Impaired brainstem and thalamic high-frequency oscillatory EEG activity in migraine between attacks

INTRODUCTION: We investigated whether interictal thalamic dysfunction in migraine without aura (MO) patients is a primary determinant or the expression of its functional disconnection from proximal or distal areas along the somatosensory pathway. METHODS: Twenty MO patients and twenty healthy volunteers (HVs) underwent an electroencephalographic (EEG) recording during electrical stimulation of the median nerve at the wrist. We used the functional source separation algorithm to extract four functionally constrained nodes (brainstem, thalamus, primary sensory radial, and primary sensory motor tangential parietal sources) along the somatosensory pathway. Two digital filters (1-400 Hz and 450-750 Hz) were applied in order to extract low- (LFO) and high- frequency (HFO) oscillatory activity from the broadband signal. RESULTS: Compared to HVs, patients presented significantly lower brainstem (BS) and thalamic (Th) HFO activation bilaterally. No difference between the two cortical HFO as well as in LFO peak activations between the two groups was seen. The age of onset of the headache was positively correlated with HFO power in the right brainstem and thalamus. CONCLUSIONS: This study provides evidence for complex dysfunction of brainstem and thalamocortical networks under the control of genetic factors that might act by modulating the severity of migraine phenotype.

Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction:a review

Support Vector Machines (SVMs) are widely used classifiers for detecting physiological patterns in Human-Computer Interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently in the literature, insufficient details about the SVM implementation and/or parameters selection are reported, making it impossible to reproduce study analysis and results. In order to perform an optimized classification and report a proper description of the results, it is necessary to have a comprehensive critical overview of the application of SVM. The aim of this paper is to provide a review of the usage of SVM in the determination of brain and muscle patterns for HCI, by focusing on electroencephalography (EEG) and electromyography (EMG) techniques. In particular, an overview of the basic principles of SVM theory is outlined, together with a description of several relevant literature implementations. Furthermore, details concerning reviewed papers are listed in tables, and statistics of SVM use in the literature are presented. Suitability of SVM for HCI is discussed and critical comparisons with other classifiers are reported.

Comparing neural correlates of configural processing in faces and objects:An ERP study of the thatcher illusion

In the Thatcher illusion, a face with inverted eyes and mouth looks abnormal when upright but not when inverted. Behavioral studies have shown that thatcherization of an upright face disrupts perceptual processing of the local configuration. We recorded high-density EEG from normal observers to study ERP correlates of the illusion during the perception of faces and nonface objects, to determine whether inversion and thatcherization affect similar neural mechanisms. Observers viewed faces and houses in four conditions (upright vs. inverted, and normal vs. thatcherized) while detecting an oddball category (chairs). Thatcherization delayed the N170 component over occipito-temporal cortex to faces, but not to houses. This modulation matched the illusion as it was larger for upright than inverted faces. The P1 over medial occipital regions was delayed by face inversion but unaffected by thatcherization. Finally, face thatcherization delayed P2 over occipito-temporal but not over parietal regions, while inversion affected P2 across categories. All effects involving thatcherization were face-specific. These results indicate that effects of face inversion and feature inversion (in thatcherized faces) can be distinguished on a functional as well as neural level, and that they affect configural processing of faces in different time windows. © 2006 Elsevier Inc.

Causality and synchronisation in complex systems with applications to neuroscience

