Tag Archives: plasticity

OMG, More Stairs?!?

When I came to Paris, I thought I was prepared for everything: the bakeries, the museums, the landmarks, the culture — but nothing could have prepared me for the walking I was about to do. Unlike the suburban areas around Emory or my hometown of Topeka, Kansas, where a car is considered necessary for most outings, the streets of Paris are easily traversable by foot, and public transportation is much more accessible. And in a city so beautiful, I had a hard time refusing the ease of foot travel. Still, with the recent muggy weather, walking hasn’t felt quite as pleasant. People always say “no pain, no gain,” and I began to wonder what all my walking was doing for me brain-wise.

My steps before and after I came to Paris. As one can see, my steps significantly increased after I came to Paris, May 22th.

Turns out, there’s a lot to be gained from regular aerobic exercise. Consistent research has pointed to the role of physical activity in cognitive function and has grown in volume over the past decade (Soga et al., 2015). General movement has been suggested to contribute to brain plasticity, which in turn facilitates interaction between cognitive and motor functioning (Doyon and Benali, 2005). Furthermore, research has also linked physical activity to academic performance (Castelli et al., 2007). While these results doesn’t necessarily mean that taking up routine walking or running will guarantee better grades or memory, the two do seem to be invariably related.

Amidst this burgeoning research, Colcombe and colleagues decided to research the cortical mechanisms beneath cardiovascular fitness-related changes in cognitive function (Colcombe et al., 2004). Functional magnetic resonance imaging (fMRI) was used to study how changes in fitness might affect the brain. Researchers particularly focused on the anterior circular cingulate (ACC), an area of the limbic system linked to brain structures responsible for sensory, motor, emotional, and cognitive information (Bush et al., 2000).

The study took place in 2 segments, with Study 1 involving high-fit (HF) older adults, and Study 2 involving adults randomly assigned to either a cardiovascular fitness training (CFT) group or a stretching and toning group (control) (Colcombe et al., 2004). All participants in both groups underwent a flanker task in which they filtered and identified incongruent cues (Colcombe et al., 2004). The flanker test allowed researchers to study participants’ ability to filter and respond to relevant information (Colcombe et al., 2004). Researchers then compared cortical mechanisms triggered by incongruent clues to those triggered by congruent ones, to see whether HF adults would demonstrate higher activation in attention- and control-related regions (Colcombe et al., 2004).

fMRI scans of the ACC illustrate activation of different cortical areas in the task-related activity (Colcombe et al., 2004).

Sure enough, fMRI scans supported the study’s hypothesis that older adults with high levels of measured cardiovascular fitness would demonstrate significantly more activation in cortical regions linked with attention selection and control (Colcombe et al., 2004). These cortical regions include the medial frontal gyrus (MFG), superior frontal gyrus (SFG), and superior parietal lobe (SPL) (Colcombe et al., 2004). Significantly less activation was observed in the ACC, which is linked with behavioral conflict and adaptation of attentional control (Colcombe et al., 2004).

One weakness of the study by Colcombe and colleagues is the cross-sectional approach taken in Study 1. Being observational, cross-sectional studies are vulnerable to non-response bias, which can lead to a participant pool unrepresentative of the population (Sedgwick, 2014). Furthermore, data can only be collected during one set period of time, leaving researchers unable to create long-term representations of cause and effect (Sedgwick, 2014). However, it is important to note that longitudinal studies might also be difficult to complete with older participants, due to possible interference from disease or other age-related complications (Sedgwick, 2014). Ultimately, the research by Colcombe and colleagues was important at the time of its publication because it expanded upon existing research regarding the underlying cortical mechanisms of cardiovascular fitness.

More recent research by Brockett and colleagues suggests that physical exercise may contribute to extensive plasticity and increased cognitive functioning (Brockett et al., 2015). Rats who ran for moderate durations of 12 days were able to better discriminate than control rats in a task testing medial prefrontal cortex (mPFC) function, though little difference was seen between both groups in a task testing perirhinal cortex (PRC) function (Brockett et al., 2015). In a second experiment, runner rats took less trials and errors than control sedentary rats to reach criteria for simple discrimination, reversal, extradimensional shift (Brockett et al., 2015). Researchers also tested whether running influences astrocytes, non-neural brain cells that communicate with neurons and suggest links to synaptic plasticity, learning, and memory (Brockett et al., 2015). Co-labelling of astrocytes with visual markers revealed increase in astrocytes cell body area in the hippocampus, mPFC, and OFC (Brockett et al., 2015). These results aligned with data from the behavioral tests, suggesting that physical exercise can enhance cognitive performance in tasks that activate the hippocampus, mPFC, and OFC (Brockett et al., 2015). The lack of significant change to the PRC suggests that routine running lacks observable relation to the PRC. Ultimately, results suggest greater cognitive performance in tasks reliant on the prefrontal cortex, as well as enhanced synaptic, dendritic, and astrocytic measures in several regions. This evidence supports the hypothesis that physical exercise contributes positively to plasticity and cognitive functioning. Together, both papers by Colcombe, Brockett, and their colleagues have contributed to the growing understanding that exercise generally promotes greater cognitive functioning.

