The central components of Brain Capital need to be formalized and developed into an investment plan. Since one cannot manage what one does not measure, developing a Brain Capital Index (BCI) to track the progress of this approach is critical.

If such an index were investable—as in the case of a mutual fund or exchange-traded fund—it would encourage investment in the entire space by opening it to the passive investor. Index-tracking funds have recently passed the $10 trillion mark of assets under management globally, surpassing assets under active management for the first time. Attracting even a small fraction of global passive investment using a BCI would transform the Brain Economy as a whole. Consequently, creating a well-defined and easily computed index is a key part of the Brain Capital Grand Strategy.

In this context, the Human Capital Index (HCI) provides a valuable example. The HCI is a tool that quantifies the contribution of health and education to the productivity of a country’s next generation of workers [5859]. Through the HCI, countries can “access how much income they are foregoing because of human capital gaps, and how much faster they can turn these losses into gains if they act now” [58]. Ultimately, HCI and the proposed BCI will enable public and private entities (e.g., governments and corporations) to objectively measure commonly overlooked indicators that fundamentally underpin overall social and economic development and monitor outcomes over time. Table 3 shows other models that can inform a BCI.

Table 3 Models that can inform the Brain Capital Index.

There are a range of potential components of the BCI (Fig. 2). Health-related metrics may include incidence and prevalence metrics, access to care, and relapse rates. As we move toward a Brain Economy, educational attainment may be helpful to approximate the Brain Skills of individuals. The output of value-adding novel products and services may help assess innovation outputs. Co-benefits of large-scale initiatives should be captured to understand tax revenue, IP, and employment benefits of such work. There are is also potential for digital metrics to be captured as we outline below.

Fig. 2: Potential components of the Brain Capital Index.

QALY quality-adjusted life year, DALY disability-adjusted life year.

Brain imaging and digital biomarkers could contribute to the Brain Capital Index

The components of the BCI are not yet determined, but recent developments may provide quantitative methods of doing so including neuroimaging and digital biomarkers.

A global example of applied neuroscience relevant to Brain Capital is the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium that includes research groups across more than 40 countries and identified brain metrics in structure and function from magnetic resonance imaging that are associated with a wide range of neurological and psychiatric conditions [60]. In addition to imaging metrics, the ENIGMA Consortium has identified associated genetic, environmental, demographic, cognitive, and psychosocial factors involved in clinical syndromes. Advances by ENIGMA along with many other longitudinal imaging studies help monitor brain health status and provide objective metrics to help quantify Brain Capital.

Identification and validation of digital biomarkers is beginning to move into mainstream. Behavioral measures such as patterns of interaction on smartphone devices, may help provide a continuous indication of mood and cognition to support diagnosis, prognosis, and treatment monitoring for different neurological and psychiatric conditions [61].

Digital biomarker-based surveillance tools are becoming increasingly ubiquitous and provide a noninvasive and highly granular approach to assessing mood, cognition, activity, and other biometrics. Ultimately, digital biomarkers may provide more comprehensive insights and thus supplement an increasingly hybrid model of care that integrates insights from technology and clinicians [62]. For example, mental health status (including specific disorders and related symptomology) can be captured and studied using behavioral and linguistic cues from social media data [6364]. Mental health condition data tracked from digital cohorts via social media correlate with statistics obtained through traditional methodologies, suggesting that this approach may be a promising complement to current epidemiological practices [64]. Artificial intelligence techniques of real time surveillance of digital medical records, for example, have proven to be superior to clinician assessments of suicide risk using structured instruments [6566]. Indeed, sensitivity and specificity permitting, digital surveillance tools enhanced by artificial intelligence may usher in a new era of more responsive and deliberate public health interventions allowing experts to track the progress or the effects of targeted interventions in near real time [6467].

Regardless of biomarker type, a careful process of translation is required to ensure they are implemented effectively. For example, the working steps of translational psychiatry as outlined by Licinio and Wong [68] include, in a one-way direction: (T0) discovery (via preclinical, clinical, and epidemiological science), (T1) bench to bedside, (T2) bedside to clinical applications (clinical trials), (T3) translation to policy and health care guidelines, (T4) assessment of health policy and usage, and (T5) global health applications.

Responsible innovation in Support of Brain Capital Innovation

Technology may well drive the future of public health surveillance by helping to collect and integrate disparate sources of information and track and quantify the outcomes of Brain Capital interventions.

Digital surveillance tools will be a core component of the transition to precision public health [69]. Digital surveillance tools can include mobile apps and link to diverse national datasets. Although biomarkers may be incorporated into a BCI, extreme caution and ethical considerations must be taken [70]. Extensive measures will be needed to mitigate the potential downsides of these new technologies, including education disparities, appropriate policies, risk management, systems design, research, and regulatory frameworks [69].

Responsible innovation is an increasingly prominent framework and is especially critical [71]. A recent OECD Recommendation on Responsible Innovation in Neurotechnology proposed the first international standard in this domain [72]. It “aims to guide governments and innovators to anticipate and address the ethical, legal and social challenges raised by novel neurotechnologies while promoting innovation in the field.” It articulates “the importance of (1) high-level values such as stewardship, trust, safety, and privacy in this technological context, (2) building the capacity of key institutions like foresight, oversight and advice bodies, and (3) processes of societal deliberation, inclusive innovation, and collaboration.” These principles can be usefully adapted to guide the development and implementation of novel technologies for Brain Capital.

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