Efficient recognition of odorous objects universally shapes animal behavior and is crucial for survival. To distinguish kin from nonkin, mate from nonmate and food from nonfood, organisms must be able to create meaningful perceptual representations of odor qualities and categories. It is currently unknown where and in what form the brain encodes information about odor quality. By combining functional magnetic resonance imaging (fMRI) with multivariate (pattern-based) techniques, we found that spatially distributed ensemble activity in human posterior piriform cortex (PPC) coincides with perceptual ratings of odor quality, such that odorants with more (or less) similar fMRI patterns were perceived as more (or less) alike. We did not observe these effects in anterior piriform cortex, amygdala or orbitofrontal cortex, indicating that ensemble coding of odor categorical perception is regionally specific for PPC. These findings substantiate theoretical models emphasizing the importance of distributed piriform templates for the perceptual reconstruction of odor object quality.
Broca's area (or, more generally, the left inferior frontal region) is implicated in many language and language-related tasks. This chapter addresses the question of whether it is legitimate to move from this assertion (supported by very large numbers of lesion studies and functional neuroimaging experiments) to the theoretical claim that the exclusive (or even the core) specialization of Broca's area is the mediation of language functions. It shows that particular neuroanatomical regions, including Broca's area, change their functions consequent upon the simultaneous activation of other regions that are effectively connected to a given region.
It is a longstanding scientific insight that understanding processes that result from the interaction of multiple elements require mathematical models of system dynamics (von Bertalanffy 1969). This notion is an increasingly important theme in neuroscience, particularly in neuroimaging, where causal mechanisms in neural systems are described in terms of effective connectivity. Here, we review established models of effective connectivity that are applied to data acquired with positron emission tomography (PET), functional magnetic resonance imaging (fMRI), electroencephalography (EEG) or magnetoencephalography (MEG). We start with an outline of general systems theory, a very general framework for formalizing the description of systems. This framework will guide the subsequent description of various established models of effective connectivity, including structural equation modeling (SEM), multivariate autoregressive modeling (MAR) and dynamic causal modeling (DCM). We focus particularly on DCM which distinguishes between neural state equations and a biophysical forward model that translates neural activity into a measured signal. After presenting some examples of applications of DCM to fMRI and EEG data, we conclude with some thoughts on pharmacological and clinical applications of models of effective connectivity.
Impairment in mismatch negativity (MMN) generation is a robust biological marker of schizophrenia. Understanding the physiological and pharmacological processes involved in its generation may therefore advance our understanding of this complex disorder. The present study tested if acute administration of nicotine modulates human auditory sensory memory as measured with MMN. ERP responses to tone duration deviants were recorded using a stimulation protocol with continuously changing (roving) standard stimuli in order to measure the effect of stimulus repetitions on encoding of new stimuli (MMN memory trace effect). Twenty healthy adult volunteers were randomly assigned to receive either a nicotine gum or placebo after a baseline ERP recording. Nicotine administration augmented MMN amplitude in the treatment group compared to the baseline recording, while no MMN change was found in the placebo group. The drug effect was due to a selective enhancement of a frontal positive potential to standard stimuli (from 80-200 ms post-stimulus), while the negativity to deviants remained unaffected. Furthermore, under nicotine stimulation this repetition positivity showed a more marked increase with stimulus repetition compared to baseline and placebo. These results have potential implications for schizophrenia by suggesting that nicotinic agonists could ameliorate patients' MMN deficits by improving stimulus encoding and sensory memory trace formation.
If one formulates Helmholtz's ideas about perception in terms of modern-day theories one arrives at a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts. Using constructs from statistical physics it can be shown that the problems of inferring what cause our sensory input and learning causal regularities in the sensorium can be resolved using exactly the same principles. Furthermore, inference and learning can proceed in a biologically plausible fashion. The ensuing scheme rests on Empirical Bayes and hierarchical models of how sensory information is generated. The use of hierarchical models enables the brain to construct prior expectations in a dynamic and context-sensitive fashion. This scheme provides a principled way to understand many aspects of the brain's organisation and responses.In this paper, we suggest that these perceptual processes are just one emergent property of systems that conform to a free-energy principle. The free-energy considered here represents a bound on the surprise inherent in any exchange with the environment, under expectations encoded by its state or configuration. A system can minimise free-energy by changing its configuration to change the way it samples the environment, or to change its expectations. These changes correspond to action and perception respectively and lead to an adaptive exchange with the environment that is characteristic of biological systems. This treatment implies that the system's state and structure encode an implicit and probabilistic model of the environment. We will look at models entailed by the brain and how minimisation of free-energy can explain its dynamics and structure.
