In an exclusive interaction with MediaBrief, Dr. Shailender Swaminathan, Director of the Sapien Labs Centre for Human Brain and Mind at Krea University, speaks about the origins of Sapien Labs, its mission to uncover the root causes of mental health challenges, and the breakthroughs that shaped its global research footprint. Swaminathan also shares how large-scale EEG (Electroencephalography) programmes are redefining population-level neuroscience, why India has emerged as a crucial dataset in the Global Mind Project, and how these insights could inform future policy, product design, and mental-health interventions. Read on.
Give us an overview of Sapien Labs and its journey so far. What was the insight behind the launch of Sapien Labs?
Mental health has been a widely discussed issue for a while now, with most countries, including India, recognizing the ubiquitous nature of the problem. Policy responses have focused on reducing the stigma associated with mental health and rapidly increasing the supply of trained personnel—psychologists, psychiatrists—who can address the issue. Schools, colleges, and employers—many of them have “counsellors”—whose job it is to provide “treatment.”
However, what was puzzling was that the focus on treatment happened in the absence of almost any understanding of cause. It was akin to treating chest pain without knowing whether the cause of the chest pain was a block in the main artery in the heart, or digestive issues, or musculoskeletal problems. So, we wanted to be able to get to the root causes of brain and mind health.
Walk us through some of the inflection points in Sapien Labs’s growth—key decisions, partnerships, or pivots that changed its trajectory.
The first inflection point happened early, when we conducted a study (even before the formal constitution of Sapien Labs) that revealed great differences in brain physiology (as measured using the EEG) even within a rather small geographic area in Tamil Nadu.
This finding turned on its head our textbook understanding of the human brain—that did not speak about individual differences in the brain or acknowledge the existence of such huge differences. This finding spurred the collection of large-scale EEG data from the field—in both rural and urban India.
The second was perhaps our Global Mind Project, an ongoing survey administered online in 85+ countries that has amassed the largest and most comprehensive database on mental health profiles and lifestyle/life context factors in the world.
This has highlighted a decline in mental health and wellbeing—or what we call “Mind Health”—in each younger generation across the modern Internet-enabled population of every country across the globe and uncovered a number of root causes. Within the Global Mind Project, India is actually the largest country dataset. Currently, we have over 200,000 respondents from India alone, and there are over 2 million respondents globally.
I think one reason we could scale this quickly is because we were able to give back to the respondents—in real time—a sense of their mind health along with some general suggestions on how to preserve/improve their individual mind health. By providing individuals with this information, we were able to collect data from more respondents.
Note that almost every other survey does not provide any real, meaningful information to the respondent. This was a real gamechanger with regards to mental health data from India.
In addition to the scale, an advantage of our data is the fact that it is collected continuously (i.e., daily). Until recently (2023–24), data on mental health statistics used the data generated as part of the first National Mental Health Survey in 2015–2016.
In the context of the rapidly changing area of mental health, even data that is a year old is not really relevant. We were able to work with policymakers who wanted a more current read on the state of mental health. The Economic Survey of 2024–2025 included a note by Sapien Labs and highlighted the importance of mental health for the economy.
Another inflection point that occurred around the same time was when researchers and organizations such as NIMHANS began to recognize the value of our transdiagnostic measure of mental health—the MHQ. Unlike previous disorder-based diagnoses (e.g., depression, ADHD, anxiety disorders, etc.), our measure provides a holistic view of mind health/distress. The MHQ is now being fielded in a few nationally representative surveys.
Collecting EEG data from streets, classrooms, and job sites is logistically complex. Can you walk us through the practical and scientific challenges of such population-level neurophysiology?
The practical challenge comes from essentially collecting quality EEG data in environments with poor/no access to the internet, various types of noise sources and disturbances under different weather conditions, recruiting participants within our sampling criteria to gather in one place, and finally, the possibility of individuals refusing participation.
With a great deal of process and quality control mechanisms now in place, in about a year, these programs in India and Tanzania have already amassed the largest neurophysiological datasets in their respective countries or continents, with samples of over 3,500 participants in Tanzania and over 4,300 in India.
As of now, we expect to reach a steady state of about 10,000 individuals in India over the next two years, which means that every year, we will be collecting data from 10,000 individuals. The scientific challenge is that of being able to collect high-quality data in the field, as opposed to the controlled environment within a lab. Achieving high-quality data in large-scale studies requires a great deal of process and training.
How do you ensure the accuracy and reliability of EEG data collected outside controlled lab environments?
With respect to EEG data, quality depends on the stability and accuracy of electrode positioning and the effective guidance to the participant to minimize unnecessary movement artifacts. These processes require patience and sustained diligence, where boredom or a rush to complete tasks can compromise data quality.
To mitigate these risks, daily monitoring and feedback are therefore essential. For each team, data is analyzed automatically upon upload, with end-of-day reports and dashboards generated for each field researcher as well as the project supervisor. This system enables rapid course corrections or retraining whenever issues arise.
With this process in place, a rigorous assessment of the first 7,300 EEG records from Tanzania and India, respectively, revealed that data quality was comparable to three highly cited lab-based benchmark datasets with similar experimental paradigms.
Are there insights from your dataset that challenge traditional assumptions about consumer decision-making in India?
We are, at this moment, focused on collecting high-quality data at scale. The analysis that would follow will shed light on how the environment impacts brain function and, in turn, mental health outcomes, and we believe will challenge some traditional assumptions about human behaviour.
