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Plasticity of cerebellar Purkinje cells in behavioral training of body balance control.

Lee RX, Huang JJ, Huang C, Tsai ML, Yen CT - Front Syst Neurosci (2015)

Bottom Line: The ability to differentiate such sensory information can lead to movement execution with better accuracy.Both PC simple (SSs; 17 of 26) and complex spikes (CSs; 7 of 12) were found to code initially on the angle of the heads with respect to a fixed reference.Using periods with comparable degrees of movement, we found that such SS coding of information in most PCs (10 of 17) decreased rapidly during balance learning.

View Article: PubMed Central - PubMed

Affiliation: Department of Life Science, National Taiwan University Taipei, Taiwan.

ABSTRACT
Neural responses to sensory inputs caused by self-generated movements (reafference) and external passive stimulation (exafference) differ in various brain regions. The ability to differentiate such sensory information can lead to movement execution with better accuracy. However, how sensory responses are adjusted in regard to this distinguishability during motor learning is still poorly understood. The cerebellum has been hypothesized to analyze the functional significance of sensory information during motor learning, and is thought to be a key region of reafference computation in the vestibular system. In this study, we investigated Purkinje cell (PC) spike trains as cerebellar cortical output when rats learned to balance on a suspended dowel. Rats progressively reduced the amplitude of body swing and made fewer foot slips during a 5-min balancing task. Both PC simple (SSs; 17 of 26) and complex spikes (CSs; 7 of 12) were found to code initially on the angle of the heads with respect to a fixed reference. Using periods with comparable degrees of movement, we found that such SS coding of information in most PCs (10 of 17) decreased rapidly during balance learning. In response to unexpected perturbations and under anesthesia, SS coding capability of these PCs recovered. By plotting SS and CS firing frequencies over 15-s time windows in double-logarithmic plots, a negative correlation between SS and CS was found in awake, but not anesthetized, rats. PCs with prominent SS coding attenuation during motor learning showed weaker SS-CS correlation. Hence, we demonstrate that neural plasticity for filtering out sensory reafference from active motion occurs in the cerebellar cortex in rats during balance learning. SS-CS interaction may contribute to this rapid plasticity as a form of receptive field plasticity in the cerebellar cortex between two receptive maps of sensory inputs from the external world and of efference copies from the will center for volitional movements.

No MeSH data available.


Related in: MedlinePlus

SS information coding and behavioral activity. (A) Schematic illustrations of good (upper left panel) and poor (upper right panel) SS information coding association and of high , low σ (lower left panel) and low , high σ (lower right panel) cases, where σ is the standard deviation of information association index  (t) which is defined as with I depicting θ or ω, and information association capability  is derived as . (B) Examples with low variations in  accompanied by high  and high  (a), and high variations in  accompanied by low  and low  (b). (C) Pooled data showing that both z (z-score of ) and the coefficient of determination r2 in fSS-I plots correlated positively with . This result shows that SS coded information better when there was more movement.
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Figure 6: SS information coding and behavioral activity. (A) Schematic illustrations of good (upper left panel) and poor (upper right panel) SS information coding association and of high , low σ (lower left panel) and low , high σ (lower right panel) cases, where σ is the standard deviation of information association index (t) which is defined as with I depicting θ or ω, and information association capability is derived as . (B) Examples with low variations in accompanied by high and high (a), and high variations in accompanied by low and low (b). (C) Pooled data showing that both z (z-score of ) and the coefficient of determination r2 in fSS-I plots correlated positively with . This result shows that SS coded information better when there was more movement.

Mentions: Having established that PCs encoded information on θ and ω (Figures 4, 5), clearly, information of θ and ω would be important to balancing on the dowel. As such information underwent significant changes with motor learning (Figure 1D), our next analysis was to test whether information coding in SS was modulated as a result of motor learning. To quantify SS information coding capability to online information, we devised a dynamical information association index (), defined as the slope between two temporally adjacent data points in the fSS-ω or fSS-θ plots. If fSS consistently correlated with updated online information of ω or θ, successive values of should be consistently close or vary within a small margin. By contrast, if fSS did not follow the updated information well, values of should vary more (Figure 6A). Therefore, we took the inverse of the standard deviation of (σ) in a time window as a measurement of the information association capability (i.e., ). At first look, the variation of was low and was high (0.240 (°/s)/Hz) during the 30-s period with high (55.4°/s2) (Figure 6Ba), and vice versa (=0.011 (°/s)/Hz), = 16.8 °/s2; Figure 6Bb). The population result (n = 17) revealed a positive linear correlation between the normalized and (r = 0.9334; p = 0.002, One-Way ANOVA; Figure 6C, left panel). The coefficient of determination r2 in fSS-ω plots also showed a positive correlation with (r = 0.8547; p = 0.0033, One-Way ANOVA; Figure 6C, right panel). These results suggest that both static (i.e., r2) and dynamic associations (i.e., ) of SS to sensory information depend upon motion level.


