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1Department of Neurobiology, University of Pittsburgh School of Medicine; and 2Department of Physical Therapy, University of Pittsburgh School of Health and Rehabilitation Sciences, Pittsburgh, Pennsylvania
Submitted 18 July 2007; accepted in final form 31 August 2007
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ABSTRACT |
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INTRODUCTION |
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). Processing of somatosensory information during active touch is rapid and, as in other sensory systems, the earliest arriving spikes are likely to be particularly important for sensory coding (VanRullen et al. 2005). Rapid perception of tactile stimuli on the human hand depends strongly on the earliest impulses evoked in populations of sensory afferent neurons (Johansson and Birznieks 2004). One implication of these latter findings is that central somatosensory circuits operate at a fast timescale to transform afferent signals that propagate through the posterior column–medial lemniscal system.
The rat whisker-to-barrel pathway is a lemniscal-like system that mediates discriminative touch from the face (Simons 1995). Animals are capable of detecting small differences in texture that are presumably signaled by responses of primary afferent neurons to rapid accelerations and decelerations of whisker hairs as the animal sweeps them across an object's surface (Carvell and Simons 1990). Trigeminal ganglion neurons respond reliably and at short latency to deflection velocity and/or acceleration (Jones et al. 2004; Shoykhet et al. 2000), and both thalamic and cortical neurons fire most robustly in response to high velocity/acceleration stimuli (Pinto et al. 2000; Temereanca and Simons 2003). Experimental and theoretical studies indicate that fast-acting circuitry within whisker-related layer 4 barrels is preferentially sensitive to initial changes in thalamic input signals (Pinto et al. 2003; Wilent and Contreras 2004). To date action potential conduction within the barreloid-to-barrel circuit has been viewed as being uniform. Indeed, studies of the ventral posterior medial thalamic nucleus (VPm), the main source of thalamocortical inputs to the barrels, indicate that neurons there are morphologically and functionally rather homogeneous (Arnold et al. 2001; Harris 1986), at least in comparison to geniculocortical pathways in cats and monkeys where neurons differ substantially in terms of cellular morphology, laminar location, receptive field properties, and axon conduction velocities (e.g., Dreher et al. 1976; Hoffman et al. 2007; Marrocco 1976).
Working in the rabbit whisker system, Swadlow and colleagues (Swadlow and Gusev 2000; Swadlow et al. 2002) used a powerful method to assess the functional impact of individual thalamocortical axons on cortical circuitry. Local field potentials (LFPs) in the cortex are recorded in response to the spontaneous firing of an individual thalamic neuron, and spike-triggered averages (STAs) are generated from several thousand samples. These STAs consist of complex waveforms having distinguishable pre- and postsynaptic components. The earliest waveform, called the axon terminal potential (AxTP), reflects the incoming thalamic spike; the existence of an AxTP is thought to indicate nearby termination of a thalamocortical neuron's axon. Subsequently, Bruno et al. (2003) showed that amplitudes of AxTPs evoked from a single thalamocortical neuron can vary within a cortical barrel and that AxTP size correlated with the angular tuning preference of both the thalamic unit and of nearby barrel cells.
On further examination of the data from the Bruno et al. study, we noticed that there was variability not only in AxTP amplitudes evoked by different thalamocortical units (TCUs) but also in their onset latencies. Moreover, AxTP latency for a given TCU could differ at different recording sites in the same barrel. In the present study, we examine AxTP latencies in detail and investigate possible relationships between thalamocortical axon conduction time and stimulus-evoked response properties. Conduction times vary fourfold, from 0.31 to 1.34 ms, and faster conducting TC neurons respond earlier and more robustly to whisker deflection. Such small asynchrony in the arrival times of thalamocortical impulses, at the submillisecond scale, could enhance response tuning of neurons at the initial stage of processing by the cortical column.
