139 research outputs found
An efficient, approximate path-following algorithm for elastic net based nonlinear spike enhancement
Unwanted spike noise in a digital signal is a common problem in digital filtering. However, sometimes the spikes are wanted and other, superimposed, signals are unwanted, and linear, time invariant (LTI) filtering is ineffective because the spikes are wideband - overlapping with independent noise in the frequency domain. So, no LTI filter can separate them, necessitating nonlinear filtering. However, there are applications in which the noise includes drift or smooth signals for which LTI filters are ideal. We describe a nonlinear filter formulated as the solution to an elastic net regularization problem, which attenuates band-limited signals and independent noise, while enhancing superimposed spikes. Making use of known analytic solutions a novel, approximate path-following algorithm is given that provides a good, filtered output with reduced computational effort by comparison to standard convex optimization methods. Accurate performance is shown on real, noisy electrophysiological recordings of neural spikes
Apport de nouvelles techniques dans l’évaluation de patients candidats à une chirurgie d’épilepsie : résonance magnétique à haut champ, spectroscopie proche infrarouge et magnétoencéphalographie
L'épilepsie constitue le désordre neurologique le plus fréquent après les maladies cérébrovasculaires. Bien que le contrôle des crises se fasse généralement au moyen d'anticonvulsivants, environ 30 % des patients y sont réfractaires. Pour ceux-ci, la chirurgie de l'épilepsie s'avère une option intéressante, surtout si l’imagerie par résonance magnétique (IRM) cérébrale révèle une lésion épileptogène bien délimitée. Malheureusement, près du quart des épilepsies partielles réfractaires sont dites « non lésionnelles ». Chez ces patients avec une IRM négative, la délimitation de la zone épileptogène doit alors reposer sur la mise en commun des données cliniques, électrophysiologiques (EEG de surface ou intracrânien) et fonctionnelles (tomographie à émission monophotonique ou de positrons). La faible résolution spatiale et/ou temporelle de ces outils de localisation se traduit par un taux de succès chirurgical décevant. Dans le cadre de cette thèse, nous avons exploré le potentiel de trois nouvelles techniques pouvant améliorer la localisation du foyer épileptique chez les patients avec épilepsie focale réfractaire considérés candidats potentiels à une chirurgie d’épilepsie : l’IRM à haut champ, la spectroscopie proche infrarouge (SPIR) et la magnétoencéphalographie (MEG).
Dans une première étude, nous avons évalué si l’IRM de haut champ à 3 Tesla (T), présentant théoriquement un rapport signal sur bruit plus élevé que l’IRM conventionnelle à 1,5 T, pouvait permettre la détection des lésions épileptogènes subtiles qui auraient été manquées par cette dernière. Malheureusement, l’IRM 3 T n’a permis de détecter qu’un faible nombre de lésions épileptogènes supplémentaires (5,6 %) d’où la nécessité d’explorer d’autres techniques.
Dans les seconde et troisième études, nous avons examiné le potentiel de la SPIR pour localiser le foyer épileptique en analysant le comportement hémodynamique au cours de crises temporales et frontales. Ces études ont montré que les crises sont associées à une augmentation significative de l’hémoglobine oxygénée (HbO) et l’hémoglobine totale au niveau de la région épileptique. Bien qu’une activation contralatérale en image miroir puisse être observée sur la majorité des crises, la latéralisation du foyer était possible dans la plupart des cas. Une augmentation surprenante de l’hémoglobine désoxygénée a parfois pu être observée suggérant qu’une hypoxie puisse survenir même lors de courtes crises focales.
Dans la quatrième et dernière étude, nous avons évalué l’apport de la MEG dans l’évaluation des patients avec épilepsie focale réfractaire considérés candidats potentiels à une chirurgie. Il s’est avéré que les localisations de sources des pointes épileptiques interictales par la MEG ont eu un impact majeur sur le plan de traitement chez plus des deux tiers des sujets ainsi que sur le devenir postchirurgical au niveau du contrôle des crises.Epilepsy is the most common chronic neurological disorder after stroke. The major form of treatment is long-term drug therapy to which approximately 30% of patients are unfortunately refractory to. Brain surgery is recommended when medication fails, especially if magnetic resonance imaging (MRI) can identify a well-defined epileptogenic lesion. Unfortunately, close to a quarter of patients have nonlesional refractory focal epilepsy. For these MRI-negative cases, identification of the epileptogenic zone rely heavily on remaining tools: clinical history, video-electroencephalography (EEG) monitoring, ictal single-photon emission computed tomography (SPECT), and a positron emission tomography (PET). Unfortunately, the limited spatial and/or temporal resolution of these localization techniques translates into poor surgical outcome rates.
