Regeneration is an ongoing process. Once axons form growth cones that cross injury zones, navigate distal stumps and approach former targets, they encounter additional challenges. The interaction of new axons with their targets involves a new series of events with new molecular requirements. Similarly axons are not fully effective the moment they reach their target. They must grow in caliber and may need to myelinate. This chapter will address these events.

Forming new nerve trunks after transection

After the transection of a nerve stump, the proximal and distal stump retract apart because they are normally under some degree of tension. Retraction leaves a gap. In some cases, the gap can be repaired by suturing nerve stumps together, but this is not always possible. Early studies successfully connected the stumps with conduits, tubes, or in one report, synovium propped open by a stainless steel spiral. Various surgical innovations are reviewed in more detail in other texts and sources [421,422,660,775]. They include nerve grafts, silicone tubes, vein–muscle conduits, fibronectin mats, veins, synthetic longitudinal filaments, collagen tubes, polymer biodegradable tubes, and others. With these strategies, eventual complete reconstitution of a severed new nerve trunk from the proximal to the distal stump can occur. This was initially described by Lundborg and colleagues [424–427].

When proximal and distal stumps are connected by tubes, an eventual connection therefore does develop as discussed in the previous chapter [610,740]. In the first week, extracellular exudative fluid and a fibrin matrix clot form within the tubes.

Introduction

Neural responses to face stimuli may seem like an unwieldy subject for investigating population activity: neurons with face-selective responses are many synapses removed from sensory input, the coding for faces appears to be very sparse, and the stimuli are complex making “proper” control stimuli difficult to come by. So why bother? To the extent that population coding underlies certain cognitive abilities, then those activities that are biological imperatives for the animal should be given “neural priority.” In the rat, foraging and spatial localization relative to “home” points is one critical natural behavior. In primates, social cognition is essential. With the face at the heart of social communication and identification of social status, it should not come as a surprise that neurons appear to “care” about face stimuli in a way not seen for many non-face objects. But the nature of perceiving and learning about facial signals, in terms of population dynamics, is very under-explored territory. Surprisingly, in regions most often associated with face-selective responses, the conclusion of some researchers has been that population activity may add little to nothing to the perception of faces. The current state of knowledge regarding neural bases of face perception will be discussed. The role, if any, of population dynamics, will then be explored. Specifically, the population interactions of face-processing systems across space (e.g. circuits), and time (e.g. oscillations) will be discussed.

Peripheral nerves are living dynamic tissues that thrive on a nutritive blood supply. The vascular supply of the peripheral nerve, termed “vasa nervorum” participates intimately in regenerative events and influences their success. There are important morphological and physiological differences among microvessels that supply nerve trunk, ganglia, and brain. Each entrains and regulates its vascular supply from differing physiological perspectives, depending on their need for metabolic support. Following injury, vasa nervorum alter their behavior in unique ways that reflect their exposure to molecules released within the microenvironment and that offer insights into the repair process.

Blood flow and microvessels of intact nerve trunks

Nerve trunk blood vessels, or vasa nervorum, are supplied by upstream arterial branches of major limb vessels. Sometimes these arteries and nerves course together as neurovascular bundles. Peripheral nerves also share their abundant blood supply with other structures in limbs such as bone, connective tissue, skin, and muscle. For this reason, major ischemic lesions are likely to target several tissues and cause widespread damage. The redundant and abundant blood suppy of nerve trunks, however, can be advantageous because the interruption of a single artery is unlikely to cause significant ischemia. There are some sites where there is ischemic vulnerability, known as watershed zones. These are found at areas supplied by terminal branches of overlapping arterial trees. For example, a nerve watershed zone has been identified in the proximal tibial nerve of rats [458].

