Husseini K. Manji, MD

There has been a recent revolution in the development of digital health solutions for various chronic conditions. However, there are relatively few examples of digital health solutions that have been widely adopted in real-world health care settings and are measurably improving patient outcomes or helping health care systems deliver efficient and patient-centric care. From our vantage, there are some fundamental reasons for this “translational gap,” which, if addressed, can go a long way in helping realize the full potential of digital health technologies.

We use mental health/illness as an exemplar because brain-related illnesses will be at the forefront of digital health revolution, as their course can be monitored, outcomes can be measured via digital phenotyping, and interventions can be delivered digitally and remotely. Furthermore, the challenges and opportunities in digital mental health have been further highlighted by the ongoing COVID-19 pandemic, which has added fuel to an already brewing mental health crisis.

Mental Illnesses: The Need and the Opportunity

Mental illnesses represent some of the most prevalent and disabling conditions. Depressive disorders ranked number 4 in age group 10-24 and number 6 in age group 25-49 in the list of leading global causes of level 3 disability-adjusted life-years (DALYS)

 and are associated with a wide range of negative outcomes, including loss of occupational function,

 A key barrier to addressing these challenges stems from inadequate metrics to assess disease status, treatment effectiveness, and patient-relevant outcomes.

Mental illnesses do not have a clear outcome measure such as HbA1C in diabetes; instead, efficacy and outcome measures utilized in clinical practice today are complex and subjective, and are only sporadically collected or recorded in databases. As a result, assessment of quality of mental health care often defaults to the more easily measured process and structure metrics, which are distal from true patient outcomes, and, although they are easier to measure, improvement in these metrics does not always translate to improvement in patient-centric outcomes.

 This hampers outcome-relevant assessment of quality, which in turns hampers creation of value-based incentive structures that would lead to true patient-centric care and direct resources based toward the most impactful interventions (“pay for performance”).

The relative value of patient-generated digital data in driving measurement- and outcome-based care will be disproportionately high in mental health since brain-based disorders manifest with changes in behavior, cognition, and mood that are ripe for digital phenotyping. For example, recent and ongoing digital phenotyping efforts in patients with depression are demonstrating correlations between symptom severity and smartphone and wearable-derived features relevant to daily functioning such as physical activity and sleep as measured by actigraphy,

 sociability as measured by blue-tooth handshakes,

 and anxiety or stress as detected in speech features (natural language processing, or NLP) or physiological read-outs such as Galvanic Skin Response (GSR) and Heart Rate/Heart Rate Variability (HR/HRV). Such digital readouts can be more sensitive to real-time measures of symptom severity, functional impairment, and recovery status than episodic clinical assessments. These digital measures may also serve as proxy measures to assess value and efficacy of clinical care and interventions.

Of course, payers and clinicians will require that evidence for this be generated through long-term digital phenotyping studies that relate digital biomarkers with clinical milestones and cost. This will require long-term adherence to digital phenotyping solutions from the ultimate data source: the patient. Adoption and adherence have been challenges

 that can only be addressed if, regardless of the eventual utility for clinicians and payers, digital phenotyping solutions treat the patient as their first and foremost customer.

Using Patient-Generated Data and Digital Tech for Patient-Centric Care

Utilizing digital health technologies and patient-generated data for enabling outcome-based care hinges on developing a “data partnership” with the patient that is built on principles of trust, mutual benefit, and informed data access and use. This will become more urgent as advances in wearable technology and mobile health provide patients greater control over, access to, and use of their personalized health data. In fact, eventually the boundary between health care data primarily housed in electronic health records (EHRs) and so-called patient-generated health data will blur as agency shifts to the patient and we realize that all data—whether in EHRs or generated by wearable devices—is ultimately patient-generated, and thus, is patient owned and controlled.

