Learning from the spinal cord: how the study of spinal cord plasticity informs our view of learning

Abstract

The paper reviews research examining whether and how training can induce a lasting change in spinal cord function. A framework for the study of learning, and some essential issues in experimental design, are discussed. A core element involves delayed assessment under common conditions. Research has shown that brain systems can induce a lasting (memory-like) alteration in spinal function. Neurons within the lower (lumbosacral) spinal cord can also adapt when isolated from the brain by means of a thoracic transection. Using traditional learning paradigms, evidence suggests that spinal neurons support habituation and sensitization as well as Pavlovian and instrumental conditioning. At a neurobiological level, spinal systems support phenomena (e.g., long-term potentiation), and involve mechanisms (e.g., NMDA mediated plasticity, protein synthesis) implicated in brain-dependent learning and memory. Spinal learning also induces modulatory effects that alter the capacity for learning. Uncontrollable/unpredictable stimulation disables the capacity for instrumental learning and this effect has been linked to the cytokine tumor necrosis factor (TNF). Predictable/controllable stimulation enables learning and counters the adverse effects of uncontrollable stimulation through a process that depends upon brain-derived neurotrophic factor (BDNF). Finally, uncontrollable, but not controllable, nociceptive stimulation impairs recovery after a contusion injury. A process-oriented approach (neurofunctionalism) is outlined that encourages a broader view of learning phenomena.

Keywords: Inflammation; Injury; Instrumental conditioning; Operant; Pavlovian conditioning; Spinal cord.

Fig. 1

Gross anatomy of the spinal cord. (A) The human spine is covered by bony segments (vertebrae) and is grouped the segments into 4 sections: cervical, thoracic, lumbar and sacral. Within each section, the segments are numbered along the rostral-caudal axis. During development, the bony covering grows faster than the underlying tissue, which is accommodated by lengthening the sensory/motor fibers. This yields a bundle of fibers at the caudal tip known as the cauda equina. (B) A cross-section section of the spinal tissue illustrating the major components of the white (outer band) and gray (inner region) matter. Sensory neurons enter through the dorsal root and project to neurons within the dorsal horn of the gray matter. Neurons carrying motor commands from the ventral horn exit through the ventral root. (C) Cells within the central gray are organized into layers known as laminae. The substantia gelatinosa plays a central role in processing sensory signals related to pain. (Adapted from Grau et al., 2006.)

Fig. 2

(A) The stimuli used to examine conditioned antinoception in spinally transected rats. Moderate shock to the left or right hind leg served as the conditioned stimuli (CS1 and CS2). An intense tailshock served as the US. (B) Tail-flick latencies before (Baseline) and after spinally transected rats had received differential conditioning in which one CS was paired with the US (CS+) while the other (CS-) was explicitly unpaired. At the start of testing, subjects exhibited longer tail-flick latencies during the CS+ (conditioned antinociception). The CS+/CS- difference waned (extinguished) over the course of testing. (C) Mean tail-flick latencies in subject that had received the CS paired with the US (CS+) or the CS presented explicitly unpaired (CS-). At the end of training, tail-flick latencies were assessed in the presence of the pretrained CS and a novel CS (provided by stimulation of the contralateral leg). Training produced a CS+/CS- difference. Tail-flick latencies during the novel CS (CS-N) were comparable to those observed during the CS+ and significantly greater than the CS-. This pattern of results suggests that a form of protection from habituation may have generated the CS+/CS- difference. (Adapted from Grau & Joynes, 2001.)

Fig. 3

(A) Three mechanisms that could generate differential performance to a paired CS (+) relative to an unpaired CS (-). The graphs illustrate the pattern of results expected for each mechanism, working in isolation, during training and testing. Because each mechanism can produce a comparable CS+/CS- difference at testing, a novel CS (N) is needed to help uncover the underlying mechanism. These idealized results assume zero stimulus generalization. For associative learning (i), only the paired cue (+) generates a response at the time of testing. In pairing specific enhanced sensitization (ii), both the unpaired (-) and novel (N) CS would generate a response, but it would be weaker than that produced by paired CS. In protection from habituation (iii), the unpaired CS generates a weaker response than both the paired and novel CS. (B) The proposed framework assumes that a Pavlovian (CS-US) relation can be encoded by multiple mechanisms. We suggest that detailing the functional process that underlies the learning will simplify the derivation of linking hypotheses by providing a formal map of how the system operates. It is assumed that multiple biological processes may generate similar functional outcomes and contribute to multiple processes. (Adapted from Grau & Joynes, 2005a.)

