How Does the Brain Learn New Motor Tasks?

Post by Ewina Pun

What’s motor learning?

Humans possess a remarkable capacity to acquire new motor skills. Some motor skills can be readily transferred to a new task or context. For example, someone who can already play the guitar will have an easier time learning to play the bass, and mastering biking on flat terrain makes it less challenging to tackle mountain biking. However, learning entirely new motor skills can be more difficult. Through practice and repetition, our brain is capable of acquiring and refining those skills. Motor learning involves modifying the nervous system to enable movement generation in response to environmental changes or through practice. This overview highlights some of the neural mechanisms that guide motor learning.

How does the brain change during motor learning?

The brain may reorganize itself on various levels when we learn a new task. First, cortical maps may change over an extended period of deliberate practice. For example, a study revealed skilled musicians have a larger volume of grey matter than non-musicians. Second, learning can also result in short-term and long-term changes in tuning properties of individual neurons, synapses, and functional networks of neurons. Motor learning is complex because these processes may occur simultaneously over different timescales, and typically involve various brain regions depending on the type of learning.

During learning, cortical neurons in the motor cortex and sensorimotor cortex may reuse existing neural patterns for fast skill adaptation or create new neural patterns when learning skills that have not been encountered before. Changes in neural population activity also occur, which can be attributed to synaptic plasticity: synapses being strengthened, pruned, or created, allowing more efficient neural communication. These changes in connections affect the neural population's firing patterns, subsequently influencing behavior.

Reward-based learning in the basal ganglia

Reward-based learning in the basal ganglia plays a crucial role in the acquisition of new motor skills and refining existing ones. Also known as reinforcement learning, this process allows the brain to associate specific motor actions with positive or negative outcomes and adjust behavior to maximize rewards and minimize punishments. The basal ganglia create a feedback loop connecting motor planning and execution from the cerebral cortex with the evaluation of outcomes. This evaluation is facilitated by dopamine-releasing neurons that encode reward prediction information. This process considers rewards independently of sensory and motor aspects and integrates reward information into movement activities.

Error-based learning in the cerebellum

The cerebellum also plays an important role in acquiring and coordinating precise movements through error-based learning. Prediction error refers to the difference between the intended movement and the actual movement produced. To predict and correct errors and fine-tune movements, the cerebellum receives and integrates inputs from sensory systems, such as vision and proprioception (the sense of body position), as well as from the cerebral cortex. Research suggests that the cerebellum is involved in externally driven movements, while the basal ganglia participate in internally generated movements.

Why study motor learning?

Motor learning is an active and ongoing area of research in neuroscience, with many unanswered questions awaiting exploration. Understanding the neural mechanisms underlying movement control and learning is essential, as it holds practical applications in fields such as sports training (to enhance athletic performance) and rehabilitation (new therapies and technologies to help disabled individuals). Furthermore, understanding how we incorporate predictions and errors in the context of motor learning may contribute to the advancement of machine learning algorithms for tackling and solving new tasks. Future research may focus on emerging techniques or technologies, such as brain-computer interfaces, virtual reality, or advanced neuroimaging methods, which could further our understanding of how the brain learns new motor tasks.

References +

C. Gaser, G. Schlaug. Brain structures differ between musicians and non-musicians. J. Neurosci. (2003).

B. Jarosiewicz et al. Functional network reorganization during learning in a brain- computer interface paradigm. Proc. Natl. Acad. Sci. (2008).

C. S. Li, C. Padoa-Schioppa, E. Bizzi. Neuronal correlates of motor performance and motor learning in the primary motor cortex of monkeys adapting to an external force field. Neuron. (2001).

R. Shadmehr and F. A. Mussa-Ivaldi. Adaptive representation of dynamics during learning of a motor task. J. Neurosci. (1994).

E. R. Oby et al. New neural activity patterns emerge with long-term learning,” Proc. Natl. Acad. Sci. 2019.

K. Doya. Complementary roles of basal ganglia and cerebellum in learning and motor control. Current Opinion in Neurobiology. (2000).

