responses of rat trigeminal ganglion neurons to longitudinal whisker

Journal of Neurophysiology JN-90511-2008, 2nd revision Article Type: Report

RESPONSES OF RAT TRIGEMINAL GANGLION NEURONS TO LONGITUDINAL WHISKER

STIMULATION

Maik C. Stüttgen1, Stephanie Kullmann1,2, Cornelius Schwarz1*

1Abteilung für Kognitive Neurologie, Hertie-Institut für Klinische Hirnforschung, Universität Tübingen, Germany

2Graduate School for Neural and Behavioural Sciences, Universität Tübingen, Germany

Running head: Longitudinal whisker stimulation

With 14 text pages, 3 figures, and 1 table

* Correspondence: Cornelius Schwarz Hertie Institut für Klinische Hirnforschung Universität Tübingen Abteilung für Kognitive Neurologie

Otfried Müller Str. 27 72076 Tübingen, Germany

Phone: +49 7071 2980462 Fax: +49 7071 295724 email: [email protected]

Articles in PresS. J Neurophysiol (August 6, 2008). doi:10.1152/jn.90511.2008

Copyright © 2008 by the American Physiological Society.

Longitudinal whisker stimulation 1

Abstract

Rats use their mobile set of whiskers to actively explore their environment. Parameters

that play a role to generate movement dynamics of the whisker shaft within the follicle,

thus activating primary afferents, are manifold: amongst them are mechanical properties

of the whiskers (curvature, elasticity and taper), active movements (head, body and

whiskers), and finally, object characteristics (surface, geometry, position and orientation).

Hence, the whisker system is confronted with forces along all three axes in space.

Movements along the two latitudinal axes of the whisker (horizontal and vertical) have

been well studied. Here we focus on movement along the whisker’s longitudinal axis that

has been neglected so far. We employed ramp-and-hold movements that pushed the

whisker shaft towards the skin, and quantified the resulting activity in trigeminal first-

order afferents in anesthetized rats. Virtually all recorded neurons were highly sensitive

to longitudinal movement. Neurons could be perfectly segregated into two groups

according to their modulation by stimulus amplitude and velocity, respectively. This

classification regimen correlated perfectly with the presence or absence of slowly

adapting responses in longitudinal stimulation but agreed with classification derived from

latitudinal stimulation only if the whisker was engaged in its optimal direction and set

point. We conclude that longitudinal stimulation is an extremely effective means to

activate the tactile pathway, and thus, is highly likely to play an important role in tactile

coding on the ascending somatosensory pathway. In addition, compared to latitudinal

stimulation, it provides a reliable and easy to use classification of trigeminal first-order

afferents.


Introduction

During exploration, rats exhibit rhythmic whisker movements in the range of 5-11 Hz

(Welker, 1964). A typical whisker stroke follows a smooth ellipsoid trajectory with its

principal axis aligned with the rostrocaudal axis of the rat (Bermejo et al., 2002). This is

one reason why virtually all studies conducted so far employed latitudinal (horizontal or

vertical) whisker stimulation protocols. However, already Zucker and Welker (1969)

reported the qualitative finding that ~50 % of neurons in the TG respond to longitudinal

stimulation (push or pull) of the whisker. Observations of whisker motion using high-

speed videography have revealed that whisker-object contacts consist of repetitive ‘stick-

slip’ events and frequently involve the bent whisker tips rather than whisker shafts (Ritt

et al., 2008), with even large whisker tip deformations having comparatively small effects

on whisker base angle (Voigts et al., 2008). In addition, a preliminary report found that

rats ‘dab’ their whisker tips against surfaces of interest during exploration (Prescott et al.,

2005). Most importantly, Gopal and Hartmann (2007) demonstrated the presence of

significant longitudinal forces by rotating a plucked whisker against a point object with

force-sensing capabilities. Notably, these longitudinal forces were observed even though

the direction of the contact force was perpendicular to the long axis of the whisker – a

situation typical of object contact during whisking (Mitchinson et al., 2007). Moreover,

depending on whisker rotational angle and radial object distance from the base,

longitudinal forces were up to eight times larger than normal forces.

