A New Foreperiod Effect on Intertrial Phase Coherence. Part I: Existence and Behavioral Relevance.

This letter makes scientific and methodological contributions. Scientifically, it demonstrates a new and behaviorally relevant effect of temporal expectation on the phase coherence of the electroencephalogram (EEG). Methodologically, it introduces novel methods to characterize EEG recordings at the single-trial level. Expecting events in time can lead to more efficient behavior. A remarkable finding in the study of temporal expectation is the foreperiod effect on reaction time, that is, the influence on reaction time of the delay between a warning signal and a succeeding imperative stimulus to which subjects are instructed to respond as quickly as possible. Here we study a new foreperiod effect in an audiovisual attention-shifting oddball task in which attention-shift cues directed the attention of subjects to impendent deviant stimuli of a given modality and therefore acted as warning signals for these deviants. Standard stimuli, to which subjects did not respond, were interspersed between warning signals and deviants. We hypothesized that foreperiod durations modulated intertrial phase coherence (ITPC, the degree of phase alignment across multiple trials) evoked by behaviorally irrelevant standards and that these modulations are behaviorally meaningful. Using averaged data, we first observed that ITPC evoked by standards closer to the warning signal was significantly different from that evoked by standards further away from it, establishing a new foreperiod effect on ITPC evoked by standards. We call this effect the standard foreperiod (SFP) effect on ITPC. We reasoned that if the SFP influences ITPC evoked by standards, it should be possible to decode the former from the latter on a trial-by-trial basis. We were able to do so showing that this effect can be observed in single trials. We demonstrated the behavioral relevance of the SFP effect on ITPC by showing significant correlations between its strength and subjects' behavioral performance.

ePPR: a new strategy for the characterization of sensory cells from input/output data.

A central goal of systems neuroscience is to characterize the transformation of sensory input to spiking output in single neurons. This problem is complicated by the large dimensionality of the inputs. To cope with this problem, previous methods have estimated simplified versions of a generic linear-nonlinear (LN) model and required, in most cases, stimuli with constrained statistics. Here we develop the extended Projection Pursuit Regression (ePPR) algorithm that allows the estimation of all of the parameters, in space and time, of a generic LN model using arbitrary stimuli. We first prove that ePPR models can uniformly approximate, to an arbitrary degree of precision, any continuous function. To test this generality empirically, we use ePPR to recover the parameters of models of cortical cells that cannot be represented exactly with an ePPR model. Next we evaluate ePPR with physiological data from primary visual cortex, and show that it can characterize both simple and complex cells, from their responses to both natural and random stimuli. For both simulated and physiological data, we show that ePPR compares favorably to spike-triggered and information-theoretic techniques. To the best of our knowledge, this article contains the first demonstration of a method that allows the estimation of an LN model of visual cells, containing multiple spatio-temporal filters, from their responses to natural stimuli.

Uncovering low-dimensional structure in high-dimensional representations of long-term recordings in people with epilepsy.

Effective representations of recordings of epileptic activity for seizure prediction are high-dimensional, which prevents their visualization. Here we introduce and evaluate methods to find low-dimensional (2D or 3D) descriptors of these high-dimensional representations, which are amenable for visualization. Once low-dimensional descriptors are found, it is useful to identify structure in them. We evaluate clustering algorithms to automatically identify this structure. In addition, typical recordings of epileptic activity are long, extending for several days or weeks. We present and assess extensions of the previous methods to handle large datasets.

Estimating nonlinear receptive fields from natural images.

The response of visual cells is a nonlinear function of their stimuli. In addition, an increasing amount of evidence shows that visual cells are optimized to process natural images. Hence, finding good nonlinear models to characterize visual cells using natural stimuli is important. The Volterra model is an appealing nonlinear model for visual cells. However, their large number of parameters and the limited size of physiological recordings have hindered its application. Recently, a substantiated hypothesis stating that the responses of each visual cell could depend on an especially low-dimensional subspace of the image space has been proposed. We use this low-dimensional subspace in the Volterra relevant-space technique to allow the estimation of high-order Volterra models. Most laboratories characterize the response of visual cells as a nonlinear function on the low-dimensional subspace. They estimate this nonlinear function using histograms and by fitting parametric functions to them. Here, we compare the Volterra model with these histogram-based techniques. We use simulated data from cortical simple cells as well as simulated and physiological data from cortical complex cells. Volterra models yield equal or superior predictive power in all conditions studied. Several methods have been proposed to estimate the low-dimensional subspace. In this article, we test projection pursuit regression (PPR), a nonlinear regression algorithm. We compare PPR with two popular models used in vision: spike-triggered average (STA) and spike-triggered covariance (STC). We observe that PPR has advantages over these alternative algorithms. Hence, we conclude that PPR is a viable algorithm to recover the relevant subspace from natural images and that the Volterra model, estimated through the Volterra relevant-space technique, is a compelling alternative to histogram-based techniques.

Brain oscillations in switching vs. focusing audio-visual attention.

Selective attention contributes to perceptual efficiency by modulating cortical activity according to task demands. The majority of attentional research has focused on the effects of attention to a single modality, and little is known about the role of attention in multimodal sensory processing. Here we employ a novel experimental design to examine the electrophysiological basis of audio-visual attention shifting. We use electroencephalography (EEG) to study differences in brain dynamics between quickly shifting attention between modalities and focusing attention on a single modality for extended periods of time. We also address interactions between attentional effects generated by the attention-shifting cue and those generated by subsequent stimuli. The conclusions from these examinations address key issues in attentional research, including the supramodal theory of attention, or the role of attention in foveal vision. The experimental design and analysis methods used here may suggest new directions in the study of the physiological basis of attention.

Assisting autistic children with wireless EOG technology.

We propose a novel intervention to train the speed and accuracy of attention orienting and eye movements in Autism Spectrum Disorder (ASD). Training eye movements and attention could not only affect those important functions directly, but could also result in broader improvement of social communication skills. To this end we describe a system that would allow ASD children to improve their fixation skills while playing a computer game controlled by an eye tracker. Because this intervention will probably be time consuming, this system should be designed to be used at homes. To make this possible, we propose an implementation based on wireless and dry electrooculography (EOG) technology. If successful, this system would develop an approach to therapy that would improve clinical and behavioral function in children and adults with ASD. As our initial steps in this direction, here we describe the design of a computer game to be used in this system, and the predictions of gaze position from EOG data recorded while a subject played this game.