Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. Here we introduce a Python-based, cross-platform, and open-source software toolbox, called PyMVPA, for the application of classifier-based analysis techniques to fMRI datasets. PyMVPA makes use of Python's ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine-learning packages. We present the framework in this paper and provide illustrative examples on its usage, features, and programmability.

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Cognition and behavior emerge from brain network interactions, such that investigating causal interactions should be central to the study of brain function. Approaches that characterize statistical associations among neural time series-functional connectivity (FC) methods-are likely a good starting point for estimating brain network interactions. Yet only a subset of FC methods ("effective connectivity") are explicitly designed to infer causal interactions from statistical associations. Here we incorporate best practices from diverse areas of FC research to illustrate how FC methods can be refined to improve inferences about neural mechanisms, with properties of causal neural interactions as a common ontology to facilitate cumulative progress across FC approaches. We further demonstrate how the most common FC measures (correlation and coherence) reduce the set of likely causal models, facilitating causal inferences despite major limitations. Alternative FC measures are suggested to immediately start improving causal inferences beyond these common FC measures.

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Brain-imaging research has largely focused on localizing patterns of activity related to specific mental processes, but recent work has shown that mental states can be identified from neuroimaging data using statistical classifiers. We investigated whether this approach could be extended to predict the mental state of an individual using a statistical classifier trained on other individuals, and whether the information gained in doing so could provide new insights into how mental processes are organized in the brain. Using a variety of classifier techniques, we achieved cross-validated classification accuracy of 80% across individuals (chance = 13%). Using a neural network classifier, we recovered a low-dimensional representation common to all the cognitiveperceptual tasks in our data set, and we used an ontology of cognitive processes to determine the cognitive concepts most related to each dimension. These results revealed a small organized set of large-scale networks that map cognitive processes across a highly diverse set of mental tasks, suggesting a novel way to characterize the neural basis of cognition.Neuroimaging has long been used to test specific hypotheses about brain-behavior relationships. However, it is increasingly being used to infer the engagement of specific mental processes. This is often done informally, by noting that previous studies have found an area to be engaged for a particular mental process and inferring that this process must be engaged whenever that region is found to be active. Such reverse inference has been shown to be problematic, particularly when regions are unselectively active in response to many different cognitive manipulations (Poldrack, 2006). However, recent developments in the application of statistical classifiers to neuroimaging data provide the means to directly test how accurately mental processes can be classified (e.g., Hanson & Halchenko, 2008;Haynes & Rees, 2006;O'Toole et al., 2007).In this study, we first examined how well classifiers can predict which of a set of eight cognitive tasks a person is engaged in, on the basis of patterns of activation from other individuals, and we found that such predications can be highly accurate. We next examined the dimensional representation of brain activity underlying this classification accuracy and found that the differences among these tasks can be described in terms of a small set of underlying dimensions. Finally, we examined how these distributed neural dimensions map onto the component cognitive processes that are engaged by the same eight diverse tasks, by mapping each task onto an ontology of mental processes. neuroimaging can in principle be used to map brain activity onto cognitive processes, rather than onto tasks.There is increasing interest in using the tools of machine learning to identify signals that can allow brain-reading, or prediction of mental states or behavior directly from neuroimaging data (O'Toole et al., 2007). These tools, known as classifiers, are first trained on functional magnet...

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The presentation of an incentive generates a small amount of arousal that decays exponentially over time. If the time interval separating successive incentives is short enough, arousal cumulates to an equilibrium level that is predictable from decay constants derived from the presentation of isolated incentives. The accumulation of arousal accounts for the "excessive" nature of scheduleinduced, or adjunctive, behaviors.These are revolutionary times for behavior scientists. At least three major lines of research -adjunctive behaviors (Falk, 1972), speciesspecific constraints on learning (Hinde & Stevenson-Hinde, 1973;Seligman & Hager, 1972), and sign tracking (Hearst & Jenkins, 1974;Schwartz & Gamzu, 1977)-have developed over the last decade in response to behavioral anomalies, with proponents of each noting the inconsistency of their subdiscipline with traditional theories of learning (cf. Bolles, 1976). The present article concerns adjunctive, or scheduled-induced, behavior and attempts to provide a rational basis for those "existentially absurd" (Falk, 1972) responses. Falk (1961 was the first to demonstrate that intermittent food schedules induce excessive drinking (polydipsia). He found that rats consumed over half their body weight in water over a 3-hour period, whereas they normally consume less than 10% over a 24-hour period.Since Falk's initial demonstration of polydipsia, a host of other schedule-induced behaviors have been identified (for review, see Falk, 1972;Staddon, 1977;Wallace & Singer, 1976). Many explanatory hypotheses, such as superstitious conditioning, have been advanced and subsequently eliminated. The most attractive rationale has been provided by Staddon

Over the past decade, object recognition work has confounded voxel response detection with potential voxel class identification. Consequently, the claim that there are areas of the brain that are necessary and sufficient for object identification cannot be resolved with existing associative methods (e.g., the general linear model) that are dominant in brain imaging methods. In order to explore this controversy we trained full brain (40,000 voxels) single TR (repetition time) classifiers on data from 10 subjects in two different recognition tasks on the most controversial classes of stimuli (house and face) and show 97.4% median out-of-sample (unseen TRs) generalization. This performance allowed us to reliably and uniquely assay the classifier's voxel diagnosticity in all individual subjects' brains. In this two-class case, there may be specific areas diagnostic for house stimuli (e.g., LO) or for face stimuli (e.g., STS); however, in contrast to the detection results common in this literature, neither the fusiform face area nor parahippocampal place area is shown to be uniquely diagnostic for faces or places, respectively.