Health Guide

Brain and Behavior

In thinking about the relationship between brain and behavior, there is a tendency to focus on constancy, rather than on change, and on similarities, rather than on differences.

Thus, as we try to map the regions of the cerebral hemispheres that are involved in processes like language; we focus on the constancies and similarities in the localization of functions across individuals. Indeed, it could be argued that one of the reasons we now know so much about human brain function is because there are so many constancies in brain organization. But it can also be argued that change and variability are as basic to brain function as uniformity. Indeed, it is only through changes in the brain that the child develops or that we are able to learn new concepts in adulthood. The recognition of the importance of change and variability in brain function has led to the study of the role of environmental events in shaping brain structure and function, a field that is often referred to as the study of brain plasticity.

In principle, there are three ways that experience could alter the brain: By modifying the ontogenetic unfolding of brain structure, or by modifying existing brain circuitry, or by creating novel circuitry. It is reasonable to suppose that the environment influences the brain in all three ways, although it is likely that a particular type of change will vary with the developmental stage of the animal. The goal of this chapter is not to review all of the literature on environmentally induced changes in brain structure and function so much as it is both to demonstrate the power of the environment in sculpting the brain during development and through adulthood and to document a large range of examples, running from insects to people.

Assumptions
I must first admit to making several assumptions First, I assume that the structural properties of the brain are important in understanding its function. Although such an assumption is self-evident to most neuroscientists, it is not as ubiquitously assumed by psychologists who do not study the brain (e.g. Pylyshyn, 1980 ; Skinner, 1938 ). An important corollary of this assumption is that changes in the structural properties of the brain reflect changes in the function of neural circuits.

Second, specific mechanisms of neural plasticity are likely to underlie more than one form of behavioral change. The nervous system is likely to be conservative in its mechanisms for change. Thus, general mechanisms that are used for one type of behavioral change, such as in development, may also form the basis of other types of behavioral change, such as in learning and memory, aging, or recovery from injury. This preconception does not exclude the possibility of specific mechanisms for different types of plasticity, but it has the advantage that it allows studies of one form of plasticity to provide insights into mechanisms involved in others. Indeed, it has become clear in recent years that the structural changes underlying experientially induced plasticity, such as in perceptual development, are remarkably similar to those underlying recovery from some types of brain injury. There is a corollary to this assumption: Similar mechanisms of plasticity are likely to be used across a broad range of species. I must admit parenthetically at this point that I am an unabashed vertebrate chauvinist, so my emphasis will be on vertebrates and especially on mammals. I do not exclude the possibility that animals such as Aplysia may use mechanisms similar to those in mammals, but my suspicion is that we will learn more about forebrain function in humans by studying animals whose brains, and thus perceptual processes, are more similar to ours than is the case for most invertebrates.

Third, I assume both that the mechanisms of cortical plasticity are most likely to be found at the synapse and that synaptic changes can be measured by analysis of either pre- or postsynaptic structure. Traditionally, the emphasis in the literature on synaptic plasticity has been upon the presynaptic, or axonal terminal, side. Although this may be a practical site to study if one is interested in reparative processes after injury to peripheral nerves (e.g. Diamond, 1988 ), it is not so practical for studies of cortical structure. In particular, one difficulty with studying presynaptic changes is that they are very difficult to locate unless one knows a priori where to look. In addition, once found, they are difficult to quantify. The ability to quantify specific morphological features is critical if one is to correlate structural change with behavior.

An alternate way to look at synaptic change is to study the postsynaptic, or dendritic, side. This requires that the complete cell body and dendritic tree be stained, such as in a Golgi-type stain. As the dendritic surface receives more than 95% of the synapses on a neuron, it is therefore possible to infer changes in synapse number from measurements of dendritic extent and spine density. One clear advantage of this measure is that one need not know a priori where to look because it is possible to stain, and to examine, the structure of cells throughout the entire brain. In addition, analysis requires only a light microscope (and a lot of time!). A strong bias of this review, therefore, will be towards studies that have utilized Golgi-type analyses of postsynaptic structure.

Fourth, although the emphasis in most studies of structure-function relationships falls on the analysis of neurons, there are solid grounds for looking at changes in the structure and number of glial cells. Glial cells play an important role in synaptic modification and thus can be a clue to the location and nature of experience-dependent changes in neurons and their synapses.

Fifth, it is implicit in the foregoing discussion that changes in the postsynaptic structure will be visible in the light microscope. Although the final verification of the nature of structural modification must be at the ultrastructural, and thus electron microscopic (EM) level, EM studies are impractical on a large scale as they are time (and money) consuming, even if one knows where to look. Practically, therefore, most studies are carried out in tissue that is stained with a Golgi-type stain (for neurons) or with other specialized histochemical procedures that identify specific proteins, such as glial fibrillary acidic protein (GFAP), in glial cells.

Sixth, the emphasis of this review will be on the cerebral cortex. As psychologists, our primary interest is in cognitive function and it is our assumption that the changes in the cerebral cortex form the principal mechanism for cognitive change. This assumption comes from several lines of evidence. For instance, it is generally agreed that the relative increase in cortical volume across mammalian evolution is associated with increased cognitive capacity. It follows that changes in cognitive functions in a particular mammal are likely to involve changes in cortical structure or organization. Furthermore, studies of decorticated rats show that although they are capable of a remarkable behavioral repertoire (e.g. Whishaw, 1990 ), there is limited functional flexibility under conditions that would normally lead to marked functional and/or structural change in intact animals (e.g. Kolb, Whishaw, & van der Kooy, 1986 ). Finally, there are marked interspecies differences in the details of cortical organization, such as in Old World and New World monkeys, and it has been assumed that these differences reflect the clear differences in perceptual and cognitive abilities (Kaas, 1987 ).

Finally, I assume that experience-dependent effects are studied most easily by: (a) placing animals in special environments; (b) training animals in specific tasks; or (c) considering ecological pressures that shape the nervous system in the daily lives of animals. I shall therefore emphasize these types of analyses.

Measuring brain plasticity: Analysis of Golgi-stained material
Once the cells are stained with a Golgi-type procedure, the dendritic length can be measured in several ways. Cells are drawn using a microscope, which is typically set at 250–400× magnification. The drawing can be done using some type of computerized imaging system or a camera-lucida procedure (Fig. 5.1) in which cells are drawn with pen and ink. The advantage of the computerized systems is that the precise length of all dendritic segments can be calculated and various statistical measurements can be made (e.g. Capowski, 1989 ). The dis-advantage is that these semi-automated procedures are very slow and, somewhat paradoxically, labor-intensive. Although only 1% of the neurons are stained in Golgi-type stains, the cells are still close together and dendritic branches from different cells overlap. The human eye can easily distinguish which branches belong to which cells, but computers cannot yet do so. This means that an operator must guide all of the computer drawing. If cells are drawn by pen and ink, then the analysis is normally done by using a procedure that estimates dendritic length. One way is to count the total number of dendritic branches, whereas another way is to place some sort of grid over the drawing and to count the number of intersections of the dendrites with the grid lines (Fig. 5.2). Although the two procedures give very different statistical views of dendritic arborization, both measures lead to the same conclusions (e.g. Stewart & Kolb, 1994 ).

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