We live in the Anthropocene age – the recent age of modern man. An age where humankind is now the dominant influence over our planet.
And although it might sometimes seem that modern life has managed to penetrate all corners of the globe, anyone who has taken the time to stray off the beaten track can tell you that the advancement of modern man has not been uniform.
In fact you don’t have to travel that far to find people whose way of life has hardly changed for hundreds or even thousands of years. People who have somehow managed to remain impervious to a modern way of life that so many of us take for granted.
But how has this modernization imprinted itself on our brain? What is the neural impact of having increased access to a good education; of using novel devices and technologies which require a different way of thinking; of travelling across the country, and the globe, at increasingly faster speeds, seeking new sights and meeting new people?
To date, scientists have often approached this kind of question from two angles – either looking the benefits of living in an “enriched environment”, or examining the impact of growing up in a financially impoverished one.
The effects of enrichment
Ever since the seminal studies by Donald Hebb in 1947 which showed that rodents who were reared in an enriched environment had improved performance on a variety of learning and memory-based tasks, researchers have been trying to determine the neurobiological mechanisms that underpin these benefits.
Across a broad range of animal studies, they have since shown that environmental enrichment has a significant impact on structural aspects of the brain. For example, living in an enriched environment can increase levels of synaptic plasticity; cause changes in the regulation of gene expression; modulate the length and spine density of dendritic branches; as well as mediate a host of other functional and neuroprotective benefits (see here for a review).
And although the majority of these studies have been done in rodents, our understanding of the importance of learning and experience on neuroplasticity in the human brain, as well as knowledge from fields such as epigenetics, suggests that similar patterns of neurobiological enhancement also likely occur in humans.
The effects of impoverished environments
From a contrasting perspective, labs around the globe have also investigated the effect of socioeconomic factors, in particular growing up in an impoverished household, on the structure of the human brain. They have found that children from low income households typically have a smaller brain surface area, reduced volume of the orbitofrontal cortex and lower frontal gamma power.
And although modernization means more than just having a satisfactory income, money is a great facilitator of modern life by affording opportunities which were previously unobtainable, or even unthinkable. The global inequality in income distribution across the globe has therefore occurred in parallel to an inequality in educational, technological and travel experiences – the backbone of what we think of as modernization.
The bigger picture
Although these studies provide useful insights into specific populations of people, they do not allow you to form a bigger picture of how modernization as a whole has impacted the human brain across diverse sectors of humanity. With large datasets from across the world, Sapien Labs hopes to enable such insight.
Moving in this direction to provide an initial view, Sapien Labs scientists Tara Thiagarajan and Dhanya Parameshwaran have used mobile EEG to measure the brain activity in people from 48 different locations across India, spanning from busy urban cities to remote villages of only 300 people, presenting initial results in three papers recently posted on BioRXiv.
In these studies, they looked at two brain metrics – EEG signal complexity and alpha oscillations during resting state EEG relating them to a range of demographic factors such as income, education, technology access, geofootprint (geographic extent of travel) and energy usage.
Their complexity metric was adapted from studies using local field potentials as a way of reflecting the relative diversity of the temporal patterns of waveforms observed in brain activity. Their study of alpha oscillations, which is one of the dominant neural oscillations in the brain, built on the existing literature which suggests this metric as having a broad role in cognitive function including attention, working memory and learning, as well as helping to maintain the balance between external (sensory) and internal (mental) focus, and the management of information into consciousness.
The studies have revealed some fascinating findings. For example:
- Modernization has made our brains more complex, with geofootprint or extent of travel being more tightly coupled to this aspect than any other factor measured.
- The strength of the alpha oscillation, a feature related to attention, was closely related to fuel consumption, a proxy for the speed at which we experience the world.
- Some features of the EEG varied as much as 1000-fold across the sample population suggesting that there is no meaningful dynamical average
- these features of brain activity scaled in lock step with access to composite elements of modernization.
- An income threshold in the range of $30-50/day was required to reach modern dynamical parity
Only by going off the beaten track, with an EEG kit in tow, is it possible to reveal these kind of findings. Findings that not only reflect a diverse range of humanity, but also include individuals who follow a “pre-modern” lifestyle. Something that is far removed from the typical university laboratory where so many of the world’s EEG studies are currently conducted.
More information on the study and the papers can be found here. In addition the data in these papers as well as additional related datasets are available to researchers on request.
References cited here for further reading
The Anthropocene: A man-made world The Economist, May 26th (2011)
Bayat, M., Sharifi, M.D., Haghani, M., & Shabani, M. (2015) Enriched environment improves synaptic plasticity and cognitive deficiency in chronic cerebral hypoperfused rats. Brain Res Bull, 119:34-40.
Rampon, C., Jiang, C.H., Dong, H., et al. (2000) Effects of environmental enrichment on gene expression in the brain. Proc Natl Acad Sci U S A 97:12880-4.
Bose, M., Munoz-Llancao, P., Roychowdhury, S., et al. (2010) Effect of the environment on the dendritic morphology of the rat auditory cortex. Synapse 64:97-110.
Nithianantharajah, J. & Hannan, A.J. (2006) Enriched environments, experience dependent plasticity and disorders of the nervous system. Nat Rev Neurosci 7:697-709.
Chaudhury, S., Sharma, V., Kumar, V., Nag, T.C., & Wadhwa, S. (2015) Activity dependent synaptic plasticity modulates the critical phase of brain development. Brain Dev 38:355–363.
Kanherkar, R.R., Bhatia-Dey, N. & Csoka, A.B. (2014) Epigenetics across the human lifespan. Front. Cell Dev. Biol., 2:49
Noble, K.G., Houston, S.M., Brito, N.H., et al. (2015) Family income, parental education and brain structure in children and adolescents. Nat Neurosci 18: 773-8.
Tomalski, P., Moore, D.G., Ribeiro, H., et al. (2013) Socioeconomic status and functional brain development – associations in early infancy. Dev Sci 16:676-87.
Holz, N.E., Boecker, R., Hohm, E., et al. (2015) The long-term impact of early life poverty on orbitofrontal cortex volume in adulthood: results from a prospective study over 25 years. Neuropsychopharmacology 40: 996-1004.
Ortiz, I. & Cummins, M. (2011) Global Inequality: Beyond the Bottom Billion: A Rapid Review of Income Distribution in 141 Countries. Unicef Social and Economic Policy Working Paper.
Thiagarajan, T.C. Lebedev, M.A. Nicolelis, M.A. & Plenz, D. (2010) Coherence potentials: loss-less, all-or-none network events in the cortex. PLoS Biol 8:e1000278.
Klimesch, W. (2012) Alpha-band oscillations, attention, and controlled access to stored information. Trends Cogn Sci. 16:606-17.
Jensen, O., Gelfand, J., Kounios, J., Lisman, J.E. (2002) Oscillations in the alpha band (9-12 Hz) increase with memory load during retention in a short-term memory task. Cereb Cortex. 12:877-82.
Wan, F., Nan, W., Vai, M.I., Rosa, A. (2014) Resting alpha activity predicts learning ability in alpha neurofeedback. Front Hum Neurosci. 8:500.
Palva, S., Palva, J.M. (2007) New vistas for alpha-frequency band oscillations. Trends Neurosci. 30:150-8.