Matters of privacy, transparent consenting, and, more broadly, trust are clearly “table stakes” for patient participation in a digital data collection regime. Beyond that, in our experience, conducting long-term multimodal digital phenotyping studies in patients with mental illnesses, 3 factors are most important for patient acceptance, engagement, and long-term adherence. First, any sustained digital phenotyping data collection, in the context of research studies or clinical care, must provide immediate “here and now” value and utility back to the patient, over the friction and burden it creates. This equation can be helped by relying on passive data collection and developing bandwidth-frugal applications with minimal impact on smartphone battery life and other factors important to patients.

Furthermore, the end-user interface and visualizations must be engaging and useful to patients.

 Designing such a solution requires a systematic data-driven understanding of patients’ attitudes, motivations, and barriers toward data collection.

 It is important to follow the common tech industry practice of co-designing solutions in close partnership with end-customers (patients), with iterative learn and improve cycles. We may find ourselves surprised at how often the end-users’/patients’ views on which features are important to them diverge from experts’ preconceived notions.

Second, altruism remains a significant motivator; most patients diagnosed with a serious condition are willing—even eager—for their data to be utilized in research that helps their fellow patients, assuming privacy and security safeguards. This propensity to be part of citizen-science efforts should be leveraged to develop “learning engines” wherein pooled, deidentified digital phenotyping data can be used to solve important problems of broad utility and impact.

Finally, we see physicians’ use of patient-generated data to improve clinical care as crucial motivator for patients to adhere to a digital data regime. In fact, this is perhaps the single most important criterion for patients with serious mental illness who are under the care of a clinician. From the physician’s perspective, the data and visualizations should make the clinic visit more efficient and should integrate readily with their current workflows. Such a system will also improve outcomes by improving patient engagement and enabling more measurement-based care,

 We believe digital health solutions that enhance the patient-physician interface and generate near-term value for both may require more effort for initial uptake, but are most likely to stick than direct-to-patient approaches often utilized by insurance companies and digital health solution providers.

While physicians’ awareness of patient-derived digital data and overall digital platforms was already on the upswing prior to COVID, the pandemic has provided further tailwinds for such solutions.

 The initial deployment of digital phenotyping in clinical care settings will necessarily rely on validated self-reported scales of symptoms, function, and quality of life such as the disease set recommendations by the International Consortium for Health Outcomes and Measurement (ICHOM)

 to implement simple, interpretable flags and known treatment algorithms to aid clinical decision making.

Eventually, growing datasets will enable validation of digital biomarkers and predictive algorithms that are leading indicators of disease burden, health care utilization, crises, relapses, and other cost-driving events that could provide payers with real-time, leading quality metrics that can duly reward risk reduction and preemption. The 

 summarizes the framework of a “learning engine”

 digital health solution that we think can be deployed in mental health to bring together patient-derived digital data and clinical data from EHRs to deliver value to all key stakeholders: patients, physicians, and payers.

Digital Health Solution Framework for Filling the Measurement Gap to Enable Outcome-Based Care in Mental Health

The previously described digital phenotyping framework could become an extension of existing EHRs and clinical decision-enabling software systems, but given the already noted challenges in integrating patient-generated health data (PGHD) into EMRs,

 we expect earlier evolution of stand-alone digital phenotyping solutions that are interoperable with multiple EMR systems.

Collection of longitudinal digital phenotyping data at scale in the real world would necessarily imply reliance on data from patient-owned consumer devices. This will require a concerted effort to develop platform-independent data standards and APIs that can ensure that digital biomarker readouts across different devices are comparable.

Increasingly, data will be analyzed and appropriate features extracted in-situ on the device, which will ease privacy concerns and reduce bandwidth requirements, both of which can severely limit scalability and deployment. We should also note that digital phenotyping solutions that collect continuous data for asynchronous use during patient-physician interaction are distinct from—and in fact, complementary to and synergistic with—synchronous telemedicine solutions that enhance physician reach and accessibility.

