Mark E PfliegerCortech Solutions, Inc., United States · Translational Solutions Center
Mark E Pflieger
PhD
About
58
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Introduction
Publications
Publications (58)
We aim to study auditory-visual integration processes in humans via an experimental analysis framework that generalizes auditory and visual evoked potential (AEP, VEP) measurements. AEPs and VEPs, measured by averaging EEG epochs aligned to single events, reflect 1st-order brain responses to stimuli. However, auditory-visual integration requires at...
INTRODUCTION. An EEG spatial filter is defined by a matrix with M columns corresponding to scalp channels (input) and D rows corresponding to derived channels (output). Typically, D<<M (e.g., M=128, D=4) so that the spatial filter reduces the data, and the derived channels selectively enhance particular brain signals of interest for an application....
Title: Translational development of solutions for participant-collaborative human brain research • Abstract: Cortech Solutions, a business based in North Carolina, provides innovative solutions for research in electrophysiology. Cortech Translational Solutions Center, located close to SDSU, is an office of Cortech that once was a small business in...
We describe and illustrate an algorithm that generates nearly uniform arrays with any specified target spacing on a given scalp surface wireframe for a bounded coverage area.
The U.S. National Institute of Mental Health (NIMH) Research Domain Criteria (RDoC) initiative is a science logic framework for developing new classifications of mental disorders based on an integrative approach that seeks to understand adaptive and maladaptive human brain functions (biological, social, cognitive, and affective functions) across st...
The concepts of singular spectral entropy (SSE) and identicality (= inverse-invariant SSE) are motivated and applied in the context of maximum entropy covariance (MaxEntCov) source estimation of intrinsic EEG activity and connectivity. The concepts are also applied to forward model selection and construction of Venn subspace filters.
We summarize theory and formulas useful for the Multimodal Resting State Network Tools project.
Motivation. In fMRI functional connectivity (fcMRI) research, analysis of resting state networks (RSNs) typically uses voxel-voxel correlation matrices which, in turn, are derived from covariance matrices accumulated from preprocessed BOLD time series da...
Despite consensus on the neurological nature of autism spectrum disorders (ASD), brain biomarkers remain unknown and diagnosis continues to be based on behavioral criteria. Growing evidence suggests that brain abnormalities in ASD occur at the level of interconnected networks; however, previous attempts using functional connectivity data for diagno...
Objective
The present study was designed to test for neural signs of impulsivity related to voice motor control in young adults with ADHD using EEG recordings in a voice pitch perturbation paradigm.
Methods
Two age-matched groups of young adults were presented with brief pitch shifts of auditory feedback during vocalization. Compensatory behaviora...
Background
Mid-frontal and mid-lateral (F3/F4 and F7/F8) EEG asymmetry has been associated with motivation and affect. We examined alpha EEG asymmetry in depressed and healthy participants before and after Behavioral Activation treatment for depression; examined the association between alpha EEG asymmetry and motivational systems and affect; and ev...
Spatial component analysis is often used to explore multidimensional time series data whose sources cannot be measured directly. Several methods may be used to decompose the data into a set of spatial components with temporal loadings. Component selection is of crucial importance, and should be supported by objective criteria. In some applications,...
Multimodal integration in the field of human brain mapping has evolved from structural-functional co-registrations toward functional-functional combinations. This paper briefly reviews fMRI-EEG, fMRI-NIRS, EEG-NIRS, and fMRI-EEG-NIRS combinations.
Connectivity measures are (typically bivariate) statistical measures that may be used to estimate interactions between brain regions from electrophysiological data. We review both formal and informal descriptions of a range of such measures, suitable for the analysis of human brain electrophysiological data, principally electro- and magnetoencephal...
An important determinant of the value of quantitative neuroimaging studies is the reliability of the derived information, which is a function of the data collection conditions. Near infrared spectroscopy (NIRS) and electroencelphalography are independent sensing domains that are well suited to explore principal elements of the brain's response to n...