Tobler et al. Adaptive Coding of Reward Value by Dopamine Neurons. Science. (2005)

W. Schultz. Reward functions of the basal ganglia. Journal of Neural Transmission (2016)

Y. Zang and E. De Schutter. Climbing Fibers Provide Graded Error Signals in Cerebellar Learning. Front. Syst. Neurosci. (2019).

Improving the Diagnosis of Pediatric CNS Tumors

Post by Lincoln Tracy

The takeaway

Integrating multi-omic approaches in the neuropathological assessment of pediatric CNS tumors improves diagnostic accuracy.

What's the science?

The World Health Organization has recently released an updated classification system for the diverse range of central nervous system (CNS) tumors that occur in children and adolescents. However, the wide variety of different tumors out there have an equally diverse range of outcomes, which makes it challenging for clinicians to make an accurate diagnosis. This week in Nature Medicine, Sturm and colleagues sought to improve diagnostic accuracy in pediatric neuro-oncology by developing a next-generation sequencing gene panel and introducing a DNA methylation-based classification for pediatric CNS tumors.

How did they do it?

The authors collected CNS tumor tissue samples from 1204 patients seeking treatment in 65 hospitals across Germany, Switzerland, Australia, and New Zealand. They first classified the CNS tumors according to the WHO classification system and the newly developed DNA methylation-based class prediction algorithm, before comparing the similarity of the two classification systems. The authors then integrated next generation sequencing (i.e., a large-scale DNA sequencing technology) to detect relevant genetic alterations in the tumors.

What did they find?

The authors made a confident diagnosis for 87% of CNS tumors using the WHO classification system, while three percent of tumors could not be assigned to any existing category. Low-grade glial/glioneuronal tumors were the most common (38%), followed by medulloblastomas and high-grade gliomas (both 16%). In contrast, 79% of tumors could be confidently assigned to a DNA methylation class. Low-grade glial/glioneuronal tumors were again the most common classification (29%), followed by medulloblastomas (16%) and high-grade gliomas (10%). While there was a strong correlation between the two classification systems, a large portion of WHO-based tumor types could not be classified by the DNA methylation-based algorithm, including 34% of WHO-defined high-grade gliomas and 20% of low-grade glial/glioneuronal tumors. Additional visualization of DNA methylation patterns suggested there were novel molecular classes not represented by the original reference cohort. Genetic alterations were detected in 60% of tumors, most commonly in the BRAF gene, although 42% of tumors had a diagnostically-relevant mutation and 15% had a therapeutically-relevant mutation.

What's the impact?

The data support incorporating DNA methylation-based classification approaches in the WHO classification for CNS tumors, showing it is a useful tool for diagnosing many types of tumors – especially those that are otherwise difficult to diagnose. This study provides an initial example of how neuropathological multi-omic approaches can be integrated into clinical practice.

Access the original scientific publication here. 

This is Your Brain on Burnout

Post by Elisa Guma

What is burnout?

Chronic physical, emotional, or mental exhaustion, decreased motivation, lowered performance, and negative attitudes towards oneself and others are all symptoms of “burnout”. This syndrome is familiar to many professionals in high-stress, demanding jobs. Burnout is usually caused by work that demands continuous, long-term cognitive, emotional, or physical efforts in concert with a perceived lack of control in the face of a demanding workload, as well as a lack of adequate social support or poor self-care (Vladut et al., 2010). While work-related stress is often the cause, burnout can also occur in other areas of life, such as parenting, caretaking, or romantic relationships.

How does burnout affect the neuroendocrine system?