In this thesis, we explore three relatively novel techniques to improve the localization of the epileptic focus for patients with drug-resistant focal epilepsy who are potential candidates for epilepsy surgery: high-field 3 Tesla (T) MRI, near-infrared spectroscopy (NIRS) and magnetoencephalography (MEG).
In the first study, we evaluated if high-field 3T MRI, providing a higher signal to noise ratio, could help detect subtle epileptogenic lesions missed by conventional 1.5T MRIs. Unfortunately, we show that the former was able to detect an epileptogenic lesion in only 5.6% of cases of 1.5T MRI-negative epileptic patients, emphasizing the need for additional techniques.
In the second and third studies, we evaluated the potential of NIRS in localizing the epileptic focus by analyzing the hemodynamic behavior of temporal and frontal lobe seizures respectively. We show that focal seizures are associated with significant increases in oxygenated haemoglobin (HbO) and total haemoglobin (HbT) over the epileptic area. While a contralateral mirror-like activation was seen in the majority of seizures, lateralization of the epileptic focus was possible most of the time. In addition, an unexpected increase in deoxygenated haemoglobin (HbR) was noted in some seizures, suggesting possible hypoxia even during relatively brief focal seizures.
In the fourth and last study, the utility of MEG in the evaluation of nonlesional drug-refractory focal epileptic patients was studied. It was found that MEG source localization of interictal epileptic spikes had an impact both on patient management for over two thirds of patients and their surgical outcome
Recommended from our members
New techniques for vibration condition monitoring: Volterra kernel and Kolmogorov-Smirnov
This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University.This research presents a complete review of signal processing techniques used, today,
in vibration based industrial condition monitoring and diagnostics. It also introduces
two novel techniques to this field, namely: the Kolmogorov-Smirnov test and Volterra
series, which have not yet been applied to vibration based condition monitoring.
The first technique, the Kolmogorov-Smirnov test, relies on a statistical comparison
of the cumulative probability distribution functions (CDF) from two time series. It
must be emphasised that this is not a moment technique, and it uses the whole CDF,
in the comparison process.
The second tool suggested in this research is the Volterra series. This is a non-linear
signal processing technique, which can be used to model a time series. The
parameters of this model are used for condition monitoring applications.
Finally, this work also presents a comprehensive comparative study between these
new methods and the existing techniques. This study is based on results from
numerical and experimental applications of each technique here discussed.
The concluding remarks include suggestions on how the novel techniques proposed here can be improved.Brunel University Department of Mechanical Engineering and CAPES, Fundacao
Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior
Dynamic models of brain imaging data and their Bayesian inversion
This work is about understanding the dynamics of neuronal systems, in particular with
respect to brain connectivity. It addresses complex neuronal systems by looking at
neuronal interactions and their causal relations. These systems are characterized using
a generic approach to dynamical system analysis of brain signals - dynamic causal
modelling (DCM). DCM is a technique for inferring directed connectivity among
brain regions, which distinguishes between a neuronal and an observation level. DCM
is a natural extension of the convolution models used in the standard analysis of
neuroimaging data. This thesis develops biologically constrained and plausible
models, informed by anatomic and physiological principles. Within this framework, it
uses mathematical formalisms of neural mass, mean-field and ensemble dynamic
causal models as generative models for observed neuronal activity. These models
allow for the evaluation of intrinsic neuronal connections and high-order statistics of
neuronal states, using Bayesian estimation and inference. Critically it employs
Bayesian model selection (BMS) to discover the best among several equally plausible
models. In the first part of this thesis, a two-state DCM for functional magnetic
resonance imaging (fMRI) is described, where each region can model selective
changes in both extrinsic and intrinsic connectivity. The second part is concerned with
how the sigmoid activation function of neural-mass models (NMM) can be
understood in terms of the variance or dispersion of neuronal states. The third part
presents a mean-field model (MFM) for neuronal dynamics as observed with
magneto- and electroencephalographic data (M/EEG). In the final part, the MFM is
used as a generative model in a DCM for M/EEG and compared to the NMM using
Bayesian model selection
The selective updating of working memory: a predictive coding account
