Movement Matters: A Look at Glial Cells

Posted on December 18, 2014


Astrocytes and Microglia

I recently took a course on advanced topics within Neurobiology. We looked at current research from 2011-2014 on topics of what glial cells are, what their functions are, where they are located, and how they influence sick-behavior, fatigue, energy metabolism, eating behaviors, depression, etc. Of course, I’m interested in athletics and tried to connect my weekly papers my favorite topic.

Below is my final paper, which was sort of a culmination of all the ideas I had throughout the semester.

Sports, dancing, playing music, and other forms of expressions of movement are integrated into many people’s lives, whether it’s for leisure or for work. Movement is an important part of our daily lives and that importance is often brought to our attention when there is either an attempt to enhance movement patterns, like sprinting faster in sport, or when there is a degradation of habitual movement patterns, like the ability to walk in people with neurodegenerative diseases. Glial cells are present in every brain area that is involved with movement. Coaches and athletes have refined movement skills to push athletes out of homeostasis, and in doing so, improve performance. This improvement can be attributed to the plasticity of the Central Nervous System into adulthood, and understanding how glial cells are involved in this alteration of behavior, may provide new approaches to sport. This may even help provide answers for neurodegenerative diseases.

The brain and Central Nervous System (CNS) cost a significant amount of energy for animals. This energy requirement, per neuron, seems to be fixed across several different species of mammals (Herculano-Houzel, 2011). In order to have complex cognition and movement, certain animals utilize specific mechanisms to solve this limitation of energy. Many animals use myelinated neurons to increase conduction velocity and efficiency (Stys, 2011). Some animals also utilize glial cells. Glial cells efficiently support the (CNS) assisting with neurogenesis, learning, restoration, and support all types of synapse function (Franklin et al., 2013; Sild & Ruthazer, 2011, Wang et al., 2011). A glial cell(s) function depends on the time of brain development in utero, the region they are located in, and the type of glial cell (i.e. astrocytes, microglia, oligodendrocytes, Bergmann Glia, and Mueller Cells) – making for a dynamic and complex role in the CNS (Barry et al., 2013). Glial cells have active roles in the brain well into adulthood, especially in the mammalian hippocampus, retina, striatum, and cerebellum (Barry et al., 2013; Nimmerjahn et al., 2009; Reichbach & Bringman, 2013; Sild & Ruthazer, 2011; Tong et. al, 2014).

Astrocyte domains encompass up to 2 million neuronal synapses, which has recently encouraged researchers to focus more on the astrocyte-neuron interaction pathways (Penky et al., 2014). Astrocytes communicate to each other through gap junctions via localized Ca2+ signaling waves, lactate, D-serine, and other gliotransmitters (Lopez-Hidalgo & Schummers, 2014; Mergenthaler et al., 2013; Sild & Ruthazer, 2011). Neuroplasticity is stimulated when there is either astrocyte activation and/or reactive gliosis (Lopez-Hidalgo & Schummers, 2014). Even specific properties of axons can be altered in adult nervous systems, which questions what cells influence changes in action potentials to occur (Stys, 2011).

Lopez-Hidalgo & Schummers (2014) point out that for an adult plasticity to be initiated, there must be extended periods of modified sensory inputs. If you want to improve your ability to sprint faster, you must do activities that stimulate the CNS in ways that force it to alter the outputs or how it perceives inputs differently than prior to the training stimulus. Coaches prescribe specific exercises or drills to alter these inputs and outputs, which ultimately moves the athlete out of homeostasis. Both inputs and outputs will encompass many different changes in the homeostasis quality of many biological systems simultaneously, such as the motor and visual systems.

There are several mechanisms or communicators that signal there is a loss of homeostasis like the release of pro-inflammatory cytokines, hormones, and neurotransmitters, which results with astrocyte activation (Alsio et al., 2012; Dantzer et al., 2008; Jakubs et al., 2008; McCusker & Kelley, 2013; Penky et al., 2014). These communicators assist in altering behavior that begins at the level of the athlete making a choice. The process of choosing, whether it’s what to do or how to do it, is called Action Selection (AS).

Action Selection variables

AS is defined as the desire to select the best action (Ozturk, 2009), that is, to select the action that considers both the organism’s current needs and choices available in the environment so it could make the best adaptive metabolic choices (Berthoud, 2011). Ozturk (2009) makes a distinction that there are two types of AS – Type 1 (Action Goal Selection) and Type 2 (Motor command selection). Simply stated, Type 1 equates to the “WHAT to do?” and Type 2 equates to the “HOW to do it?” (Ozturk, 2009). FMRI studies have shown activation of the basal ganglia (BG) with regards to Type 1 AS and two-way communication between the BG and cerebellum with Type 2 AS commands (Ozturk, 2009).