Repetition suppression (RS) phenomena, such as those observed using paired-identical-stimulus (S1-S2) paradigms, likely reflect adaptive functions such as habituation and, more specifically, sensory gating.
To better characterize the neural networks underlying RS, we analyzed auditory S1-S2 data from electrodes placed on the cortices of 64 epilepsy...
A method is described that combines linear source estimation (beamformers) with non-parametric statistical significance testing to yield vector time series estimates for brain regions of interest. These source time series are a suitable starting point for functional connectivity analysis.
Slides with prepared text of a lecture presented at a week-long course (see http://www.risc.cnrs.fr/pdf/2008/27-04-08-models_in_neurosc.pdf). The lecture introduced two frameworks for modeling ERP and EEG experimental data--event-related Volterra modeling and quasi-causal information measures--from the aim of taking a small step toward inferences a...
To further explore the roles of medial temporal structures in mediating sensory gating of incoming irrelevant or redundant auditory input, twenty-seven patients with intractable epilepsy with depth electrodes implanted in the medial temporal lobe for presurgery evaluation underwent evoked response recording to auditory paired-stimuli (S1-S2). Seven...
Electrocorticographic (ECoG) signals, acquired from consenting epilepsy patients for presurgical evaluation and functional mapping, provide a rare opportunity to study human brain electrical activity. ECoG functional connectivity measures (coherence, phase synchronization, Granger causality, etc.) quantify relational characteristics of large-scale...
We studied adaptive and non-adaptive beamformers for source space time series estimation from MEG data, using simulated data and the interference function as a metric. Both filter types show significant interference from non-target locations. We describe a method to obtain additional interference suppression. Modeling error suppression is also disc...
Background: Sensory gating dysfunction, assessed via paired-click extracranial EEG/MEG measures, is a useful endophenotype for schizophrenia and bipolar disorder. Intracranial EEG (iEEG), obtained by consent of epilepsy patients, provides a unique opportunity to study the complex and poorly understood brain network dynamics of sensory gating. In th...
Clarification of the cortical mechanisms underlying auditory sensory gating may advance our understanding of brain dysfunctions associated with schizophrenia. To this end, data from nine epilepsy patients who participated in an auditory paired-click paradigm during pre-surgical evaluation and had grids of electrodes covering temporal and frontal lo...
We report a preliminary analysis of statistical effective connectivity between brain regions using intracranial EEG data recorded from an epilepsy patient who consented to participate in an auditory paired-click sensory gating paradigm.
Cognitive neuroscientists often conduct trial-based experiments, having two or more events (e.g., sensory stimulus and motor response) per trial, conjointly with neurophysiological time series acquisition and analysis of event-related transient brain activity. Hansen decomposition [Hansen, 1983] is a frequency-domain technique for separating multip...
A computationally efficient method is described for estimating first-order and associated higher-order kernels of general multi-input Volterra systems expressed in the oblique (nonsymmetric) form. A first-order kernel characterizes how a system responds to isolated inputs (i.e., an isolated input-output transformation), and the oblique form interpr...
Linear estimators have been used widely in the bioelectromagnetic inverse problem, but their properties and relationships have not been fully characterized. Here, we show that the most widely used linear estimators may be characterized by a choice of norms on signal space and on source space. These norms depend, in part, on assumptions about the si...
Main Idea: A 3D spatial filter that estimates regional brain activity may be designed offline, using structural MRI, previously recorded EEG, and (optionally) functional MRI. Then it may be applied in real-time via rapid matrix multiplication. To handle multiple, simultaneous ROIs, the matrices are simply concatenated.
Causality analytic techniques based on conditional mutual information are described. Causality analysis may be used to infer linear and nonlinear causal relations between selected brain regions, and can account for identified non-causal confounds. The analysis results in a directed graph whose nodes are brain regions, and whose edges represent info...