Goal-relevant information maintained in working memory is remarkably robust and resistant to distractions. However, our nervous system is endowed with exceptional flexibility; therefore such information can be updated almost effortlessly. A scenario – not uncommon in our daily life – is that selective maintaining and updating information can be achieved concurrently. This is an intriguing example of how our brain balances stability and flexibility, when organising its knowledge. A possibility – one may draw upon to understand this capacity – is that working memory is represented as beliefs, or its probability densities, which are updated in a context-sensitive manner. This means one could treat working memory in the same way as perception – i.e., memories are based on inferring the cause of sensations, except that the time scale ranges from an instant to prolonged anticipation. In this setting, working memory is susceptible to prior information encoded in the brain’s model of its world. This thesis aimed to establish an interpretation of working memory processing that rests on the (generalised) predictive coding framework, or hierarchical inference in the brain. Specifically, the main question it asked was how anticipation modulates working memory updating (or maintenance). A novel working memory updating task was designed in this regard. Blood-oxygen-level dependent (BOLD) imaging, machine learning, and dynamic causal modelling (DCM) were applied to identify the neural correlates of anticipation and the violation of anticipation, as well as the causal structure generating these neural correlates. Anticipation induced neural activity in the dopaminergic midbrain and the striatum. Whereas, the fronto-parietal and cingulo-operculum network were implicated when an anticipated update was omitted, and the midbrain, occipital cortices, and cerebellum when an update was unexpected. DCM revealed that anticipation is a modulation of backward connections, whilst the associated surprise is mediated by forward and local recurrent modulations. Two mutually antagonistic pathways were differentially modulated under anticipatory flexibility and stability, respectively. The overall results indicate that working memory may as well follow the cortical message-passing scheme that enables hierarchical inference
Recommended from our members
Advanced robust non-invasive foetal heart detection techniques during active labour using one pair of transabdominal electrodes
The thesis proposes and evaluates three state-of-the-art signal processing techniques to detect fetal heartbeats within each maternal cardiac cycle, during labour contractions, using only a pair of transabdominal electrodes. The first and second techniques are, namely, the structured third- order cumulant-slice-template matching and the bispectral-contours-template matching for fetal QRS identification, respectively. The third technique is based on the modified and appropriately weighted spectral multiple signal classification (MUSIC) with incorporated covariance matrix for uterine contraction noise-like interfering signals also contaminated with noise. Essentially, two modifications to the standard MUSIC have been developed in order to enhance the performance of the spectral estimator in our applied work. The first modification involves the introduction of an optimised weighting function to the segmented ECG covariance matrix, and is chiefly aimed at enhancing the fetal QRS major spectral peak which occurs at around 30 Hz against the mother QRS major spectral peak usually occurring around 17 Hz and all other noise contributions. Additional optional pseudo-bispectral enhancement to sharpen the maternal and fetal spectral peaks, in particular when the mother and fetal R-waves are temporally coincident, have been achieved. The second modification to the spectral MUSIC is the removal of the unjustified assumption that only white Gaussian noise is present and the incorporation of the actual measured labour uterine contraction covariance matrix in reconfigured subspace analysis. This inevitably leads to the generalised eigenvectors - eigenvalues decomposition modern signal processing. This is now coined the modified, interference incorporated pseudo-spectral MUSIC. The above mentioned first and second techniques are higher-order statistics-based (HOS) and hybrid involving both signal processing and NN classifiers. The third technique is second-order statistics-based (SOS). In all techniques, the removal of signal non-linearity with the aid of non-linear Volterra synthesisers plays a crucial part in the fetal detection integrity.
Accurately assessed fetal heart classification rates as high as 95% have been achieved during labour, thus helping to provide non-invasive transparency to fetal intrapartum welfare. Performance analysis and evaluation processes involved more than 30 critical cases classified as “fetal under stress in labour” recorded in a London hospital database and used both transbadominal ECG electrodes and fetal scalp electrodes. The latter facilitates detection of the instantaneous fetal heart rate which is then used as the Reference Fetal Heart Rate in the assessment of the classification rate of each of the above mentioned techniques. It will be shown that the fetal heartbeats are completely masked by uterine activity and noise artefacts in all the recorded transabdominal maternal ECG signals. The fetal scalp electrode was, therefore, deemed necessary to provide the highest accurate measure of fetal heart functionality (from the hospital viewpoint), and in the assessment of the three non-invasive techniques presented in this thesis. The techniques may also be used during gestation and as early as 10 weeks
Imaging functional and structural networks in the human epileptic brain
Epileptic activity in the brain arises from dysfunctional neuronal networks involving cortical and subcortical grey matter as well as their connections via white matter fibres. Physiological brain networks can be affected by the structural abnormalities causing the epileptic activity, or by the epileptic activity itself. A better knowledge of physiological and pathological brain networks in patients with epilepsy is critical for a better understanding the patterns of seizure generation, propagation and termination as well as the alteration of physiological brain networks by a chronic neurological disorder. Moreover, the identification of pathological and physiological networks in an individual subject is critical for the planning of epilepsy surgery aiming at resection or at least interruption of the epileptic network while sparing physiological networks which have potentially been remodelled by the disease.