In addition to lack of actionable patient-generated outcomes data, true patient-centric and value-based care in mental health is also hindered due to fissures between mental and physical care (ie, a lack of a “whole person” approach). For instance, there is mounting evidence that patients suffering from mental health conditions have markedly poorer outcomes while incurring significantly higher direct medical costs for their physical comorbidities.

 However, such comorbid depression is often undiagnosed or otherwise poorly treated, if at all. Remote assessments and digital diagnostics now offer new scalable ways to detect depression and anxiety and set individuals on a path to formal diagnosis. Digital health solutions can also provide evidence-based tools for self-monitoring, self-care, and management of depression, anxiety, and stress for patients with a primary physical health diagnosis.

Furthermore, comorbid patients can be provided access to high-quality behavioral health care virtually. The virtual behavioral health team can work in partnership with primary/secondary providers treating their chronic physical comorbidities. In this context, use of physician-supervised or autonomous digital therapeutics can also be considered to further address behavioral health co-morbidities in a scalable and effective manner.

The COVID-19 pandemic has illustrated the value of digital technology in health care and has created significant momentum that should be leveraged. Although significant advances have been made, we believe digital solutions can play an even greater role in transforming care. This is particularly true in mental health, where there are inadequate objective measures of disease status or progression. These gaps hinder effective measurement-based patient care as well as an opportunity to better align incentives and scarce resources through more meaningful outcomes-based care. We believe an appropriately designed and safeguarded digital health solution can be used to improve patient care and, at the same time, begin to address critical data gaps needed to enable patient-centric, value-based care.

head of strategy and innovation, Davos Alzheimer’s Collaborative. 

 is the founder and CEO of Holmusk Technologies Inc. 

 is chairman of the board at TheSteinberg Institute. 

 is lead of the Brain Capital Alliance, co-lead of the OECD Neuroscience-inspired Policy Initiative in collaboration with the PRODEO Institute, and senior fellow for Brain Capital with the Meadows Mental Health Policy Institute. 

 is director of the San Bernardino County Department of Behavioral Health. 

 is visiting professor, Oxford University and Duke University, and previously global therapeutic head of neuroscience for Janssen Research & Development, LLC.

 Dr Insel is an advisor to Alto Neuroscience, Cerebral, Compass Pathways, Koa Health, and Valera Health. Drs Narayan and Manji are employees of Jansen Research & Development, LLC, and may hold company stock.

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Articles

Considering challenges and opportunities in digital health solutions for mental health care.

Digital Health as an Enabler for Patient-Centric, Outcome-Based Care

“Brain health conditions are a key contributor to the global burden of disease and disability. The devastating impact of these conditions is even more apparent as the world faces global challenges such as the COVID-19 pandemic, climate change, societal inequity, and more. To make progress, we need a fundamental shift in our approach. Convergence Brain Health deftly makes the case that a convergence approach is urgently needed to tackle the global brain health crisis and provides a roadmap for change.”

— Victor Dzau MD, President, National Academy of Medicine

Even before the COVID-19 pandemic, the world was in the midst of an escalating global brain health crisis with severe physical, social, and economic ramifications. According to a recent World Health Organization report, around 450 million individuals were suffering from mental health conditions, marking mental health disorders among the leading causes of ill-health and disability worldwide.

 The Organisation for Economic Co-operation and Development (OECD) estimates that the consequences of mental ill health cost up to 

Due to social and physical distancing, unemployment and underemployment, stress and other factors, there have been significant increases in issues such as depression, anxiety, social isolation, substance abuse, loneliness, and cognitive decline in older adults.

 In addition, as observed after the SARS pandemic, it is expected that COVID-19 survivors may endure long term cognitive and psychiatric consequences, including suicide, posttraumatic stress disorders, and depression.

 Furthermore, brain health is a prototypical 

 in that it is extremely complex and textured by innumerable variables from biology, sociology, genetics, psychology, and the natural environment to economics and beyond.

