be sustained by the system itself.15
Management of adaptive capacity does not necessarily achieve recovery to a previous regime or the creation of a novel one, and it is often limited to mitigating the effects of disturbances. This is due to previous coping regimes having become maladaptive or obsolete, leading to deeply entrenched feedbacks that stabilize the novel, alternative, or degraded regime (Figure 1). This can be exemplified in therapeutic interventions that merely mitigate mental illness without recovering patients to a symptomless regime where further therapy or medication is unnecessary.16 Similarly, biological and technological solutions often only mitigate turbid, nutrient-enriched lakes with toxic algal blooms and fail to create a clear-water regime with abundant plants and good water quality.17
Alternative regimes underscore the importance of accounting for transformation in theory and practice because adaptation and transformation both need to be considered when managing resilient multi-level systems. However, transformative management is plagued by high uncertainty. Despite developing early warning scores for impending system transitions used in psychiatry, these often have limited predictability within ecosystems.18-23 This uncertainty is exemplified by the increased mental health challenges associated with the COVID-19 pandemic, where the future trajectory is impossible to predict.24 As such, it is currently not possible to ascertain whether the global mental health crisis is a mirror image of social-ecological systems having already flipped into novel regimes, possibly triggered by the pandemic and social media, or whether regime shifts will likely occur in the future.
Consider the Russian invasion of Ukraine, which has had international ramifications through military rearmament, inflation, mass migration, and supply of resources. All these considerations must be accounted for if the transformation goal is to create desirable, resilient, and self-organizing multi-level systems of neuroscience, and ultimately meet the United Nations Sustainable Development goals for health and resilience of social-ecological systems.
Despite this complexity, transformative management can be informed by planning competing scenarios using science-based predictive models, including simulations and group scenarios with stakeholders. Such scenarios can envision different social, environmental, technological, and economic realities that can be reiteratively tested as we move toward an uncertain future.25 There are, however, many challenges that need to be accounted for in scenario planning and transformation, which may be informed by interdisciplinary research, exemplified by the cross-pollination of economics and physics.26 Relevant factors range from the levels of neurons to societies, resilience by design, intervention in health care logistics, responsible governance, economic and energetic transformation, and media communication.27
Demonstrating a Multi-Level Neuroscience System of Resilience
The complexity that crystalizes the resilience of multi-level neuroscience systems initially requires a simplified conceptualization as seen in Figure 2.
A scaffold for a conceptual model for multi-level neuroscience systems resilience can be built through research that increasingly unravels its inherent complexity, including the connectivity and “information flow” across system levels, through a constant iterative learning process.
One example would be how genetic susceptibility to mental illness at the brain level can trigger disorders following stressful life events. This transforms a healthy regime with fragile resilience into a potentially dysfunctional, highly resistant regime that may need constant health care management to mitigate symptom expressions.16 The brain-level changes resulting from transformation influence the next system level: the individual. At this scale, symptoms affect psychological, physiological, and behavioral functioning that may need to be managed therapeutically. At the population level, it becomes necessary to implement systems that manage patients and other related brain problems for multiple individuals experiencing the same problem. These treatments have economic repercussions at the societal level through loss of productivity and cost of health care.
This chain of events demonstrates a clear “bottom-up” component in transmitting information in a multi-level neuroscience system. However, “top-down” events also further influence system processes and feedbacks. For instance, public illiteracy about mental illness and stigma, limited resources for managing mental health, and ineffective policies foster the negative societal repercussions of mental illness as well as reinforce stressful environments for patients. Bottom-up and top-down factors combined form negative feedbacks to maintain this demonstrated neuroscience system in a resilient undesired regime. However, they also identify scales in the system where management can be leveraged to create a more desirable regime through transformation.
Leveraging System Management for Multi-Level Neuroscience Systems of Resilience
There is a wide range of system management possibilities that may contribute to creating a resilient, multi-level neuroscience system. Management options may likely encompass a wide spectrum of molecular, clinical, technological, educational, economic, and ecological tools and approaches as outlined in Table 2.
These options can be exploited for adaptive and transformative management, but specific approaches may be most suitable for only a few specific hierarchical levels that comprise such a system. For instance, at the brain level, the development of nanotechnology may provide opportunities to create nanoparticle-based drugs that improve psychotherapeutic approaches.28 Similarly, experimental data corroborate that mesenchymal stem cell therapy could be a potential treatment for depression based on anti-inflammatory and neurotrophic properties.29
An example of a novel therapeutic approach is the addition of cytokine markers to antidepressants that provide a protective effect on executive functioning and treatment response in older adults with depression.30,31 Pharmacogenetic innovations could transform the clinical space and complement or refine current adaptive treatment approaches based on medication adjustment for brain disorders. This allows adaptive and transformative management options to target neurocircuits and developmental processes at the next higher scale: the brain.