This thesis presents an investigation, of synchronisation and causality, motivated by problems in computational neuroscience. The thesis addresses both theoretical and practical signal processing issues regarding the estimation of interdependence from a set of multivariate data generated by a complex underlying dynamical system. This topic is driven by a series of problems in neuroscience, which represents the principal background motive behind the material in this work. The underlying system is the human brain and the generative process of the data is based on modern electromagnetic neuroimaging methods . In this thesis, the underlying functional of the brain mechanisms are derived from the recent mathematical formalism of dynamical systems in complex networks. This is justified principally on the grounds of the complex hierarchical and multiscale nature of the brain and it offers new methods of analysis to model its emergent phenomena. A fundamental approach to study the neural activity is to investigate the connectivity pattern developed by the brain’s complex network. Three types of connectivity are important to study: 1) anatomical connectivity refering to the physical links forming the topology of the brain network; 2) effective connectivity concerning with the way the neural elements communicate with each other using the brain’s anatomical structure, through phenomena of synchronisation and information transfer; 3) functional connectivity, presenting an epistemic concept which alludes to the interdependence between data measured from the brain network. The main contribution of this thesis is to present, apply and discuss novel algorithms of functional connectivities, which are designed to extract different specific aspects of interaction between the underlying generators of the data. Firstly, a univariate statistic is developed to allow for indirect assessment of synchronisation in the local network from a single time series. This approach is useful in inferring the coupling as in a local cortical area as observed by a single measurement electrode. Secondly, different existing methods of phase synchronisation are considered from the perspective of experimental data analysis and inference of coupling from observed data. These methods are designed to address the estimation of medium to long range connectivity and their differences are particularly relevant in the context of volume conduction, that is known to produce spurious detections of connectivity. Finally, an asymmetric temporal metric is introduced in order to detect the direction of the coupling between different regions of the brain. The method developed in this thesis is based on a machine learning extensions of the well known concept of Granger causality. The thesis discussion is developed alongside examples of synthetic and experimental real data. The synthetic data are simulations of complex dynamical systems with the intention to mimic the behaviour of simple cortical neural assemblies. They are helpful to test the techniques developed in this thesis. The real datasets are provided to illustrate the problem of brain connectivity in the case of important neurological disorders such as Epilepsy and Parkinson’s disease. The methods of functional connectivity in this thesis are applied to intracranial EEG recordings in order to extract features, which characterize underlying spatiotemporal dynamics before during and after an epileptic seizure and predict seizure location and onset prior to conventional electrographic signs. The methodology is also applied to a MEG dataset containing healthy, Parkinson’s and dementia subjects with the scope of distinguishing patterns of pathological from physiological connectivity.

Disordered connectivity in the autistic brain:challenges for the 'new psychophysiology'

In 2002, we published a paper [Brock, J., Brown, C., Boucher, J., Rippon, G., 2002. The temporal binding deficit hypothesis of autism. Development and Psychopathology 142, 209-224] highlighting the parallels between the psychological model of 'central coherence' in information processing [Frith, U., 1989. Autism: Explaining the Enigma. Blackwell, Oxford] and the neuroscience model of neural integration or 'temporal binding'. We proposed that autism is associated with abnormalities of information integration that is caused by a reduction in the connectivity between specialised local neural networks in the brain and possible overconnectivity within the isolated individual neural assemblies. The current paper updates this model, providing a summary of theoretical and empirical advances in research implicating disordered connectivity in autism. This is in the context of changes in the approach to the core psychological deficits in autism, of greater emphasis on 'interactive specialisation' and the resultant stress on early and/or low-level deficits and their cascading effects on the developing brain [Johnson, M.H., Halit, H., Grice, S.J., Karmiloff-Smith, A., 2002. Neuroimaging of typical and atypical development: a perspective from multiple levels of analysis. Development and Psychopathology 14, 521-536].We also highlight recent developments in the measurement and modelling of connectivity, particularly in the emerging ability to track the temporal dynamics of the brain using electroencephalography (EEG) and magnetoencephalography (MEG) and to investigate the signal characteristics of this activity. This advance could be particularly pertinent in testing an emerging model of effective connectivity based on the balance between excitatory and inhibitory cortical activity [Rubenstein, J.L., Merzenich M.M., 2003. Model of autism: increased ratio of excitation/inhibition in key neural systems. Genes, Brain and Behavior 2, 255-267; Brown, C., Gruber, T., Rippon, G., Brock, J., Boucher, J., 2005. Gamma abnormalities during perception of illusory figures in autism. Cortex 41, 364-376]. Finally, we note that the consequence of this convergence of research developments not only enables a greater understanding of autism but also has implications for prevention and remediation. © 2006.

Can we observe collective neuronal activity from macroscopic aggregate signals?