Introduction

Traditionally, neurophysiological investigations in awake non-human primates have largely focused on the study of single-unit activity (SUA), recorded extracellularly in behaving animals using microelectrodes. The general aim of these studies has been to uncover the neural basis of cognition and action by elucidating the relation between brain activity and behavior. This is true for studies in sensory systems such as the visual system, where investigators are interested in how SUA covaries with aspects of visually presented stimuli, as well as for studies in the motor system where SUA covariation with movement targets and dynamics are investigated. In addition to these SUA studies, there has been increasing interest in the local field potential (LFP), a signal that reflects aggregate activity across populations of neurons near the tip of the microelectrode. In this chapter, we will describe recent progress in our understanding of brain function in awake behaving monkeys using LFP recordings. We will show that the combination of recording the activity of single neurons and local populations simultaneously offers a particularly promising way to gain insight into cortical brain mechanisms underlying cognition and memory.

Measures of neural activity at the level of neurons and networks

The activity of single neurons (SUA) is estimated by amplifying and collecting the comprehensive broadband electrical signal, which can be detected in the brain by using microelectrodes. This signal is digitized at rates of 20 kHz or higher, and high-pass filtering to remove its low-frequency components at a typical cut-off frequency of 300 Hz.

In this chapter I discuss the earliest events of peripheral nerve regeneration. What will emerge from this information is a remarkable panoply of cells and molecules that come together for regrowth to occur. Considered separately, the anatomical events, the signaling interactions, and how they influence each other speak to a coordinated beauty. While the anatomical, or cellular features of early nerve regrowth have been described for many years, the molecular cascades involved are only now being considered. Some of this information, such as growth cone signaling and guidance, has not been examined in the specific situation of peripheral nerve regrowth. Nevertheless, there is strong evidence that it eventually will be identified as important in peripheral neurons as in the experimental and central systems studied to date. Thus, some of the material presented relates to the general neurobiology of the growth cone and axon growth, only summarized here. Seen as the forest, rather than the trees, however, it is a critical part of the peripheral nerve regeneration story.

Early axonal events

Immediate responses to injury

When an axon is physically disrupted, a rapid series of molecular events ensues over the next seconds, minutes, and hours. Injured axons undergo rapid depolarization, generating immediate retrogradely propagated “injury potentials.” These critical signals, perhaps associated with a calcium wave, from distal axon to proximal portions of the neuron, may be the first to initiate a shift in its properties. That is, the neuron begins to relinquish its role as a stable transmitter to one primed for regeneration.

Introduction

Despite almost 100 years of research into the pathophysiology of schizophrenia, the causes and mechanisms underlying the disease remain poorly understood. For a long time, biological research has focused on finding regionally specific pathophysiological processes in this disorder. In the last decade, however, theories of schizophrenia have laid emphasis upon pathophysiological mechanisms, which involve multiple cortical areas and their coordination. These theories suggest that the core impairment underlying both dysfunctional cognition and the overt symptoms of the disorder arise from a dysfunction in the integration and coordination of distributed neural activity (Andreasen, 1999; Friston, 1999; Phillips and Silverstein, 2003).

Interestingly, a disturbance of integrative processing had already been suggested by Bleuler (1950). He coined the term schizophrenia (“split mind”) to highlight the fragmentation of mental functions. According to him, the fragmentation of mental functions constituted the primary disturbance in schizophrenia that represented a direct manifestation of the organic pathology whilst other symptoms, such as delusions and hallucinations, were accessory or secondary manifestations of the disease process. Contemporary models of schizophrenia for instance by Friston (1999) suggest that the core pathology is an impaired control of (experience-dependent) synaptic plasticity that manifests as abnormal functional integration of neural systems, i.e. dysconnectivity. Andreasen (1999) used “cognitive dysmetria” to refer to the fact that patients with diverse clinical and cognitive deficits share a common underlying deficit in the “timing or sequencing component of mental activity” across multiple brain regions.

Introduction

Analogies between the brain and the digital computer have been out of fashion for a long time. The differences between brains and computers are numerous. Computers run pre-specified programs written by people. Computers store programs and data in specialized RAM circuits, and have one CPU (or at most a handful) which follows a coded list of instructions to the letter. A computer has a central clock, which allows all of its components to march through a program in lockstep. The brain, on the other hand, has billions of neurons operating in parallel, no central clock, no externally supplied list of instructions, and no separation of RAM and CPU. Although the inventors of the modern computer held the brain as a model, the analogy is rarely taken seriously today.

