Department of Biology, Marquette University, Milwaukee, Wisconsin 53201-1881
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ABSTRACT |
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Buchanan, James T. and Stefan Kasicki. Segmental Distribution of Common Synaptic Inputs to Spinal Motoneurons During Fictive Swimming in the Lamprey. J. Neurophysiol. 82: 1156-1163, 1999. These experiments were designed to measure the degree of shared synaptic inputs coming to pairs of myotomal motoneurons during swimming activity in the isolated spinal cord of the lamprey. In addition, the experiments measured the decrease in the degree of shared synaptic inputs with the distance between the motoneurons to assess the segmental distribution of these shared inputs. Intracellular microelectrode recordings of membrane potential were made simultaneously on pairs of myotomal motoneurons during swimming activity induced with an excitatory amino acid. The swim cycle oscillations of motoneuron membrane potentials were removed with a digital notch filter, thus leaving the fast synaptic activities that underlie these slower oscillations. Cross-correlations of the fast synaptic activities in two simultaneously recorded motoneurons were made to measure the degree of shared inputs. The cross-correlation was done on time windows restricted to one swim cycle or to part of a swim cycle, and 50 consecutive swim cycle cross-correlograms then were averaged. The peak coefficients of the cross-correlations exhibited a wide range, even for pairs of motoneurons located near one another (range = 0.06-0.74, for pairs located within 2 segments). This observation suggests that there may be different functional classes of myotomal motoneurons with inputs originating from different sets of premotor interneurons. In spite of this variability, the mean peak correlation coefficients of motoneuron pairs clearly decreased with the distance between them. With separations of more than five segments, there was little or no clear correlation between the motoneurons (range = 0.04-0.10). These results suggest that common synaptic inputs to motoneurons during fictive swimming originate from local premotor interneurons and that beyond five spinal segments, common premotor inputs are rare or weak to motoneurons. Thus the premotor signals originating from the locomotor network have relatively short distribution lengths, on the order of 5 segments of 120 total spinal segments.
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INTRODUCTION |
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The lamprey, a primitive vertebrate fish, swims
forward with lateral body waves that propagate from head to tail. The
spinal cord contains the central pattern generator (CPG) for swimming as shown by exposure of the isolated spinal cord to an excitatory amino
acid such as glutamate. Under these conditions, the spinal cord
exhibits rhythmic alternating ventral root bursting (i.e., fictive
swimming) similar to the pattern of electromyographic activity observed
during intact swimming (Cohen and Wallén 1980; Wallén and Williams 1984
). As few as two to three
spinal segments isolated from any rostrocaudal level of the spinal cord
can generate rhythmic bursting, so the CPG for swimming is proposed to
be a continuous network of rhythm generators that can be reduced to a
minimum unit oscillator of two to three segments. In this model, each
unit oscillator generates the basic alternating pattern of activity
between motoneurons on opposite sides of the spinal cord, and coupling
among the unit oscillators by interneurons produces the head-to-tail
propagation of the rhythmic activity characteristic of forward swimming
(Cohen et al. 1992
; Grillner et al. 1991
, 1995
). The present study addresses the segmental distribution lengths of synaptic outputs from the locomotor network to motoneurons.
On the basis of intracellular recordings from pairs of spinal neurons
(Buchanan 1982, 1986
; Buchanan et al.
1989
), a network for the unit oscillator in the lamprey has
been proposed (Buchanan 1986
; Buchanan and
Grillner 1987
). Modeling studies have demonstrated that the
proposed network can generate rhythmic activity with the appropriate
phase relationships among motoneurons and interneurons (Buchanan
1992
; Grillner et al. 1988
; Wallén
et al. 1992
). The three types of interneurons of the network
are known to have multisegmental axonal projections (Buchanan
1982
; Buchanan et al. 1989
; Rovainen 1974
), and two of these, the CC interneurons and the excitatory interneurons, have direct outputs to motoneurons. Therefore these interneurons have been proposed to be responsible not only for rhythm-generation activity but also for interoscillator coupling and
for premotor inputs to motoneurons. Modeling studies have demonstrated
the feasibility of this proposal (Buchanan 1992
; Williams 1992
). In these models, the interoscillator
coupling consists of spread of the same synaptic connectivities found
within the unit oscillator to neighboring oscillators. The synaptic
strengths of these connections are modeled to be greatest within the
oscillator of origin and then to decline with distance from that origin
(Williams 1992
). This is supported by split-bath
experiments in which measurements were made of the amplitudes of the
summed excitatory and inhibitory synaptic inputs to motoneurons
projecting from a fictively swimming half of spinal cord into a passive
half. These experiments demonstrated a decline in synaptic amplitude
with the distance from the active half up to eight segments from the
diffusion barrier (Dale 1986
).