Taken together, these findings suggest that longitudinal forces occurring during natural

whisking could generate responses at least in a subset of primary afferent neurons. These


responses may be behaviorally relevant to the animal and, due to mechanical processing

in the follicle, could be both quantitatively and qualitatively different from those evoked

by rostrocaudal stimulation. Here, we investigated the quantitative relationship between

kinematics of longitudinal whisker stimulation and neuronal responses in the trigeminal

ganglion.

Methods

Data were obtained from 10 adult female Sprague-Dawley rats aged 12-16 weeks. All

experiments were performed in accordance with standards of the Society for

Neuroscience and the German law for the protection of animals. At the end of the

experiment the rat was killed with an overdose of pentobarbital. Our methods have been

described previously (Stüttgen et al., 2006). Briefly, rats were under i.p. ketamine and

xylazine anesthesia (100 and 10 mg/kg, respectively). To expose the trigeminal ganglion

at the base of the skull, the overlying bone was removed, the left cerebral hemisphere was

aspirated and the dura covering the trigeminal ganglion was carefully teased away. The

ganglion was kept moist with physiological saline (0.9% sodium chloride) throughout the

experiment.

Recordings were made with laboratory-built, pulled and ground glass coated platinum

tungsten electrodes (4-8 MΩ). Bandpass filtered (200-5000 Hz) voltage traces were


recorded at 20 kHz sampling rate. An automated spike- sorting algorithm was applied to

sort out stimulation artifacts and multiunit activity (Hermle et al., 2004). Only single unit

spike trains entered the data set. Once a unit was isolated, its receptive field was

determined by deflecting individual whiskers using a cotton-tipped probe. The

corresponding whisker was trimmed to ~ 5 mm. The whisker stump was attached to a

glass capillary glued to a piezoelectric actuator. Stimuli were constructed such that the

stimulator pushed the whisker along its longitudinal axis into the follicle. The shortened

whisker stumps were sufficiently stiff such that longitudinal push did not result in

bending; for every cell recorded, we checked that the stimulator exerted its force at an

angle identical to the long axis of each whisker using a dissecting microscope. We

sampled exclusively caudal whiskers (whiskers 1-4 in rows A-E, and straddlers). Each

stimulus was composed of a fast half cosine wave, pushing the whisker into the follicle,

followed by a 500 ms plateau and another half cosine wave at 0.5 Hz for a very slow

return (stimulus waveforms are depicted in Fig. 1). The steepness of the half cosine wave

was varied to achieve different peak velocities. For longitudinal stimulation, we used a

total of 15 stimuli (3 different amplitudes, 95, 155, 285 µm; 5 different peak velocities, 5,

22, 43, 87, 130 mm/s, where ‘peak velocity’ refers to the maximum velocity achieved

during the onset ramp). Ten presentations per stimulus were presented in pseudorandom

order at an interstimulus interval of 2.5 s.

A subset of cells were also recorded during latitudinal whisker deflection. Here, both

onset and return of the ramp-and-hold stimuli (applied in the rostral direction) were

composed of a half cosine wave (see Fig. 3 for stimulus waveform traces). The amplitude

of all stimuli was 1020 µm (~20° at a contact point 3 mm from the skin); 8 different peak


velocities were employed (5, 22, 43, 87, 130, 174, 260, 350 mm/s corresponding to about

105, 415, 825, 1655, 2480, 3325, 4965, and 6685 °/s).

Since whisker-responsive ganglion neurons do not display spontaneous firing, neurons

were classified as ‘responsive’ if they responded to any of the three fastest whisker

deflections with on average more than 1 spike per stimulus.