The ability of a few interventions to target more than 1 scale in a multi-level neuroscience system has implications for resilience. Specifically, medications currently used to treat disorders include redundant management options that target neuronal molecular structures and physiology and structures at the brain level. Creating redundant management interventions within scales or across them can foster resilience in multi-level neuroscience systems like those proposed for climate change mitigation.32 Brain-level management options cannot directly be used to manage higher hierarchical scales. For example, at the scale of individuals, management targeting suicide rates may build on current clinical tools such as individually adjusted therapy through adaptation. Novel self-reflexive approaches, such as mental health apps monitoring mood states, could provide improved insight through transformation.27
We acknowledge that our examples focus broadly on factors that directly affect the multi-level neuroscience systems of resilience like the genetic proclivity of mental illness. However, other issues affecting these systems indirectly, like the mental health impact of sickle cell anemia, need to be accounted for. Success in rewiring the brain toward the best possible health can reduce the burden on mental health care systems and associated economic costs. Meanwhile, creating equal and fair access to health care can cascade down to lower levels and contribute to reinforcing improved brain health.
Within-scale and cross-scale redundancy of management options at the individual and societal level shows the interconnectedness of hierarchical scales that ultimately influences its resilience through the flow of information from the lowest to the highest hierarchical scale in the system and vice versa. It will also be essential to identify additional levels not noted in the previously outlined model, such as organizational levels in which maintaining values and promoting growth at the individual level will nurture desirable behaviors at the society level. Companies, schools, volunteer agencies, and clubs play an important role in shaping adaptive and maladaptive behavior. A toxic or unethical organizational climate can have ill effects that spread to the wider levels, but a positive one may be transformative for a multi-level system.
Creating and Managing Resilient Multi-Level Neuroscience Systems
A major goal of management is to create self-organizing systems that need minimal intervention in providing necessary goods and services. These can range from food, clean water, and financial security to brain health, sustainable workload, and access to health care. This complexity leads to a multi-level neuroscience system as pictured in Figure 3A.
Despite comprising an apparent individual entity, such a system must be understood as an integral part of broader social-ecological systems.27 Therefore, the evolution of multi-level neuroscience systems is likely developed in tandem with the dynamics and change of socio-ecological systems and their underlying economic, social, technological, and ecological drivers.
The increasing global mental health crisis is projected to incur substantial economic and societal costs. This suggests that creating self-organizing, desirable, and resilient multi-level neuroscience systems will likely be impossible.33 Thus, substantial and incrementally intensifying management will be needed to coerce multi-level neuroscience systems into a regime that artificially maintains the desired functioning. There may be contrasting scenarios in which management will either succeed in emulating multi-level neuroscience systems conditions that approximate those of a resilient desired regime (Figure 3B) or fail to do so (Figure 3C).
A Way Forward
We have suggested scenarios comprising opposites between best-case and worst-case outcomes of managing coerced multi-level neuroscience systems of resilience. Although scenario planning is a useful tool to suggest how management may be geared for creating best-possible multi-level neuroscience systems of resilience, benchmarked against the worst-case, scenarios must not be seen as static endpoints. They rather need to be refined and recalibrated iteratively. They also can be informed by machine learning to accommodate the unforeseen change that may result from technological, economic, and societal innovation. At the same time, there is a pervasive inability to abate many of the crises that have challenged sustainable development.25,34 The “unforeseeable” is an essential part of the high uncertainty inherent in the dynamics of complex systems of individuals and nature. This means that in addition to known unknowns, we need to account for unknown unknowns.35 Addressing the unknown unknowns comprises a dilemma because defining social-ecological, including brain health, challenges that we do not know to comprise is an absurd contradiction.36
Compartmentalization across spheres of society is ill-suited to deal with the unknown unknowns. Fundamental changes in academia are needed to move away from models emphasizing isolation and metric fulfillment. More room needs to be created for intuitive thinking and collaborative work that allows for novelty and innovation, which arises at the intersection between, rather than within, distinctly different knowledge domains and life experiences.37 Such collaborations will need to be truly transdisciplinary and include cooperation across all spheres of society. Such partnerships can be inspired by a creative, alternative, provocative, and unorthodox dialog at the intersection of the arts, sciences, philosophy, and spirituality. Such collaborations may ultimately facilitate the planning of scenarios that may provide management targets for creating the best-possible resilient multi-level neuroscience systems desirable for human societies.