The length of the interoscillator coupling signals in the lamprey is
not known with certainty. A theoretical analysis of coupled oscillators, which has been applied to the lamprey locomotor system, has assumed that the interoscillator coupling signals are weak and
occur only between nearest-neighbor oscillators (Williams et al.
1990). However, correlational analysis of ventral root discharges during fictive swimming has suggested that long-range coupling also may be present in the lamprey spinal cord (Mellen et al. 1995
). In addition, experiments in which activity in
intervening middle segments of spinal cord were blocked with low
calcium or with inhibitory transmitters have demonstrated that
detectable coupling can be achieved between fictively swimming regions
separated by
20 segments (Miller and Sigvardt 1996
;
Rovainen 1985
).
One approach to determining the functional lengths of the
intersegmental coupling signals would be to compare the degree of similarity in synaptic inputs during fictive swimming to neurons separated by various distances. Rhythmically active neurons located in
the same segment are presumably controlled mainly by the unit oscillator of that same segment but also may be influenced by the
spread of synaptic connections from unit oscillators located in other
segments. Thus two related neurons separated by several segments should
have a subset of common synaptic inputs originating from the same unit
oscillator, even though the two neurons each receive their strongest
inputs from different unit oscillators. The distribution lengths of
these common inputs should be measurable by making cross-correlations
of the synaptic activities in neurons during fictive swimming. The
present study takes this approach, focusing on the correlation of
synaptic inputs to pairs of motoneurons. Although the motoneurons have
not been considered to be a part of the rhythm-generating network
(Wallén and Lansner 1984; but see Perrins
and Roberts 1995
), they do receive excitatory and inhibitory
inputs from the network and therefore may reflect the distribution
lengths of synaptic output from the network. It is possible, though,
that the segmental distribution lengths of outputs from the locomotor
network may be different for rhythm-generating interneurons compared
with motoneurons. As a first step, the present study sought to measure
the segmental distribution of common synaptic inputs to motoneurons
occurring during fictive swimming by using intracellular recordings of
membrane potential from pairs of motoneurons separated by various
distances. The results indicate that the spread of signals from the
unit oscillators to motoneurons occurs over relatively short distances,
about five segments.
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METHODS |
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Preparation and dissection
The experiments were done on seven adult silver lampreys
(Ichthyomyzon unicuspis), 18-30 cm in length. The animals
were anesthetized by immersion in a 0.01% solution of tricaine
(3-aminobenzoic acid ethyl ester) (Sigma) until reflexes were lost. The
animals then were decapitated, the brain destroyed mechanically, and a
spinal cord/notochord preparation dissected in cooled Ringer solution as previously described (Rovainen 1974). The lengths of
the preparations ranged from 15 to 56 spinal segments. Four of the
preparations were between 46 and 56 segments in length, extending from
midgill to the beginning of the fin; one preparation was 35 segments in length, and three were 15 segments in length. The four shorter preparations were taken from the midbody region between the last gill
opening and the beginning of the fin. Each preparation was pinned to
the silicone elastomer (Sylgard)-lined bottom of an experimental
chamber which was perfused with cooled Ringer solution (8-9°C) at a
rate of 2-4 ml/min. The Ringer solution consisted of the following (in
mM): 91 NaCl, 2.1 KCl, 2.6 CaCl2, 1.8 MgCl2, 4 glucose, and 20 NaHCO3. The solution was continuously bubbled with 98% O2-2% CO2
(pH 7.4).
Recording techniques
Fictive swimming was induced in the spinal cord/notochord
preparation by bath perfusion with
N-methyl-D,L-aspartate (NMA) (0.2-0.3 mM) or
with D-glutamate (0.5-0.7 mM). Swim activity was monitored
with extracellular ventral root recordings made by placing the tip of a
suction electrode onto a ventral root at its exit point from the spinal
cord. The membrane potentials of motoneurons were recorded with
intracellular microelectrodes consisting of micropipettes filled with 4 M potassium acetate. The motoneurons (n = 78) had
action potentials of 80 mV in amplitude and were identified by the
presence of one-for-one spikes in the nearby ventral root while
eliciting intracellular action potentials with current injection. A
total of 62 ipsilateral pairs of motoneurons were recorded (Fig.
