Applying stochastic spike train theory for high-accuracy human MEG/EEG

Background The accuracy of electroencephalography (EEG) and magnetoencephalography (MEG) in measuring neural evoked responses (ERs) is challenged by overlapping neural sources. This lack of accuracy is a severe limitation to the application of ERs to clinical diagnostics. New method We here introduce a theory of stochastic neuronal spike timing probability densities for describing the large-scale spiking activity in neural assemblies, and a spike density component analysis (SCA) method for isolating specific neural sources. The method is tested in three empirical studies with 564 cases of ERs to auditory stimuli from 94 humans, each measured with 60 EEG electrodes and 306 MEG sensors, and a simulation study with 12,300 ERs. Results The first study showed that neural sources (but not non-encephalic artifacts) in individual averaged MEG/EEG waveforms are modelled accurately with temporal Gaussian probability density functions (median 99.7 %–99.9 % variance explained). The following studies confirmed that SCA can isolate an ER, namely the mismatch negativity (MMN), and that SCA reveals inter-individual variation in MMN amplitude. Finally, SCA reduced errors by suppressing interfering sources in simulated cases. Comparison with existing methods We found that gamma and sine functions fail to adequately describe individual MEG/EEG waveforms. Also, we observed that principal component analysis (PCA) and independent component analysis (ICA) does not consistently suppress interference from overlapping brain activity in neither empirical nor simulated cases. Conclusions These findings suggest that the overlapping neural sources in single-subject or patient data can be more accurately separated by applying SCA in comparison to PCA and ICA.Peer reviewe

Identification of detailed time-frequency components in somatosensory evoked potentials

Somatosensory evoked potential (SEP) usually contains a set of detailed temporal components measured and identified in time domain, providing meaningful information on physiological mechanisms of the nervous system. The purpose of this study is to reveal complex and fine time-frequency features of SEP in time-frequency domain using advanced time-frequency analysis (TFA) and pattern classification methods. A high-resolution TFA algorithm, matching pursuit (MP), was proposed to decompose a SEP signal into a string of elementary waves and to provide a time-frequency feature description of the waves. After a dimension reduction by principle component analysis (PCA), a density-guided K-means clustering was followed to identify typical waves existed in SEP. Experimental results on posterior tibial nerve SEP signals of 50 normal adults showed that a series of typical waves were discovered in SEP using the proposed MP decomposition and clustering methods. The statistical properties of these SEP waves were examined and their representative waveforms were synthesized. The identified SEP waves provided a comprehensive and detailed description of time-frequency features of SEP. © 2006 IEEE.published_or_final_versio

Spatial-temporal modelling of fMRI data through spatially regularized mixture of hidden process models.

Previous work investigated a range of spatio-temporal constraints for fMRI data analysis to provide robust detection of neural activation. We present a mixture-based method for the spatio-temporal modelling of fMRI data. This approach assumes that fMRI time series are generated by a probabilistic superposition of a small set of spatio-temporal prototypes (mixture components). Each prototype comprises a temporal model that explains fMRI signals on a single voxel and the model's "region of influence" through a spatial prior over the voxel space. As the key ingredient of our temporal model, the Hidden Process Model (HPM) framework proposed in Hutchinson et al. (2009) is adopted to infer the overlapping cognitive processes triggered by stimuli. Unlike the original HPM framework, we use a parametric model of Haemodynamic Response Function (HRF) so that biological constraints are naturally incorporated in the HRF estimation. The spatial priors are defined in terms of a parameterised distribution. Thus, the total number of parameters in the model does not depend on the number of voxels. The resulting model provides a conceptually principled and computationally efficient approach to identify spatio-temporal patterns of neural activation from fMRI data, in contrast to most conventional approaches in the literature focusing on the detection of spatial patterns. We first verify the proposed model in a controlled experimental setting using synthetic data. The model is further validated on real fMRI data obtained from a rapid event-related visual recognition experiment (Mayhew et al., 2012). Our model enables us to evaluate in a principled manner the variability of neural activations within individual regions of interest (ROIs). The results strongly suggest that, compared with occipitotemporal regions, the frontal ones are less homogeneous, requiring two HPM prototypes per region. Despite the rapid event-related experimental design, the model is capable of disentangling the perceptual judgement and motor response processes that are both activated in the frontal ROIs. Spatio-temporal heterogeneity in the frontal regions seems to be associated with diverse dynamic localizations of the two hidden processes in different subregions of frontal ROIs

Automatic signal and image-based assessments of spinal cord injury and treatments.

