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Somato-dendritic morphology and dendritic signal transfer properties differentiate between fore- and hindlimb innervating motoneurons in the frog Rana esculenta.

Stelescu A, Sümegi J, Wéber I, Birinyi A, Wolf E - BMC Neurosci (2012)

Bottom Line: On the other hand no segregation was observed by the steady-state current transfer except under high background activity.We found size-dependent and size-independent differences in morphology and electrical structure of the limb moving motoneurons based on their spinal segmental location in frogs.Location specificity of locomotor networks is therefore partly due to segmental differences in motoneurons driving fore-, and hindlimbs.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Anatomy, Histology and Embryology, Faculty of Medicine, Medical and Health Science Center, University of Debrecen, Nagyerdei krt 98, Debrecen, H-4032, Hungary.

ABSTRACT

Background: The location specific motor pattern generation properties of the spinal cord along its rostro-caudal axis have been demonstrated. However, it is still unclear that these differences are due to the different spinal interneuronal networks underlying locomotions or there are also segmental differences in motoneurons innervating different limbs. Frogs use their fore- and hindlimbs differently during jumping and swimming. Therefore we hypothesized that limb innervating motoneurons, located in the cervical and lumbar spinal cord, are different in their morphology and dendritic signal transfer properties. The test of this hypothesis what we report here.

Results: Discriminant analysis classified segmental origin of the intracellularly labeled and three-dimensionally reconstructed motoneurons 100% correctly based on twelve morphological variables. Somata of lumbar motoneurons were rounder; the dendrites had bigger total length, more branches with higher branching orders and different spatial distributions of branch points. The ventro-medial extent of cervical dendrites was bigger than in lumbar motoneurons. Computational models of the motoneurons showed that dendritic signal transfer properties were also different in the two groups of motoneurons. Whether log attenuations were higher or lower in cervical than in lumbar motoneurons depended on the proximity of dendritic input to the soma. To investigate dendritic voltage and current transfer properties imposed by dendritic architecture rather than by neuronal size we used standardized distributions of transfer variables. We introduced a novel combination of cluster analysis and homogeneity indexes to quantify segmental segregation tendencies of motoneurons based on their dendritic transfer properties. A segregation tendency of cervical and lumbar motoneurons was detected by the rates of steady-state and transient voltage-amplitude transfers from dendrites to soma at all levels of synaptic background activities, modeled by varying the specific dendritic membrane resistance. On the other hand no segregation was observed by the steady-state current transfer except under high background activity.

Conclusions: We found size-dependent and size-independent differences in morphology and electrical structure of the limb moving motoneurons based on their spinal segmental location in frogs. Location specificity of locomotor networks is therefore partly due to segmental differences in motoneurons driving fore-, and hindlimbs.

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Steps of data processing to create standardized area weighted distributions of signal transfer values. Example is based on current transfers of MN C-IC82 but steps are similar for other measures of signal transfers we used. (A) Frequency distribution of somatopetal current transfers measured from mid-points of each compartment. These raw values were area weighted (B) to give proportionally bigger weight to transfers from compartments with bigger surface area. Then the area weighted distribution was standardized (C) to eliminate variable size effects of MNs on signal transfers. Shape of standardized and area weighted distribution of transfers was quantified by the 10th, 25th, 50th, 75th and 90th percentiles of the distribution (see lower horizontal axis in part C).
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Figure 1: Steps of data processing to create standardized area weighted distributions of signal transfer values. Example is based on current transfers of MN C-IC82 but steps are similar for other measures of signal transfers we used. (A) Frequency distribution of somatopetal current transfers measured from mid-points of each compartment. These raw values were area weighted (B) to give proportionally bigger weight to transfers from compartments with bigger surface area. Then the area weighted distribution was standardized (C) to eliminate variable size effects of MNs on signal transfers. Shape of standardized and area weighted distribution of transfers was quantified by the 10th, 25th, 50th, 75th and 90th percentiles of the distribution (see lower horizontal axis in part C).