This work describes the combination of neuroimaging methods to study the functional epileptic networks in the brain, structural connectivity changes of the motor networks in patients with localisation-related or generalised epilepsy and finally structural connectivity of the epileptic network. The combination between EEG source imaging and simultaneous EEG-fMRI recordings allowed to distinguish between regions of onset and propagation of interictal epileptic activity and to better map the epileptic network using the continuous activity of the epileptic source. These results are complemented by the first recordings of simultaneous intracranial EEG and fMRI in human. This whole-brain imaging technique revealed regional as well as distant haemodynamic changes related to very focal epileptic activity. The combination of fMRI and DTI tractography showed subtle changes in the structural connectivity of patients with Juvenile Myoclonic Epilepsy, a form of idiopathic generalised epilepsy. Finally, a combination of intracranial EEG and tractography was used to explore the structural connectivity of epileptic networks. Clinical relevance, methodological issues and future perspectives are discussed
Imaging brain networks in focal epilepsy: a prospective study of the clinical application of simultaneous EEG-fMRI in pre-surgical evaluation
Epilepsy is a common disorder with significant associated morbidity and mortality. Despite advances in treatment, there remain a minority of people with pharmacoresistant focal epilepsy for whom surgery may be beneficial. It has been suggested that not enough people are offered surgical treatment, partly owing to the fact that current non-invasive techniques do not always adequately identify the seizure onset zone so that invasive EEG is required. EEG-fMRI is an imaging technique, developed in the 1990s (Ives, Warach et al. 1993) which identifies regions of interictal epileptiform discharge associated haemodynamic changes, that are concordant with the seizure onset zone in some patients (Salek-Haddadi, Diehl et al. 2006). To date there has been no large scale prospective comparison with icEEG and postoperative outcome. This thesis presents a series of experiments, carried out in a cohort of patients scanned using EEG-fMRI as part of a multi-centre programme, designed to investigate the relationship between EEG-fMRI and intracranial EEG and to assess its potential role in pre-surgical evaluation of patients with focal epilepsy. The results suggested that positive, localised IED-related BOLD signal changes were sensitive for the seizure onset zone, as determined on icEEG, both in patients neocortical epilepsies, but were not predictive of outcome. Widespread regions of positive IEDrelated BOLD signal change were associated with widespread or multifocal abnormalities on icEEG and poor outcome. Patterns of haemodynamic change, identified using both data driven and EEG derived modeling approaches, correspond to regions of seizure onset on icEEG, but improvements for modeling seizures are required. A study of a single seizure in a patient who underwent simultaneous icEEGfMRI, showed similar findings.. An exploratory investigation of fMRI-DCM in EEG-fMRI, suggested it can provide information about seizure propagation and this opens new avenues for the non-invasive study of the epileptic network and interactions with function
Functional MRI of focal and generalised interictal epileptiform discharges.
Localizing the source of epileptic discharges is important in gaining a greater understanding of the disease, classifying epilepsy, and identifying areas suitable for potentially curable surgical resection. Functional imaging measures haemodynamic, metabolic or neurochemical correlates to localise neural activity. Combining EEG with functional MRI (EEG-fMRI) allows the localisation of haemodynamic correlates of neuronal events recorded on surface EEG. The work in this thesis aims to identify the spatial haemodynamic correlates of interictal epileptiform discharges (IED) in patients with epilepsy using EEG-fMRI. Five studies form the main body of this thesis. In the first study, 46 patients with frequent generalised spike wave activity (GSW) were studied with EEG-fMRI on a 1.5 Tesla scanner. The main finding was of a characteristic pattern of fMRI signal decrease in frontal, parietal and posterior cingulate cortex, areas of association cortex, during GSW. In the second study, 4 patients from this first series were re-studied with a 3 Tesla scanner. A high degree of reproducibility was seen in the spatial distribution of fMRI changes. Perfusion MRI with an arterial spin label sequence was used that showed a decrease in blood flow to these areas during GSW. In the third study, a novel method for the analysis of fMRI data in epilepsy, temporal clustering analysis (TCA) was assessed. The technique was confounded by subject motion, and we were unable to reliably detect correlates of IED. The fourth study moves away from correlating visually identified IEDs on the EEG, and correlates power fluctuations in the delta frequency band with simultaneously acquired fMRI. Finally a combination of EEG-fMRI and MR tractography were used to study a patient with temporal lobe epilepsy. The issues surrounding potential use of EEG-fMRI as a clinical tool are discussed
- …