The organization of thinking: What functional brain imaging reveals about the neuroarchitecture of complex cognition

Recent findings in brain imaging, particularly in fMRI, are beginning to reveal some of the fundamental properties of the organization of the cortical systems that underpin complex cognition. We propose an emerging set of operating principles that govern this organization, characterizing the system as a set of collaborating cortical centers that operate as a large-scale cortical network. Two of the network’s critical features are that it is resource constrained and dynamically configured, with resource constraints and demands dynamically shaping the network topology. The operating principles are embodied in a cognitive neuroarchitecture, 4CAPS, consisting of a number of interacting computational centers that correspond to activating cortical areas. Each 4CAPS center is a hybrid production system, possessing both symbolic and connectionist attributes. We describe 4CAPS models of sentence comprehension, spatial problem solving, and complex multitasking and compare the accounts of these models with brain activation and behavioral results. Finally, we compare 4CAPS with other proposed neuroarchitectures.     

Oscillatory brain dynamics, wavelet analysis, and cognition

On the basis of a systems theoretical approach it was hypothesized that event-related potentials (ERPs) are superpositions of stimulus-evoked and time-locked EEG rhythms reflecting resonance properties of the brain (Ba?ar, 1980). This approach led to frequency analysis of ERPs as a way of analyzing evoked rhythms. The present article outlines the basic features of ERP frequency analysis in comparison to ERP wavelet analysis, a recently introduced method of time-frequency analysis. Both methods were used in an investigation of the functional correlates of evoked rhythms where auditory and visual ERPs were recorded from the cat brain. Intracranial electrodes were located in the primary auditory cortex and in the primary visual cortex thus permitting "cross-modality" experiments. Responses to adequate stimulation (e.g., visual ERP recorded from the visual cortex) were characterized by high amplitude alpha (8-16 Hz) responses which were not observed for inadequate stimulation. This result is interpreted as a hint at a special role of alpha responses in primary sensory processing. The results of frequency analysis and of wavelet analysis were quite similar, with possible advantages of wavelet methods for single-trial analysis. The results of frequency analysis as performed earlier were thus confirmed by wavelet analysis. This supports the view that ERP frequency components correspond to evoked rhythms with a distinct biological significance.     

Neural modeling and imaging of the cortical interactions underlying syllable production

This paper describes a neural model of speech acquisition and production that accounts for a wide range of acoustic, kinematic, and neuroimaging data concerning the control of speech movements. The model is a neural network whose components correspond to regions of the cerebral cortex and cerebellum, including premotor, motor, auditory, and somatosensory cortical areas. Computer simulations of the model verify its ability to account for compensation to lip and jaw perturbations during speech. Specific anatomical locations of the model's components are estimated, and these estimates are used to simulate fMRI experiments of simple syllable production.     

Social cognition and the human brain

Humans are exceedingly social animals, but the neural underpinnings of social cognition and behavior are not well understood. Studies in humans and other primates have pointed to several structures that play a key role in guiding social behaviors: the amygdala, ventromedial frontal cortices, and right somatosensory-related cortex, among others. These structures appear to mediate between perceptual representations of socially relevant stimuli, such as the sight of conspecifics, and retrieval of knowledge (or elicitation of behaviors) that such stimuli can trigger. Current debates concern the extent to which social cognition draws upon processing specialized for social information, and the relative contributions made to social cognition by innate and acquired knowledge.     

Levels of processing: A view from functional brain imaging

This paper briefly reviews two central assumptions of the levels-of-processing framework in the light of findings from recent PET and fMRI studies: First, to address the suggestion that memory traces can be seen as records of analyses carried out for the purposes of perception and comprehension, studies on encoding-retrieval overlap in brain activation patterns are considered. Second, to address the suggestion that deeper, more semantic, processing results in more durable traces, studies of how encoding activity relates to processing depth and subsequent memory performance are examined. The results show that some of the sensory regions that are activated during initial perception are subsequently reactivated during retrieval, and activity in frontal and medial-temporal brain regions is related to depth of processing and level of memory performance. Collectively, these results provide support for central components of the levels framework.     