 Compounding these issues are challenges at all levels of innovation around brain health, listed in 

Table 1. Challenges and Opportunities for the Brain Health Field

Progress in the 21st century will be molded by our collective response to the global brain health crisis.

 Fortunately, beacons of hope are beginning to shine. Stigma has been reduced in high-income country settings, allowing individuals to overcome the barriers to seeking care.

 The COVID-19 pandemic has—in a positive sense—lowered the perceived and actual barriers to technology adoption, ushering in a new era of technology-driven brain health care. Some governments are beginning to increase spending on brain health, and venture capital funding for brain health start-ups has never been more in vogue. PitchBook data shows that 146 deals generated nearly $1.6 billion in venture capital investments as of December 10th, 2020. A decade ago, there were only 3 deals, worth $6.6 million, whereas last year saw a total of 

However, these advances on their own cannot solve the myriad global challenges posed by the brain health emergency. A paradigm shift is needed to address the unprecedented complexities associated with the current global brain health crisis—a crisis now inextricably linked to concurrent interacting crises surrounding discrimination and inequality, mass misinformation, environmental catastrophes, accelerated climate change, entrenched economic models, population displacements and immigrant status, and more.

 Governments, employers, and healthcare systems around the world need to acknowledge the scope of the problem and embrace solutions to empower consumers to take charge of their own brain health and to protect existing levels of health, well-being, and social inclusion.

In response to the evolving issues, convergence science integrates knowledge, tools, and thought strategies from diverse fields to address challenges that exist at the interface of 

 Convergence science not only recognizes the need to respond to apparent societal problems, but also emphasizes approaches which allow us to consider a myriad of the known unknowns and improves the odds of us quickly adapting to newly identified gaps.

 Acknowledging the serendipity of accidentally obtained knowledge permits 

 Taking these approaches when investigating solutions to brain health challenges permits greater flexibility and utility when unforeseen challenges are inevitably met.

Brains are the indispensable drivers of human progress. Why not invest more heavily in them? There are currently 

 allocated to Environment, Sustainability, and Governance investing around the world.

 If only a portion of this amount were invested in brain health, we could produce major improvements for our society.

 note that this present moment is a clarion call for the philanthropic community, which is uniquely positioned to lead an approach in 2 distinct ways. In the short term, the immediate mental health crisis can be mitigated by supporting interventions for the most vulnerable, the development and deployment of rapidly available mental health tools and technologies, and research to drive evidence-based responses to COVID-19-related mental health challenges. Much work is needed to address the long-term mental health impacts caused by the pandemic. There is an ongoing and urgent need for targeted investment in research to better understand and treat mental health conditions, build capacity and diversity within the mental health system, and advocate for social and policy solutions to improve the ecosystem of mental health care.

Given the critical need to nurture healthier, more resilient, and increasingly flexible brains worldwide, we have introduced the concept of Brain Capital—broadly defined as a form of capital which prioritizes brain skills and brain health.

The OECD Neuroscience-inspired Policy Initiative seeks to place Brain Capital at the center of a new narrative to fuel societal resilience in mitigating economic shocks, and to create preparedness should future societies fundamentally transform, for instance through irreversible damage to the environment. The OECD’s New Approaches to Economic Challenges Unit has created the Neuroscience-inspired Policy Initiative. This initiative seeks to reconceptualize and revitalize the economy, laying the groundwork to identify relevant metrics while building a transdisciplinary network of stakeholders. The initiative will rapidly refine and advance the concept of Brain Capital via a series of research projects, economic modelling, seminars, and clear policy analyses and recommendations.

In this context, our Brain Capital Grand Strategy supports the focus of the OECD initiative. The Grand Strategy has 3 main components: considering Brain Capital in all policies, developing a comprehensive investment plan to support Brain Capital, and generating a Brain Capital Index.

