Author Type

Graduate Student

Date of Award

2001

Document Type

Thesis

Publication Status

Version of Record

Submission Date

October 2026

Abstract

This dissertation is an investigation of the sources of commonly observed event-related transients in statistical measures of interdependence: variance, cross-correlation, power spectrum density and coherence spectrum density time functions. These measures are often employed in the analysis of spatio-temporal interdependence patterns in neural activity. In order to understand the phenomenon, the origins of the variability of event-related responses are revisited. The time series of single trial cortical event-related potentials typically have a random appearance, and their trial-to-trial variability is commonly explained by the classic signal-plus-noise model, in which random ongoing background noise activity is linearly combined with a stereotyped evoked response. Here, we demonstrate that more realistic models, challenging both the linear superposition and the trial-to-trial stationarity of the event-related responses, can account for such event-related transients. In particular, two effects are considered: the nonlinear gain modulation in neural networks coupled through sigmoid functions and the trial-to-trial variability in amplitude and latency of the event phase-locked responses. An extensive analysis and characterization of both effects in interdependence measures is carried out through both analytical and numerical simulations in Chapter 2. Chapter 3 presents the outcome of testing the predicted effects on UP data recorded from implanted intracortical electrodes in monkeys performing a visuo-motor pattern discrimination task. Overall, the results point to a large contribution of the trial-to-trial variability of event phase-locked responses on the observed event-related transient in statistical interdependence measures. Because variability of the event-related responses is commonly ignored, event-related modulations in power spectral density, cross-correlation, and spectral coherence are often attributed to dynamic changes in functional connectivity within and among neural populations. It becomes then crucial the separation or removal of the trial-to-trial amplitude and latency variability effect from the statistical measures. In order to achieve this goal, the reconstruction of the single trial event phase-locked potentials is required. In Chapter 4, we approach this problem from a Bayesian inference perspective. The posterior probability density is derived for a specified number of event phase-locked components using data from single or multiple sensors. The Maximum A Posteriori solution is used to obtain the phase-locked component waveforms and their single trial parameters. The outcome is a further and definitive support for predominance of the effect of the nonstationarity of the phase-locked responses on the statistical quantities. Based on the theoretical and experimental analysis conducted in Chapters 2, 3 and 4, a framework for the statistical analysis of dynamic spatio-temporal interdependence patterns in Local Field Potential data is articulated.

Extent

218 p.

Comments

FAU Electronic Theses and Dissertations Collection|Advisers: Mingzhou Ding; Steven L. Bressler.|Thesis (Ph.D.)--Florida Atlantic University, 2001.|FAU Electronic Theses and Dissertations Collection

Identifier

FADT11975

Format

pdf

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