1A). In most experiments, an
intracellular recording was maintained in one motoneuron while the
second intracellular electrode was used to impale several other
motoneurons. In two experiments, a single motoneuron was impaled
simultaneously with two microelectrodes (Fig. 3A). These
dual impalements were accomplished under visual control by virtue of
the visibility of the cell bodies in the lamprey spinal cord using a
stereomicroscope. All intracellular recordings of motoneurons were done
within the midbody region to ensure that the cells were myotomal
motoneurons and not fin motoneurons. Recordings were done in
current-clamp bridge mode using an Axoclamp-2A amplifier (Axon
Instruments). The bath fluid level was kept as low as possible over the
spinal cord to minimize electrode capacitance, and a grounded aluminum
foil shield was placed between the two intracellular electrodes to
minimize interelectrode coupling.
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The intracellular recordings initially were low-pass filtered with a cutoff frequency of 3.0 kHz (Axon Instruments, model 2130) and stored on an eight-channel DAT recorder (Biologic) for later off-line analysis. For the off-line analysis, the intracellular signals were further low-pass filtered and then digitized using a Cambridge Electronic Design (CED) 1401 computer interface, Spike2 software, and a 486 IBM-type computer. The analysis consisted of performing cross-correlations of the synaptic activity in the motoneuron pairs. To correlate the fast synaptic activities of the cells, it was first necessary to remove the slow oscillations of fictive swimming from the membrane potential records; this would otherwise dominate the cross-correlograms. This was done using a digital notch filter provided by the Spike2 software, leaving the fast synaptic activity and action potentials (Fig. 1, B-D).
Filtering
The digital notch filter implemented a finite impulse response
filter consisting of 101 coefficients. The first data points of the
waveform were multiplied by the coefficients and the result of the
multiplications summed to produce an output data point. The set of
coefficients then were shifted one step to the right and the process
repeated. The peak of the notch filter was set as a fraction of the
digitizing rate, and the same fraction (0.001) was used on all files.
The swim frequency to be removed ranged in the different preparations
from 0.6 to 2 cycles/s, mean = 1.3 ± 0.4 (SD) cycles/s.
Therefore the digitizing rate had to range from 0.6 kHz for the slowest
swimming to 2 kHz for the fastest swimming. To ensure that the
digitizing rate was sufficient to provide an accurate representation of
the data (i.e., that the digitizing rate was at least twice the highest
frequency components in the waveform), all recordings were low-pass
filtered at 0.3 kHz before digitizing. This filter frequency was
considered adequate to preserve the major frequency components of fast
synaptic potentials because lamprey motoneurons have membrane time
constants of ~10 ms, as shown with step current injections
(Buchanan 1993). Additional evidence for the adequacy of
the 0.3-kHz filtering is that white-noise impedance measurements of
motoneurons, done with a frequency range of 0.5-200 Hz, have been used
to accurately simulate synaptic potentials using the inverse transform
of the impedance function (Buchanan et al. 1992
).
Finally, tests of the dependence of the cross-correlations on the value
of the low-pass filtering found only small changes in the peak
amplitude of the cross-correlograms. For example (Fig.
2B), in a pair of motoneurons
digitized at 2 kHz, the cross-correlation was compared when the data
had been filtered at 0.3 versus 1.0 kHz. There was a nonsignificant
11% drop in the peak amplitude of the cross-correlation at 1.0 kHz compared with 0.3 kHz (P > 0.2, Student's
t-test; Fig. 2B). It thus was concluded that the
0.3 kHz filter and the digitizing rate of at least two times this value
were sufficient to preserve the character of the fast synaptic
activity.