However, even until now, we know that the brain physiology of individuals varies greatly (measured using the EEG) and is increasingly diverging across populations in the modern world. This suggests that consumer decision-making may vary significantly across populations, making traditional, one-size-fits-all assumptions increasingly less generalizable.
In your cross-country pilots, have you noticed differences in brain responses to the same stimuli between India and other Global South populations?
The one thing to note is that heterogeneity in brain physiology in India will, by definition, be much more than that in other countries, including the Global South (we are only collecting EEG data from Tanzania and India at the moment). The diversity in India has been talked about for decades now, and that diversity will naturally also express itself in terms of the diversity in brain (EEG) physiology.
We speak varied languages, our diets are as varied as one can imagine, we have millions under the poverty line on the one hand and billionaires on the other, the quality of education and healthcare varies, and occupations are diverse.
Given that brain physiology is a reflection of our lifetime experiences, the within-India heterogeneity in brain physiology will not be similar to that obtained in Tanzania. However, we note that even within Tanzania, there is heterogeneity—because you have the Hadzabe and other tribes alongside the office-going segment of the population.
How do digital habits, such as smartphone or social media usage, shape mental well-being and decision-making in different demographics?
We recently published work on this question. The global rise in smartphone and social media use has dramatically reshaped childhood and adolescence, with algorithmically engineered digital environments increasingly influencing how young people develop mental capabilities and perspectives.
This paper draws on data from the Global Mind Project to examine the population-level impacts of childhood smartphone ownership on the mental health and capability outcomes of young adults. Our analysis reveals that receiving a smartphone before age 13 is associated with poorer mind health outcomes and diminished capabilities in young adulthood, particularly among females, including suicidal thoughts, detachment from reality, aggression, and diminished self-worth.
These correlations persist after controlling for several other factors and are mediated through social media, cyberbullying, disrupted sleep, and diminishing family relationships, among other things. This trend appears consistently across all global regions, with the magnitude greatest in English-speaking nations.
Based on these findings, we advocate for the adoption of a precautionary principle and propose the implementation of a developmentally appropriate, society-wide policy approach—similar to those regulating access to alcohol and tobacco—that restricts smartphone and social media access for children under 13, mandates digital literacy education, and enforces corporate accountability to protect the foundational mental health and capabilities of future generations.
How could brands or policymakers ethically use population-level brain and behavioural data to design better products or services?
The insights that we believe our data will deliver are twofold. First, how different products, nutrients, chemicals, and social structures impact brain physiology and behaviour, and second, a more cause-based diagnostic framework for brain/mind challenges. This can aid in more effective policies, products, and services on various dimensions.
For example, at the policy level, this can hypothetically guide legislation to regulate a toxin that might destabilize the nervous system and lead to a lack of emotional control and regulation. At a product level, it can help companies design more “mind-healthy” products and more cause-based treatments.
How are your findings being translated into actionable interventions for mental health, both in India and globally?
I think the first thing to note is that our instrument to collect mental/mind/behavioural health data—the Mental Health Quotient (MHQ)—is beginning to be fairly widely used. It is now being used both in research, including a current study at NIMHANS, and is being piloted in paediatric primary care in the US.
Traditional measures for mental health—both in India and worldwide—have used a disorder-based approach, where disorders are essentially theoretical symptom groupings that lack any cause-based understanding.
The approach forces individuals with very different symptom profiles and/or potentially distinct etiologies into the same “diagnosis” and therefore care pathways. The MHQ overcomes these challenges by providing a comprehensive symptom profile along with potential causal/risk factors which are informed by the research.
The potential policy implications of the work we do are also massive. Consider how we came to an understanding of the underlying causes or drivers of cardiovascular disease. There were studies like the Framingham Heart Study that were conducted over decades from a sample living in Framingham, Massachusetts, USA.The study unravelled striking findings—that blood pressure, diet, and smoking are central to the evolution of heart disease.
These findings led to huge policy changes; smoking was banned in several places, blood pressure was closely monitored in physician offices, and changes in diet and physical activity were recommended as key preventative strategies. None of this would be possible without such large-scale data. Yet, in the case of brain health, our earlier findings suggested that there is large diversity in the human brain—which meant that a Framingham-like study, based in one small geographic location, would not be sufficient to get to the root drivers.
As our data continue to grow in scale, the analysis will involve parsing these multivariate effects. We recognize that this represents a formidable challenge, but one that is essential for identifying the true drivers of neurological and mental health outcomes in the context of diverse and changing environments. By enabling this level of inquiry, large-scale EEG datasets can offer entirely new perspectives on brain function, helping to develop more precise, cause-based diagnostics and to inform public policy aimed at creating environments that support optimal brain health.
How do you see AI or machine learning playing a role in interpreting your dataset?
To be able to use AI and machine learning tools to predict disease/mental distress is something that is not only possible but also extremely useful. There are numerous applications in integrating multiple data streams and identifying structure in these multivariate datasets.
Beyond EEG, are you exploring other neurotechnologies or digital biomarkers to understand the human brain at scale?
To do this at scale (bearing in mind both the practical and cost implications), the EEG is perhaps the most versatile. We are not exploring other neurotechnologies at the moment but are exploring other physiological measures such as blood and urine assays and cognitive and emotional tasks that together will provide a more holistic picture of the impacts of various environmental factors on the brain and behaviour.
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