Every year, Georgia Tech’s STEMcomm Vertically Integrated Project (VIP), a project group centered around enhancing public understanding and enthusiasm for science through writing, exhibits, and events, partners with organizations like Science ATL and hosts an event at the Atlanta Science Festival, with the 2025 event being the Amazing Race; STEM Edition. With different legs of the race centered around 4 main focus areas in STEM, involving Physics in the Speed of Light, Chemistry in Shocking Conductors, Computer Science in the Cyber Quest, and Neuroscience in the Reflex Relay, groups of participants raced through Georgia Tech’s campus to complete interactive hands-on STEM-related tasks and objectives as quickly as possible.
Focusing in on the Reflex Relay, which involved 2 stations where participants had to successfully perform a ruler drop experiment centered around reaction time and then move into an experiment centered around the electrophysiological activity associated with motor command. At this 2nd station, participants took turns exploring the electrophysiology of the nervous system through the usage of a Backyard Brains Human-Human Interface to “control” each other’s arms. A common neuroscience experiment performed in many intro neuroscience courses and labs, as described by Greg Gage’s Ted Talk, How to Control Someone Else’s Arm with Your Brain, the human-human interface arm control experiment is a fun and interactive way for people to explore neuroscience in a more hands-on fashion through directly participating in the transmittance of information from one person’s arm into another person’s arm using the Human-Human Interface to do so (Backyard Brains, n.d.).
Throughout the race, however, to everyone’s surprise, a major issue arose regarding the Human-Human Interface’s ability to read certain older individuals. This observed age-related decline in electrophysiological readability was especially shocking for many of the neuroscience group members as well as VIP team leads because much of the group in general had prior experience with the kits as well as a neuroscience background and had never experienced or even heard of this issue before. However, this unexpected linkage between electrophysiological readability and age can be described as electrophysical age and is ultimately something that, while not typically even thought of in neuroscience, is a studied topic of interest and is an interesting byproduct of neuronal noise.
Aging in general is typically associated with some form of mental decline and/or detriment on a more cognitive level, and this generally understood age-related decline in cognitive functioning isn’t typically observed until far later in life. However, some niche research related to changes in electrophysiological noise, specifically what is considered 1/f noise, has proven that, similar to many of the neurobiological precursors linked to typical cognitive decline, there are similar electrophysiological precursors associated with age-related cognitive decline. Electrophysiology is the study of electrical properties associated with biological systems, and since the nervous system is bioelectric, electrophysiology is often very heavily intertwined with the discipline. Through data collected by using intracranial electroencephalography (EEG), a technique neuroscientists use to read the electrophysiological activity in the brain, Voytek et al.’s study titled Age-Related Changes in 1/f Neural Electrophysiological Noise done in 2015 ultimately defines characteristics of 1/f neural noise. Neural noise is interesting in that it is a byproduct of the brain’s constant processing of stimuli; however, in being a ‘noisy’ system, the nervous system can sometimes be challenging to record through devices such as EEG. In defining that the frequency of 1/f neural noise can fall rapidly to what can be considered “white noise” due to the reciprocal nature of the function (f), Voytek et al.’s study is really interesting in that it connects and applies mathematical concepts to neurobiological principles. It was also found that even at rest, the 1/f noise mediated many aspects of cortical functioning like memory, which is especially interesting considering that it directly supports the electrophysiologically based neural noise hypothesisc of aging “that the signal-to-noise ratio of the central nervous system degrades during aging, leading to deficits in processing speed” first proposed by Robert Kail in 1997 (McCormick et al., 2023). Furthermore, neuroelectrophysiological studies have shown that electrophysiological changes that occur due to age happen as a result of decreases in cellular excitability or the rate in wich neurons can communicate with eachother, which in turn constraints intraneuronal communication by limiting the rate at which neurons can commintate with eachother through the alteration of parameters that are integral to neural functioning (Cepeda et al., 1992). Ultimately, these observed electrophysiological changes can contribute to many of the cognitive and behavioral effects that are commonly associated with aging.
Overall, understanding the key neurobiological functions that degrade over time and lead to cognitive decline is a topic that has been continually researched over the past decade, and more often than not, due to complexity, the electrophysiological components associated with these processes are not thoroughly explored within neuroscience. Even though many computational neuroscientists can contextualize these long-standing problems through different models, such as computational models, much of neuroscience in general looks at noise as troublesome, especially regarding the long-standing issues in computational cognitive modeling where noise is typically thought to indicate a deficit within intraneural communication and is therefore filtered out extensively. Ultimately, however, this phenomenon, known as stochastic resonance, may not always be a bad thing as it is a signaling process that naturally occurs with age in non-linear systems (McDonnell & Abbott, 2009). In a dynamic system, such as the nervous system, extra noise can end up playing a somewhat counterintuitive role as systematically, the noise generated can cause deterministic and predictable changes that end up leading to an overall improved quality of neuronal signaling on a microscopic level while making the signals themselves harder to read on a macroscopic level.
Overall, what the Reflex Relay group found, while unexpected, is a great example of the many neural processes that go unrecognized due to the overall complexity of a continuing, evolving, and relatively newer field. Ultimately, this unexpected realization linking age to a decline of electrophysiological readability is just another example of where we may initially perceive something as abnormal or inefficient, but it may, in turn, actually be an unknowing observation of an eloquently nuanced solution not typically considered.
References
Backyard Brains. (n.d.). Human-human interface. Human-Human Interface. https://backyardbrains.com/products/human-human-interface?srsltid=AfmBOopmTALs9blNPlYVv8nNty1nJUDUmp5rtV5m2wH9T8Tc8lcWJJhC
Cepeda, C., Lee, N., Buchwald, N. A., Radisavljevic, Z., & Levine, M. S. (1992). Age-induced changes in electrophysiological responses of neostriatal neurons recorded in vitro. Neuroscience, 51(2), 411–423. https://doi.org/10.1016/0306-4522(92)90325-v
McCormick, E. M., Cambridge Centre for Ageing and Neuroscience, & Kievit, R. A. (2023). Poorer White Matter Microstructure Predicts Slower and More Variable Reaction Time Performance: Evidence for a Neural Noise Hypothesis in a Large Lifespan Cohort. The Journal of neuroscience: the official journal of the Society for Neuroscience, 43(19), 3557–3566. https://doi.org/10.1523/JNEUROSCI.1042-22.2023
McDonnell, M. D., & Abbott, D. (2009). What is stochastic resonance? Definitions, misconceptions, debates, and its relevance to biology. PLoS computational biology, 5(5), e1000348. https://doi.org/10.1371/journal.pcbi.1000348
TED. (2015, April 28). How to control someone else’s arm with your brain | Greg Gage. YouTube. https://www.youtube.com/watch?v=rSQNi5sAwuc
Voytek, B., Kramer, M. A., Case, J., Lepage, K. Q., Tempesta, Z. R., Knight, R. T., & Gazzaley, A. (2015). Age-Related Changes in 1/f Neural Electrophysiological Noise. The Journal of neuroscience: the official journal of the Society for Neuroscience, 35(38), 13257–13265. https://doi.org/10.1523/JNEUROSCI.2332-14.2015