Levels of processing: a view from functional brain imaging

This paper briefly reviews two central assumptions of the levels-of-processing framework in the light of findings from recent PET and fMRI studies: First, to address the suggestion that memory traces can be seen as records of analyses carried out for the purposes of perception and comprehension, studies on encoding-retrieval overlap in brain activation patterns are considered. Second, to address the suggestion that deeper, more semantic, processing results in more durable traces, studies of how encoding activity relates to processing depth and subsequent memory performance are examined. The results show that some of the sensory regions that are activated during initial perception are subsequently reactivated during retrieval, and activity in frontal and medial-temporal brain regions is related to depth of processing and level of memory performance. Collectively, these results provide support for central components of the levels framework.     

Reading and controlling human brain activation using real-time functional magnetic resonance imaging

Understanding how to control how the brain's functioning mediates mental experience and the brain's processing to alter cognition or disease are central projects of cognitive and neural science. The advent of real-time functional magnetic resonance imaging (rtfMRI) now makes it possible to observe the biology of one's own brain while thinking, feeling and acting. Recent evidence suggests that people can learn to control brain activation in localized regions, with corresponding changes in their mental operations, by observing information from their brain while inside an MRI scanner. For example, subjects can learn to deliberately control activation in brain regions involved in pain processing with corresponding changes in experienced pain. This may provide a novel, non-invasive means of observing and controlling brain function, potentially altering cognitive processes or disease.     

Neural implementation of probabilistic models of cognition

Bayesian models of cognition hypothesize that human brains make sense of data by representing probability distributions and applying Bayes’ rule to find the best explanation for available data. Understanding the neural mechanisms underlying probabilistic models remains important because Bayesian models provide a computational framework, rather than specifying mechanistic processes. Here, we propose a deterministic neural-network model which estimates and represents probability distributions from observable events—a phenomenon related to the concept of probability matching. Our model learns to represent probabilities without receiving any representation of them from the external world, but rather by experiencing the occurrence patterns of individual events. Our neural implementation of probability matching is paired with a neural module applying Bayes’ rule, forming a comprehensive neural scheme to simulate human Bayesian learning and inference. Our model also provides novel explanations of base-rate neglect, a notable deviation from Bayes.     

Recharging cognition with DC brain polarization

Electrical direct current (DC) has been applied to the human head throughout history for various reasons and with claims of behavioral effects and clinical benefits. This technique has recently been rediscovered and its effects validated with modern quantitative techniques and experimental designs. Despite the very weak current used, DC polarization applied to specific brain areas can alter verbal fluency, motor learning and perceptual thresholds, and can be used in conjunction with transcranial magnetic stimulation. Compact and safe, this old technique seems poised to allow major advances cognitive science and therapy.     

Plasticity of brain and cognition in older adults

Aging is typically related to changes in brain and cognition, but the aging process is heterogeneous and differs between individuals. Recent research has started investigating the influence of cognitive and physical training on cognitive performance, functional brain activity, and brain structure in old age. The functional relevance of neural changes and the interactions among these changes following interventions is still a matter of debate. Here we selectively review research on structural and functional brain correlates of training-induced performance changes in healthy older adults and present exemplary longitudinal intervention studies sorted by the type of training applied (i.e., strategy-based training, process-specific training, and physical exercise). Although many training studies have been conducted recently, within each task domain, the number of studies that used comparable methods and techniques to assess behavioral and neural changes is limited. We suggest that future studies should include a multimodal approach to enhance the understanding of the relation between different levels of brain changes in aging and those changes that result from training. Investigating inter-individual differences in intervention-induced behavioral and neuronal changes would provide more information about who would benefit from a specific intervention and why. In addition, a more systematic examination of the time course of training-related structural and functional changes would improve the current level of knowledge about how learning is implemented in the brain and facilitate our understanding of contradictory results.     