Brockett and colleagues’ research has made me wonder how much I would have to run to achieve the human equivalent of a rat’s 12-day regimen. As a student, it’s incredibly easy to get sucked into the grind and become deskbound. But the grind is exactly why brain power is important for the students, and optimizing my brain power in exchange for a few minutes and some physical effort has started to sound like a much better idea than the old me would have thought.


Brockett AT, LaMarca EA, Gould E (2015) Physical exercise enhances cognitive flexibility as well as astrocytic and synaptic markers in the medial prefrontal cortex. Public Library of Science ONE 10(5): e0124859. https://doi.org/10.1371/journal.pone.0124859.

Bush G, Luu P, Posner MI (2000) Cognitive and emotional influences in anterior cingulate cortex. Trends in Cognitive Sciences. 4(6):215-222. https://doi.org/10.1016/S1364 6613(00)01483-2.

Castelli DM, Hillman CH, Buck SM, Erwin HE (2007) Physical fitness and academic achievement in third- and fifth-grade students. Journal of Sport and Exercise Psychology 29(2):239-252. https://doi.org/10.1123/jsep.29.2.239.

Colcombe SJ, Kramer AF, Erickson KI, Scalf  P, McAuley E, Cohen NJ, Webb A, Jerome GJ, Marquez DX, Elavsky S (2004) Cardiovascular fitness, cortical plasticity, and aging. Proceedings of the National Academy of Sciences of the United States of America            101(9):3316-3321. https://doi.org/10.1073/pnas.0400266101.

Doyon J, Benali H (2005) Reorganization and plasticity in the adult brain during learning of motor skills. Current Opinion in Neurobiology 15(2):161-167. https://doi.org/10.1016/j.conb.2005.03.004.

Sedgwick P (2014) Cross sectional studies: Advantages and disadvantages. BMJ 348. https://doi.org/10.1136/bmj.g2276.

Soga K, Shishido T, Nagatomi R (2015) Executive function during and after acute moderate aerobic exercise in adolescents. Psychology of Sport and Exercise 16:7-17. https://doi.org/10.1016/j.psychsport.2014.08.010.

Image 1 taken by myself.

Image 2 from Colcombe et al., 2004.

Paul Cézanne, Museum Fatigue Advocate

Have you ever experienced museum fatigue? I thought that I made up this term to describe my own experiences, but upon performing a quick Google search, I discovered that this is actually a phenomenon first described in 1916 (Gilman, 1916).

Interior of the Musée d’Orsay (Image from TripSavy.com)

Going to a museum may seem like a passive process, but to me, it is actually quite a bit of work!

Navigating large crowds and carrying a heavy backpack for several hours is enough to wear me out. But even more so, interpreting piece after piece of artwork—each of which leaves a lot of room for interpretation—is a laborious effort leading to mental exhaustion. Though it is uncomfortable, I think that this is the way it should be. If you don’t experience some fatigue, are you fully engaged with and appreciating the art?

Exterior of Musée d’Orsay (Image from SortiraParis.com)

One particular French artist I have learned about in class is Paul Cézanne, and he seems to have been an especially avid proponent of museum fatigue; although his works were rejected from museums during his lifetime, it seems as if he were intentionally inducing this exhaustion. In the Post-Impressionistic style (abandoning the detailed, picture-perfect landscapes characteristic of Realism), Cézanne produced blurry, unfinished images in order to accentuate the mind’s interpretation process. Leaving blank spots peeking through the blobs of color is a technique called nonfinito, and it’s a bit like trailing off in the middle of a sentence—a visual ellipsis. In this way, the viewer’s interpretation is unique to the way the mind fills in the gaps at that particular moment, influenced by all of the emotions and experiences one brings to the table.

It turns out that this reflects how the brain works when interpreting all visual stimuli: even looking at the same things twice may trigger different responses from neurons dedicated to processing visual information (Jeon et al., 2018).