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METHODS |
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. Briefly, rats were initially anesthetized with halothane (1.5–2.0%) and craniotomies were made in the dorsal skull overlying the ventral posterior medial thalamus (VPm) and the posteromedial cortical barrel field; all wound margins were sutured closed and warm saline was applied to an acrylic dam around the craniotomy. For neural recordings, the animal's head was held by means of a steel post attached to the skull and the rat was maintained in a lightly sedated state using a synthetic opiate agonist [fentanyl, 
10.0 µg·kg–1·h–1, administered intravenously (iv)]. To prevent spontaneous facial movements, neuromuscular blockade was produced using pancuronium bromide (1.6 mg·kg–1·h–1, iv), the core temperature was maintained at 37 °C using a heating blanket, and the animal was ventilated with a positive-pressure respirator (Inspiron, Harvard Apparatus). The animal's physiological condition was continuously monitored using a computer to track femoral arterial blood pressure, pulse rate, electroencephalogram, and tracheal airway pressure waveform. At the conclusion of the experiment, or if normative physiological parameters (see Fraser et al. 2006) could not be maintained, the rat was deeply anesthetized with pentobarbital sodium and perfused transcardially for subsequent histological evaluation of recording sites using cytochrome oxidase (CO) histochemistry.
Extracellular recordings were made using 2- to 6-M
electrodes constructed from pulled and beveled quartz-coated platinum–tungsten (90/10%) filaments (Uwe Thomas Recording, Giessen, Germany). Single microelectrodes were advanced into VPm using a hydraulic microdrive (David Kopf Instruments, Tujunga, CA). In eight experiments, cortical LFP recordings were obtained with a multielectrode array consisting of two to four microelectrodes mounted in a Thomas multichannel manipulator and head-stage system. All of the cortical electrodes were positioned within the same barrel (confirmed histologically) at distances of 75–300 µm using a surgical microscope equipped with a reticule, and electrode placements were noted with respect to surface blood vessels. The thalamic electrode was placed in the topographically aligned thalamic barreloid. In one experiment, only a single cortical electrode was used. Thalamic recordings were obtained from well-isolated single units using conventional amplification and filtering (0.3–10 kHz). Cortical LFPs were filtered at 10 Hz and 10 kHz. Analog signals of both the thalamic spikes and the one to four channels of cortical data were digitized at 32 kHz and saved to disk. Histological evaluation verified that all thalamic recording sites were located within the VPm nucleus, as viewed in coronal sections. For cortical recordings, the cortex was removed and sectioned in a tangential plane. The barrel of interest and the overlying blood vessels, visible in the most superficial sections, were aligned. It was not feasible to locate precisely the position of each electrode within the barrel; histology was used only to verify that electrode penetrations were within the barrel center, as defined by CO staining.
For cortical recordings, we identified the principal whisker (PW) as the whisker that evoked the largest multiunit responses elicited by manual whisker deflections; the PW was subsequently confirmed histologically by matching it to the barrel in which the electrodes were placed. Thalamic recordings were obtained from homologous thalamic barreloids determined from similar physiological identification of the PW.
Responses of TCUs to controlled whisker deflections were subsequently obtained using an electromechanical stimulator that deflected the PW in eight randomly interleaved directions in 45° increments (Simons 1983). The electromechanical stimulator was attached to the whisker 10 mm from the skin. Ramp-and-hold deflections of 1.0-mm amplitude had onset and offset velocities of about 125 mm/s and sustained displacements of 200 ms. Batteries of eight deflections were delivered 10 or 20 times (total of 80 or 160 trials). For constructing poststimulus time histograms (PSTHs), spike times were binned at 0.1 ms and examined either at this resolution or after a further recombination using 1.0-ms bins. Responses to stimulus onsets (ON) and offsets (OFF) were measured during a 20-ms epoch that captured the large transient responses; plateau activity was measured during the latter 100 ms of the sustained deflection. Similarly, spontaneous activity was measured for a 100-ms period preceding deflection onset. Angular tuning of ON responses was quantified by a directional sensitivity index (DS) as the ratio of the maximal angle response to the response computed over all eight deflection angles.
