2,103 research outputs found
Dynamic Decomposition of Spatiotemporal Neural Signals
Neural signals are characterized by rich temporal and spatiotemporal dynamics
that reflect the organization of cortical networks. Theoretical research has
shown how neural networks can operate at different dynamic ranges that
correspond to specific types of information processing. Here we present a data
analysis framework that uses a linearized model of these dynamic states in
order to decompose the measured neural signal into a series of components that
capture both rhythmic and non-rhythmic neural activity. The method is based on
stochastic differential equations and Gaussian process regression. Through
computer simulations and analysis of magnetoencephalographic data, we
demonstrate the efficacy of the method in identifying meaningful modulations of
oscillatory signals corrupted by structured temporal and spatiotemporal noise.
These results suggest that the method is particularly suitable for the analysis
and interpretation of complex temporal and spatiotemporal neural signals
Validation of Transcranial Electrical Stimulation (TES) Finite Element Modeling Against MREIT Current Density Imaging in Human Subjects
abstract: Transcranial electrical stimulation (tES) is a non-invasive brain stimulation therapy that has shown potential in improving motor, physiological and cognitive functions in healthy and diseased population. Typical tES procedures involve application of weak current (< 2 mA) to the brain via a pair of large electrodes placed on the scalp. While the therapeutic benefits of tES are promising, the efficacy of tES treatments is limited by the knowledge of how current travels in the brain. It has been assumed that the current density and electric fields are the largest, and thus have the most effect, in brain structures nearby the electrodes. Recent studies using finite element modeling (FEM) have suggested that current patterns in the brain are diffuse and not concentrated in any particular brain structure. Although current flow modeling is useful means of informing tES target optimization, few studies have validated tES FEM models against experimental measurements. MREIT-CDI can be used to recover magnetic flux density caused by current flow in a conducting object. This dissertation reports the first comparisons between experimental data from in-vivo human MREIT-CDI during tES and results from tES FEM using head models derived from the same subjects. First, tES FEM pipelines were verified by confirming FEM predictions agreed with analytic results at the mesh sizes used and that a sufficiently large head extent was modeled to approximate results on human subjects. Second, models were used to predict magnetic flux density, and predicted and MREIT-CDI results were compared to validate and refine modeling outcomes. Finally, models were used to investigate inter-subject variability and biological side effects reported by tES subjects. The study demonstrated good agreements in patterns between magnetic flux distributions from experimental and simulation data. However, the discrepancy in scales between simulation and experimental data suggested that tissue conductivities typically used in tES FEM might be incorrect, and thus performing in-vivo conductivity measurements in humans is desirable. Overall, in-vivo MREIT-CDI in human heads has been established as a validation tool for tES predictions and to study the underlying mechanisms of tES therapies.Dissertation/ThesisDoctoral Dissertation Biomedical Engineering 201
Functional Connectivity Analysis on Resting-State Electroencephalography Signals Following Chiropractic Spinal Manipulation in Stroke Patients
Stroke impairments often present as cognitive and motor deficits, leading to a decline in quality of life. Recovery strategy and mechanisms, such as neuroplasticity, are important factors, as these can help improve the effectiveness of rehabilitation. The present study investigated chiropractic spinal manipulation (SM) and its effects on resting-state functional connectivity in 24 subacute to chronic stroke patients monitored by electroencephalography (EEG). Functional connectivity of both linear and non-linear coupling was estimated by coherence and phase lag index (PLI), respectively. Non-parametric cluster-based permutation tests were used to assess the statistical significance of the changes in functional connectivity following SM. Results showed a significant increase in functional connectivity from the PLI metric in the alpha band within the default mode network (DMN). The functional connectivity between the posterior cingulate cortex and parahippocampal regions increased following SM, t (23) = 10.45, p = 0.005. No significant changes occurred following the sham control procedure. These findings suggest that SM may alter functional connectivity in the brain of stroke patients and highlights the potential of EEG for monitoring neuroplastic changes following SM. Furthermore, the altered connectivity was observed between areas which may be affected by factors such as decreased pain perception, episodic memory, navigation, and space representation in the brain. However, these factors were not directly monitored in this study. Therefore, further research is needed to elucidate the underlying mechanisms and clinical significance of the observed changes
LOW RESOLUTION ELECTROMAGNETIC TOMOGRAPHY (LORETA) ANALYSIS OF THE BRAINS ELECTROPHYSIOLOGICAL RESPONSE TO EMOTIONAL VISUAL STIMULI UNDER DIFFERING CONDITIONS
Current methods of diagnosing and monitoring stress include: observing changes in the severity of existing symptoms, the development of new symptoms, hormone level tests, and stress self-assessment surveys. Self-assessment surveys are subject to bias and false reporting. This project focuses on analyzing electroencephalogram (EEG) using Low Resolution Electromagnetic Tomography (LORETA) to identify differences within current source location of emotionally elicited event related potentials (ERPs), in order to aid physicians in stress diagnostics and management. For this study twenty-one participants took the Penn State Worry Questionnaire which classifies the participants into high-stress and low-stress groups. The individuals had their EEG recorded while viewing pleasant, neutral, and unpleasant stimuli. CURRY, the current reconstruction program, was used to filter, epoch, and average the data to obtain event related potentials (ERPs) for each participant. Using group-averaged ERPs as the data input, LORETA was used to calculate the current distribution within the brain. One and two-tailed t-tests were performed to examine for current source distribution differences between high-stress/low-stress conditions and pleasant, unpleasant and neutral stimuli. The results of the experiment indicate that there is a difference in current source location between high-stress and low-stress individuals. The current source distribution differences are within regions of the frontal lobe and the parietal lobe associated with emotional processing
Imaging the spatial-temporal neuronal dynamics using dynamic causal modelling
Oscillatory brain activity is a ubiquitous feature of neuronal dynamics and
the synchronous discharge of neurons is believed to facilitate integration both
within functionally segregated brain areas and between areas engaged by the same
task. There is growing interest in investigating the neural oscillatory networks in
vivo. The aims of this thesis are to (1) develop an advanced method, Dynamic
Causal Modelling for Induced Responses (DCM for IR), for modelling the brain
network functions and (2) apply it to exploit the nonlinear coupling in the motor
system during hand grips and the functional asymmetries during face perception.