First, let’s start with some background information about vision and how our

The occipital lobe, shown in yellow (Image from The Science of Pscychotherapy.com)

brains process signals coming from our eyes.

Light enters the eye and reaches the retina at the very back. There, it stimulates light-responsive cells called photoreceptors (rods and cones). Signals from all these cells go through the optic nerve, the optic tract, a structure called the thalamus, and eventually reach the part of the brain that deals with visual information. This area is called the occipital lobe, and the section that is first to receive these signals is called the primary visual cortex, or V1. Here, there are cells that have been shown to respond to basic details of a scene like the width and orientation of lines (Gawne, 2015). Each cell is “tuned” to respond best to a certain width and a certain orientation, and logically, this is called neuronal tuning (Butts and Goldman, 2006). The conditions determining the responsivity of the neurons get more and more complex as the signals are processed (Tsunoda et al., 2001).

The perception of visual information (Image from Slideplayer.com)

As one views the same image, it would make sense that the same neurons respond each time. But, this is not exactly the case: In one experiment by Jeon et al. 2018 in the journal Nature, researchers found that the same neurons aren’t reliably activated by the same stimuli.

In the study, the researchers showed mice lines of different orientations and widths. Using a technique called two-photon calcium imaging, they looked at the activity of neurons in the V1 (Jeon et al., 2018). This technique involves installing an apparatus on the head of a mouse. Based on the movement of fluorescing ions, it lets us see what neurons are active as the mouse is awake and interacting with the world (Mitani and Komiyama, 2018).

Some of the images shown to mice in the Jeon et al. (2018) experiment (Image from the journal Nature)

Tracking around 300 neurons, the researchers determined the qualities of the image (such as the angle and the width of the lines) for which a neuron was most likely to respond. Then, performing the test one week later and again two weeks later, they compared the preferences of the neurons. While the majority of individual qualities were relatively stable over time, the researchers found that fewer than half of the neurons had exactly all of the same preferences as before.

What does this all mean? In the past it has been shown that the visual cortex is highly plastic, or able to rearrange and reorganize its connections based on new information (Hofer et al., 2009).  However, these results provide even more insight into how our visual systems adapt and change: some parts can remain stable while others change their responsivity in order to incorporate new information, altering our perception of the world around us.

So, our perception of static scenes is actually not static at all; it is being altered constantly! That boulangerie we pass on the way to class is not perceived by our brains in exactly the same manner every day.

Portrait of a Woman by Paul Cezanne (Image from the Metropolitan Museum of Art)

That leads me to wonder: especially when looking at one of Cézanne’s paintings—since he left so much for the viewer’s mind to fill in—do we ever experience the same thing twice?  This may very well be the most intriguing thing about his work, making it both timeless and malleable. A perfect excuse to visit the Musée d’Orsay just one more time.  The unfortunate result is only that this “museum fatigue” may become an increasingly common affliction. However, it’s likely already a common experience for all the museum-goers of the world, and I’m not afraid. It certainly won’t deter me from absorbing all of the Post-Impressionism art I can while I’m here!



Butts, D.A., Goldman, M.S. (2006). Tuning curves, neuronal variability, and sensory coding. PLOS Biology. 4:92. doi: 10.1371/journal.pbio.0040092.

Gawne, T. (2015). The responses of V1 cortical neurons to flashed presentations of orthogonal single lines and edges. Journal of Neurophysiology. 113:2676-2681. doi: 10.1152/jn.00940.2014

Gilman, B. I. (1916). Museum Fatigue. The Scientific Monthly. 2:62–74.

Hofer, S. B., Mrsic-Flogel, T. D., Bonhoefer, T. & Hubener, M. (2009). Experience leaves a lasting structural trace in cortical circuits. Nature. 457:313–317.

Jeon, B. B., Swain, A.D., Good, J. T., Chase, S. M., Kuhlman, S.J. (2018). Feature selectivity is stable in primary visual cortex across a range of spatial frequencies. Nature. 8:15288. doi:10.1038/s41598-018-33633-2.

Mitani, A., Komiyama, T. (2018). Real-time processing of two-photon calcium imaging data including lateral motion artifact correction. Frontiers in Neuroinformatics. 12:98. doi: 10.3389/fninf.2018.00098

Tsunoda, K., Yamane, Y., Nishizaki, M., Tanifuji, M. (2001). Complex objects are represented in macaque inferotemporal cortex by the combination of feature columns. Nature Neuroscience. 4:832-838. doi: 10.1038/90547.


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