Stephen Johnston

Neurofeedback: A promising tool for the self-regulation of emotion networks

Real-time functional magnetic resonance imaging (fMRI) affords the opportunity to explore the feasibility of self-regulation of functional brain networks through neurofeedback. We localised emotion networks individually in thirteen participants using fMRI and trained them to upregulate target areas, including the insula and amygdala. Participants achieved a high degree of control of these networks after a brief training period. We observed activation increases during periods of upregulation of emotion networks in the precuneus and medial prefrontal cortex and, with increasing training success, in the ventral striatum. These findings demonstrate the feasibility of fMRI-based neurofeedback of emotion networks and suggest a possible development into a therapeutic tool.

Random Subspace Ensembles for fMRI Classification

Classification of brain images obtained through functional magnetic resonance imaging (fMRI) poses a serious challenge to pattern recognition and machine learning due to the extremely large feature-to-instance ratio. This calls for revision and adaptation of the current state-of-the-art classification methods. We investigate the suitability of the random subspace (RS) ensemble method for fMRI classification. RS samples from the original feature set and builds one (base) classifier on each subset. The ensemble assigns a class label by either majority voting or averaging of output probabilities. Looking for guidelines for setting the two parameters of the method-ensemble size and feature sample size-we introduce three criteria calculated through these parameters: usability of the selected feature sets, coverage of the set of ¿important¿ features, and feature set diversity. Optimized together, these criteria work toward producing accurate and diverse individual classifiers. RS was tested on three fMRI datasets from single-subject experiments: the Haxby data (Haxby, 2001.) and two datasets collected in-house. We found that RS with support vector machines (SVM) as the base classifier outperformed single classifiers as well as some of the most widely used classifier ensembles such as bagging, AdaBoost, random forest, and rotation forest. The closest rivals were the single SVM and bagging of SVM classifiers. We use kappa-error diagrams to understand the success of RS.

Task-irrelevant visual motion and flicker attenuate the attentional blink

Our reduced ability to correctly report two sequentially presented targets is seen in the robust effect known as the attentional blink (AB; Raymond, Shapiro, & Arnell, 1992). One recent report (Olivers & Nieuwenhuis, 2005) strikingly reveals the AB to be virtually abolished when non-task-demanding music occurs in the background. The authors suggest that a diffuse attentional state is the mediating factor. Here, we seek to broaden the finding’s generality by determining if task-irrelevant visual motion and flicker also attenuate the AB. In our experiments, the AB task was presented together with a background field of moving dots that could moveaway from ortoward the central AB task, or flicker. In the control condition, the dots remained static. The AB was attenuated—though to different degrees—in all experimental conditions, but not in the static condition. Our findings add to the generality of the previous conclusions, and we emphasize an account based on the overallocation of attention.

Neural Correlates of Enhanced Visual Short-Term Memory for Angry Faces: An fMRI Study

Background Fluid and effective social communication requires that both face identity and emotional expression information are encoded and maintained in visual short-term memory (VSTM) to enable a coherent, ongoing picture of the world and its players. This appears to be of particular evolutionary importance when confronted with potentially threatening displays of emotion - previous research has shown better VSTM for angry versus happy or neutral face identities. Methodology/Principal Findings Using functional magnetic resonance imaging, here we investigated the neural correlates of this angry face benefit in VSTM. Participants were shown between one and four to-be-remembered angry, happy, or neutral faces, and after a short retention delay they stated whether a single probe face had been present or not in the previous display. All faces in any one display expressed the same emotion, and the task required memory for face identity. We find enhanced VSTM for angry face identities and describe the right hemisphere brain network underpinning this effect, which involves the globus pallidus, superior temporal sulcus, and frontal lobe. Increased activity in the globus pallidus was significantly correlated with the angry benefit in VSTM. Areas modulated by emotion were distinct from those modulated by memory load. Conclusions/Significance Our results provide evidence for a key role of the basal ganglia as an interface between emotion and cognition, supported by a frontal, temporal, and occipital network.

Functional specialization in the supplementary motor complex

, Nachev, Kennard and Husain consider data from a broad range of studies involving structures within the supplementary motor complex (SMC) comprising the supplementary motor area (SMA), the supplementary eye fields (SEF) and the pre-supplementary motor area (preSMA). These include studies contrasting self-initiated with externally triggered move ments, studies concerning the observation of graspable objects and studies concerning the planning of motor sequences, motor learning and cognitive control (for example, task switching). Nachev et al. suggest that functional characterizations of the SMC may be better cast in terms of a rostrocaudal continuum of graded change in structure and function than in terms of subregions of discrete or modular functional specializa tion. They discuss one hypothesis, drawn from the data presented, that this continuum

A polarity effect in misoriented object recognition: The role of polar features in the computation of orientation-invariant shape representations

This study investigated the contribution of polar features and internal shape axes to misoriented object recognition. A recognition memory paradigm was used to examine the effects of stimulus orientation on the recognition of previously memorized 2-D novel objects. In contrast to some recent reports, Experiment 1 showed that orientation-invariant performance can be found from the outset of testing with objects containing a salient axis of symmetry. In Experiments 2 and 3 it was found that the removal of a single salient polar feature, while preserving the axis of elongation, was sufficient to increase stimulus orientation time costs. This polarity effect suggests that polar features, and shape axes, play a role in the computation of orientation-invariant shape representations. It is proposed that shape axes facilitate the localization of polar features, which, in turn, are used to resolve the polarity of shape representations during recognition.

On-Line fMRI Data Classification Using Linear and Ensemble Classifiers

The advent of real-time fMRI pattern classification opens many avenues for interactive self-regulation where the brain’s response is better modelled by multivariate, rather than univariate techniques. Here we test three on-line linear classifiers, applied to a real fMRI dataset, collected as part of an experiment on the cortical response to emotional stimuli. We propose a random subspace ensemble as a fast and more accurate alternative to component classifiers. The on-line linear discriminant classifier (O-LDC) was found to be a better base classifier than the on-line versions of the perceptron and the balanced winnow.