DCM for IR models the time-varying power over a range of
frequencies of coupled electromagnetic sources. The model parameters encode
coupling strength among areas and allows the differentiations between linear
(within frequency) and nonlinear (between-frequency) coupling. I applied DCM
for IR to show that, during hand grips, the nonlinear interactions among neuronal
sources in motor system are essential while intrinsic coupling (within source) is
very likely to be linear. Furthermore, the normal aging process alters both the
network architecture and the frequency contents in the motor network.
I then use the bilinear form of DCM for IR to model the experimental
manipulations as the modulatory effects. I use MEG data to demonstrate
functional asymmetries between forward and backward connections during face
perception: Specifically, high (gamma) frequencies in higher cortical areas
suppressed low (alpha) frequencies in lower areas. This finding provides direct
evidence for functional asymmetries that is consistent with anatomical and
physiological evidence from animal studies. Lastly, I generalize the bilinear form of DCM for IR to dissociate the induced responses from evoked ones in terms of
their functional role. The backward modulatory effect is expressed as induced, but
not evoked responses
Graph analysis of functional brain networks: practical issues in translational neuroscience
The brain can be regarded as a network: a connected system where nodes, or
units, represent different specialized regions and links, or connections,
represent communication pathways. From a functional perspective communication
is coded by temporal dependence between the activities of different brain
areas. In the last decade, the abstract representation of the brain as a graph
has allowed to visualize functional brain networks and describe their
non-trivial topological properties in a compact and objective way. Nowadays,
the use of graph analysis in translational neuroscience has become essential to
quantify brain dysfunctions in terms of aberrant reconfiguration of functional
brain networks. Despite its evident impact, graph analysis of functional brain
networks is not a simple toolbox that can be blindly applied to brain signals.
On the one hand, it requires a know-how of all the methodological steps of the
processing pipeline that manipulates the input brain signals and extract the
functional network properties. On the other hand, a knowledge of the neural
phenomenon under study is required to perform physiological-relevant analysis.
The aim of this review is to provide practical indications to make sense of
brain network analysis and contrast counterproductive attitudes
Neural Connectivity with Hidden Gaussian Graphical State-Model
The noninvasive procedures for neural connectivity are under questioning.
Theoretical models sustain that the electromagnetic field registered at
external sensors is elicited by currents at neural space. Nevertheless, what we
observe at the sensor space is a superposition of projected fields, from the
whole gray-matter. This is the reason for a major pitfall of noninvasive
Electrophysiology methods: distorted reconstruction of neural activity and its
connectivity or leakage. It has been proven that current methods produce
incorrect connectomes. Somewhat related to the incorrect connectivity
modelling, they disregard either Systems Theory and Bayesian Information
Theory. We introduce a new formalism that attains for it, Hidden Gaussian
Graphical State-Model (HIGGS). A neural Gaussian Graphical Model (GGM) hidden
by the observation equation of Magneto-encephalographic (MEEG) signals. HIGGS
is equivalent to a frequency domain Linear State Space Model (LSSM) but with
sparse connectivity prior. The mathematical contribution here is the theory for
high-dimensional and frequency-domain HIGGS solvers. We demonstrate that HIGGS
can attenuate the leakage effect in the most critical case: the distortion EEG
signal due to head volume conduction heterogeneities. Its application in EEG is
illustrated with retrieved connectivity patterns from human Steady State Visual
Evoked Potentials (SSVEP). We provide for the first time confirmatory evidence
for noninvasive procedures of neural connectivity: concurrent EEG and
Electrocorticography (ECoG) recordings on monkey. Open source packages are
freely available online, to reproduce the results presented in this paper and
to analyze external MEEG databases
Multimodal Functional Network Connectivity: An EEG-fMRI Fusion in Network Space
EEG and fMRI recordings measure the functional activity of multiple coherent networks distributed in the cerebral cortex. Identifying network interaction from the complementary neuroelectric and hemodynamic signals may help to explain the complex relationships between different brain regions. In this paper, multimodal functional network connectivity (mFNC) is proposed for the fusion of EEG and fMRI in network space. First, functional networks (FNs) are extracted using spatial independent component analysis (ICA) in each modality separately. Then the interactions among FNs in each modality are explored by Granger causality analysis (GCA). Finally, fMRI FNs are matched to EEG FNs in the spatial domain using network-based source imaging (NESOI). Investigations of both synthetic and real data demonstrate that mFNC has the potential to reveal the underlying neural networks of each modality separately and in their combination. With mFNC, comprehensive relationships among FNs might be unveiled for the deep exploration of neural activities and metabolic responses in a specific task or neurological state
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