To tease apart the contribution of variations in peak velocity and amplitude to the

variation in the units’ spike responses, we employed two analyses. Firstly, η2 was used as

measure of effect size, representing the proportion of the total variance that is attributable

to an independent variable (IV), totalsquaresofsumIVsquaresofsum

. η2 for an IV (here, amplitude or

peak velocity) varies between 0 and 1, with 1 implying that the entire variance in the

dependent variable (DV; here, spike counts) is due to variation of the IV, and 0 implying

that variance in the DV is unaffected by variations of the IV. Secondly, for each neuron,

we entered amplitude and peak velocity as predictors into a multiple regression equation

with number of spikes fired in response as criterion variable. The magnitude of the beta

weights for the predictors, obtained from the multiple regression equation, can be

interpreted as the standardized contribution of the given predictor’s variance to variation

in the criterion variable. Positive values of beta indicate a positive correlation between

predictor and criterion, negative values indicate a negative correlation. The relative

magnitudes of beta for the two predictors can be used to infer the relative importance of

the predictors in explaining a unit’s spike counts. See Kline (2004) for detailed

descriptions of the above measures. All analyses were performed in Matlab 7.0

(Mathworks, Natick, MA).


It is well known that the velocity- and amplitude-sensitivities of trigeminal units depends

on the time windows over which spikes are counted (Shoyket et al., 2000; Stüttgen et al.,

2006). However, as a first approach, we decided to analyze spike counts during the whole

stimulus period because we sought to conduct our analyses as free from prior

assumptions as possible.

Results and discussion

Out of 38 TG single units in our sample, 33 responded to a subset of the longitudinal

ramp-and-hold stimuli presented using the piezo bender. Notably, all responsive neurons

gave responses already to the lowest amplitude presented in this study (95µm), given the

peak velocity was high enough. The 5 unresponsive ones (not analyzed further)

responded readily to a manual dab against the whisker trunk, displaying rapidly adapting

(RA) responses. Figures 1ab show example peri-stimulus time histograms (PSTHs) of

two representative TG units. Qualitatively, neuronal activity in Fig. 1a is clearly

modulated by stimulus amplitude (rows), but hardly by stimulus peak velocity (columns),

and displays a slowly adapting (SA) response profile. In contrast, firing of the cell in Fig.

1b is sparse, rapidly adapting, and seemingly not affected by stimulus amplitude but by

peak velocity. Importantly, compared to the previously mentioned one, this cell started to

respond only at higher peak velocities (44 mm/s). Figure 1c displays superimposed

normalized PSTHs for all responsive neurons, color-coded according to whether their


response pattern resembled the SA type (orange) or the RA type (green). Quantitative

justification for this classification will be provided below.

Tuning properties of the responsive neurons are presented in figure 2. Panel a depicts the

neurons’ spike counts as a function of peak deflection velocity, averaged over amplitudes

while panel b plots the neurons’ spike counts as a function of amplitudes averaged over

peak velocity. Response strengths covered a wide range, from 0 to >150 spikes per

stimulus. Comparing the neurons that generated low vs. high spike counts (cf. plots on

the right in Figs. 2ab), revealed two well-separated groups of neurons. The first group

yielded high spikes counts, increased the response with high stimulus amplitudes, but did

not show any dependency on peak velocity (orange, cf. Fig. 1a). The second group was

composed of neurons that responded sparsely and showed the opposite preference (green,

cf. Fig. 1b): they increased their response with increasing peak velocity but were

relatively independent from amplitude. To work out this possible classification more

quantitatively, we asked how much of the response variance of each individual neuron is

explained by either peak velocity or amplitude. To this end we calculated η2 (‘proportion

of variance explained’, see methods), entering amplitude and peak velocity as

independent variables and number of spikes per stimulus as dependent variable.

Inspection of the scatter plot in figure 2c revealed that TG neurons are either sensitive to

amplitude (orange; η2amplitude > 0.2) or peak velocity (green; η2

velocity > 0.2). No TG

neuron was sensitive to both. The impression of a response dichotomy was confirmed by

plotting the standardized regression coefficients for amplitude and peak velocity for each

neuron based on multiple regression analysis (Fig. 2d). From these results we conclude

that response profiles of TG neurons can be well classified into amplitude-responsive


(AR) vs. velocity responsive (VR) neurons, based on η2amplitude or η2

velocity > 0.2 (broken

line in Fig. 2c), respectively.