The fundamental problem faced by noninvasive neuroimaging techniques such as EEG/MEG1 is to elucidate functionally important aspects of the microscopic neuronal network dynamics from macroscopic aggregate measurements. Due to the mixing of the activities of large neuronal populations in the observed macroscopic aggregate, recovering the underlying network that generates the signal in the absence of any additional information represents a considerable challenge. Recent MEG studies have shown that macroscopic measurements contain sufficient information to allow the differentiation between patterns of activity, which are likely to represent different stimulus-specific collective modes in the underlying network (Hadjipapas, A., Adjamian, P., Swettenham, J.B., Holliday, I.E., Barnes, G.R., 2007. Stimuli of varying spatial scale induce gamma activity with distinct temporal characteristics in human visual cortex. NeuroImage 35, 518–530). The next question arising in this context is whether aspects of collective network activity can be recovered from a macroscopic aggregate signal. We propose that this issue is most appropriately addressed if MEG/EEG signals are to be viewed as macroscopic aggregates arising from networks of coupled systems as opposed to aggregates across a mass of largely independent neural systems. We show that collective modes arising in a network of simulated coupled systems can be indeed recovered from the macroscopic aggregate. Moreover, we show that nonlinear state space methods yield a good approximation of the number of effective degrees of freedom in the network. Importantly, information about hidden variables, which do not directly contribute to the aggregate signal, can also be recovered. Finally, this theoretical framework can be applied to experimental MEG/EEG data in the future, enabling the inference of state dependent changes in the degree of local synchrony in the underlying network.

Induced gamma activity in primary visual cortex is related to luminance and not color contrast: an MEG study

Gamma activity in the visual cortex has been reported in numerous EEG studies of coherent and illusory figures. A dominant theme of many such findings has been that temporal synchronization in the gamma band in response to these identifiable percepts is related to perceptual binding of the common features of the stimulus. In two recent studies using magnetoencephalography (MEG) and the beamformer analysis technique, we have shown that the magnitude of induced gamma activity in visual cortex is dependent upon independent stimulus features such as spatial frequency and contrast. In particular, we showed that induced gamma activity is maximal in response to gratings of 3 cycles per degree (3 cpd) of high luminance contrast. In this work, we set out to examine stimulus contrast further by using isoluminant red/green gratings that possess color but not luminance contrast using the same cohort of subjects. We found no induced gamma activity in V1 or visual cortex in response to the isoluminant gratings in these subjects who had previously shown strong induced gamma activity in V1 for luminance contrast gratings.

Normative MEG data to flash and pattern reversal stimuli

We have attempted to establish normative values of components of the magnetic evoked field to flash and pattern reversal stimuli prior to clinical use of the MEG. Full visual field, binocular evoked magnetic fields were recorded from 100 subjects 16 to 86 years of age with a single channel dc Squid (BTI) second-order gradiometer at a point 5-6cm above the inion. The majority of subjects showed a large positive component (out going magnetic field) of mean latency 115 ms (SD range 2.5 -11.8 in different decades of life) to the pattern reversal stimulus. In many subjects, this P100M was preceeded and succeeded by negative deflections (in going field). About 6% of subjects showed an inverted response i.e. a PNP wave. Waveforms to flash were more variable in shape with several positive components; the most consistent having a mean latency of 110ms (SD range 6.4-23.2). Responses to both stimuli were consistent when measured on the same subject on six different occasions (SD range 4.8 to 7.3). The data suggest that norms can be established for evoked magnetic field components, in particular for the pattern reversal P100M, which could be used in the diagnosis of neuro-opthalmological disease.

The effectiveness of lamotrigine in the treatment of photosensitive epilepsy

Photosensitive epilepsy and associated pattern sensitivity are more prevalent in females and are usually treated with sodium valproate. Sodium valproate has an adverse effect profile, which particularly affects females, including teratogenicity, association with the polycystic ovary syndrome and weight gain. It would be useful therefore if an alternative treatment for photosensitive epilepsy could be found. The principle aim of this study was to investigate the effectiveness of lamotrigine in the treatment of photosensitive epilepsy in adults and children. Patients were either drug-naive, commencing lamotrigine therapy or were transferring from other antiepileptic drugs to lamotrigine (primarily sodium valproate) due to lack of response, adverse effects or desired pregnancy. The photoparoxsymal response in the electroencephalograph was used as the primary measure of photo and pattern sensitivity. In addition the effects of lamotrigine on occipital spikes and normal responses in the EEG to visual stimuli were investigated. Secondary measures also included the resting EEG, seizures, body mass index, menstrual function, mood and cognitive function. The results suggest that in adult patients lamotrigine is efficacious in the treatment of photosensitive epilepsy, although it appears inferior to sodium valproate. Lamotrigine does however have a more favourable adverse effect profile than valproate. The results indicate that lamotrigine therapy is suitable for photosensitive epilepsy in women of childbearing age or in patients experiencing unacceptable adverse effects with valproate therapy. Patients are more likely to respond to lamotrigine treatment if they present with sensitivity to a limited number of frequencies. Lamotrigine does not seem to be as efficacious in the treatment of children, although against it may be considered a second line drug if the child does not respond to or will not tolerate sodium valproate.

