Joset A. Etzel

Primary somatosensory cortex discriminates affective significance in social touch

Another person’s caress is one of the most powerful of all emotional social signals. How much the primary somatosensory cortices (SIs) participate in processing the pleasantness of such social touch remains unclear. Although ample empirical evidence supports the role of the insula in affective processing of touch, here we argue that SI might be more involved in affective processing than previously thought by showing that the response in SI to a sensual caress is modified by the perceived sex of the caresser. In a functional MRI study, we manipulated the perceived affective quality of a caress independently of the sensory properties at the skin: heterosexual males believed they were sensually caressed by either a man or woman, although the caress was in fact invariantly delivered by a female blind to condition type. Independent analyses showed that SI encoded, and was modulated by, the visual sex of the caress, and that this effect is unlikely to originate from the insula. This suggests that current models may underestimate the role played by SI in the affective processing of social touch.

Searchlight analysis: Promise, pitfalls, and potential

Multivariate pattern analysis (MVPA) is an increasingly popular approach for characterizing the information present in neural activity as measured by fMRI. For neuroimaging researchers, the searchlight technique serves as the most intuitively appealing means of implementing MVPA with fMRI data. However, searchlight approaches carry with them a number of special concerns and limitations that can lead to serious interpretation errors in practice, such as misidentifying a cluster as informative, or failing to detect truly informative voxels. Here we describe how such distorted results can occur, using both schematic illustrations and examples from actual fMRI datasets. We recommend that confirmatory and sensitivity tests, such as the ones prescribed here, should be considered a necessary stage of searchlight analysis interpretation, and that their adoption will allow the full potential of searchlight analysis to be realized.

An introduction to anatomical ROI-based fMRI classification analysis

Modern cognitive neuroscience often thinks at the interface between anatomy and function, hypothesizing that one structure is important for a task while another is not. A flexible and sensitive way to test such hypotheses is to evaluate the pattern of activity in the specific structures using multivariate classification techniques. These methods consider the activation patterns across groups of voxels, and so are consistent with current theories of how information is encoded in the brain: that the pattern of activity in brain areas is more important than the activity of single neurons or voxels. Classification techniques can identify many types of activation patterns, and patterns unique to each subject or shared across subjects. This paper is an introduction to applying classification methods to functional magnetic resonance imaging (fMRI) data, particularly for region of interest (ROI) based hypotheses. The first section describes the main steps required for such analyses while the second illustrates these steps using a simple example.

Rapid transfer of abstract rules to novel contexts in human lateral prefrontal cortex

Flexible, adaptive behavior is thought to rely on abstract rule representations within lateral prefrontal cortex (LPFC), yet it remains unclear how these representations provide such flexibility. We recently demonstrated that humans can learn complex novel tasks in seconds. Here we hypothesized that this impressive mental flexibility may be possible due to rapid transfer of practiced rule representations within LPFC to novel task contexts. We tested this hypothesis using functional MRI and multivariate pattern analysis, classifying LPFC activity patterns across 64 tasks. Classifiers trained to identify abstract rules based on practiced task activity patterns successfully generalized to novel tasks. This suggests humans can transfer practiced rule representations within LPFC to rapidly learn new tasks, facilitating cognitive performance in novel circumstances.

Reward Motivation Enhances Task Coding in Frontoparietal Cortex

Reward motivation often enhances task performance, but the neural mechanisms underlying such cognitive enhancement remain unclear. Here, we used a multivariate pattern analysis (MVPA) approach to test the hypothesis that motivation-related enhancement of cognitive control results from improved encoding and representation of task set information. Participants underwent two fMRI sessions of cued task switching, the first under baseline conditions, and the second with randomly intermixed reward incentive and no-incentive trials. Information about the upcoming task could be successfully decoded from cue-related activation patterns in a set of frontoparietal regions typically associated with task control. More critically, MVPA classifiers trained on the baseline session had significantly higher decoding accuracy on incentive than non-incentive trials, with decoding improvement mediating reward-related enhancement of behavioral performance. These results strongly support the hypothesis that reward motivation enhances cognitive control, by improving the discriminability of task-relevant information coded and maintained in frontoparietal brain regions.

The impact of certain methodological choices on multivariate analysis of fMRI data with support vector machines

Multivoxel pattern analysis of functional magnetic resonance imaging (fMRI) data is continuing to increase in popularity. Like all fMRI analyses, these analyses require extensive data processing and methodological choices, but the impact of these decisions on the final results is not always known. This study explores the impact of four methodological choices on analysis outcomes and introduces the technique of partitioning on random runs for characterizing temporal dependencies and evaluating partitioning methods. The analyses were performed on two fMRI data sets, which were repeatedly analyzed with support vector machines, varying the method of temporal compression, smoothing, voxel-wise detrending, and partitioning into training and testing sets. Smoothing sometimes slightly increased classification accuracy. Partitioning other than on the runs increased classification accuracy, and the random runs technique allowed us to attribute this improvement to the increased amount of training data, rather than to bias. The impact of the temporal compression and detrending methods varied so strongly with data set that general recommendations could not be drawn. These interactions suggest that, rather than searching for a universally superior set of methodological choices, researchers must carefully consider each choice in the context of each experiment.

MVPA significance testing when just above chance, and related properties of permutation tests

Parametric statistical tests (e.g., t-tests) can sometimes return highly significant results in cases that would be considered uninformative, such as when the individuals’ accuracies are just above chance. This paper demonstrates that permutation tests can produce the expected non-significant results in these datasets. The properties of null distributions underlying this difference in significance are illustrated: their relative insensitivity to dataset information content, but sensitivity to dataset characteristics such as number of participants, examples, and runs.

Looking outside the searchlight

Searchlight analysis (information mapping) with pattern classifiers is a popular method of multivariate fMRI analysis often interpreted as localizing informative voxel clusters. Applicability and utility of this method is limited, however, by its dependency on searchlight radius, the assumption that information is present at all spatial scales, and its susceptibility to overfitting. These problems are demonstrated in a dataset in which, contrary to common expectation, voxels identified as informative do not clearly contain more information than those not so identified.

High dimensional exploration: A comparison of PCA, distance concentration, and classification performance in two fMRI datasets

fMRI (functional magnetic resonance imaging) studies frequently create high dimensional datasets, with far more features (voxels) than examples. It is known that such datasets frequently have properties that make analysis challenging, such as concentration of distances. Here, we calculated the probability of distance concentration and proportion of variance explained by PCA in two fMRI datasets, comparing these measures with each other, as well as with the number of voxels and classification accuracy. There are clear differences between the datasets, with one showing levels of distance concentration comparable to those reported in microarray data [1, 2]. While it remains to be determined how typical these results are, they suggest that problematic levels of distance concentration in fMRI datasets may not be a rare occurrence.

Marigolds: an evolutionary algorithm simulation

A novel evolutionary algorithm was developed to simulate plant growth in a 3D environment with and without mortality. After adaptation the populations exhibit characteristics resembling natural strategies: dividing into unique types, promoting seed dispersal through flower placement, and maximizing energy production. The distance between branches decreased when mortality was enabled.