For cognitive neuroscience to go forward a more explicit effort is needed to use neurophysiology to constrain how the brain produces human mental functions. This review begins with the suggestion that two fundamental features may be critical for this effort. The first is the connectivity of the brain, which occupies an intermediate position between complete redundant interconnections and independence. The term semiconnected is presented as a designation, which is an obvious derivation of the term semiconductors as used in engineering. The second is transient response plasticity where a given neuron or collection of neurons may show rapid changes in response characteristics depending on experience. Response plasticity is a ubiquitous property of the brain rather than a unique characteristic of "neurocognitive" regions. These two properties may be brought together when brain areas interact such that their aggregate function embodies cognition. Three examples are used to illustrate these ...

Many kinds of complex systems exhibit characteristic patterns of temporal correlations that emerge as the result of functional interactions within a structured network. One such complex system is the brain, composed of numerous neuronal units linked by synaptic connections. The activity of these neuronal units gives rise to dynamic states that are characterized by specific patterns of neuronal activation and co-activation. These patterns, called functional connectivity, are possible neural correlates of perceptual and cognitive processes. Which functional connectivity patterns arise depends on the anatomical structure of the underlying network, which in turn is modified by a broad range of activity-dependent processes. Given this intricate relationship between structure and function, the question of how patterns of anatomical connectivity constrain or determine dynamical patterns is of considerable theoretical importance. The present study develops computational tools to analyze networks in terms of their structure and dynamics. We identify different classes of network, including networks that are characterized by high complexity. These highly complex networks have distinct structural characteristics such as clustered connectivity and short wiring length similar to those of large-scale networks of the cerebral cortex. � 2002 Wiley Periodicals, Inc.

Throughout the early history of neurology and neuroscience, most theoretical accounts of brain function have emphasized either aspects of localization or distributed properties [1]. Instead, modern views focus extensively on the structure and dynamics of large-scale neuronal networks, especially those of the cerebral cortex and associated thalamocortical

This essay introduces the massive redeployment hypothesis, an account of the functional organization of the brain that centrally features the fact that brain areas are typically employed to support numerous functions. The central contribution of the essay is to outline a middle course between strict localization on the one hand, and holism on the other, in such a way as to account for the supporting data on both sides of the argument. The massive redeployment hypothesis is supported by case studies of redeployment, and compared and contrasted with other theories of the localization of function.

common regions mediate? By comparing patterns of brain activity across different cognitive functions, answers to this question can be generated. ........ Figure 1 about here ........ The matrix in Figure 1 illustrates the difference between the traditional within-function approach and the cross-function approach we are advocating in this chapter. Let us assume that in functional neuroimaging studies Cognitive Function A typically is associated with activations in Brain Regions 1 and 3, Cognitive Function B with activations in Brain Regions 2 and 3, and Cognitive Function C with activations in Brain Regions 1 and 2. In the standard within-function approach, functional neuroimaging researchers are primarily concerned with one cognitive function and interpret activations in relation to this particular function. Thus, in a situation like the one depicted in Figure 1, researchers of Function A would attribute the activation of Region 1 to a certain aspect of Function A, whereas researchers

INTRODUCTION During the past decade, the field of functional neuroimaging of cognition has grown exponentially. From a handful of studies in the early 1990s, this research domain expanded to more than 800 studies by the early 2000s. Today, positron emission tomography (PET) and functional MRI (fMRI) studies cover almost every aspect of human cognition, from motion perception to moral reasoning. If each study is seen as a tree, the field has grown from minimal vegetation to a luxuriant tropical forest in less than 10 years. Yet, functional neuroimaging researchers sometimes focus exclusively on their own cognitive domain and do not see the forest through the trees. The goal of the present chapter is to call attention to the forest--- that is, to what many functional neuroimaging studies of cognition have in common. When we say that most researchers are focused on the trees, we refer to the fact that the vast majority of functional neuroimaging studies investigate a single cognitive fu

Effective instructional methods are now an important national issue. We reviewed the current research techniques used in cognitive neuroscience and what is currently known about the neurocognition of reading and mathematics. We found that while the neurocognition aspects of reading and mathematics share common processes associated with language, certain aspects of semantics and comprehension are unique to reading and certain aspects of mathematics entail visual-spatial processing not observed during reading. We conclude that although significant advances have been made in the understanding of the underlying neurocognitive process in the last decade more research is needed before the neurosciences can make a direct contribution to instructional practice.

ated between 100,000 and 10,000,000 km [5]. Despite this massive connectivity, cortical networks are exceedingly sparse, with an overall connectivity factor (number of connections present out of all possible) of around 10 #6 . Brain networks are not random, but form highly specific patterns. A predominant feature of brain networks is that neurons tend to connect predominantly with other neurons in local groups. Thus, local connectivity ratios can be significantly higher than those suggested by random topology. Networks in the brain can be analyzed at multiple levels of scale. Within small and localized region of the brain, neurons form characteristic sets of connections, socalled local circuits [6]. For example, neurons forming cortical columns show specific patterns of connectivity between morphologically and pharmacologically distinct classes of cells in different layers. At a higher level of scale, such columns communicate through "tangential" or "horizontal" connections, forming