A maximum unscaled entropy solution to the spatial covariance inverse problem is presented, and the theory is applied to task-related EEG in four frequency bands. Whereas the obtained second order solutions are of interest in their own right, this new method also may be used, in principle, to improve underlying source covariance and forward models...
Different modeling frameworks (such as error analyses for dipole localization [Fuchs, 1998] [Huizenga, 2001]; crosstalk and point spread analyses for linear estimators [Liu, 2002]; etc.) have demonstrated improved three-dimensional (3D) resolution for combined MEG/EEG (or EMEG) source estimation. Complementary to these, an empirical analysis of 2D...
Methods are described for non-parametric significance testing from event-related encephalographic data, using randomization tests. These methods may be applied in both signal space and source space. The methods include within-subject between-condition comparisons, paired and unpaired comparisons, and within-group and between-group comparisons. Test...
This commentary highlights methods for using scalp EEG to make inferences about contextual field interactions, which, in view of the target article, may be specially relevant to the study of schizophrenia. Although scalp EEG has limited spatial resolution, prior knowledge combined with experimental manipulations may be used to strengthen inferences...
While conventional group averaging assumes that electrode location differences across subjects are small compared with inter-electrode distances, 256-channel event-related potential (ERP) datasets have by necessity much shorter inter-electrode distances (1.7 cm) that can invalidate this assumption even with the meticulous application of the electro...
Technical Note presented as a poster at the EMSE Workshop, Princeton, 7-8 Sep 2001. This technical note supplements a workshop demonstration by providing (a) pointers to issues in the literature regarding the correction of EEG (or MEG) recordings for blink and eye movement artifacts, and (b) a theoretical description of a spatial filter for removin...
This commentary discusses three features of the general theoretical framework proposed by Nunez: (1) Functional concepts, such as computation and control, are not foundational. (2) A mismatch between the concept of subcortical input and EEG output is problematic for the input/output operator concept of cortical dynamics. (3) The concept of brain st...
Although MEG and EEG measurement modalities are empirically distinct, there is a theoretically unified statistical-biophysical framework for posing and (in a limited sense) solving the electromagnetic inverse problem. In addition, biomagnetic and bioelectric measurements are differentially sensitive to the same type of intracranial signals, i.e., m...
MEG and EEG share a common theoretical framework. Thus, there is considerable appeal for the working hypothesis that these " twin " modalities provide complementary information about the macroscopic ionic currents in the brain that generate fields and potentials at the outer head surface. The ultimate objective is to exploit complementarity to impr...
Local maxima in linear underdetermined inverse tomographies for
EEG may be useful for initializing nonlinear overdetermined localization
of multiple dipoles. The feasibility of this suggestion is studied by
applying four distributed inverse approaches (MUSIC and three types of
minimum norm) to a large number of simulated dipole configurations. A
va...
To fully characterize the brain processes underlying sensorimotor and cognitive function, the spatial distribution of active regions, their interconnected regions must be measured. We describe methods for imaging brain sources from surface-recorded EEG and magnetoencephalographic data, called electromagnetic source imaging (EMSI). EMSI provides bra...
We report a preliminary analysis of statistical effective connectivity between brain regions using intracranial EEG data recorded from an epilepsy patient who consented to participate in an auditory paired-click sensory gating paradigm.
Paradigm. 100 pairs of clicks (first click = S1, second click = S2; 1500 Hz, 4 ms duration, 1.2 ms Gaussian envel...
If we adopt the simplified view that the brain is a deterministic system having EEG as output, with isolated task events as input, then we can use average event-related potentials (ERPs) to approximate impulse response functions. In an actual experiment, however, task events are not isolated. Rather, they interact via brain memory systems, and thei...
Thesis (Ph. D.)--Ohio State University, 1991. Includes bibliographical references. Advisor: Karl Kornacker, Biophysics Graduate Program.