Spike-triggered averages
Neural signals from each cortical electrode were acquired on the occurrence of a spontaneous thalamic action potential. Mean spontaneous activity was 15.7 Hz (range 1.5–36.8, median = 13.8, n = 48). Cortical STAs were constructed from >2,000 such events. STAs consist of a rapid, short-latency initially positive-going biphasic or triphasic waveform superimposed on a long-duration negative-going potential that peaks variably approximately 3.0 ms after the thalamic spike (see Fig. 1 and following text;, also see Fig. 1 from Swadlow and Gusev 2000). The early waveform (i.e., the AxTP) reflects the incoming action potential, whereas the later negativity, termed the focal synaptic negativity (FSN), is a postsynaptic field response (Bereshpolova et al. 2006; Swadlow and Gusev 2000). In experiments in which the array was used to record cortical activity, we obtained data from 45 thalamic units and 153 cortical recording sites, 109 of which yielded an AxTP. For the remainder of the analyses, AxTP latencies are reported based on a single value, when only one AxTP was present, or on the average value, when AxTPs were observed on two or more of the cortical electrodes. Virtually identical results were obtained when only the fastest (or sole) AxTPs were analyzed. In one experiment only a single cortical electrode was used; this experiment yielded four thalamic units with four AxTPs. Data from 24 TCUs (and accompanying STAs) were taken from the previous study of Bruno et al. (2003).
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RESULTS |
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For a given thalamocortical neuron AxTP latency was correlated with its whisker-evoked response. Figure 2 shows data from three TCUs. The TCU in Fig. 2A had a rapid axonal conduction time, as revealed by its short-latency AxTP (0.34 ms), and it responded robustly to whisker deflection as shown by the PSTH below. As illustrated in the polar plot, the TCU was moderately tuned for deflection angle (DS = 1.76). Figure 2C shows data from the TCU having the longest AxTP latency (1.34 ms); the cell responded with few spikes to whisker deflection but was well tuned for deflection angle (DS = 2.50). Figure 2B shows data from a thalamic cell having intermediate values of AxTP latency and response magnitude (DS = 2.03).
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Strongly responding TCUs are more likely than weakly responding cells to fire at nonpreferred as well as at the preferred (maximally effective) deflection angles; that is, they are more uniformly responsive. We quantified angular tuning by means of a directional sensitivity (DS) index, calculated as the ratio of the response evoked by the maximally effective angle to the response averaged over all eight angles; larger values of DS indicate poorer angular tuning, i.e., more circular polar plots. More strongly responsive TCUs are less angularly tuned (Fig. 4A, R2 = 0.14, P = 0.008). Consistent with the aforementioned finding that more strongly responsive TCUs have faster conduction velocities (e.g., Fig. 3A), faster conducting TCUs tend to display broader angular tuning (Fig. 4B, R2 = 0.06, P = 0.086). The relationship is more robust when the two outliers having DS values >3.5 are removed (R2 = 0.12, P = 0.016), but not when the two cells having the longest AxTP latencies are additionally omitted (P = 0.24). The statistical significance of the relationship between DS and AxTP latency (R2 = 0.05, P < 0.015) is also greater when the regression coefficient is calculated for all 109 AxTPs rather than average values. To illustrate the combined effects of response magnitude, angular tuning and conduction velocity, we constructed population polar plots for the TCUs having the 10 fastest and 10 slowest average AxTP latencies, i.e., from 0.39 to 0.465 ms and from 0.72 to 1.34 ms. Each individual polar plot was first rotated to a common angle (upward). Figure 4C shows that whisker deflection in any direction will evoke, on average, more spikes from the more rapidly conducting population. The population polar plot for the fastest conducting cells is larger and slightly more circular, consistent with the data of Fig. 4, A and B.