Brain health issues should be addressed in the context of improving performance within the fields of innovation and entrepreneurship. Because most 

 are created by firms less than 5 years old,

 creative and innovative entrepreneurs are needed to drive social and economic growth at the regional and national levels. In many ways, together with unprecedented government investment in many countries, entrepreneurs stand as a critical firewall against recession and deprivation. They serve as economic first responders when (exogenous) catastrophic events produce national and global economic shocks. 

In the United States, deaths due to suicide, opioid overdoses, and other addictions and poisonings (deaths of despair) are a barrier to complete economic recovery from COVID-19.

 Approximately 20% of prime-aged males who have dropped out of the labor force are 

 in these deaths, and many of them will not take up jobs even when available due to the severity of their addiction.

A senior economist at The Brookings Institution is leading an initiative to coordinate the many efforts to deal with this crisis. An important role for the task force would be to coordinate with the many burgeoning local level efforts to address addiction and despair, and these local organizations could greatly benefit from ties to a federal-level coordinating agency. Among other things, this agency could serve as an 

 Without addressing the linked issues of mental health and labor market drop-out simultaneously, it will be difficult to solve the tragic mental health crisis or achieve the goal of widely shared economic prosperity.

It is also essential that, given the complexity of the challenges being addressed, freedom to fail is offered liberally. Flat organizational structures, which are agile and empower individuals to make decisions in their specific areas and roles without need to constantly consult with managers at higher levels, are important. However, these decisions should be aligned with the mission and not deviate from the already traced goals and strategy. In 

, we provide examples of organizations and buildings which foster Convergence Brain Health activities.

Table 2. Infusing Convergence Brain Health Practices Into Various Levels of Innovation

Existing models of innovation are not adequate—suffering from siloing and a lack of speed. There are now a number of more contemporary models which may propel the brain health field forward more quickly and effectively. 

For example, The Davos Alzheimer Collaborative (DAC) is a public-private partnership committed to a collective global response against the challenges Alzheimer disease presents to millions of families around the world.

 The DAC aims to raise around USD $700 million for a 6-year plan to accelerate and diversify innovation in Alzheimer disease research. The 3 main components of DAC include a global cohort developed to identify new targets for potential treatments, a global clinical trial support platform to reduce the cost and time to bring new treatments to market, and a healthcare system prepared to get new treatments to patients. The DAC project will enable novel biomarker development, connect global researchers using the data platform provided by the Alzheimer Disease Data Initiative, and keep individuals with the lived experience of Alzheimer disease at the center of its efforts.

The first big data wave occurred over a decade ago, fueled by the reduced cost of -omics techniques (eg, genomics, transcriptomics), increased access to neuroimaging equipment, and the transition to electronic medical records. Insights from this data, however, have not translated into dramatically improved outcomes for patients. Diagnostic clarity is often elusive and treatment selection remains primarily trial-and-error. A second wave of big data proliferation is already underway with the increased implementation of mobile applications and wearable devices.

 Solutions are desperately needed to resolve current bottlenecks in transforming data into insights and action. 

Furthermore, according to economist Klaus Schwab, PhD, we are on the verge of the Fourth Industrial Revolution, which will see a rise in cyber-physical systems that blur “the lines between the physical, digital, and biological spheres.”

 Virtual reality environments, brain-computer interfaces, wearable or implantable devices, and mobile applications will continue to contribute to the current data explosion. Artificial intelligence integrates knowledge from a myriad of disciplines, including computer science, data science, mathematics, engineering, physics, statistics, linguistics, signal processing, ethics, and philosophy. Artificial intelligence is no longer a novelty, but a requisite set of convergence tools for navigating the future.

As psychological disorders are mediated by complex interactions between genetic and environmental factors it will be imperative to dedicate significant effort to understanding the modifiable risk factors emerging from the environment.