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Data analysis
After the slow swim frequency was removed from the pair of waveforms with the notch filter, a cross-correlation was performed using a program written in the Spike2 script language that allowed averaging of multiple cross-correlograms (Fig. 1E). Averages were made on 50 consecutive swim cycles. When performing the cross-correlations, the more rostral motoneuron was used as the trigger and the caudal motoneuron as the reference. The cross-correlations were performed on windows of membrane potential activity that consisted of either a full or a partial swim cycle: if action potentials were absent in the records of both cells, a full swim cycle was used for the cross-correlation (peak depolarization to peak depolarization of 1 swim cycle, as assessed in prenotch filtered records displayed simultaneously with the notch-filtered records); if action potentials were present, the cross-correlations were made within windows between action potentials. High-gain records of the notch-filtered membrane potentials were used to place the window boundaries between action potentials, avoiding the slow afterhyperpolarization after the action potential. The afterhyperpolarizations were visible in the notch-filtered records, allowing placement of the beginning of the window at a point when the membrane potential had returned to its mean level. This tended to occur at a latency of ~50 ms after the peak of the action potential. The end of the window was placed at a similar latency before the first action potential of the next cycle. These partial swim cycles were used exclusively in 51 pairs of motoneurons; full cycles were used exclusively in 5 pairs, and both full and partial cycles were used in 6 pairs. To assess whether there was any significant differences between full and partial swim cycle correlations, both types of analysis were done on three pairs of motoneurons (Fig. 2C). No significant differences were found between the peak cross-correlations using the two types of windowing (P > 0.2, Student's t-test). This finding suggests that the synaptic inputs occurring during the excitatory and inhibitory phases are correlated to similar degrees in given pairs of motoneurons. Therefore the results from these two windowing procedures were pooled.
The peak coefficient of the averaged correlograms is expressed as the mean ± SD. The peak coefficient represents the maximum positive value found in the correlogram. For well-correlated records, the maximum value occurred at a distinct peak (Fig. 3, A3-D3). However, records with poor correlation often did not have distinct peaks (see Fig. 3E3). To provide a measure of the maximum positive values that would indicate a correlation, cross-correlations were done on noncorrelated data, that is, on pairs of motoneurons in which each member of the pair was recorded separately at different times (n = 3). These correlograms had peak maximum values of 0.029, 0.045, and 0.052, respectively. Therefore peak coefficients <0.06 are not likely to be correlated.
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RESULTS |
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When two motoneurons were located close to one another in the same spinal segment and on the same side of the spinal cord (Fig. 1A), the membrane potential activities of the two cells could be highly similar when recorded simultaneously with intracellular microelectrodes. The similarities were apparent both in the shapes of the slow swim cycle oscillations and in some details of the faster synaptic activities (Fig. 1B). After removal of the membrane potential oscillations of the swim cycle using a digital notch filter, the underlying fast synaptic activity could be seen more clearly. For the two motoneurons illustrated in Fig. 1, a highly similar pattern of synaptic activity was then apparent after notch filtering (Fig. 1, C and D). Cross-correlation of the fast synaptic activity occurring in the portions of the swim cycle without action potentials revealed a high peak correlation coefficient between these two motoneurons (Fig. 1E; peak = 0.71 ± 0.11). The high cross-correlation coefficient confirms and quantifies the impression that the two motoneurons have similar synaptic inputs during fictive swimming.
The highest peak correlation observed between two motoneurons was 0.74, indicating that there was never a perfect correlation of activity.
Physiological factors certainly play a major role in this such as
having similar but nonidentical sets of active premotor interneurons
synapsing on the two motoneurons or because of the different
electrophysiological properties of the two motoneurons [e.g., input
resistances and membrane time constants (Buchanan 1993)]. However, nonphysiological factors also could play a
role in limiting the degree of correlation such as differences in the filter characteristics of the two microelectrodes.
To provide some indication of the contribution of differences in the
microelectrodes to the outcome of the cross-correlations, two
microelectrodes were inserted into the same motoneuron during fictive
swimming in two experiments (Fig. 3A). The peak coefficients of the averaged cross-correlations in these two-electrode recordings made from the same motoneuron (n = 2) were 0.83 and
0.87, respectively. These values were higher than the peak coefficients
observed with the microelectrodes in two separate motoneurons, but
these values demonstrate that even with a two-electrode recording of a
single cell, there was not a perfect correlation of the fast synaptic activity. It is likely that this difference is due in part to differences in the filter characteristics of the microelectrodes. However, there also appear to be real differences in the synaptic activity recorded by the two electrodes as revealed by close inspection of the high gain traces of Fig. 3A2. Thus although both
electrodes were in the same motoneuron, they apparently could record
differences in the spatial distributions of synaptic inputs to the
cell. These dual-impalement cross-correlation values place an upper
limit on the peak correlation coefficients attainable using two
microelectrodes and appear in Fig. 4 at
the 0-distance point ().