Spinal cord injury (SCI) is one of the most common sources of motor disabilities in humans that often deeply impact the quality of life in individuals with severe and chronic SCI. In this dissertation, we have developed advanced engineering tools to address three distinct problems that researchers, clinicians and patients are facing in SCI research. Particularly, we have proposed a fully automated stochastic framework to quantify the effects of SCI on muscle size and adipose tissue distribution in skeletal muscles by volumetric segmentation of 3-D MRI scans in individuals with chronic SCI as well as non-disabled individuals. We also developed a novel framework for robust and automatic activation detection, feature extraction and visualization of the spinal cord epidural stimulation (scES) effects across a high number of scES parameters to build individualized-maps of muscle recruitment patterns of scES. Finally, in the last part of this dissertation, we introduced an EMG time-frequency analysis framework that implements EMG spectral analysis and machine learning tools to characterize EMG patterns resulting in independent or assisted standing enabled by scES, and identify the stimulation parameters that promote muscle activation patterns more effective for standing. The neurotechnological advancements proposed in this dissertation have greatly benefited SCI research by accelerating the efforts to quantify the effects of SCI on muscle size and functionality, expanding the knowledge regarding the neurophysiological mechanisms involved in re-enabling motor function with epidural stimulation and the selection of stimulation parameters and helping the patients with complete paralysis to achieve faster motor recovery

NeuroImage 84 (2014) 657–671 Contents lists available at ScienceDirect

journal homepage: www.elsevier.com/locate/ynimg Spatial–temporal modelling of fMRI data through spatially regularize

Applying stochastic spike train theory for high-accuracy MEG/EEG

The accuracy of electroencephalography (EEG) and magnetoencephalography (MEG) is challenged by overlapping sources from within the brain. This lack of accuracy is a severe limitation to the possibilities and reliability of modern stimulation protocols in basic research and clinical diagnostics. As a solution, we here introduce a theory of stochastic neuronal spike timing probability densities for describing the large-scale spiking activity in neural networks, and a novel spike density component analysis (SCA) method for isolating specific neural sources. Three studies are conducted based on 564 cases of evoked responses to auditory stimuli from 94 human subjects each measured with 60 EEG electrodes and 306 MEG sensors. In the first study we show that the large-scale spike timing (but not non-encephalographic artifacts) in MEG/EEG waveforms can be modeled with Gaussian probability density functions with …Non peer reviewe

Biophysical Sources of 1/f Noises in Neurological Tissue

High levels of random noise are a defining characteristic of neurological signals at all levels, from individual neurons up to electroencephalograms (EEG). These random signals degrade the performance of many methods of neuroengineering and medical neuroscience. Understanding this noise also is essential for applications such as real-time brain-computer interfaces (BCIs), which must make accurate control decisions from very short data epochs. The major type of neurological noise is of the so-called 1/f-type, whose origins and statistical nature has remained unexplained for decades. This research provides the first simple explanation of 1/f-type neurological noise based on biophysical fundamentals. In addition, noise models derived from this theory provide validated algorithm performance improvements over alternatives. Specifically, this research defines a new class of formal latent-variable stochastic processes called hidden quantum models (HQMs) which clarify the theoretical foundations of ion channel signal processing. HQMs are based on quantum state processes which formalize time-dependent observation. They allow the quantum-based calculation of channel conductance autocovariance functions, essential for frequency-domain signal processing. HQMs based on a particular type of observation protocol called independent activated measurements are shown to be distributionally equivalent to hidden Markov models yet without an underlying physical Markov process. Since the formal Markov processes are non-physical, the theory of activated measurement allows merging energy-based Eyring rate theories of ion channel behavior with the more common phenomenological Markov kinetic schemes to form energy-modulated quantum channels. These unique biophysical concepts developed to understand the mechanisms of ion channel kinetics have the potential of revolutionizing our understanding of neurological computation. To apply this theory, the simplest quantum channel model consistent with neuronal membrane voltage-clamp experiments is used to derive the activation eigenenergies for the Hodgkin-Huxley K+ and Na+ ion channels. It is shown that maximizing entropy under constrained activation energy yields noise spectral densities approximating S(f) = 1/f, thus offering a biophysical explanation for this ubiquitous noise component. These new channel-based noise processes are called generalized van der Ziel-McWhorter (GVZM) power spectral densities (PSDs). This is the only known EEG noise model that has a small, fixed number of parameters, matches recorded EEG PSD\u27s with high accuracy from 0 Hz to over 30 Hz without infinities, and has approximately 1/f behavior in the mid-frequencies. In addition to the theoretical derivation of the noise statistics from ion channel stochastic processes, the GVZM model is validated in two ways. First, a class of mixed autoregressive models is presented which simulate brain background noise and whose periodograms are proven to be asymptotic to the GVZM PSD. Second, it is shown that pairwise comparisons of GVZM-based algorithms, using real EEG data from a publicly-available data set, exhibit statistically significant accuracy improvement over two well-known and widely-used steady-state visual evoked potential (SSVEP) estimators

