Stefan Pollmann

Prefrontal cortex activation in task switching: an event-related fMRI study.

When a switch between two tasks has to be carried out, performance is slower than in trials where the same task is performed repeatedly. This finding has been attributed to time-consuming control processes required for task switching. Previous results of other paradigms investigating cognitive control processes suggested that prefrontal cortex is involved in executive control. We used event-related fMRI to investigate prefrontal cortex involvement in task switching. Regions in the lateral prefrontal and premotor cortex bilaterally, the anterior insula bilaterally, the left intraparietal sulcus, the SMA/pre-SMA region and the cuneus/precuneus were activated by the task repetition condition and showed additional activation in the task switch condition. This confirmed the hypothesis that lateral prefrontal cortex is involved in task switching. However, the results also showed that this region is neither the only region involved in task switching nor a region specifically involved in task switching.

PyMVPA: a Python Toolbox for Multivariate Pattern Analysis of fMRI Data

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.

Covert Reorienting and Inhibition of Return: An Event-Related fMRI Study

Using event-related fMRI, we analyzed the functional neuroanatomy of covert reorienting and inhibition of return (IOR). Covert reorienting to a target appearing within 250 msec after an invalid contralateral location cue elicited increased activation in the left fronto-polar cortex (LFPC), right anterior and left posterior middle frontal gyrus, and right cerebellum, areas that have previously been associated with attentional processes, specifically attentional change. In contrast, IOR, which leads to prolonged response times to targets that appear at the cued location at a stimulus-onset-asynchrony (SOA)>250 msec, was accompanied by increased activation in brain areas involved in oculomotor programming, such as the right medial frontal gyrus (supplementary eye field; SEF) and the right inferior precentral sulcus (frontal eye field; FEF), supporting the oculomotor bias theory of IOR. Pre-SEF and pre-FEF areas were involved both in covert reorienting and IOR. The supramarginal gyri were bilaterally involved in IOR, with the right supramarginal gyrus additionally involved in covert reorienting.

A Fronto-Posterior Network Involved in Visual Dimension Changes

Objects characterized by a unique visual feature may pop out of their environment. When participants have to search for such odd-one-out targets, detection is facilitated when targets are consistently defined within the same feature dimension (e.g., color) compared with when the target dimension is uncertain (e.g., color or motion). Further, with dimensional uncertainty, there is a cost when a given target is defined in a different dimension to the preceding target, relative to when the critical dimension remains the same. Behavioral evidence suggests that a target dimension change involves a shift of attention to the new dimension. The present fMRI study revealed increased activation in the left frontopolar cortex, as well as in posterior visual areas of the dorsal and ventral streams, specific to changes in the target dimension. In contrast, activation in the striate cortex was decreased. This pattern suggests control of cross-dimensional attention shifts by the frontopolar cortex, modulating visual cortical processing by increased activation in higher-tier visual areas and suppression of activation in lower-tier areas.

Object working memory and visuospatial processing: functional neuroanatomy analyzed by event-related fMRI

Abstract. We report an event-related functional magnetic-resonance-imaging (fMRI) experiment that investigates the relationship of transient visual object memory, visuospatial orienting, and object recognition. Delayed object matching and visuospatial orienting involved a highly overlapping network of brain areas. Common areas were the frontal eye fields (FEF), the pre-supplementary motor area (pre-SMA)/SMA complex, the precentral gyri, and the horizontal and descending branches of the intraparietal sulcus (IPS). Selective delay activation was observed anterior to the FEF and in the ascending part of the IPS. Right dorsolateral prefrontal cortex was involved in goal-directed visual search, but showed no delay activity.

Separating distractor rejection and target detection in posterior parietal cortex - An event-related fMRI study of visual marking

Successful survival in a competitive world requires the employment of efficient procedures for selecting new in preference to old information. Recent behavioral studies have shown that efficient selection is dependent not only on properties of new stimuli but also on an intentional bias that we can introduce against old stimuli. Event-related analysis of functional magnetic resonance imaging data from a task involving visual search across time as well as space indicates that the superior parietal lobule is specifically involved in processes leading to the efficient segmentation of old from new items, whereas the temporoparietal junction area and the ascending limb of the right intraparietal sulcus are involved in the detection of salient new items and in response preparation. The study provides evidence for the functional segregration of brain regions within the posterior parietal lobe.