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During fictive swimming, intracellular recordings were made among 62 pairs of motoneurons separated by various distances. The peak correlation coefficients of the cross-correlograms showed considerable variability, even for pairs of motoneurons located close to one another. For example, pairs of motoneurons located within two segments of one another had peaks that ranged from 0.06 to 0.74 (mean = 0.44 ± 0.20, n = 29; median = 0.49). Pairs of motoneurons located from two to four segments of one another had peaks that ranged from 0.08 to 0.33 (mean = 0.20 ± 0.08, n = 14; median = 0.21). At five segments and greater separating the motoneurons, the peaks ranged from 0.04 to 0.21 (mean = 0.08 ± 0.04, n = 19; median = 0.08). These means are significantly different from one another (P < 0.001, Student's t-test). The observation that motoneurons within the same segment may have a fairly high correlation (e.g., 0.7) or low or no correlation (0.06) suggests that motoneurons located within the same segment do not necessarily receive inputs from the same set of premotor interneurons. This suggests the existence of different functional groupings of myotomal motoneurons (see DISCUSSION).
In spite of the variability in the peak cross-correlation coefficients in pairs separated by similar distances, there was a clear decrease in the mean values of the peaks with the distance separating the motoneuron pairs. Individual examples of motoneuron pairs separated by various distances are shown in Fig. 3. The peak coefficients of the averaged cross-correlations were plotted versus the distance (in segments) between the pairs of motoneurons in Fig. 4. It should be noted that the data for this plot were based mostly on correlations taken during the inhibitory phases to exclude action potentials from the cross-correlograms (see METHODS). There was a clear tendency for the peak coefficients to decrease with the distance separating the motoneurons. Beyond about five segments, little or no correlation remained. As described in the Methods, peak correlations of 0.03-0.05 were obtained when doing the cross-correlation analysis on noncorrelated cells (i.e., cells recorded separately at different times) and most of the pairs at distances more than five segments had peak correlations in this range, although 4 of the 14 cells had peaks between 0.08 and 0.10. For the smooth curve in Fig. 4, a single-exponential function was fit to the higher correlations at each distance (n = 44 pairs, plus the 2 motoneurons recorded with dual impalements). The rationale for using the cells with the higher correlations at each distance is that if the variability in the peak correlations indicates different functional groupings of myotomal motoneurons, using those with the higher peaks allows a comparison of related motoneurons. The rationale for including the two motoneurons recorded with dual impalements in the curve is that these represent the maximum observed correlation obtained with two microelectrodes.
The peaks of the averaged correlograms often occurred at nonzero lags (Fig. 3, C3 and D3); this could indicate that the correlated synaptic potentials tended to occur with a particular temporal relationship between the two motoneurons. One obvious factor that could contribute to nonzero lags would be the conduction time along common presynaptic axons. The relationship between the distance separating the motoneuron pairs and the offset of the peak correlation coefficient should then reveal the conduction velocity and the direction of propagation of the signals between the cells. However, as shown in Fig. 5, there was little or no correlation between the distance separating the motoneurons and the lag (R2 = 0.25; m = 0.9 mm/ms). The data points used for Fig. 5 were those pairs of motoneurons separated by distances less than five segments that had sufficiently high correlations to determine the lag for the peak correlation coefficient. Most of the nonzero lags were positive (13 positive, 4 negative), which would be associated with rostral-to-caudal propagation of the signal.
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DISCUSSION |
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During fictive swimming, the membrane potentials of motoneurons
oscillate due to alternating phases of excitatory and inhibitory synaptic inputs (Buchanan and Cohen 1982;
Buchanan and Kasicki 1995
; Kahn 1982
;
Russell and Wallén 1983
). It was shown in the present study that the synaptic potentials underlying these
oscillations can be highly correlated between motoneurons located near
one another within the same spinal segment. This result suggests that during fictive swimming, some neighboring motoneurons receive synaptic
inputs from similar sets of premotor interneurons. Motoneurons separated by greater distances showed lower correlations of their synaptic activities, suggesting that their synaptic inputs come from
different sets of premotor interneurons. The relationship between the
correlation of synaptic activity versus the distance between the
motoneurons (Fig. 4) indicates that the main region of influence of
premotor interneurons is within about five spinal segments. This is a
relatively short distance considering that the lamprey spinal cord
contains ~120 spinal segments. Possible candidates for these local
inputs are the excitatory interneurons, which have short ipsilateral
axons (Buchanan et al. 1989
), and the commissural
interneurons with axons of less than five segments (Ohta et al.