However, before we conclude that stimulus kinematics are the decisive factor affecting

spike counts, we have to consider that all cosine ramps employed in this study are of

different duration, which is a joint function of amplitude and velocity. Therefore, we

added ramp duration as an extra predictor in the multiple regression equation. Beta

weights for durations were symmetrically distributed around 0 (mean = 0.098) and left

the weights of amplitude and velocity virtually unaffected. This indicates that stimulus

duration has no additional effect on spike counts.

Next, we provide three more characteristics that in addition help to differentiate the two

classes of cells that we have based so far on different responses to stimulus kinematics.

Firstly, and most importantly, as suggested by the raw data shown in figure 1, the two

classes display different adaptation profiles that resembled closely the classical scheme of

slowly and rapidly adapting cells found with latitudinal stimulation (SA vs. RA). To

investigate this quantitatively, we adapted the classification scheme developed by

Lichtenstein et al. (1990) designed for latitudinal stimulation for the present purpose to

separate responses to longitudinal stimuli: According to this strategy, SA from RA

responses are separated by assessing the spike count during the hold phase of a fast, high-

amplitude stimulus. SA responses are characterized by spike counts significantly larger

than zero (one-sample t-test at p < 0.025). For the longitudinal stimulation used here we

chose a test stimulus of 285 µm amplitude and 130 mm/s peak velocity and found a

perfect congruence of SAlongitudinal with AR, as well as of RAlongitudinal with VR classes.

The same holds true for all other peak velocities at this amplitude. The second


characteristic that differentiated well between the classes was mentioned earlier: spike

counts of AR cells were substantially higher than that of VR cells (grand averages across

all stimuli are 53 (n = 22) and 2.7 (n = 11), respectively). A third property was the

velocity threshold which is highlighted by the application of high-amplitude, low-

velocity stimuli (e.g. 285 µm, 5 mm/s, Fig. 2e). To this kind of stimulus, neurons of the

VR kind (asterisks in Fig. 2e) exhibit very low responses (in fact it was zero for 10 out of

11 VR cells and one for the remaining cell). In contrast, the AR cells all generated more

than one spike in response to this stimulus. In summary, we found that small-amplitude

longitudinal stimuli less than 100 µm routinely evoke responses in trigeminal ganglion

units. The TG units were found to be superbly classifiable based on a) responses to

amplitude and peak velocity, b) patterns of adaptation, and c) presence of a response to

very low velocities (5 mm/s).

Next we posed the question if membership in one of the response classes derived from

longitudinal stimulation (AR/SA vs. VR/RA) predicts membership in the corresponding

response class derived from classic latitudinal stimulation. To test this, we recorded a

subset of neurons under both stimulation conditions. To securely surpass the amplitude

threshold of SA units in latitudinal direction we presented high-amplitude stimuli (~20°)

(Stüttgen et al., 2006). Applying the criteria of Lichtenstein et al. (1990), as above,

showed that 7 out of 8 cells that classified as RA using longitudinal stimulation were

correctly classified as RA using latitudinal stimulation. Nevertheless, the overall match of

the two classification methods was quite poor (52% correct) due to the fact that two-

thirds of SAs as assessed with longitudinal stimuli were classified as RA using

rostrocaudal stimuli (Table 1, 1st row). We believe that this result does not imply a


genuine difference between response properties under different stimulation axes; rather,

we suspect that our latitudinal classification method was deficient. As reported by

Lichtenstein et al. (1990), we found that some SA neurons were directionally sensitive,

characterized by a rapidly adapting response in one direction and a slowly adapting

response in the reverse direction (stimulus return; see Fig. 3a for an example). In order to

capture these cases, we extended our SA classification criterion to include units with

significant activity during a 100-ms-period starting 50 ms after stimulus end. As can be

seen in Table 1 (2nd row), this procedure increased the match of the two classification

methods, but still left many non-matching cases (68% correct). In a previous paper

(Stüttgen et al., 2006), we reported that SAs have markedly lower velocity activation

thresholds compared to RAs (> 250°/s for SAs vs. > 750°/s for RAs). We, therefore, used

this feature to further improve our ability to capture SA responses. Indeed, classifying

units as SA that responded to the slowest stimulus (105°/s) with more than 1 spike on

average, improved the match considerably (88% correct) (table 1, 3rd row).