Decoding Electrophysiological Correlates of Selective Attention by Means of Circular Data

Sustaining our attention to a relevant sensory input in a complex listening environment, is of great importance for a successful auditory communication. To avoid the overload of the auditory system, the importance of the stimuli is estimated in the higher levels of the auditory system. Based on these information, the attention is drifted away from the irrelevant and unimportant stimuli. Long-term habituation, a gradual process independent from sensory adaptation, plays a major role in drifting away our attention from irrelevant stimuli. A better understanding of attention-modulated neural activity is important for shedding light on the encoding process of auditory streams. For instance, these information can have a direct impact on developing smarter hearing aid devices in which more accurate objective measures can be used to re ect the hearing capabilities of patients with hearing pathologies. As an example, an objective measures of long-term habituation with respect to di erent level of sound stimuli can be used more accurately for adjustment of hearing aid devices in comparison to verbal reports. The main goal of this thesis is to analyze the neural decoding signatures of long-term habituation and neural modulations of selective attention by exploiting circular regularities in electrophysiological (EEG) data, in which we can objectively measure the level of attentional-binding to di erent stimuli. We study, in particular, the modulations of the instantaneous phase (IP) in event related potentials (ERPs) over trials for di erent experimental settings. This is in contrast to the common approach where the ERP component of interest is computed through averaging a su ciently large number of ERP trials. It is hypothesized that a high attentional binding to a stimulus is related to a high level of IP cluster. As the attention binding reduces, IP is spread more uniformly on a unit circle. This work is divided into three main parts. In the initial part, we investigate the dynamics of long-term habituation with di erent acoustical stimuli (soft vs. loud) over ERP trials. The underlying temporal dynamics in IP and the level of phase cluster of the ERPs are assessed by tting circular probability functions (pdf) over data segments. To increase the temporal resolution of detecting times at which a signi cant change in IP occurs, an abrupt change point model at di erent pure-tone stimulations is used. In a second study, we improve upon the results and methodology by relaxing some of the constrains in order to integrate the gradual process of long-term habituation into the model. For this means, a Bayesian state-space model is proposed. In all of the aforementioned studies, we successfully classi ed between di erent stimulation levels, using solely the IP of ERPs over trials. In the second part of the thesis, the experimental setting is expanded to contain longer and more complex auditory stimuli as in real-world scenarios. Thereby, we study the neural-correlates of attention in spontaneous modulations of EEG (ongoing activity) which uses the complete temporal resolution of the signal. We show a mapping between the ERP results and the ongoing EEG activity based on IP. A Markov-based model is developed for removing spurious variations that can occur in ongoing signals. We believe the proposed method can be incorporated as an important preprocessing step for a more reliable estimation of objective measures of the level of selective attention. The proposed model is used to pre-process and classify between attending and un-attending states in a seminal dichotic tone detection experiment. In the last part of this thesis, we investigate the possibility of measuring a mapping between the neural activities of the cortical laminae with the auditory evoked potentials (AEP) in vitro. We show a strong correlation between the IP of AEPs and the neural activities at the granular layer, using mutual information.Die Aufmerksamkeit auf ein relevantes auditorisches Signal in einer komplexen H orumgebung zu lenken ist von gro er Bedeutung f ur eine erfolgreiche akustische Kommunikation. Um eine Uberlastung des H orsystems zu vermeiden, wird die Bedeutung der Reize in den h oheren Ebenen des auditorischen Systems bewertet. Basierend auf diesen Informationen wird die Aufmerksamkeit von den irrelevanten und unwichtigen Reizen abgelenkt. Dabei spielt die sog. Langzeit- Habituation, die einen graduellen Prozess darstellt der unabh angig von der sensorischen Adaptierung ist, eine wichtige Rolle. Ein besseres Verst andnis der aufmerksamkeits-modulierten neuronalen Aktivit at ist wichtig, um den Kodierungsprozess von sog. auditory streams zu beleuchten. Zum Beispiel k onnen diese Informationen einen direkten Ein uss auf die Entwicklung intelligenter H orsysteme haben bei denen genauere, objektive