1991
), which may be predominately inhibitory (Buchanan 1982
).
An interpretation of these experimental results is shown in Fig. 6. In this scheme, the spinal cord contains a continuous locomotor network, which can be reduced to about two or three segments as the minimal unit oscillator or CPG. Each unit CPG has a distributed set of outputs that are strongest to the motoneurons of the same segment and progressively weaker in rostral and caudal directions (Fig. 6A). Therefore a given motoneuron will not only receive input from its local CPG but also from more distant CPGs. Two motoneurons in the same segment will have a high correlation of synaptic activity because they both receive strong inputs from the same CPG interneurons. Two motoneurons located in different segments will have a lower correlation of synaptic activity because their strongest inputs will be originating from different sets of CPG premotor interneurons. But because of the distributed nature of the CPG outputs, the two motoneurons will have some overlapping inputs (compare MN3 and MN5 in Fig. 6B), and thus will still show some correlation. However, because the strengths of the CPG outputs decline with distance, the local inputs to each of these motoneurons located in different segments will be strong and uncorrelated and will tend to obscure the weaker correlated inputs to the two cells.
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The premotor interneurons that provide the output to motoneurons
during fictive swimming also synapse among themselves to form the
proposed unit oscillator (Buchanan 1982; Buchanan
and Grillner 1987
; Buchanan et al. 1989
).
Therefore it is our working hypothesis that the same set of locomotor
interneurons is responsible not only for providing output to
motoneurons, but also for rhythm generation and for interoscillator
coupling (Fig. 6C). The feasibility of this scheme has been
demonstrated with computer simulations of the network (Buchanan
1992
; Williams 1992
). This hypothesis would
predict that paired recordings between locomotor interneurons, i.e., CC
interneurons, excitatory interneurons, and lateral interneurons, would
yield correlations that vary with distance in a manner similar to
motoneurons. The alternative hypothesis is that the outputs from the
unit CPGs have different distribution lengths depending on their
targets. It is possible, for example, that the CPG outputs to
motoneurons are short while the CPG outputs to other CPG interneurons are longer. This scheme would perhaps help to stabilize the
rostral-to-caudal phase coupling of the oscillators but allow for more
local control of the motoneurons. This hypothesis would predict that
paired recordings between locomotor interneurons would show a longer length constant than that found for motoneurons in Fig. 4.
In the present study, cross-correlations between motoneurons separated
by more than five segments showed low correlations (range = 0.04-0.10). Tests on noncorrelated data gave peak correlations coefficients of <0.06 (see METHODS), so many of these
motoneuron pairs separated by more than five segments showed no
significant correlations. However, these results do not exclude the
existence of intersegmental signals longer than five segments but
suggest that such long-range connections are much weaker than the
short-range connections. There are several experimental studies in
lamprey demonstrating the existence of long-range signals. For example, in lesion studies of the lamprey spinal cord during fictive swimming, shortening of the spinal cord to three segments reduced the synaptic drive in motoneurons to ~30% of the value recorded in the 18-segment preparation before shortening (Wallén et al.
1993). This finding suggested that the summated synaptic inputs
from more distant segments were stronger than the local synaptic
inputs. However, these experiments probably underestimated the local
synaptic drive because spinal cord lengths less than four segments are
only marginally capable of generating regular rhythmic activity
(Cohen and Wallén 1980
). Thus the observed
reduction in synaptic drive may have simply reflected a depressed state
of the local generator rather than the loss of more distant premotor
inputs. In another study, modeling of correlations among ventral root
discharges suggested that long-range coupling exists (Mellen et
al. 1995
). Additional evidence for long-range coupling has been
obtained using three-bath experiments in which a middle region of the
spinal cord is inhibited by application of glycine or low-calcium
solutions. In these experiments, coupling of fictive swimming could be
maintained across inhibited spinal cord
20 segments in length
(Miller and Sigvardt 1996
; Rovainen
1985
). Some interneurons of the proposed locomotor network have
axons projecting 30-50 spinal segments (Buchanan 1982
;
Rovainen 1974
), so the anatomic substrate for long-range
connections exists. In the present experiments, some of the motoneuron
pairs separated by 10-20 segments may have shown weak correlations
(0.08-0.10). The higher correlations at these distances may be due to
input from inhibitory CC interneurons, some of which have been shown to
project
30 segments and synapse directly on motoneurons
(Buchanan 1982
). Thus although it is likely that pairs
of motoneurons separated by more than five segments will receive some
common inputs, the cross-correlation technique used here does not
readily demonstrate them. The presumed reason for this is that these
distant inputs are only small contributors to the total synaptic drive.