Plasticity of cerebellar Purkinje cells in behavioral training of body balance control.

Lee RX, Huang JJ, Huang C, Tsai ML, Yen CT - Front Syst Neurosci (2015)

SS information coding and behavioral activity. (A) Schematic illustrations of good (upper left panel) and poor (upper right panel) SS information coding association and of high , low σ (lower left panel) and low , high σ (lower right panel) cases, where σ is the standard deviation of information association index  (t) which is defined as with I depicting θ or ω, and information association capability  is derived as . (B) Examples with low variations in  accompanied by high  and high  (a), and high variations in  accompanied by low  and low  (b). (C) Pooled data showing that both z (z-score of ) and the coefficient of determination r2 in fSS-I plots correlated positively with . This result shows that SS coded information better when there was more movement.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
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Figure 6: SS information coding and behavioral activity. (A) Schematic illustrations of good (upper left panel) and poor (upper right panel) SS information coding association and of high , low σ (lower left panel) and low , high σ (lower right panel) cases, where σ is the standard deviation of information association index (t) which is defined as with I depicting θ or ω, and information association capability is derived as . (B) Examples with low variations in accompanied by high and high (a), and high variations in accompanied by low and low (b). (C) Pooled data showing that both z (z-score of ) and the coefficient of determination r2 in fSS-I plots correlated positively with . This result shows that SS coded information better when there was more movement.
Mentions: Having established that PCs encoded information on θ and ω (Figures 4, 5), clearly, information of θ and ω would be important to balancing on the dowel. As such information underwent significant changes with motor learning (Figure 1D), our next analysis was to test whether information coding in SS was modulated as a result of motor learning. To quantify SS information coding capability to online information, we devised a dynamical information association index (), defined as the slope between two temporally adjacent data points in the fSS-ω or fSS-θ plots. If fSS consistently correlated with updated online information of ω or θ, successive values of should be consistently close or vary within a small margin. By contrast, if fSS did not follow the updated information well, values of should vary more (Figure 6A). Therefore, we took the inverse of the standard deviation of (σ) in a time window as a measurement of the information association capability (i.e., ). At first look, the variation of was low and was high (0.240 (°/s)/Hz) during the 30-s period with high (55.4°/s2) (Figure 6Ba), and vice versa (=0.011 (°/s)/Hz), = 16.8 °/s2; Figure 6Bb). The population result (n = 17) revealed a positive linear correlation between the normalized and (r = 0.9334; p = 0.002, One-Way ANOVA; Figure 6C, left panel). The coefficient of determination r2 in fSS-ω plots also showed a positive correlation with (r = 0.8547; p = 0.0033, One-Way ANOVA; Figure 6C, right panel). These results suggest that both static (i.e., r2) and dynamic associations (i.e., ) of SS to sensory information depend upon motion level.

Bottom Line: The ability to differentiate such sensory information can lead to movement execution with better accuracy.Both PC simple (SSs; 17 of 26) and complex spikes (CSs; 7 of 12) were found to code initially on the angle of the heads with respect to a fixed reference.Using periods with comparable degrees of movement, we found that such SS coding of information in most PCs (10 of 17) decreased rapidly during balance learning.

View Article: PubMed Central - PubMed

Affiliation: Department of Life Science, National Taiwan University Taipei, Taiwan.

ABSTRACT
Neural responses to sensory inputs caused by self-generated movements (reafference) and external passive stimulation (exafference) differ in various brain regions. The ability to differentiate such sensory information can lead to movement execution with better accuracy. However, how sensory responses are adjusted in regard to this distinguishability during motor learning is still poorly understood. The cerebellum has been hypothesized to analyze the functional significance of sensory information during motor learning, and is thought to be a key region of reafference computation in the vestibular system. In this study, we investigated Purkinje cell (PC) spike trains as cerebellar cortical output when rats learned to balance on a suspended dowel. Rats progressively reduced the amplitude of body swing and made fewer foot slips during a 5-min balancing task. Both PC simple (SSs; 17 of 26) and complex spikes (CSs; 7 of 12) were found to code initially on the angle of the heads with respect to a fixed reference. Using periods with comparable degrees of movement, we found that such SS coding of information in most PCs (10 of 17) decreased rapidly during balance learning. In response to unexpected perturbations and under anesthesia, SS coding capability of these PCs recovered. By plotting SS and CS firing frequencies over 15-s time windows in double-logarithmic plots, a negative correlation between SS and CS was found in awake, but not anesthetized, rats. PCs with prominent SS coding attenuation during motor learning showed weaker SS-CS correlation. Hence, we demonstrate that neural plasticity for filtering out sensory reafference from active motion occurs in the cerebellar cortex in rats during balance learning. SS-CS interaction may contribute to this rapid plasticity as a form of receptive field plasticity in the cerebellar cortex between two receptive maps of sensory inputs from the external world and of efference copies from the will center for volitional movements.

No MeSH data available.


Related in: MedlinePlus