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Fast-conducting thalamocortical neurons having more robust whisker-evoked responses could have a differential effect on cortical circuitry if their signals arrived there earlier, that is, if in addition to faster conduction times they also responded to whisker deflection at the same time as or earlier than the more slowly conducting TCUs. We therefore examined whether conduction velocity of thalamic neurons is related to their whisker-evoked response latencies (Fig. 3D). For each TCU we computed the average time of the first spike evoked by a whisker deflection in the cell's preferred angular direction. We examined the maximally responsive deflection angle because TCUs, like trigeminal ganglion cells, often display weak or null responses to stimuli in nonpreferred directions. First-spike latency was positively correlated with AxTP latency (R2 = 0.46; P 
0.0001); the correlation across all deflection angles was also significant (R2 = 0.20; P = 0.0003).
We further examined TCU response profiles by constructing population PSTHs from the 10 fastest and 10 slowest conducting TCUs. Spike times were delayed by each cell's AxTP latency; each population PSTH thus reflects the thalamic signal as it arrives in the barrel neuropil. Response profiles in Fig. 5 confirm the finding that the fastest conducting TCUs have more robust whisker-evoked responses and show, further, that differences in firing rate are most pronounced during the first few milliseconds of the response. We quantified the initial slope of the population firing rate by computing 40% of the total response magnitude (within the response window) and dividing this value by the time required to reach the 40th percentile spike. This yields a population measure of spikes/ms called temporal contrast (TC40), which robustly predicts the responses of barrel neurons (Pinto et al. 2000). The population TC40 value for the slowest TCUs is 0.066 spikes/ms, that of the fastest TCUs is 0.192 spikes/ms. Based on the data of Pinto et al. the slow and fast population input signals would evoke cortical responses of approximately 1.1 and 1.7 spikes per stimulus, respectively.
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DISCUSSION |
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); axon distances may be somewhat longer in the rabbit but axons are likely to be of larger diameter. Swadlow and Gusev showed that AxTP conduction times correlate well with independently measured antidromic latencies.
The spike-triggered–averaging approach assumes that the evoked activity in the cortex reflects the influence of the single thalamic neuron that is being recorded (Bereshpolova et al. 2006; Swadlow and Gusev 2000). As noted by Swadlow and colleagues, in the case of AxTPs, the waveforms are so brief that they are unlikely to be produced by multiple cells, which would have to fire virtually simultaneously. Under our recording conditions the synchrony of spontaneous firing is low (Bruno and Sakmann 2006). Nevertheless, to avoid possible contamination in the spike-triggered averages from other synchronously firing thalamocortical neurons, we obtained data only from well-isolated, spontaneous action potentials. Whisker-evoked responses were acquired from cells after we found them to produce an AxTP based on their spontaneous firing. Our sample is therefore biased to TCUs having at least some low level of background activity. However, in our recording conditions almost all barreloid neurons fire spontaneously, and we found no correlation between such firing rates and AxTP latency. We explicitly recorded only in topographically aligned thalamic barreloids and cortical barrels. Although AxTPs may be observed with nonaligned recordings (Swadlow and Gusev 2000), the incidence and strength of the STA activity are small, and we therefore assume that our data reflect the major VPm inputs to single barrels.
Variation in conduction times among thalamocortical axons likely reflects differences in axon myelination and/or diameter. Also, action potential propagation may be slowed at branch points (Debanne 2004). Thalamocortical axons branch extensively within the cortical gray matter and specifically within the layer 4 barrel (Arnold et al. 2001; Bernardo and Woolsey 1987), and therefore local branching patterns might account for our finding that a single TCU can evoke AxTPs differing in latency at different locations within the same cortical barrel. Recording locations associated with longer latencies from a TCU may receive axon collaterals from it that have more local branch points. Similarly, uniformly faster conducting thalamocortical axons may have overall simpler terminal arbors. Anatomical evidence indicates that a given thalamocortical axon may branch more profusely in one part of a barrel than in another (e.g., see Fig. 7 in Arnold et al. 2001). AxTP (and FSN) amplitudes also vary within a barrel, and the pattern of this functional innervation corresponds to the receptive field properties of nearby cortical neurons (Bruno and Simons 2002).