The role of the cortical occipital spike in photosensitive epilepsy

Chapters one to three are an introduction to photosensitive epilepsy, electroencephalography (EEG) and the magnocellular and parvocellular visual pathways. Photoparoxysmal response (PPR) are strongly associated with photosensitive epilepsy. Chapters four to nine investigated whether occipital spikes were associated with PPR and hence with photosensitive epilepsy. The chapters investigated whether the response types showed similar dependence on stimulus characteristics using EEG. Chapters four and five found that occipital spikes and PPR showed different dependence on colour and luminance contrast. The differences were consistent with the magnocellular pathway mediating occipital spikes and the pavocellular pathway mediating PPR. The study in chapter eight found that monocular occlusion had a significantly greater effect on PPR than on occipital spikes, which is further evidence against an association between the two types of response. Chapters six and seven showed that occipital spikes and PPR had similar optimum spatial and temporal frequencies. Chapter nine showed that both response types could be generated via stimulation of the periphery of the retina. However, these three chapters are not strong evidence of an association, as the results do not contradict the theory that the responses are generated via different pathways. The magnocellular and pavocellular pathways have similar optimum temporal and spatial frequencies and both are present in the periphery. In chapter ten, magnetoencephalography was used to estimate the source of activity underlying the components of the VEP and occipital spike. Changes in the amplitude and latency in the components of the normal VEP are associated with epilepsy. However, the source underlying the occipital spikes was not related to that underlying the components of the VEP so this is also removed as a source of evidence for an association between occipital spikes and photosensitive epilepsy.

Cognitive neuroscience: the evolution of a discipline

Preface. The evolution of cognitive neuroscience has been spurred by the development of increasingly sophisticated investigative techniques to study human cognition. In Methods in Mind, experts examine the wide variety of tools available to cognitive neuroscientists, paying particular attention to the ways in which different methods can be integrated to strengthen empirical findings and how innovative uses for established techniques can be developed. The book will be a uniquely valuable resource for the researcher seeking to expand his or her repertoire of investigative techniques. Each chapter explores a different approach. These include transcranial magnetic stimulation, cognitive neuropsychiatry, lesion studies in nonhuman primates, computational modeling, psychophysiology, single neurons and primate behavior, grid computing, eye movements, fMRI, electroencephalography, imaging genetics, magnetoencephalography, neuropharmacology, and neuroendocrinology. As mandated, authors focus on convergence and innovation in their fields; chapters highlight such cross-method innovations as the use of the fMRI signal to constrain magnetoencephalography, the use of electroencephalography (EEG) to guide rapid transcranial magnetic stimulation at a specific frequency, and the successful integration of neuroimaging and genetic analysis. Computational approaches depend on increased computing power, and one chapter describes the use of distributed or grid computing to analyze massive datasets in cyberspace. Each chapter author is a leading authority in the technique discussed.

Migraine, A study of environmental and intrapersonal factors

A study was carried out of 45 migrainous patients with visually induced migraine (VIM), and 25 migrainous students, each having an age and sex matched control. The study utilised questionnaires, interviews, electroencephalography (EEG) and visual evoked potentials (VEP). The experimental work and analysis was carried out in the Neuropsychology Unit in collaboration with the Birmingham Migraine Clinic, over a period of five years. The study suggests: 1. The literature on a possible relationship between migraine and epilepsy hitherto published is unreliable (supporting evidence is given). 2. That a much greater precision is needed in defining migraine for research purposes. 3. A revised methodology for the selection of controls is needed and this is proposed. 4. That despite what are now seen to be superficial similarities, there are clear distinctions of a fundamental nature between photo-sensitive epilepsy (PSE) and VIM. 5. Caution be used when taking headache as a symptom, since many of the precipitants of migrainous headache can also precipitate non-migrainous headache (NMH). 6. The list of visual precipitants of migraine is expanded (particularly flicker and pattern). 7. That colour (principally red) is a previously unreported precipitant of migraine. 8. The extended range of responses to flicker (the 'H' response) has no significant difference in its frequency of occurrence in patients and normal controls, which contradicts previous literature. 9. The mechanisms thought to underlie migraine serve to explain previously unexplained EEG findings. 10. Further research is needed and proposed.

