Dr. Kim 'Avrama' Blackwell from the Computational and Experimental Neuroplasticity Laboratory of the Krasnow Institute at GMU has done research on the computational modelling of neurological diseases. Of high importance in the development of these diseases is dopamine, a neurotransmitter involved in habits, addiction, and reward-centered behaviors. Of the various illnesses affected by dopamine, common ones include: ADHD, schizophrenia, and Parkinson's.
Dopamine is produced in various structures of the brain, including the substantia nigra (SN). In the basal ganglia circuitry, the SN sends dopaminergic pathways to the striatum, which is then directed to the ventrolateral thalamus and primary motor cortex. Since dopamine promotes movement, if neurons within the SN die and the SN diminish in size, movement is inhibited. This is clearly depicted in patients with Parkinson's disease who have slow movement and rigidity of the body. Dopamine is not only constricted to movement processes, but is also involved in the formation of habits, skill learning, and reward learning.
As mentioned above, dopamine is responsible for learning and it does so by inducing synaptic plasticity. A question posed by Dr. Blackwell was how do neurons know to increase certain synapses and not others? The main component of synaptic plasticity is calcium, however it cannot do the job alone. In order to understand the signaling pathways involved in synaptic plasticity, temporal and spatial specificity must be observed. Long-term depression (LTD) and long-term potentiation (LTP) have been found to be underlying factors in cognitive functions such as learning and memory by way of synaptic plasticity. In terms of spatial aspects, protein kinase A (PKA) location is important for LTP within the striatum. PKA must be anchored near the enzyme adenyl cyclase help produce spatial specificity. It is typically known that high frequency stimulation (~100Hz) produce LTD. In the study conducted by Dr. Blackwell, it was found that theta burst stimulation produced striatal LTP. This temporal pattern differentiated for LTD or LTP in the striatum. In order to further discriminate temporal patterns, other molecules and pathways were observed. Dopamine interaction with ACh activated Gq pathways, which resulted in the endocannabinoid 2AG in LTD and protein kinase C in LTP. LTD and LTP are expressed in synaptic models and are shown to affect the amount of plasticity induced., thus impacting learning and memory.
The mechanisms behind LTD and LTP are still not fully understood. Their activities are dependent upon which circuits they operate and temporal, as well as spatial, specificity. There can never be an end to enhancing computational modeling of neural networks to further understand the interactions of different molecules in various pathways. In order to better the research that has been produced, Dr. Blackwell plans to evaluate responses of cortical activation with various levels of dopamine and derive plasticity rules to incorporate into models of striatal networks.
During Dr. Blackwell's lecture, I can surely affirm that LTP was at work in order for me to learn and store the newly acquired information. It is amazing to know how simple tasks that we as humans do without much deliberation is carried out by such intricate networks within our brains. Learning and memory differs across different situations. Learning to play an instrument activates different areas of the brain compared to learning calculus. How do we know how to differentiate learning and how can we optimize our learning capabilities? In addition, hopefully with further studies focusing on the neural networks behind learning and memory, those with learning disabilities such as ADD, Autism, or dyslexia will be able to learn more effectively and easily. Just as how learning never ends, research on this topic will only further advance the field of neuroscience.