Dr Angeler is a transdisciplinary researcher at the Swedish University of Agricultural Sciences, adjunct professor at the University of Nebraska-Lincoln, collaborator at the PRODEO Institute and honorary fellow at the Institute for Mental and Physical Health and Clinical Translation (IMPACT) at Deakin University. Dr Lundin is a psychiatry registrar with Barwon Health, an affiliate lecturer with IMPACT, and a PhD candidate with the University of Auckland. Dr Hunter is assistant professor of psychology in the College of Liberal Arts at The University of Massachusetts at Boston.Ms Smith is an Atlantic Fellow for Equity in Brain Health at the Global Brain Health Institute, a Thiel Fellow at Stanford University, and a Steering Committee member for the OECD Neuroscience-inspired Policy Initiative. Dr Wister is director of the Gerontology Research Centre and a professor in the Department of Gerontology at Simon Fraser University. Dr Hynes is an associate fellow at the Johns Hopkins School of Advanced International Studies. Dr Berk is the director of IMPACT at Deakin University. Dr Lavretsky is a professor in residence in the Department of Psychiatry at UCLA in Los Angeles, California. Her work on geriatric depression and integrative mental health using mind-body interventions has received national attention, and she has won numerous grants supporting that work. She is the president-elect of the American Association for Geriatric Psychiatry, a distinguished fellow of the American Psychiatric Association and of the American Association for Geriatric Psychiatry, and a fellow of the American College of Neuropsychopharmacology. She is also on the board of Psychiatric Times™. Dr Trump is senior research social scientist at U.S. Army Engineer Research and Development Center (ERDC). Dr Linkov is senior scientific and technical manager at the US Army Engineer Research and Development Center, where he manages crisis response and resilience project portfolio. Dr Benight is professor, executive director of the Lyda Hill Institute of Human Resilience, University of Colorado at Colorado Springs. Ms Estafany is an associate with The PRODEO Institute Dr Winter is a physician-scientist, social entrepreneur, and health policy analyst. Besides training in neurology at the Charité University Hospital and Berlin Institute of Health, Germany, he is a postdoctoral researcher at the Massachusetts General Hospital at Harvard Medical School, Boston, USA. Mr Edmonds is research associate professor and Master of Humanities & Master of Social Science Programs at The University of Colorado at Denver. Dr Lister is senior clinical system analyst at Stanford Health Care. Dr Storch is professor, vice chair, and McIngvale Presidential Endowed Chair in the Department of Psychiatry and Behavioral Sciences at Baylor College of Medicine. Dr Matthews is professor of engineering psychology, United States Military Academy (West Point). Dr Eyre is lead of the Brain Capital Alliance, co-lead of the OECD Neuroscience-inspired Policy Initiative, senior fellow for Brain Capital with the Meadows Mental Health Policy Institute and advisor to the Euro-Mediterranean Economists Association.
Dr Lavretsky was supported by an AT009198 NIH grant. No other authors have acknowledgments, and no authors report relevant conflicts of interest.
References
1. Holling CS. Resilience and stability of ecological systems. Annu Rev Ecol Syst. 1973;4(1):1-23.
2. Eyre HA, Berk M, Lavretsky H, Reynolds C, eds. Convergence Mental Health: A Transdisciplinary Approach to Innovation. Oxford University Press; 2021:229-254.
3. Patz JA, Frumkin H, Holloway T, et al. Climate change: challenges and opportunities for global health. JAMA. 2014;312(15):1565-1580.
4. Hayes K, Blashki G, Wiseman J, et al. Climate change and mental health: risks, impacts and priority actions. Int J Ment Health Syst. 2018;12:28.
5. Patel V, Saxena S, Lund C, et al. The Lancet Commission on global mental health and sustainable development [published correction appears in Lancet. 2018 Oct 27;392(10157):1518]. Lancet. 2018;392(10157):1553-1598.
6. Berry HL, Bowen K, Kjellstrom T. Climate change and mental health: a causal pathways framework. Int J Public Health. 2010;55(2):123-132.
7. Eyre HA, Lavretsky H, Forbes M, et al. Convergence science arrives: how does it relate to psychiatry? Acad Psychiatry. 2017;41(1):91-99.
8. Angeler DG, Fried-Petersen H, Allen CR, et al. Adaptive capacity in ecosystems. Adv Ecol Res. 2019;60:1-24.
9. Allen CR, Angeler DG, Chaffin BC, et al. Resilience reconciled. Nat Sustain. 2019;2:898-900.
10. McEwen BS. Stress, adaptation, and disease. allostasis and allostatic load. Ann N Y Acad Sci. 1998;840:33-44.
11. Baho DL, Allen CR, Garmestani AS, et al. A quantitative framework for assessing ecological resilience. Ecol Soc. 2017;22(3):1-17.