 Since the environment includes biological, psychosocial, and ecological components, it is likely to be more complex than the genes, proteins, and metabolites that are currently analyzed by a variety of tools. Therefore, within the framework of convergence brain health it will be necessary to develop highly powerful and complex tools that record, measure, and analyze the myriad environmental factors. This effort will be pursued as it has been with the omics technologies but with a much larger scope, ranging from psychophysiological data collected from millions of individual smartphones to satellite tracking of air pollution in big cities. Important ethical issues, however, will have to be addressed with some urgency.

Operationalizing a convergent clinical framework will require effective direction and utilization of the continuing data explosion.

 The role of digital clinical operating systems is therefore critical for allowing effective patient care, as well as ongoing research. These clinical operating systems house data from clinical encounters, imaging, pathology, personal medical devices, wearables, and finance. Analytics methods include the use of mathematical and algorithmic-based processing of data resources, including subfields of artificial intelligence, such as machine learning, natural language processing, and visual analytics to generate descriptive, predictive, and prescriptive models to analyze and derive insight from data. Machine learning has been utilized in predictive risk assessment, clinical decision support, home health monitoring, finance, and resource allocation.

Convergent approaches often require consideration of many different data types. Data harmonization involves creating a single consistent macro-data source from multiple, potentially disparate micro-datasets. Contributing datasets are structured to remove inconsistencies, creating harmony, such that the final dataset provides a cohesive view of a subject from many different perspectives. Machine learning enables automatization of this process, permitting integration of clinical, behavioral, -omic, imaging, neurosignals, and other datatypes for data mining and predictive modelling. Deep learning in combination with multidimensional biological data (eg, genomics, proteomics) enables examination of the impact of genetic variation or other system disturbances on transcription, translation, signaling, and resultant phenotypic variation. 

Perioperative brain health is an emerging example of the need for a convergence approach in brain health science. The average individual undergoes 9 surgical procedures in his or her lifetime, typically at an older age when the brain is more vulnerable to perioperative insults such as stress, illness, and immobility.

 As such, the perioperative period is a very high-risk period for older adults, and brain health impairments are abundant with considerable negative impacts. For example, the US National Health and Aging Trends Study found clinically significant depressive and anxiety symptoms in 39% and 24% older adults in rehabilitation facilities, respectively, in older adults recently in rehabilitation facilities (vs. 10% and 9% for older adults in the community).

Similarly, a study in the UK of postoperative Intensive Care Unit patients found rates of 40% and 46%, respectively.

 Depression and anxiety are connected to poor postoperative outcomes, including persistent pain, delirium, longer hospital stays, poor functional recovery, lower quality of life, and increased chance of both falls and rehospitalization.

A new field of perioperative mental health is responding to the need to integrate mental and cognitive healthcare with surgical and postsurgical care. The increasing number of older adults undergoing surgery creates high demand for this innovation, so that care can move beyond an emphasis only on surgical success and anesthetic safety, to a convergent brain health view of outcomes, such as improved functional recovery, reduced rehospitalization, and decreased long-term morbidity. These innovations are further driven by bundled care models, which include preoperative optimization, in-hospital care, and postoperative rehabilitation. With the recent genesis of the Center for Perioperative Mental Health, funded by the National Institute of Mental Health (P50MH122351), surgeons, anesthesiologists, nurses, physical and occupational therapists, and surgical patients will work together with psychiatrists in an accelerated laboratory for translating scientific advances to practical clinical care that improves the lives of older adults.

With the emergence of so many digital innovations, care providers are presented with more tools than ever. However, such a crowd of novel options adds complexity and uncertainty. To be clinically useful, such point solutions must first be vetted and then clustered into symphonies shown to play well together to optimize collective benefit for specific conditions. In the dementia field, for example, it is encouraging to see multidomain interventions being deployed.39 In the future, a therapeutic guidance system may make sense, similar to those used to advance precision medicine, such as oncology and neurosurgery.