It would be predicted that these distant common synaptic inputs would
be demonstrated easily with this cross-correlation technique in a quiescent preparation.
The wide range of peak correlation coefficients between pairs of
motoneurons located even within the same segment (Fig. 4) suggests that
some motoneurons receive significantly different inputs during fictive
swimming in spite of their close proximity. It has been shown
previously that motoneurons in the same segment, but innervating
muscles located at different dorsoventral levels of the body, can show
different oscillatory patterns during fictive swimming
(Wallén et al. 1985). In particular, motoneurons
innervating the midline part of a myotome tended to show different
oscillatory patterns than motoneurons innervating the lateral part of a
myotome. The midline-innervating motoneurons also have dendrites that
cross the midline of the spinal cord while the laterally innervating motoneurons do not. It then would be expected that midline-innervating motoneurons would receive inputs from different sets of premotor interneurons compared with laterally innervating motoneurons. The wide
range of cross-correlations between close pairs of motoneurons therefore may indicate functional differences among motoneurons and may
imply that the unit locomotor oscillator is fractionated into
components related to different dorsoventral levels of the body.
The estimate of conduction velocity of the intersegmental signals from
the plot of distance versus the offset of the peak correlation
coefficients suggested a caudally projecting signal with a conduction
velocity of 0.9 mm/ms (Fig. 5). Although conclusions cannot be made
from the weak relationship of Fig. 5, it is interesting to note that
this conduction velocity is consistent with the conduction velocities
of medium-sized interneurons such as CC interneurons, which have
caudally projecting axons (Buchanan 1982). The lack of a
strong relationship between distance and the offset is not surprising.
Motoneurons receive both ascending and descending signals (Dale
1986
), which would result in both phase leads and phase lags of
synaptic potentials in the more caudal motoneuron. In the
cross-correlograms this mixture of phase relations would tend to
obscure phase relations due to particular populations of ascending or
descending neurons. To make a clearer distinction between the
contributions of ascending versus descending signals, it would be
possible to perform a split-bath experiment in which a diffusion
barrier separates the bath into independently-perfused pools with
fictive swimming occurring in only the rostral or the caudal half of
the preparation. In this way, it would be possible to separately
examine the correlations associated with ascending versus descending
signals (Dale 1986
). This might also reveal longer
coupling signals because the mixture of phase leads and lags may
degrade the correlations, especially in widely separated cells.
Several factors may contribute to the lengths of intersegmental coupling signals: the average lengths of the axons involved in coupling, how the number of synaptic outputs are distributed along the lengths of the axons (e.g., more frequent axonal outputs close to the cell body vs. at a distance), and how the synaptic strengths are distributed along the lengths of the axons (e.g., stronger connections close to the cell body of origin vs. at a distance). Although the present study does not address the anatomic and physiological determinants of coupling strength, it does give an overall measure of the functional lengths of intersegmental signals from the unit CPG for fictive swimming to the motoneurons. These signals appear to be relatively short, characterized mainly as nearest-neighbor in length.
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ACKNOWLEDGMENTS |
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Our thanks to T. Strauss for help with data analysis.
This work was supported by National Institute of Neurological Disorders and Stroke Grants NS-28369 and NS-35725 to J. T. Buchanan and National Institutes of Health Fogarty Central and Eastern Europe Senior Fellowship in the Neurosciences 5 F05 TW04648 to S. Kasicki.
Present address of S. Kasicki: Dept. of Neurophysiology, Nencki Institute of Experimental Biology, 3 Pasteura, 02-093 Warsaw, Poland.
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FOOTNOTES |
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Address for reprint requests: J. T. Buchanan, Dept. of Biology, Marquette University, P.O. Box 1881, Milwaukee, WI 53201-1881.
The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Received 14 September 1998; accepted in final form 13 May 1999.
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