Neural blackboard architectures of combinatorial structures in cognition

Human cognition is unique in the way in which it relies on combinatorial (or compositional) structures. Language provides ample evidence for the existence of combinatorial structures, but they can also be found in visual cognition. To understand the neural basis of human cognition, it is therefore essential to understand how combinatorial structures can be instantiated in neural terms. In his recent book on the foundations of language, Jackendoff described four fundamental problems for a neural instantiation of combinatorial structures: the massiveness of the binding problem, the problem of 2, the problem of variables, and the transformation of combinatorial structures from working memory to long-term memory. This paper aims to show that these problems can be solved by means of neural "blackboard" architectures. For this purpose, a neural blackboard architecture for sentence structure is presented. In this architecture, neural structures that encode for words are temporarily bound in a manner that preserves the structure of the sentence. It is shown that the architecture solves the four problems presented by Jackendoff. The ability of the architecture to instantiate sentence structures is illustrated with examples of sentence complexity observed in human language performance. Similarities exist between the architecture for sentence structure and blackboard architectures for combinatorial structures in visual cognition, derived from the structure of the visual cortex. These architectures are briefly discussed, together with an example of a combinatorial structure in which the blackboard architectures for language and vision are combined. In this way, the architecture for language is grounded in perception. Perspectives and potential developments of the architectures are discussed.     

Neural correlates of establishing, maintaining, and switching brain states

Although the study of brain states is an old one in neuroscience, there has been growing interest in brain state specification owing to MRI studies tracing brain connectivity at rest. In this review, we summarize recent research on three relatively well-described brain states: the resting, alert, and meditation states. We explore the neural correlates of maintaining a state or switching between states, and argue that the anterior cingulate cortex and striatum play a critical role in state maintenance, whereas the insula has a major role in switching between states. Brain state may serve as a predictor of performance in a variety of perceptual, memory, and problem solving tasks. Thus, understanding brain states is critical for understanding human performance.     

In vivo neuroanatomy of Alzheimer's disease: evidence from structural and functional brain imaging

In vivo structural (CT, MRI) and functional (SPECT, PET) brain imaging techniques have been widely used to study the neuroanatomy and neurophysiology of Alzheimer's disease (AD) and to identify definite biological markers of the disease. We used meta-analytic methods to synthesize this literature to determine what neuroanatomical structures best differentiate patients with AD from healthy normal controls. A total of 125 studies published between 1984 and 2000 that included 3543 patients with AD and 1698 normal healthy controls met inclusion criteria. We found that measures of the temporal cortices, including the amygdala, hippocampus, and inferior temporal lobes, along with the anterior cingulate cortex, associated with the largest magnitudes of effects and, hence, could serve as the most useful structures to help clinicians differentiate AD from healthy normal aging.     

A space for measuring mind and brain: interdisciplinarity and digital tools in the development of brain mapping and functional imaging, 1980-1990

Brain mapping is said to have opened up the possibility of a new collaboration between the sciences of mind and the sciences of the brain, potentially leading to a new kind of scientist, sometimes called "cognitive neuroscientist." This article traces the recent history of brain mapping and analyzes the processes that have led to a new "close working relationship" between the sciences of mind and brain. A key part of the working relationship is shown to be constituted through the development of the Talairach system, a digital space in which to measure structure and function. The development of meaningful brain mapping data involves the creation of measurement spaces that allow interdisciplinary collaboration and is not the result solely of theoretical developments or of the application of a technology.     

State-dependency in brain stimulation studies of perception and cognition

We address the importance of understanding initial states of neuronal populations and of state-dependent responses in cognitive neuroscience experiments with special emphasis on brain stimulation studies of perception and cognition. The approach we present is based on evidence that behavioural and perceptual effects of transcranial magnetic stimulation (TMS) are determined by initial neural activation state; by systematically manipulating neural activation states before application of TMS, one can selectively target specific, even spatially overlapping neural populations within the affected region. This approach is potentially of great benefit to cognitive neuroscience and remediation programmes as it combines high spatial and functional resolution with the ability to assess causality.