The remaining deficiency was most likely due to missing of those SA responses that

would have been detected only when adjusting the stimulator exactly in the direction of

cell’s preference (Lichtenstein et al., their Fig. 1), and to a another complicating feature

of SA units, the positional sensitivity (also mentioned in the previous study). Latitudinal

stimulation, identical in its kinematic parameters but starting from different start points

were observed to yield qualitatively different responses. The responses in panels a and b

of figure 3 were obtained by ramp-and-hold stimuli that differed only in the starting

position (difference ~1 mm). As can be seen, the slowly adapting responses to the offset


ramp visible in panel a are markedly reduced in b, leaving the impression of a rapidly

adapting neuron.

In summary, our results suggest that ganglion cells and their associated mechanoreceptors

display matching response profiles under latitudinal and longitudinal whisker stimulation.

However, this could be demonstrated only when three different classification approaches

for latitudinal stimulation – rate of adaptation to stimulus onset or offset, and velocity

threshold – were taken into account simultaneously. This fact highlights a noteworthy

spin-off of the present results for the practical implementation of TG cell classification:

Presentation of a single longitudinal ramp-and-hold stimulus completely suffices for

perfect classification simply using ‘rate of adaptation’ as a criterion. To reach the same

classification result with latitudinal stimulation, a whole battery of stimulus directions has

to be applied to secure stimulation of each vibrissa in a unit’s preferred direction

(Lichtenstein et al., 1990). In addition, even multi-angle stimulation is likely to suffer

from offset position effects (cf. Fig. 3). These multiple measurements consume valuable

experimental time and require special equipment (e.g. a multi-angle stimulator (Simons,

1983)). Taken together, we conclude that classification based on longitudinal stimulation

is convenient, time saving, and much less prone to error.

As outlined in the introduction, the richness of tactile sensory input generated by the rat

during exploration cannot be reduced to latitudinal deflections. This is the first

quantitative study providing evidence that the whisker system is highly sensitive to

longitudinal movement. For instance, if one imagines a hypothetical object engaging the

whisker at a distance 3 cm from the face, the known SA amplitude threshold of ~3° with

latitudinal stimulation (corresponding to 1.6 mm at the point 3 cm from the face,


(Stüttgen et al., 2006)) is far outrun by the longitudinal amplitude of 95 µm, sufficient in

most cases to evoke vigorous responses from SA neurons (on average ~35, maximally

113 spikes). While such considerations are helpful in shaping an intuition for the different

sensitivities along longitudinal and latitudinal axes, it is clear that a realistic tactile

situation is highly unlikely to engage one of the whisker axes in isolation, as is the case in

all studies employing precise whisker stimuli to date. Since all neurons we recorded were

responsive to either force vector when presented in isolation, and in addition displayed

remarkably similar response profiles, it seems that trigeminal neurons cannot tell the

difference between the axes of force vectors. This is surprising given the abundance of

different nerve endings at several locations inside the whisker follicle (Ebara et al., 2002),

and suggests that the rat must base 3D discrimination on a code in just one set of neurons.

An important hint to the functional significance of longitudinal forces comes from the

insight gained from biomechanical measurements that this force component acting on the

whisker while being swept (in rostrocaudal direction) over surfaces is high and

potentially carries significant information about object distance (Gopal and Hartmann,

2007), a parameter shown to be represented neuronally and of relevance behaviorally

(Szwed et al., 2006; Krupa et al., 2001). Our present results suggest that a lot can be

learned about tactile signal acquisition using whiskers by studying the interplay and

dependencies of information captured by longitudinal and latitudinal forces acting on

follicle receptors in a concerted way.


Acknowledgements

We are indebted to Ursula Pascht for excellent technical assistance.

Grants

Research was supported by a grant from the Deutsche Forschungsgemeinschaft (SFB 550-B11).