To develop a workforce that can harness and deploy a convergence brain health framework, increased inclusion and empowerment of women and other traditionally under-represented populations is required. Building institutions that are both inclusive and meritocratic is health promoting in its own right. But more importantly, it is a powerful mechanism for producing the additional talent, fresh perspectives, and new ideas that are needed.

Meanwhile, certain behavioral characteristics among individual innovators should be cultivated. MacRae and Furnham

 articulated 3 dimensions of a high-potential individual suited to convergent teamwork building on Silzer and Church’s model of potential.

 There are 3 core dimensions: foundational, growth, and career dimensions. These dimensions explain how personality traits are foundational to potential because they represent fundamental and stable individual differences. Personality traits, especially conscientiousness and adjustment, contribute to success in nearly any type of work and have, “higher predictive ability of potential in roles that are more complex, challenging, and demanding such as that of the convergence practitioner.”

Career dimensions are the unique and specific skills, expertise, and occupational knowledge required for success in a specific career or role: “Convergence practice requires sublimating one’s own objectives into the vision and the objectives of the team.”

 MacRae and Furnham explain in detail how personality traits fit within a model of convergence practitioner potential and expand on 10 behaviors that are related to career dimensions of potential.

 These behavioral and workplace cultural characteristics allow us to formulate screening criteria for potential new entrants into convergent brain health work, identify those who are high potential convergence practitioners, as well as consider training approaches for those within our existing brain health structures.

Leaders must assume a critical role in optimizing transdisciplinary collaboration and facilitating the emergence of discoveries from these endeavors.

 Gray outlined 3 responsibilities of transdisciplinary leaders: cognitive leadership, structural leadership, and processual leadership:

. Effective cognitive leadership provides a vision that links and motivates transdisciplinary researchers to step beyond their disciplinary lens, relax old assumptions, and search for creative frame-breaking solutions.

. Effective structural leadership adds value by creating bridges and links among unconnected individuals, labs, groups, or departments.

- Processual Leadership: Effective processual leadership encourages trust and turns conflict into constructive interactions.

These leadership approaches should be kept in mind when fostering Convergence Brain Health within an organization. Other 

 of a convergence workplace include a flat organizational structure with minimal discipline silos, a culture of trust and mutual respect, and an open acceptance of the need to respectfully challenge entrenched ways of thinking.

 Teams should be humble in acknowledging that nobody has cracked the many complex problems to date, and likely never will due to unsolvable uncertainty. However, being open to novel and seemingly eccentric ideas may help navigate such problems and identify ways for their best possible management.

Teams in a convergence workplace must embrace the three somehow radical tenets outlined in Jim Dator, PhD’s 

: 1) The future cannot be predicted because the future does not exist; 2) Any useful idea about the future should appear to be ridiculous; and 3) We shape our tools and thereafter our tools shape us.

 An optimal convergence science environment should have a strong guiding vision, will be capable of identifying myriad possible alternative futures, and better guide the organization toward the desired future.

Convergence Brain Health provides direction for transformational change in how teams could work and how organizations need to be built and operated. As we embark on building the new normal of our post-COVID-19 world, Convergence Brain Health will play a key role in this rapidly unfolding change. Indeed, Convergence Brain Health provides an opportunity to grow global Brain Capital and enter a new era of innovation and progress, leading to greater health justice for all people and preparing for the unprecedented brain health challenges of the 21st century.

is cofounder of the PRODEO Institute, adjunct associate professor with IMPACT at Deakin University, instructor in brain health diplomacy at the Global Brain Health Institute, and co-lead of the OECD Neuroscience-inspired Policy Initiative.

is Director of Innovation at a major mental health association and expert in residence at Georgetown University, and the author of Wired This Way.

is an associate with the PRODEO Institute and Thiel Fellow at Stanford University.

is a professor of psychiatry at University of California in Los Angeles (UCLA).

is Distinguished and Emeritus Professor of Psychiatry, University of Pittsburgh, Pittsburgh, PA.