Mentions: In our investigations current and voltage transfers as well as somatic to dendritic ratios of shape parameters of transient EPSPs were weighted by the surface of the dendritic compartment whose mid-point was used to generate the signal (see Figure 1A–B for illustration). Area weighting is useful to give proportionally higher weight for transfer values that approximate transfers of PSPs of more synapses received by a bigger dendritic compartment. This area weighting is especially appropriate for spinal MNs of frogs where it was shown that areal synaptic density (the number of synapses received by a unit dendritic surface) is the same over the whole dendritic arborization, independently of the distance from the soma and the diameter of the dendrite [37]. This way the area of a compartment is directly proportional to the number of synapses received by that compartment. Area weighted voltage and current transfers were then standardized (Figure 1C). Standardization is a well-known mathematical transformation that replaces each area weighted measurement by its sample standard score (z score) so that distributions have a mean value of zero and a standard deviation of 1. E.g. if X is an area weighted transfer value then its z score becomes (X-μ)/σ, where μ and σ are the mean and standard deviation of the distribution of X values. So z scores indicate how far above or below the mean a given score in the distribution is in standard deviation units. Standardization preserves the shape of area weighted distributions while differences due to variance in size of MNs are eliminated. This allowed comparison of shapes of signal transfer distributions independently of the size differences of MNs. Shape of these standardized distributions were described by their 10th, 25th, 50th, 75th and 90th percentiles; the values in the distributions, below which the corresponding percents of all observations fall. Since the frequency of dendrites with very low and high transfer values to soma is limited, the errors associated with increasingly lower and higher percentiles are disproportionally bigger than the error associated with the 50th percentile [34]. In order to compensate for these percentile dependent errors, percentiles were weighted relative to the 50th percentile (or median value, whose weight was 1). Weighting process was performed by two different sets of weighting factors to check if our results are independent on the particular weighting strategy. First, the 10th, 90th and 25th, 75th percentiles had 0.2 and 0.8 weights respectively, while in the second case, the weights were 0.33 and 0.67. The two sets of these weighted percentiles were then used as descriptors in hierarchical cluster analysis to classify MNs based on their dendritic signal transfer properties and the percentiles are shown as box plots in figures.


Somato-dendritic morphology and dendritic signal transfer properties differentiate between fore- and hindlimb innervating motoneurons in the frog Rana esculenta.

Stelescu A, Sümegi J, Wéber I, Birinyi A, Wolf E - BMC Neurosci (2012)

Steps of data processing to create standardized area weighted distributions of signal transfer values. Example is based on current transfers of MN C-IC82 but steps are similar for other measures of signal transfers we used. (A) Frequency distribution of somatopetal current transfers measured from mid-points of each compartment. These raw values were area weighted (B) to give proportionally bigger weight to transfers from compartments with bigger surface area. Then the area weighted distribution was standardized (C) to eliminate variable size effects of MNs on signal transfers. Shape of standardized and area weighted distribution of transfers was quantified by the 10th, 25th, 50th, 75th and 90th percentiles of the distribution (see lower horizontal axis in part C).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3472316&req=5