Chronic stress conditions like burnout can cause dysregulation of the neuroendocrine system through the hypothalamic-pituitary-adrenal (HPA) axis (Oosterholt et al., 2014). When faced with an acute stressor (ex: seeing a snake in the grass), the body responds by increasing cortisol concentrations in the body, triggering the fight or flight response. Once the threat has passed cortisol levels fall and return to baseline. However, in chronic stress conditions like burnout, the body is unable to bring cortisol levels back to baseline, which wreaks havoc on our body’s neuroendocrine system. Prolonged stress and chronically elevated cortisol levels can lead to abnormally low baseline cortisol levels, (Oosterholt et al., 2014), increasing inflammation in the body and the risk for heart disease, diabetes, high blood pressure, and vulnerability to illness (Toker et al., 2012). It can also increase susceptibility to anxiety, depression, or misuse of drugs and alcohol (Ogbonnaya UC et al., 2022).

How does burnout affect brain structure and function?

Chronic stress, a key underlying feature of burnout, has been associated with increasing risk for both mental health conditions and physical illness, as well as changes in brain structure and function (Miranda et al., 2022). Neuroimaging studies of individuals experiencing chronic occupational stress have shown alterations to limbic and paralimbic networks that are commonly observed in other chronic stress conditions such as post-traumatic stress disorder or childhood maltreatment (Savic 2015, Savic et al., 2018; Blix et al., 2013).  These include cortical thinning of the prefrontal cortex (PFC), implicated in higher-order executive function and decision-making, as well as reduced functional connectivity of the PFC to other brain regions. Importantly, some studies demonstrate that cortical thinning is more pronounced in females than males, highlighting potential sex-specific vulnerabilities (Savic 2015, Savic et al., 2018; Blix et al., 2013). Alterations to limbic structures have also been reported, including reduced anterior cingulate cortex volume, and reduced functional connectivity between that region and the amygdala (Golkar et al. 2014). Additional studies demonstrate that females under chronic stress have enlarged amygdala volume while males have enlarged caudate volume (Savic et al., 2018). Importantly, the magnitude of these changes has been associated with the degree of perceived stress, pointing to some potential dose-response relationship.  

Burnout has also been associated with cognitive deficits. Those experiencing burnout are more likely to have attentional lapses and memory impairments, with effects often more pronounced in women than men (Morgan et al., 2011; Deligkaris et al., 2014) and may have more trouble switching attention between visual stimuli than non-stressed controls (Deligkaris et al., 2014). These cognitive and attentional challenges may lead to difficulties with work performance and the ability to feel rewarded and satisfied by the work (Liston et al., 2009).

What’s the connection between burnout and depression?

The symptoms associated with burnout are like those of depression (Schonfeld et al., 2015). Interestingly, reduced serotonin binding in the anterior cingulate cortex, hippocampus, and anterior insular cortex (regions involved in emotional processing), has been reported in the brains of those undergoing chronic occupational stress (Jovanovic et al. 2011). These brain regions, along with the serotonin system are involved to some extent in the pathology of depression, highlighting possible mechanistic overlaps. Although this relationship requires further investigation, similar attentional-behavioral changes have been reported between individuals with burnout and those with depression (Liston et al., 2009).

While burnout and depression may be highly correlated, there are some important distinctions. Depression is currently a diagnosable mental health condition, whereas burnout is not (Schonfeld et al., 2015). Additionally, burnout occurs in response to situational stress, while depression is not necessarily triggered by a specific event. However, it’s important to note that both conditions can require professional help.

How can we bounce back from burnout?

Recognizing you have burnout is an important first step. Give yourself grace and take time to care for your mental health. This can include seeing a therapist, taking breaks throughout the workday, exercising, practicing mindfulness, and trying to foster better work-life balance by engaging in non-work-related activities and social connections (Maslach et al., 2015).

Previous research in rodents has found that chronic stress impairs attention-shifting ability in rats and damages neurons in the prefrontal cortex, but these deficits are reversed after three weeks of relaxation (Arnsten AFT, 2009). Similarly, longitudinal brain imaging studies in humans have shown that cortical thinning and decreased functional connectivity of the prefrontal cortex and attention impairments can be reversed when stress exposure is reduced (Savic, et al., 20018, Deligkaris et al., 2014). This suggests that even though the effects of burnout are quite deleterious on the brain and body, there is room for improvement if stressful conditions can be managed.