This points to the fact that movement has a clear pathway of areas in the brain. The striatum, an area of the brain that coordinates body movement by planning and modulating movement, receives inputs from the cortex and outputs into the BG. Dysfunctional striatum astrocytes have been linked to Hunnington’s Disease (Tong et al., 2014). The BG, an area of the brain associated with voluntary movement, habits, and procedural learning, as noted previously, communicates with the cerebellum. Dantzer et al. (2014) notes that the BG mediates inflammation, making this a key brain area where inflammation-based fatigue and AS are both located. The cerebellum is associated with motor control and attention. Researchers have also shown glycogen super-compensation in the brain after prolonged exercise, with the highest levels located in the cerebellum, demonstrating high activation of this brain area during movement (Matsui et al., 2012).

The degradation of movement in patients with neurodegenerative disease can be seen as a series of execution errors with regards to AS. Of course, there are physiological factors that influence such errors. Current research demonstrates that low levels of Kir4, a potassium ion, in astrocytes in mice genetically altered to model Huntington’s Disease (HD) (Tong et al., 2014). Researchers injected Kir4 locally into astrocytes in these mice and it resulted in improved motor performance (walking) and prolonged life (Tong et al., 2014). Also, researchers have shown Ca2+ signaling waves in Bergmann Glia in the cerebellum prior to the onset of walking in normal functioning mice (Hoogland & Kuhn, 2009; Nimmerjahn et al., 2009). Bergmann Glia is a type of radial glia specific to the cerebellum and any dysfunction of such cells often results in dysfunctional motor processes (Barry et al., 2014). Recall that Ca2+ signaling is the communication pathway astrocytes and other glial cells use (Sild & Ruthazer, 2011). These two findings highlight the role of glia cells and movement. And, since human glial cells are specific to our species (Han et al., 2013), what qualities can affect motor function?

There are a myriad of factors here that can contribute to reduced motor function. Chronic inflammation (i.e. cytokine expression), if present for long enough, can disrupt the dopaminergic reward system. The alterations to this system show a decrease in sustained response, errors in appropriate selection of an action, and the reduction of habits (Dantzer, 2014). Due to inflammation, the Neuroimmune communication channels encourage the agent to engage different behavioral priorities or action selections (Dantzer et al., 2014; McCusker & Kelley, 2013). Internal errors in action selection – execution and choice errors – (Ozturk, 2009) may give us a platform to research differences between normal and diseased behavior or, in the context of this paper, normal and/or dysfunctional movement patterns.

Since dysfunctional movement can be influenced by inflammation, stress, fatigue, an athlete who is not handling stressors (i.e. training load (volume/intensity/frequency, academics, social life, etc.) well, can easily move into a state of periods of overtraining – otherwise seen as non-homeostatic periods of time. This may lead to a varying degree of sick and fatigue like behavior(s) (Dantzer et al., 2014, Dantzer et al., 2008; Jakubs et al., 2008; McCusker & Kelley, 2013). Coaches must understand the temporal pattern of inflammation caused by training. Knowing that the immune system can form synapses as a reaction to inflammation within minutes, while the nervous system can take days or weeks (Dustin, 2012), can help coaches with adjusting training loads to optimize performance on a micro (i.e. daily/weekly) or macro (i.e. monthly) level. Studies have also shown how sleep affects the CNS. Animals who were sleep deprived had up to 40% higher synaptic activation, which may imply that sleep prepares the synapse for the day ahead and if the athlete lacks the proper amount of sleep it will put them into a state where learning opportunities may be reduced (Wang et al., 2011).

Even though we have looked at glial cells and cognition, we most also see how glial cells are involved in movement. Movement is such a pivotal part of our every day lives, and seeing how glial cells influence and alter motor performance sheds light on the complexity of their properties. Glial cells help CNS plasticity through development and adulthood, which may explain why athletes can experience improvements in motor patterns well into the latter parts of their lives. Seth Godin once said, “The person who created the ship also simultaneously invented the shipwreck.” Since humans have evolved to utilize glial cells to solve some of the high-energy costs of complex cognition and movement, we will also, in turn, be susceptible to dysfunction the moment there are issues with glial cells. Coaches can take the current research done in the fields of neurodegenerative diseases to enhance the process of improving sporting performance and reducing dysfunctional movement patterns. The reverse could be said for doctors and patients looking at the changes in the CNS and other qualities of athletes learning new skills or improving on older ones to enhance movement patterns. More research needs to be done linking glial cells and movement patterns to help us better understand human lives.