Figure 1: Steps of data processing to create standardized area weighted distributions of signal transfer values. Example is based on current transfers of MN C-IC82 but steps are similar for other measures of signal transfers we used. (A) Frequency distribution of somatopetal current transfers measured from mid-points of each compartment. These raw values were area weighted (B) to give proportionally bigger weight to transfers from compartments with bigger surface area. Then the area weighted distribution was standardized (C) to eliminate variable size effects of MNs on signal transfers. Shape of standardized and area weighted distribution of transfers was quantified by the 10th, 25th, 50th, 75th and 90th percentiles of the distribution (see lower horizontal axis in part C).
Mentions: In our investigations current and voltage transfers as well as somatic to dendritic ratios of shape parameters of transient EPSPs were weighted by the surface of the dendritic compartment whose mid-point was used to generate the signal (see Figure 1A–B for illustration). Area weighting is useful to give proportionally higher weight for transfer values that approximate transfers of PSPs of more synapses received by a bigger dendritic compartment. This area weighting is especially appropriate for spinal MNs of frogs where it was shown that areal synaptic density (the number of synapses received by a unit dendritic surface) is the same over the whole dendritic arborization, independently of the distance from the soma and the diameter of the dendrite [37]. This way the area of a compartment is directly proportional to the number of synapses received by that compartment. Area weighted voltage and current transfers were then standardized (Figure 1C). Standardization is a well-known mathematical transformation that replaces each area weighted measurement by its sample standard score (z score) so that distributions have a mean value of zero and a standard deviation of 1. E.g. if X is an area weighted transfer value then its z score becomes (X-μ)/σ, where μ and σ are the mean and standard deviation of the distribution of X values. So z scores indicate how far above or below the mean a given score in the distribution is in standard deviation units. Standardization preserves the shape of area weighted distributions while differences due to variance in size of MNs are eliminated. This allowed comparison of shapes of signal transfer distributions independently of the size differences of MNs. Shape of these standardized distributions were described by their 10th, 25th, 50th, 75th and 90th percentiles; the values in the distributions, below which the corresponding percents of all observations fall. Since the frequency of dendrites with very low and high transfer values to soma is limited, the errors associated with increasingly lower and higher percentiles are disproportionally bigger than the error associated with the 50th percentile [34]. In order to compensate for these percentile dependent errors, percentiles were weighted relative to the 50th percentile (or median value, whose weight was 1). Weighting process was performed by two different sets of weighting factors to check if our results are independent on the particular weighting strategy. First, the 10th, 90th and 25th, 75th percentiles had 0.2 and 0.8 weights respectively, while in the second case, the weights were 0.33 and 0.67. The two sets of these weighted percentiles were then used as descriptors in hierarchical cluster analysis to classify MNs based on their dendritic signal transfer properties and the percentiles are shown as box plots in figures.

Bottom Line: On the other hand no segregation was observed by the steady-state current transfer except under high background activity.We found size-dependent and size-independent differences in morphology and electrical structure of the limb moving motoneurons based on their spinal segmental location in frogs.Location specificity of locomotor networks is therefore partly due to segmental differences in motoneurons driving fore-, and hindlimbs.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Anatomy, Histology and Embryology, Faculty of Medicine, Medical and Health Science Center, University of Debrecen, Nagyerdei krt 98, Debrecen, H-4032, Hungary.

ABSTRACT

Background: The location specific motor pattern generation properties of the spinal cord along its rostro-caudal axis have been demonstrated. However, it is still unclear that these differences are due to the different spinal interneuronal networks underlying locomotions or there are also segmental differences in motoneurons innervating different limbs. Frogs use their fore- and hindlimbs differently during jumping and swimming. Therefore we hypothesized that limb innervating motoneurons, located in the cervical and lumbar spinal cord, are different in their morphology and dendritic signal transfer properties. The test of this hypothesis what we report here.

Results: Discriminant analysis classified segmental origin of the intracellularly labeled and three-dimensionally reconstructed motoneurons 100% correctly based on twelve morphological variables. Somata of lumbar motoneurons were rounder; the dendrites had bigger total length, more branches with higher branching orders and different spatial distributions of branch points. The ventro-medial extent of cervical dendrites was bigger than in lumbar motoneurons. Computational models of the motoneurons showed that dendritic signal transfer properties were also different in the two groups of motoneurons. Whether log attenuations were higher or lower in cervical than in lumbar motoneurons depended on the proximity of dendritic input to the soma. To investigate dendritic voltage and current transfer properties imposed by dendritic architecture rather than by neuronal size we used standardized distributions of transfer variables. We introduced a novel combination of cluster analysis and homogeneity indexes to quantify segmental segregation tendencies of motoneurons based on their dendritic transfer properties. A segregation tendency of cervical and lumbar motoneurons was detected by the rates of steady-state and transient voltage-amplitude transfers from dendrites to soma at all levels of synaptic background activities, modeled by varying the specific dendritic membrane resistance. On the other hand no segregation was observed by the steady-state current transfer except under high background activity.

Conclusions: We found size-dependent and size-independent differences in morphology and electrical structure of the limb moving motoneurons based on their spinal segmental location in frogs. Location specificity of locomotor networks is therefore partly due to segmental differences in motoneurons driving fore-, and hindlimbs.

Show MeSH
Related in: MedlinePlus