While personal-level interventions are necessary, there must be change at the organizational level as well. For example, managers should be trained to recognize signs of burnout in their staff and companies should be encouraged to improve the work environment and conditions to foster a supportive work culture (Vladut et al., 2010). Ideally, individuals recognize the first signs of burnout and take steps to intervene, but chronic stress that manifests into burnout can be challenging to handle on your own. Considering professional support when burnout becomes an issue should be encouraged.

References +

Arnsten AFT. (2009). Stress signalgin pathways that impair prefrontal cortex structure and fuction. Nature Reviews Neuroscience, 10: 410-422.

Blix, E., Perski, A., Berglund, H., & Savic, I. (2013). Long-term occupational stress is associated with regional reductions in brain tissue volumes. PLOS ONE 8: e64065. doi:10.1371/journal.pone.0064065

Deligkaris, P., Panagopoulou, E., Montgomery, A. J., & Masoura, E. (2014). Job burnout and cognitive functioning: A systematic review. Work & Stress, 28, 107–123. doi:10.1080/02678373.2014.909545

Golkar, A., Johansson, E., Kasahara, M., Osika, W., Perski, A., & Savic, I. (2014). The influence of work-related chronic stress on the regulation of emotion and on functional connectivity in the brain. PLOS ONE 9: e104550. doi:10.1371/journal.pone.0104550

Jovanovic, H, Perki, A, Berglund, H, Savic, I (2011). Chronic stress is linked to 5-HT1A receptor changes in functional disintegration of the limbic networks. Neuroimage, 55, 1178-1188.

Liston, C., McEwen, B. S., & Casey, BJ. (2009). Psychosocial stress reversibly disrupts prefrontal processing and attentional control. Proceedings of the National Academy of Sciences, 106, 912–917. doi:10.1073/pnas.0807041106

Miranda, F. (2022). The neural correlates of burnout: a systematic review. Digitala etenskapliga Arkivet.

Maslach, C., & M. P. Leiter, (Eds.). (2015). It’s time to take action on burnout. Burnout Research, 2, iv–v. doi:10.1016/j.burn.2015.05.002

Morgan, C. A., Russell, B., McNeil, J., Maxwell, J., Snyder, P. J., Southwick, S. M., & Pietrzak, R. H. (2011). Baseline burnout symptoms predict visuospatial executive function during survival school training in special operations military personnel. Journal of the International Neuropsychological Society, 17, 494–501. doi:10.1017/S1355617711000221

Ogbonnaya UC, Thiese MS, Allen J. (2022). Burnout and engagement’s relationship to drug abuse in laywers and law professionals. J Occup Environ Med, 64: 621-627.

Oosterholt, B. G., Maes, J. H., Van der Linden, D., Verbraak, M. J., & Kompier, M. A. (2015). Burnout and cortisol: Evidence for a lower cortisol awakening response in both clinical and nonclinical burnout. Journal of Psychosomatic Research, 78, 445–451. doi:10.1016/j.jpsychores.2014.11.003

Savic, I. (2015). Structural changes of the brain in relation to occupational stress. Cerebral Cortex, 25, 1554–1564. doi:10.1093/cercor/bht348

Savic, I, Perksi A, Osika, W. (2018). MRI shows that exhaustion syndrome due to chronic occupational stress is associated with partially reversible cerebral changes. Cerebral Cortex, 28: 894-906.

Schonfeld IS, Bianchi R. (2015). Burnout and depression: two entities or one? Journal of clinical psychology, 71:22-37.

Toker, S., Melamed, S., Berliner, S., Zeltser, D., & Shapira, I. (2012). Burnout and risk of coronary heart disease: A prospective study of 8838 employees. Psychosomatic Medicine, 74, 840–847. doi:10.1097/PSY.0b013e31826c3174

Vladut, CI., Kallay, E. (2010). Work stress, personal life, and burnout. Causes, consequences, possible remedies: a theoretical review. Cognition, Brain, Behaviour, 14, 261-280. https://www.proquest.com/docview/856042813?fromopenview=true&pq-origsite=gscholar