Fig. 4

(A) The apparatus used for instrumental training with spinally transected rats. Shock is provided to the tibialis anterior muscle, which elicits a flexion response. An insulated rod is taped to the rat's paw and used to monitor limb position. Whenever the tip of the rod contacts the underlying salt solution, it completes a computer-monitored circuit. Tape is used to help stabilize the leg. (B) Rats given response-contingent shock (Master) exhibit a progressive increase in flexion duration over the course of 30 min of training. Rats that are experimentally coupled (Yoked) to subjects in the Master group, and receive shock at the same time, do not exhibit an increase in flexion duration. (C) Testing under common conditions with response-contingent shock. Prior to testing, flexion force was equated and subjects then received training with controllable stimulation. Previously trained (Master) subjects learned faster than subjects that were naïve (Unshocked). Rats that had previously received uncontrollable shock (Yoked) failed to learn. (D) Yoked rats exhibited the highest level of responding during testing. (Adapted from Grau et al., 2006.)

Fig. 5

A model summarizing key features of spinally-mediated instrumental learning. The model assumes that nociceptive signals the occur within a regular proprioceptive context are interpreted as controllable, producing a constellation of effects that include an adaptive increase in response duration and the induction of a BDNF-dependent process that enables learning and attenuates the adverse consequences of uncontrollable stimulation. Exposure to uncontrollable stimulation induces a central sensitization-like state that inhibits learning, enhances mechanical reactivity and pain, and disrupts recovery. The adverse effect of uncontrollable stimulation has been linked to an alteration in GABA and the cytokine TNF. The initial state assumes incoming nociceptive signals are related to behavior, providing a bias in favor of behavioral control (indicated by the angle of the nociceptive gate). We propose that spinally-mediated instrumental learning involves learning the relation between proprioceptive signals (an index of position [the response]) and the onset of nociceptive stimulation (the outcome). This learning is fostered by the brain-derived neurotrophic factor (BDNF). Descending serotonergic (5HT) fibers provide a physiological brake that inhibits the over-excitation of spinal neurons and counters the adverse effect of uncontrollable stimulation. (Adapted from Grau et al., 2012.)

Fig. 6

A Pavlovian analysis of spinally-mediated instrumental learning. (A) It is assumed that proprioceptive cues (P1-P8) provide an index of leg position (the response). Learning is initiated by the onset of leg shock (the outcome [O]) which occurs at a regular position (P6). (B) It is proposed that the proprioceptive cue (P6) functions as a Pavlovian CS, which is paired with shock onset (the US). As a result of this pairing, the CS acquires the capacity to generate a flexion response (the CR). (Adapted from Grau et al., 2012.)

Fig. 7

The proposed relation between instrumental and operant behavior. The four criteria for learning about a response-stimulus (a.k.a. response-outcome [R-O]) relation (instrumental behavior) were listed earlier in Table 1. It is assumed that R-O learning can affect both biologically prepared behaviors, involving an elicited response (a respondent), and unprepared systems. The latter support a wider range of behavioral responses and learning can be reinforced using a variety of outcomes (reinforcers) (Advanced Criteria 5 and 6). A behavior system that meets Criteria 5 and 6 allows the organism to operate on its environment in a flexible manner, as implied by Skinner's definition of operant behavior (Skinner, 1938). From this perspective, operant behavior represents a subset of instrumental learning and both depend on a common set of core criteria (1-4). While this is represented in terms of an embedded Venn diagram, it is recognized that the boundary is fuzzy and a continuum of possibilities exist. Spinal learning would seem to lie on one end of this continuum, meeting the basic criteria (1-4) for instrumental behavior within a biological prepared system, but lacking the flexibility (Criteria 5 and 6) exhibited by brain-dependent operant behavior.