12. Fried EI, Papanikolaou F, Epskamp S. Mental health and social contact during the COVID-19 pandemic: an ecological momentary assessment study. Clin Psychol Sci. 2021;10(2):340-354.
13. Rist L, Felton A, Nyström M, et al. Applying resilience thinking to production ecosystems. Ecosphere. 2014;5(6):1-11.
14. Lake J, Turner MS. Urgent need for improved mental health care and a more collaborative model of care. Perm J. 2017;21:17-024.
15. Angeler DG, Chaffin BC, Sundstrom SM, et al. Coerced regimes: management challenges in the Anthropocene. Ecol Soc. 2020;25(1):1-4.
16. Angeler DG, Allen CR, Persson ML. Resilience concepts in psychiatry demonstrated with bipolar disorder. Int J Bipolar Disord. 2018;6(1):2.
17. Gulati RD, Dionisio Pires LM, Van Donk E. Lake restoration studies: failures, bottlenecks and prospects of new ecotechnological measures. Limnologica. 2008;38(3-4):233-247.
18. Wichers M, Groot PC; Psychosystems, ESM Group, EWS Group. Critical slowing down as a personalized early warning signal for depression. Psychother Psychosom. 2016;85(2):114-116.
19. Kramer AA, Sebat F, Lissauer M. A review of early warning systems for prompt detection of patients at risk for clinical decline. J Trauma Acute Care Surg. 2019;87(1S Suppl 1):S67-S73.
20. de Mooij SMM, Blanken TF, Grasman RPPP, et al. Dynamics of sleep: exploring critical transitions and early warning signals. Comput Methods Programs Biomed. 2020;193:105448.
21. Burthe SJ, Henrys PA, Mackay EB, et al. Do early warning indicators consistently predict nonlinear change in long-term ecological data?J Appl Ecol. 2015;53(3):666-656.
22. Teasdale GM. National early warning score (NEWS) is not suitable for all patients. BMJ. 2012;345:e5875.
23. Trefois C, Antony PM, Goncalves J, et al. Critical transitions in chronic disease: transferring concepts from ecology to systems medicine. Curr Opin Biotechnol. 2015;34:48-55.
24. Usher K, Bhullar N, Jackson D. Life in the pandemic: social isolation and mental health. J Clin Nurs. 2020;29(15-16):2756-2757.
25. Herrmann DL, Schwarz K, Allen CR, et al. Iterative scenarios for social-ecological systems. Ecol Soc. 2021;26(4):1-9.
26. Hynes W, Trump BD, Kirman A. Systemic resilience in economics. Nat Phys. 2022;18(4):381-384.
27. Angeler DG, Eyre HA, Berk M, et al. Adaptation, transformation and resilience in healthcare comment on “Government Actions and Their Relation to Resilience in Healthcare During the COVID-19 Pandemic in New South Wales, Australia and Ontario, Canada.”Int J Health Policy Manag. 2022;11(9):1949-1952.
28. Milanesi E, Maj C, Bocchio-Chiavetto L, Maffioletti E. Nanomedicine in psychiatry: new therapeutic opportunities from research on small RNAs. Drug Dev Res. 2016;77(8):453-457.
29. do Prado-Lima PAS, Costa-Ferro ZSM, Souza BSF, et al. Is there a place for cellular therapy in depression? World J Psychiatry. 2021;11(9):553-567.
30. Van Dyk K, Siddarth P, Rossetti M, et al. Memantine can protect against inflammation-based cognitive decline in geriatric depression. Brain Behav Immun Health. 2020;9:100167.
31. Grzenda A, Siddarth P, Laird KT, et al. Transcriptomic signatures of treatment response to the combination of escitalopram and memantine or placebo in late-life depression. Mol Psychiatry. 2021;26(9):5171-5179.
32. Angeler DG, Allen CR. Scales of coercion: resilience, regimes, panarchy. In: Gunderson L, Allen CR, Garmestani A (Eds). Applied Panarchy: Applications and Diffusions Across Disciplines. Island Press; 2022.
33. The Lancet Global Health. Mental health matters. Lancet Glob Health. 2020;8(11):e1352.
34. Grzenda A, Speier W, Siddarth P, et al. Machine learning prediction of treatment outcome in late-life depression. Front Psychiatry. 2021;12:738494.
35. Angeler DG, Allen CR, Carnaval A. Convergence science in the Anthropocene: navigating the known and unknown. People Nat. 2020;2(1):96-102.
36. Szent-Györgyi A. Dionysians and apollonians. Science. 1972;176(4038):966.
37. Johansson F. The Medici Effect, With a New Preface and Discussion Guide: What Elephants and Epidemics Can Teach Us About Innovation. Harvard Business Review Press; 2017.