A more popular analogy for the brain in recent years has been artificial neural networks (ANNs). ANNs, although typically simulated on a digital computer, have an apparently more “brain-like” design. They consist of elements that function (at least a bit) like neurons, connected by “synapses” whose strength can be modified by the network's history. ANNs do not need an external program, but “learn” from a set of training examples. The most successful of these, the multilayer perceptron or “backprop” net, is good enough at generalizing from training examples to be used in real-world information processing tasks, by people who have no interest in how the brain works.

The pace of molecular discovery relevant to nerve regeneration has accelerated. New insights into regeneration, however, have not necessarily been partnered with rigorous approaches to measure regeneration. The purpose of this chapter is to engender readers with a healthy appreciation of new findings, based on rigorous approaches, that confirm the complexity and beauty of the regenerative process. Similarly, the reader should be skeptical of approaches that do not live up to that standard. Assays of regeneration should ideally encompass all of the crucial steps involved in the regenerative timetable: early sprouting, axon elongation, regrowth of axon radial caliber or girth, remyelination of larger caliber axons, repopulation of nerve trunks by mature axons, and extension to target tissues. During regeneration axons regain electrophysiological properties that they have lost, features that can be carefully assayed. Finally, it is critical to know whether there has been a resumption of function, addressed through “functional” or behavioral endpoints. This chapter presents a summary of regeneration assays and a discussion of their strengths and limitations.

Structural (histological) approaches

Few other measures can convey the structural beauty of regeneration captured in a high-quality histological snapshot. Histological techniques demand strict attention toward the details of specific protocols and they require optimal handling of specimens that are appropriately sampled. Their exactitude sets a standard of quality that is enormously satisfying. Unfortunately, classical histological approaches are frequently dismissed and substituted with easier or more colorful techniques that have lower resolution.

Introduction

I am trying to understand how ideas and concepts are generated and manipulated in networks of neurons; I want to understand how we think. You probably share my curiosity and believe that the human brain creates and processes mental objects like the ideas and concepts that make up thoughts. We probably also agree that a key to understanding these mental processes is to understand how neurons represent abstract information.

It is less certain we agree on what to do to discover how neurons represent this sort of information. While I suspect we will get quite far by studying mental processes in animals, I admit that I don't know whether or not animals have ideas, concepts, and thoughts. Such open questions do not invalidate the quest to understand thought because the pursuit is founded on the conviction that mental objects are properties of neural systems and that the neural systems in the human brain are fundamentally similar to the systems in the fascinating brains of lower mammals like the laboratory rat. If we restrict the discussion to the non-moral question of how neurons give rise to thought, then the question of animal mentalism need not be asked, because the answer is not important for directing a rigorous scientific effort to understand the neurophysiology of thought.

Introduction

A major advantage conferred by recording from populations of neurons from any brain area is the potential to determine how that population encodes or represents information about a sensory input, behavioral task, motor movement, or cognitive decision. The ultimate purpose of populations of neural ensemble, recording and analysis can then be characterized as understanding: (1) what does the ensemble encode? (2) how does the ensemble encode it? and finally, (3) how do brain structures use that ensemble code?

In the hippocampus, the anatomy has been studied extensively such that connections between the major principal cell groups are well characterized and the local “functional” circuitry is currently under intense investigation. Neurons have been recorded in all major subfields in the hippocampus, and cell identification via firing signature or local analysis is not a problem in most cases. In the same manner, anatomical connections between subfields are also known; therefore, it is possible to position recording electrodes along specific anatomic projections to record ensembles of neurons with known anatomic connectivity. Given these factors, we have used multineuron recording techniques to determine how neural activity within hippocampal circuits is integrated with behavioral and cognitive events. However, as in many brain systems, the make-up of the ensembles is at least as critical as the techniques used to analyze the ensemble data, or “codes.” In addition, the functional connectivity that gives rise to such codes may not be constant; in fact variations in functional connectivity may produce different codes for different cognitive events.