is the global head of Science for Minds at Johnson & Johnson.

is a health policy researcher at the Global Brain Health Institute.

is a social gerontologist at the Global Brain Health Institute.

is a research professor and Director of Chambers-Grundy Center for Transformative Neuroscience at the University of Nevada, Las Vegas.

is a venture partner at Alsop Louie Partners.

is professor and vice chair of Psychiatry & Behavioral Services at Baylor College of Medicine.

is special advisor to the OECD secretary general and head of the OECD New Approaches to Economic Challenges Unit.

is the Wallace & Lucille Renard Professor of Psychiatry and the Director of the Healthy Mind Lab at Washington University School of Medicine in St. Louis, Missouri.

is head of strategic projects and alliances at Delix Therapeutics.

is a professor in the department of Integrative Structural and Computational Biology at Scripps Research Institute.

is Head Stakeholder Liaison Alzheimer disease at Biogen.

is a professor of Law and Health Sciences at the University of Surrey School of Law and adjunct assistant professor of Medicine at the David Geffen School of Medicine at UCLA.

is the founding director of the Center for Brain Health at UT Dallas.

is a T. Boone Pickens Distinguished Scientist at the Center for BrainHealth, a co- director at Global Brain Health Institute, professor emeritus at Trinity College Dublin, and founding director of its Institute of Neuroscience.

is the Leo Pasvolsky Senior Fellow at the Brookings Institution, a College Park Professor at the University of Maryland, and a Senior Scientist at Gallup.

is a postdoctoral researcher, Center for Precision Health School of Biomedical Informatics at the University of Texas Health Science Center at Houston.

is Researcher at the Department of Aquatic Sciences and Assessment; Section for Ecology and Biodiversity at the Sveriges lantbruksuniversitet.

is Assistant Clinical Professor of Psychiatry and Biobehavioral Sciences at David Geffen School of Medicine at UCLA and UCLA Olive View Medical Center. 

is a neuroscientist at the Global Brain Health Institute.

MB is supported by a NHMRC Senior Principal Research Fellowship (1156072). MB has received Grant/Research Support from the NIH, Cooperative Research Centre, Simons Autism Foundation, Cancer Council of Victoria, Stanley Medical Research Foundation, Medical Benefits Fund, National Health and Medical Research Council, Medical Research Futures Fund, Beyond Blue, Rotary Health, A2 milk company, Meat and Livestock Board, Woolworths, Avant and the Harry Windsor Foundation, has been a speaker for Abbot, Astra Zeneca, Janssen and Janssen, Lundbeck and Merck and served as a consultant to Allergan, Astra Zeneca, Bioadvantex, Bionomics, Collaborative Medicinal Development, Janssen and Janssen, Lundbeck Merck, Pfizer and Servier – all unrelated to this work. HAE is an employee of PRODEO LLC. ES is an employee of PRODEO LLC. ASC is an official employee of Biogen Int and the VP of Euresearch. Her contribution to this work is based on her personal opinion and might not reflect the one of the organizations with whom she is affiliated. AI has received Grant/Research Support from ANID/FONDECYT Regular (1210195); FONCYT-PICT 2017-1820; ANID/FONDAP/15150012; Alzheimer’s Association GBHI ALZ UK-20-639295; Sistema General de Regalías [BPIN2018000100059] Universidad del Valle [CI 5316], and the Multi-Partner Consortium to Expand Dementia Research in Latin America [ReDLat, supported by National Institutes of Health, National Institutes of Aging (R01 AG057234), Alzheimer’s Association (SG-20-725707), Rainwater Charitable Foundation (Tau Consortium), and Global Brain Health Institute)]. EJL has received support from the NIH, Patient-Centered Outcomes Research Institute, Taylor Family Institute for Innovative Psychiatric Disorders, and Center for Brain Research in Mood Disorders at Washington University.

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