Group B4: Premraj Yogarajah, Ruth Pedrosa, Shawn Thadeaus, Netra Malhotra
CRITIQUE OF M8
“STRENGTHENING OF HORIZONTAL CORTICAL CONNECTION FOLLOWING SKILL LEARNING”
SUMMARY
Purpose: To see whether skill learning is accompanied by changes in the
strength of connections within adult rat M1
Method: trained rats in reaching task and used coronial brain slices to
analyze effect of training on strength of horizontal intracortical connections
Result: skill learning changes strength of connections in M1\
Conclusion: Proves that plasticity of cortical connections is associated with
learning a new motor skill
Control for Variables
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The unilateral training of the rats restricts the effects of training to just one hemisphere allowing, the other
hemisphere to act as a control.
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This eliminates any interindividual variability that may be present, such as age or motor threshold, because the
individual acts as both the test subject and it’s own control.
LTP and Memory
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Stimulation-induced increment in synaptic strength was greater in the untrained hemisphere when compared to
the trained hemisphere
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By inducing low and high frequency stimulation during the training process, acquisition of the learning task was
disrupted
o
this underlies the importance of LTP mechanisms in the acquisition of memory
Effect of Aging on LTP
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Long-term potentiation (LTP) shares many properties with memory and is currently the most popular model
of memory
Loss of functional synapses in aged rats reduces the postsynaptic response
Decreased strength of NMDA-receptor mediated response can cause an increase in threshold for LTP
induction
Calcium dysregulation contributes to an increase in the threshold for LTP induction and a decrease in the
threshold for LTD induction.
Balance between LTP and LTD is thought to share mechanisms with memory storage and memory erasure
Shift in balance can cause slower task learning
Activation for LTP in adult rats may induce LTD in aged rats
Motor Learning in Humans
 Same methodology was used when analyzing neural changes associated with learning a unique motor task in
humans
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Subjects were schizophrenics and healthy volunteers
Trained to move the handle of a robotic arm
What differs in this study – use of Positron Emission Topography (PET), as to coronial brain slices, for analysis in
neuronal connections
Found - different patterns of neuronal plasticity in both subjects
Schizophrenics rely more on adaptive properties of the visual field cortex, and healthy volunteers rely more on
the properties of motor cortex
What we know today
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Motor learning occurs as an interaction between intracortical serial connections (horizontal arrows) and corticobasal ganglia/cortico-cerebellum loop circuits (vertical arrows), but in different coordinates (spatial and motor),
at diverse speeds with different levels of attention and awareness
During learning: motor movements are individually executed through the horizontal connections
After learning: motor movements are sequenced by at least two networks  spatial sequence support by the
parietal-prefrontal cortical loop and a motor sequence supported by the motor cortical loops (vertical
connections)
- spatial sequence: explicit processing – quickly acquired, but need maximum attention – correlated with
activation of prefrontal cortex and presupplementary motor areas
- motor sequence: implicit processing – slowly acquired, but need minimum attention – speed is maintained
even without awareness – may be due to multiple neural mechanisms, each contributing to different learning
aspects
Basal Ganglia:
- Dopamine depletion – disrupts performance of sequential movement
- Striatal neurons change with long-term motor learning
Anterior straitum blockade (associative region) – learning new sequence deficit
- Posterior straitum (motor region) – disrupts execution of learned sequence
Cerebellum:
- Blockade – disrupts learning goal-directed behaviour
- Lesions – impair motor sequence learning
- LTM for motor skills may be stored here
Advantage of this model: if one spatial/motor mechanisms is inactive, the other mechanism can still learn the
sequence, but not as efficiently as the original mechanism