What other properties, related to axon diameter/myelination, might further distinguish VPm neurons? By analogy with other systems (Clamann and Henneman 1976) VPm cells with faster conducting axons may have larger cell bodies. Faster conducting TCUs respond earlier and more vigorously to whisker deflection. Additionally, TCUs that respond more vigorously to PW deflections do so also to deflections of adjacent whiskers (Bruno and Simons 2002), and thalamic neurons having larger somata tend to have larger receptive fields (Harris 1986). These properties do not appear to reflect the cells' receipt of synaptic inputs from similarly rapidly conducting neurons in the brain stem principal sensory nucleus (PrV) because PrV cells having faster conducting axons, measured by antidromic stimulation, are somewhat less responsive to whisker deflection (BS Minnery and DJ Simons, unpublished observations). This latter finding suggests that the robust whisker-evoked responses of fast conducting TCUs reflects greater convergence and/or stronger (e.g., more proximal) trigeminothalamic synapses. Such inputs would be consistent with a requirement for greater spatiotemporal summation by postsynaptic cells having larger somata and/or dendritic fields.
It is presently unclear whether faster versus slower conducting thalamocortical neurons are scattered within a barreloid or whether they are located preferentially in one part of it versus another. One possibility is that fast and slow conducting cells reside in different compartments within a barreloid, such as the side versus the center. Another is that the fastest conducting neurons are located dorsomedially in the barreloid, near the VPm–medial posterior (POm) border. Barreloid cells closer to the VPm–POm border have larger dendritic fields (Varga et al. 2002). Dorsomedial aspects of barreloids stain more darkly for cytochrome oxidase, suggesting higher activity-related metabolism (Land et al. 1995). Neurons there tend to have larger whisker receptive fields (Sugitani et al. 1990), and thalamocortical cells that fire more robustly to adjacent whiskers also respond more robustly to the principal whisker (Bruno and Simons 2002). Similarly, the slowest conducting, longest latency, and weakest responding TCUs might be preferentially situated in the ventrolateral aspects of VPm where neurons may receive longer-latency inputs from the spinal trigeminal complex as well as from PrV (Pierret et al. 2000). Electrophysiological recordings incorporating single-cell labeling are needed for addressing such fine-scale functional topography within VPm.
In the whisker-to-barrel system thalamocortical conduction is fast, having a mean conduction time of about 0.6 ms. In processing the incoming thalamic signal, barrel circuitry also operates quickly. Rapid, strong, feedforward inhibition coupled with recurrently interconnected local neurons render barrel circuitry highly sensitive to the timing of thalamocortical inputs—on the millisecond scale (Gabernet et al. 2005; Pinto et al. 2003; Swadlow 2003). For example, in response to the approximately 125 mm/s deflections used here, the cortical response is largely determined by the firing rate during the first 2–4 ms of the thalamic response (Pinto et al. 2000). Thus a spread of conduction times of about 1 ms is potentially significant. In this regard, it is interesting that the most strongly responsive TCUs preferentially contact fast-spike units (FSUs; Bruno and Simons 2002), which in the barrel are thought to correspond to GABAergic interneurons. Together with the present findings, these data raise the possibility that FSU/inhibitory interneurons, in contrast to excitatory barrel neurons, may be contacted by a subpopulation of broadly tuned, faster conducting TCUs that respond to whisker deflections more robustly and at shorter latency. Such a specialization within the thalamocortical system would provide yet another mechanism for ensuring that feedforward inhibition is fast and powerful.
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GRANTS |
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ACKNOWLEDGMENTS |
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Present address of R. M. Bruno: Columbia University, Hammer Health Sciences Building, 701 West 168th Street, New York, NY 10032.
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FOOTNOTES |
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Address for reprint requests and other correspondence: D. J. Simons, Department of Neurobiology, University of Pittsburgh School of Medicine, E1452 Biomedical Science Tower, 200 Lothrop Street, Pittsburgh, PA 15261 (E-mail: cortex{at}pitt.edu)
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ABSTRACT
INTRODUCTION