PyMVPA: A Unifying Approach to the Analysis of Neuroscientific Data

The Python programming language is steadily increasing in popularity as the language of choice for scientific computing. The ability of this scripting environment to access a huge code base in various languages, combined with its syntactical simplicity, make it the ideal tool for implementing and sharing ideas among scientists from numerous fields and with heterogeneous methodological backgrounds. The recent rise of reciprocal interest between the machine learning (ML) and neuroscience communities is an example of the desire for an inter-disciplinary transfer of computational methods that can benefit from a Python-based framework. For many years, a large fraction of both research communities have addressed, almost independently, very high-dimensional problems with almost completely non-overlapping methods. However, a number of recently published studies that applied ML methods to neuroscience research questions attracted a lot of attention from researchers from both fields, as well as the general public, and showed that this approach can provide novel and fruitful insights into the functioning of the brain. In this article we show how PyMVPA, a specialized Python framework for machine learning based data analysis, can help to facilitate this inter-disciplinary technology transfer by providing a single interface to a wide array of machine learning libraries and neural data-processing methods. We demonstrate the general applicability and power of PyMVPA via analyses of a number of neural data modalities, including fMRI, EEG, MEG, and extracellular recordings.

Retinotopic activation in response to subjective contours in primary visual cortex.

Objects in our visual environment are arranged in depth and hence there is a considerable amount of overlap and occlusion in the image they generate on the retina. In order to properly segment the image into figure and background, boundary interpolation is required even across large distances. Here we study the cortical mechanisms involved in collinear contour interpolation using fMRI. Human observers were asked to discriminate the curvature of interpolated boundaries in Kanizsa figures and in control configurations, which contained identical physical information but did not generated subjective shapes. We measured a spatially precise spin-echo BOLD signal and found stronger responses to subjective shapes than non-shapes at the subjective boundary locations, but not at the inducer locations. The responses to subjective contours within primary visual cortex were retinotopically specific and analogous to that to real contours, which is intriguing given that subjective and luminance-defined contours are physically fundamentally different. We suggest that in the absence of retinal stimulation, the observed activation changes in primary visual cortex are driven by intracortical interactions and feedback, which are revealed in the absence of a physical stimulus.

fMRI Reveals a Common Neural Substrate of Illusory and Real Contours in V1 after Perceptual Learning

Perceptual learning involves the specific and relatively permanent modification of perception following a sensory experience. In psychophysical experiments, the specificity of the learning effects to the trained stimulus attributes (e.g., visual field position or stimulus orientation) is often attributed to assumed neural modifications at an early cortical site within the visual processing hierarchy. We directly investigated a neural correlate of perceptual learning in the primary visual cortex using fMRI. Twenty volunteers practiced a curvature discrimination on Kanizsa-type illusory contours in the MR scanner. Practice-induced changes in the BOLD response to illusory contours were compared between the pretraining and the posttraining block in those areas of the primary visual cortex (V1) that, in the same session, had been identified to represent real contours at corresponding visual field locations. A retinotopically specific BOLD signal increase to illusory contours was observed as a consequence of the training, possibly signaling the formation of a contour representation, which is necessary for performing the curvature discrimination. The effects of perceptual training were maintained over a period of about 10 months, and they were specific to the trained visual field position. The behavioral specificity of the learning effects supports an involvement of V1 in perceptual learning, and not in unspecific attentional effects.

Comparing the Neural Basis of Monetary Reward and Cognitive Feedback during Information-Integration Category Learning

The dopaminergic system is known to play a central role in reward-based learning (Schultz, 2006), yet it was also observed to be involved when only cognitive feedback is given (Aron et al., 2004). Within the domain of information-integration category learning, in which information from several stimulus dimensions has to be integrated predecisionally (Ashby and Maddox, 2005), the importance of contingent feedback is well established (Maddox et al., 2003). We examined the common neural correlates of reward anticipation and prediction error in this task. Sixteen subjects performed two parallel information-integration tasks within a single event-related functional magnetic resonance imaging session but received a monetary reward only for one of them. Similar functional areas including basal ganglia structures were activated in both task versions. In contrast, a single structure, the nucleus accumbens, showed higher activation during monetary reward anticipation compared with the anticipation of cognitive feedback in information-integration learning. Additionally, this activation was predicted by measures of intrinsic motivation in the cognitive feedback task and by measures of extrinsic motivation in the rewarded task. Our results indicate that, although all other structures implicated in category learning are not significantly affected by altering the type of reward, the nucleus accumbens responds to the positive incentive properties of an expected reward depending on the specific type of the reward.