Brain ictal state characterisation through multimodal information integration

This Thesis addresses the problem of automated false-positive free detection of epileptic events by the fusion of information extracted from simultaneously recorded electro-encephalographic (EEG) and the electrocardiographic (ECG) time-series. The approach relies on a biomedical case for the coupling of the Brain and Heart systems through the central autonomic network during temporal lobe epileptic events: neurovegetative manifestations associated with temporal lobe epileptic events consist of alterations to the cardiac rhythm. From a neurophysiological perspective, epileptic episodes are characterised by a loss of complexity of the state of the brain. The description of arrhythmias, from a probabilistic perspective, observed during temporal lobe epileptic events and the description of the complexity of the state of the brain, from an information theory perspective, are integrated in a fusion-of-information framework towards temporal lobe epileptic seizure detection. The main contributions of the Thesis include the introduction of a biomedical case for the coupling of the Brain and Heart systems during temporal lobe epileptic seizures, partially reported in the clinical literature; the investigation of measures for the characterisation of ictal events from the EEG time series towards their integration in a fusion-of-knowledge framework; the probabilistic description of arrhythmias observed during temporal lobe epileptic events towards their integration in a fusion-of-knowledge framework; and the investigation of the different levels of the fusion-of-information architecture at which to perform the combination of information extracted from the EEG and ECG time-series. The performance of the method designed in the Thesis for the false-positive free automated detection of epileptic events achieved a false-positives rate of zero on the dataset of long-term recordings used in the Thesis.

Watermarking biomedical time series data

This thesis addresses the problem of information hiding in low dimensional digital data focussing on issues of privacy and security in Electronic Patient Health Records (EPHRs). The thesis proposes a new security protocol based on data hiding techniques for EPHRs. This thesis contends that embedding of sensitive patient information inside the EPHR is the most appropriate solution currently available to resolve the issues of security in EPHRs. Watermarking techniques are applied to one-dimensional time series data such as the electroencephalogram (EEG) to show that they add a level of confidence (in terms of privacy and security) in an individual’s diverse bio-profile (the digital fingerprint of an individual’s medical history), ensure belief that the data being analysed does indeed belong to the correct person, and also that it is not being accessed by unauthorised personnel. Embedding information inside single channel biomedical time series data is more difficult than the standard application for images due to the reduced redundancy. A data hiding approach which has an in built capability to protect against illegal data snooping is developed. The capability of this secure method is enhanced by embedding not just a single message but multiple messages into an example one-dimensional EEG signal. Embedding multiple messages of similar characteristics, for example identities of clinicians accessing the medical record helps in creating a log of access while embedding multiple messages of dissimilar characteristics into an EPHR enhances confidence in the use of the EPHR. The novel method of embedding multiple messages of both similar and dissimilar characteristics into a single channel EEG demonstrated in this thesis shows how this embedding of data boosts the implementation and use of the EPHR securely.

Disconnected brains:what is the role of fMRI in connectivity research?

In this paper we consider how functional Magnetic Resonance Imaging (fMRI) has been used to study cortical connectivity in autism and autistic spectrum disorders (ASD). We discuss those studies that have contributed to the evidence supporting a model of disordered cortical connectivity in autism and (ASD), with a focusing emphasis on the application to research into the underconnectivity model. We note that the analytical techniques employed are limited and do not allow interpretation in terms of effective, or directional connectivity, nor do they provide information about the temporal or spectral characteristics of the functional networks being studied. We highlight how currently the features of neural generators that are being assessed by functional connectivity in fMRI are unclear. In addition, we note the importance in clinical studies of considering the consequences of task choice for the nature of the imaging data that can be collected and also of individual differences in participant state and trait characteristics for the accurate interpretation of imaging data. We discuss how alternative techniques such as EEG/MEG may address the limitations of fMRI in assessing brain connectivity, and additionally consider the potential of multimodal approaches. We conclude that fMRI has made significant contributions towards our understanding of the brain in terms of neural systems but that the conclusions drawn from its application in the sphere of autism research need to be approached with caution. It is important in research of this kind that we are aware of the need to examine the methodological and analytical techniques closely when applying findings in clinical populations, not only when they are used to support the development of theoretical models but also to inform diagnostic or treatment decisions.