Hualou Liang

Short-window spectral analysis of cortical event-related potentials by adaptive multivariate autoregressive modeling: data preprocessing, model validation, and variability assessment

Abstract. In this article we consider the application of parametric spectral analysis to multichannel event-related potentials (ERPs) during cognitive experiments. We show that with proper data preprocessing, Adaptive MultiVariate AutoRegressive (AMVAR) modeling is an effective technique for dealing with nonstationary ERP time series. We propose a bootstrap procedure to assess the variability in the estimated spectral quantities. Finally, we apply AMVAR spectral analysis to a visuomotor integration task, revealing rapidly changing cortical dynamics during different stages of task processing.

A backward progression of attentional effects in the ventral stream

The visual processing of behaviorally relevant stimuli is enhanced through top-down attentional feedback. One possibility is that feedback targets early visual areas first and the attentional enhancement builds up at progressively later stages of the visual hierarchy. An alternative possibility is that the feedback targets the higher-order areas first and the attentional effects are communicated “backward” to early visual areas. Here, we compared the magnitude and latency of attentional enhancement of firing rates in V1, V2, and V4 in the same animals performing the same task. We found a reverse order of attentional effects, such that attentional enhancement was larger and earlier in V4 and smaller and later in V1, with intermediate results in V2. These results suggest that attentional mechanisms operate via feedback from higher-order areas to lower-order ones.

2008 Special Issue: BSMART: A Matlab/C toolbox for analysis of multichannel neural time series

We have developed a Matlab/C toolbox, Brain-SMART (System for Multivariate AutoRegressive Time series, or BSMART), for spectral analysis of continuous neural time series data recorded simultaneously from multiple sensors. Available functions include time series data importing/exporting, preprocessing (normalization and trend removal), AutoRegressive (AR) modeling (multivariate/bivariate model estimation and validation), spectral quantity estimation (auto power, coherence and Granger causality spectra), network analysis (including coherence and causality networks) and visualization (including data, power, coherence and causality views). The tools for investigating causal network structures in respect of frequency bands are unique functions provided by this toolbox. All functionality has been integrated into a simple and user-friendly graphical user interface (GUI) environment designed for easy accessibility. Although we have tested the toolbox only on Windows and Linux operating systems, BSMART itself is system independent. This toolbox is freely available (http://www.brain-smart.org) under the GNU public license for open source development.

Artifact reduction in electrogastrogram based on empirical mode decomposition method

Severe contamination of the gastric signal in electrogastrogram (EGG) analysus by respiratory, motion, cardiac artifacts, and possible myoelectrical activity from other organs, poses a major challenge to EGG interpretation and analysis. A generally applicable method for removing a variety of artifacts from EGG recordings is proposed based on the empirical mode decomposition (EMD) method. This decomposition technique is adaptive, and appears to be uniquely suitable for nonlinear, non-stationary data analysis. The results show that this method, combined with instantaneous frequency analysis, effectively separate, identify and remove contamination from a wide variety of artifactual sources in EGG recordings.

Synchronized activity in prefrontal cortex during anticipation of visuomotor processing

It is commonly presumed, though not well established, that the prefrontal cortex exerts top-down control of sensory processing. One aspect of this control is thought to be a facilitation of sensory pathways in anticipation of such processing. To investigate the possible involvement of prefrontal cortex in anticipatory top-down control, we studied the statistical relations between prefrontal activity, recorded while a macaque monkey waited for presentation of a visual stimulus, and subsequent sensory and motor events. Local field potentials were simultaneously recorded from prefrontal, motor, occipital and temporal cortical sites in the left cerebral hemisphere. Spectral power and coherence analysis revealed that during stimulus anticipation three of five prefrontal sites participated in a coherent oscillatory network synchronized in the &bgr;-frequency range. Pre-stimulus network power and coherence were highly correlated with the amplitude and latency of early visual evoked potential components in visual cortical areas, and with response time. The results suggest that synchronized oscillatory networks in prefrontal cortex are involved in top-down anticipatory mechanisms that facilitate subsequent sensory processing in visual cortex. They further imply that stronger top-down control leads to larger and faster sensory responses, and a subsequently faster motor response.