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Figure legends

Figure 1: Example raster plots and peri-stimulus time histograms (PSTHs) of two

representative neurons in response to longitudinal whisker stimulation. a) A slowly

adapting cell displaying high amplitude and low velocity sensitivity. Rows: three

different amplitudes, columns: five different peak velocities. PSTHs (bin width: 1 ms)

were smoothed with a Gaussian filter with SD = 1.5 ms. Light gray lines in PSTHs depict

stimulus waveforms (scale bar to the right). b) A rapidly adapting cell displaying low

amplitude and high velocity sensitivity. Conventions as in a). c) PSTHs of all neurons

superimposed. The curve of each neuron is normalized to the maximum firing rate of this

neuron across all stimuli. Orange traces represent amplitude-responsive neurons, green

traces represent velocity-responsive neurons (see results for explanation).

Figure 2: Quantitative analysis of spike responses to longitudinal whisker stimulation. a)

Spike counts of individual units (n = 33; gray lines) as a function of deflection velocity,

averaged over amplitudes. Right: magnified view of units with low spike counts. b) Spike

counts of individual units as a function deflection amplitude, averaged over velocities.

Right: magnified ordinate as before. c) Scatter plot of individual units’ η2 for amplitude

(abscissa) and velocity (ordinate). Units with high amplitude sensitivity are colored

orange, units with high velocity sensitivity (η2amplitude or η2

velocity >0.2, respectively;

thresholds indicated by dotted lines) are colored green. d) Scatter plot of individual units’

β weights obtained from multiple regression analysis. Conventions as in c). e) Bar chart


for all neurons’ spike response to a slow high-amplitude longitudinal stimulus (285 µm, 5

mm/s), sorted by response rate. Asterisks mark neurons classified as RA (see results).

Colors throughout this figure correspond to the units clustered in c) and d).

Figure 3: PSTHs for the same cell as in Fig. 1a. a) Responses to rostrocaudal whisker

stimulation (rostral first). Light gray traces depict stimulus waveforms, scale bar to the

right: 1 mm. Stimulus waveforms are truncated at 1 s due to fixed recording duration

during calibration. b) Same as a), but with offset of the absolute starting position by ~ 1

mm.


Table 1. Classification agreement longitudinal vs. latitudinal stimulation.

Class membership

using longitudinal

stimulation

AR / SA VR / RA row totals

SA 6 1 7 class membership using

latitudinal stimulation –

adaptation in hold phase only RA 11 7 18

column totals 17 8 25


adaptation in hold and return

phases RA 7 7 14



adaptation in hold and return

phases and velocity thresholds RA 2 7 9


Figure 1

Responses of Rat Trigeminal Ganglion Neurons to LongitudinalWhisker Stimulation

M.C. Stüttgen, S. Kullmann, C. Schwarz

time (s) relative to stimulus onset

155 µm

285 µm

counts

/bin

5 mm/s 22 mm/s 130 mm/s44 mm/s 87 mm/s

a

b

time (ms) relative to stimulus onset

95 µm

5

0

0 1

5

00 1 0 1 0 1 0 1

counts

/bin

10

0

0 150

10

00 150 0 1500 1500 150

5

0

10

0

velocity

am

plit

ude


velocity

am

plit

ude

c

time (ms) relative to stimulus onset


velocity

am

plit

ude

norm

aliz

ed

firing

rate

155 µm

285 µm

95 µm

155 µm

285 µm

95 µm

0 50 100 0 50 100 0 50 100 0 50 100 0 50 1000

1

0

1

0

1

deflection velocity (mm/s)

87 130

deflection amplitude (µm)

155 285

deflection velocity (mm/s)

225

neurons sorted byspike count to slow

high-amplitude stimulus

spik

es/s

tim

ulu

s

80

0

120

40

160

200

e

* ** * ** * * ** *

amplitude

0 1

velo

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spik

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sspik

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2amplitude

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deflection amplitude (µm)

44 87 130225 44

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Figure 2



Figure 3



5 mm/s 22 mm/s 44 mm/s 130 mm/s87 mm/s

counts

/bin

8

0

time (s)

0 1

8

00 1 0 1 0 1 0 1

1m

m

whiskeroffset ~ 1 mm

whisker inrestingposition

a

counts

/bin

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Documents

responses of rat trigeminal ganglion neurons to longitudinal whisker