Alsio, J., Olszewski, P.K., Levine, A.S. and Schioth, H.B. (2012) Feed-Forward mechanisms: Addiction-like behavioral and molecular adaptations in overeating. Front. Neuroendo. 33: 127-139

Barry, D.S., Pakan, J.M.P. and McDermott, K.W. (2014) Radial glial cells: Key organizers in CNS development. Int. J. Biochem. Cell Biol. 46:76-79

Berthoud, H. (2011) Metabolic and hedonic drives in the neural control of appetite: who is the boss? Curr. Opin. Neurobiol. 21:888-896.

Bilbo, S.D., Smith, S.H. and Schwarz, J.M. (2012) A lifespan approach to neuroinflammatory and cognitive disorders: A critical role for glia. J. Neuroimmune Pharmacol. 7: 24-41.

Dantzer, R., Heijnen, C.J., Kavelaars, A., Laye, S., and Capuron, L. (2014) The Neuroimmune basis of fatigue. TINS 37(1): 39-46.

Dantzer, R., O’Connor, J.C., Freund, G.G., Johnson, R.W. and Kelley, K.W. (2008) From inflammation to sickness and depression: when the immune system subjugates the brain. Nature Reviews Neuroscience 9: 46-56.

Dustin, M.L. (2012) Signaling at neuro/immune synapses. J. Clin. Invest. 122(4): 1149-1155.

Han, X., Chen, M., Wang, S., Shanz, S., Xu, Q., Oberheim, N.A., Bekar, S., Silva, A.J., Takano, T., Goldman, S.A., Nedergaard, M. (2013) Forebrain Engraftment by Human Glial Progenitor Cells Enhances Synaptic Plasticity and Learning in Adult Mice. Cell Stem Cell 12(3): 342-353.

Jakubs, K., Bonde, S., Iosif, R.E., Ekdahl, C.T., Kokaia, Z., Kokaia, M., and Lindvall, O. (2008) Inflammation Regulates Functional Integration of Neurons Born in Adult Brain. J. Neurosci. 28: 12477-12488.

Kenney, M.J. and Ganta, C.K. (2014) Autonomic nervous system and immune system interactions. Comp. Physiol. 4: 1177-1200.

Lopez-Hidalgo, M., Schummers, J. (2014) Cortical maps: A role for astrocytes? Curr. Opin. Neurobio. 24: 176-189.

Herculano-Houzel, S. 2011. Scaling of brain metabolism with a fixed energy budget per neuron: Implications for neuronal activity, plasticity and evolution. PlosOne 6(3): e17514.

Matsui, T., Ishikawa, T., Ito, H., Okamoto, M., Inoue, K., Lee, M., Fujikawa, T., Ichitani, Y., Kawanaka, K., Soya, H. (2012) Brain glycogen supercompensation following exhaustive exercise. Journal of Physiology 590.3; pg 607-616

Mergenthaler, P., Lindauer, U., Dienel, G.A. and Meisel, A. (2013) Sugar for the brain: the role of glucose in physiological and pathological brain function. TINS 38(10): 587-597.

McCusker, R.H. and Kelley, K.W. (2013) Immune-neural connections: How the immune system’s response to infectious agents influences behavior. J. Exp. Biol. 216: 84-98.

Nimmerjahn, A., Mukamel, E.A., Schnitzer, M.J. (2009) Motor Behavior Activates Bergmann Glial Networks. Neuron 62(3): 400

Ozturk, P. Levels and Types of Action Selection: The Action Selection Soup. Adaptive Behavior. 2009 17; 537 originally published online 30 September 2009.

Pekny, M., Wilhelmsson, U., Pekna, M. (2014) The dual role of astrocyte activation and reactive gliosis. Neurosci. Lett. 565: 30-38.

Reichenbach, A. and Bringmann, A. 2013. New Functions of Muller Cells. Glia 61(5): 651-678.

Sild, M., Ruthazer, E.S. (2011) Radial Glia: Progenitor, Pathway, and Partner. The Neuroscientist. 17(3): 288-302.

Stys, P. (2011) The axo-myelinic synapse. TINS 34(8): 393-400.

Tong, X., Ao, Y., Faas, G.C., Nwaobi, S.E., Xu, J., Haustein, H.D., Anderson, M.A., Mody, I., Olsen, M.L., Sofroniew, M.V., Khakh, B. (2014) Astrocyte Kir4.1 ion channel deficits contribute to neuronal dysfunction in Huntington’s disease model mice. Nature Neuroscience. Vol 17 (5); 694-704

Wang, G., Grone, B., Colas, D., Appelbaum, L. and Mourrain, P. (2011) Synaptic plasticity in sleep: learning, homeostasis and disease TINS 34: 452-463.

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