Empirical mode decomposition: a method for analyzing neural data

Almost all processes that are quantified in neurobiology are stochastic and nonstationary. Conventional methods that characterize these processes to provide a meaningful and precise description of complex neurobiological phenomenon may be insufficient. Here, we report on the use of the data-driven empirical mode decomposition (EMD) method to study neuronal activity in visual cortical area V4 of macaque monkeys performing a visual spatial attention task. We found that local field potentials were resolved by the EMD into the sum of a set of intrinsic components with different degrees of oscillatory content. High-frequency components were identified as gamma band (35-90Hz) oscillations, whereas low-frequency components in single-trial recordings contributed to the average visual evoked potential (AVEP). Comparison with Fourier analysis showed that EMD may offer better temporal and frequency resolution. The EMD, coupled with instantaneous frequency analysis, may prove to be a vital technique for the analysis of neural data.

Semiblind Spatial ICA of fMRI Using Spatial Constraints

Independent component analysis (ICA) utilizing prior information, also called semiblind ICA, has demonstrated considerable promise in the analysis of functional magnetic resonance imaging (fMRI). So far, temporal information about fMRI has been used in temporal ICA or spatial ICA as additional constraints to improve estimation of task‐related components. Considering that prior information about spatial patterns is also available, a semiblind spatial ICA algorithm utilizing the spatial information was proposed within the framework of constrained ICA with fixed‐point learning. The proposed approach was first tested with synthetic fMRI‐like data, and then was applied to real fMRI data from 11 subjects performing a visuomotor task. Three components of interest including two task‐related components and the “default mode” component were automatically extracted, and atlas‐defined masks were used as the spatial constraints. The default mode network, a set of regions that appear correlated in particular in the absence of tasks or external stimuli and is of increasing interest in fMRI studies, was found to be greatly improved when incorporating spatial prior information. Results from simulation and real fMRI data demonstrate that the proposed algorithm can improve ICA performance compared to a different semiblind ICA algorithm and a standard blind ICA algorithm. Hum Brain Mapp, 2010.

Incremental Integration of Global Contours through Interplay between Visual Cortical Areas

The traditional view on visual processing emphasizes a hierarchy: local line segments are first linked into global contours, which in turn are assembled into more complex forms. Distinct from this bottom-up viewpoint, here we provide evidence for a theoretical framework whereby objects and their parts are processed almost concurrently in a bidirectional cortico-cortical loop. By simultaneous recordings from V1 and V4 in awake monkeys, we found that information about global contours in a cluttered background emerged initially in V4, started ∼40 ms later in V1, and continued to develop in parallel in both areas. Detailed analysis of neuronal response properties implicated contour integration to emerge from both bottom-up and reentrant processes. Our results point to an incremental integration mechanism: feedforward assembling accompanied by feedback disambiguating to define and enhance the global contours and to suppress background noise. The consequence is a parallel accumulation of contour information over multiple cortical areas.

Causal influences in primate cerebral cortex during visual pattern discrimination

Anatomical studies of the visual cortex demonstrate the existence of feedforward, feedback and lateral pathways among multiple cortical areas. Yet relatively little evidence has previously been available to show the causal influences of these areas on one another during visual information processing. We simultaneously recorded event-related local field potentials (LFPs) from surface-to-depth bipolar electrodes at six sites in the ventral region of the right hemisphere visual cortex in a highly trained macaque monkey during performance of a visual pattern discrimination task. Applying a new statistical measure, the short-time directed transfer function (STDTF), to the LFP data set, we charted the changing strength and direction of causal influence between these cortical sites on a fraction-of-a-second time scale. We present results showing, for the first time, the dynamics of distinct feedforward, feedback and lateral influences in the ventral portion of the primate visual cortex during visual pattern processing.

Empirical mode decomposition of field potentials from macaque V4 in visual spatial attention

Empirical mode decomposition (EMD) has recently been introduced as a local and fully data-driven technique for the analysis of non-stationary time-series. It allows the frequency and amplitude of a time-series to be evaluated with excellent time resolution. In this article we consider the application of EMD to the analysis of neuronal activity in visual cortical area V4 of a macaque monkey performing a visual spatial attention task. We show that, by virtue of EMD, field potentials can be resolved into a sum of intrinsic components with different degrees of oscillatory content. Low-frequency components in single-trial recordings contribute to the average visual evoked potential (AVEP), whereas high-frequency components do not, but are identified as gamma-band (30–90 Hz) oscillations. The magnitude of time-varying gamma activity is shown to be enhanced when the monkey attends to a visual stimulus as compared to when it is not attending to the same stimulus. Comparison with Fourier analysis shows that EMD may offer better temporal and frequency resolution. These results support the idea that the magnitude of gamma activity reflects the modulation of V4 neurons by visual spatial attention. EMD, coupled with instantaneous frequency analysis, is demonstrated to be a useful technique for the analysis of neurobiological time-series.