Messungen verwendet werden k onnen, um die H orf ahigkeiten von Patienten mit H orpathologien widerzuspiegeln. So kann beispielsweise ein objektives Ma f ur die Langzeit- Habituation an unterschiedliche Schallreize genutzt werden um - im Vergleich zu subjektiven Selbsteinsch atzungen - eine genauere Anpassung der H orsysteme zu erreichen. Das Hauptziel dieser Dissertation ist die Analyse neuronaler Dekodierungssignaturen der Langzeit- Habituation und neuronaler Modulationen der selektiver Aufmerksamkeit durch Nutzung zirkul arer Regularit aten in elektroenzephalogra schen Daten, in denen wir objektiv den Grad der Aufmerksamkeitsbindung an verschiedene Reize messen k onnen. Wir untersuchen insbesondere die Modulation der Momentanphase (engl. Instantaneous phase, IP) in ereigniskorrelierten Potenzialen (EKPs) in verschiedenen experimentellen Settings. Dies steht im Gegensatz zu dem traditionellen Ansatz, bei dem die interessierenden EKP-Komponenten durch Mittelung einer ausreichend gro en Anzahl von Einzelantworten im Zeitbereich ermittelt werden. Es wird vermutet, dass eine hohe Aufmerksamkeitsbindung an einen Stimulus mit einem hohen Grad an IP-Clustern verbunden ist. Nimmt die Aufmerksamkeitsbindung hingegen ab, so ist die Momentanphase uniform auf dem Einheitskreis verteilt. Diese Arbeit gliedert sich in drei Teile. Im ersten Teil untersuchen wir die Dynamik der Langzeit-Habituation mit verschiedenen akustischen Reizen (leise vs. laut) in EKP-Studien. Die zugrundeliegende zeitliche Dynamik der Momentanphase und die Ebene des Phasenclusters der EKPs werden durch die Anpassung von zirkul aren Wahrscheinlichkeitsfunktionen (engl. probability density function, pdf) uber Datensegmente bewertet. Mithilfe eines sog. abrupt change-point Modells wurde die zeitliche Au osung der Daten erh oht, sodass signi kante Anderungen in der Momentanphase bei verschiedenen Reintonstimulationen detektierbar sind. In einer zweiten Studie verbessern wir die Ergebnisse und die Methodik, indem wir einige der Einschr ankungen lockern, um den gradualen Prozess der Langzeit-Habituation in das abrupt changepoint Modell zu integrieren. Dazu wird ein bayes`sches Zustands-Raum-Modell vorgeschlagen. In den zuvor genannten Studien konnte erfolgreich mithilfe der Momentanphase zwischen verschiedenen Stimulationspegeln unterschieden werden. Im zweiten Teil der Arbeit wird der experimentelle Rahmen erweitert, um komplexere auditorische Reize wie in realen H orsituationen untersuchen zu k onnen. Dabei analysieren wir die neuronalen Korrelate der Aufmerksamkeit anhand spontaner Modulationen der kontinuierlichen EEG-Aktivit at, die eine zeitliche Au osung erm oglicht. Wir zeigen eine Abbildung zwischen den EKP-Ergebnissen und der kontinuierlichen EEG-Aktivit at auf Basis der Momentanphase. Ein Markov-basiertes Modell wird entwickelt, um st orende Variationen zu entfernen, die in kontinuierlichen EEG-Signalen auftreten k onnen. Wir glauben, dass die vorgeschlagene Methode als wichtiger Vorverarbeitungsschritt zur soliden objektiven Absch atzung des Aufmerksamkeitsgrades mithilfe von EEG-Daten verwendet werden kann. In einem dichotischen Tonerkennungsexperiment wird das vorgeschlagene Modell zur Vorverarbeitung der EEG-Daten und zur Klassi zierung zwischen gerichteten und ungerichteten Aufmerksamkeitszust anden erfolgreich verwendet. Im letzten Teil dieser Arbeit untersuchen wir den Zusammenhang zwischen den neuronalen Aktivit aten der kortikalen Laminae und auditorisch evozierten Potentialen (AEP) in vitro im Tiermodell. Wir zeigen eine starke Korrelation zwischen der Momentanphase der AEPs und den neuronalen Aktivit aten in der Granularschicht unter Verwendung der Transinformation

Dynamic causal modelling for EEG and MEG

Dynamic Causal Modelling (DCM) is an approach first introduced for the analysis of functional magnetic resonance imaging (fMRI) to quantify effective connectivity between brain areas. Recently, this framework has been extended and established in the magneto/encephalography (M/EEG) domain. DCM for M/EEG entails the inversion a full spatiotemporal model of evoked responses, over multiple conditions. This model rests on a biophysical and neurobiological generative model for electrophysiological data. A generative model is a prescription of how data are generated. The inversion of a DCM provides conditional densities on the model parameters and, indeed on the model itself. These densities enable one to answer key questions about the underlying system. A DCM comprises two parts; one part describes the dynamics within and among neuronal sources, and the second describes how source dynamics generate data in the sensors, using the lead-field. The parameters of this spatiotemporal model are estimated using a single (iterative) Bayesian procedure. In this paper, we will motivate and describe the current DCM framework. Two examples show how the approach can be applied to M/EEG experiments