Analysis of brain's electrophysiological activity

Feature extraction and classification
Various features are extracted from brain electrical activity recordings -using autoregressive and multivariate autoregressive models, harmonic analysis, wavelet transformation, energy of the  characteristic frequency rhythms (α,β,δ,θ) in order to reduce dimensionality and to focus on specific signal characteristics. Using these features, biosignals are classified to healthy and pathological patterns, while internal brain structures are identified from their signal signature, by means of intelligent computational methods.

Seizure prediction
Epilepsy afflicts about 1% of the world's population, or more than 50 million people worldwide. A most disabling characteristic of epilepsy is the unforeseen way seizures occur. That is why one of the most important problems in epileptology is seizure prediction, to which computer science and signal processing are called, in an interdisciplinary effort with the clinicians, to provide a solution. Algorithms with high sensitivity and specificity, leading to an early prediction of an upcoming seizure, need to be designed to "drive" "intelligent" implantable or non-implantable devices to electrically stimulate, or, alternatively, infuse drugs to appropriately chosen regions of the brain to avert the seizure occurrence. Alternatively, such devices could just inform the patient about the upcoming seizure so that he could take the necessary actions to avoid injury.
Various studies have come up with evidence for the existence of a pre-ictal period that could be detected using various characteristic measures.  The ultimate goal of our group's work is to achieve early prediction of epileptic seizures in an individualized manner (patient-specific) with high sensitivity and specificity, based on surface EEG (sEEG) recordings of the patients, through implementation and application on sEEG of appropriately chosen algorithms. The data used in our project are acquired in the Long-Term Video - EEG Monitoring Unit of the Epilepsy Surgery Unit at "Evangelismos" Hospital in Athens, Greece, during pre-surgery evaluation of patients with refractory Temporal Lobe Epilepsy (TLE), who are candidates for epilepsy surgery.
Within our group, various algorithms are being investigated towards the goal of seizure prediction. These include both univariate and bivariate measures (for across channel similarity assessment), linear and non-linear, with emphasis given on the non-linear ones. These measures are derived mainly from non-linear dynamics (e.g., fractal dimension) and information theory (e.g., mutual information), while other methods, such as wavelets, are considered as well. Moreover, emphasis is given on statistical validation of results, in order to quantify their significance and determine the specificity and sensitivity achieved by the methodologies.
One important issue concerning our work, which should be emphasized, is the type of the data used. Until now, different groups working on seizure prediction have carried out most of their research work using intracranial EEG data (obtained with depth electrodes), which is a relatively "clean" signal in comparison to sEEG which is contaminated with various artifacts (related to eye blinks, heart beat, muscle movements, for example). In our group, efforts are focused on developing methodologies capable of achieving high sensitivity and specificity in seizure prediction when applied not only to intracranial but also to sEEG data, since the latter could be used in a non-invasive approach. Such an approach, will definitely be more attractive and will lead to a safer and practical seizure prediction and abortion protocol. Towards that direction, additional methodologies for automated artifact detection or suppression are also investigated.

Surface EEG of an epileptic patient
The vertical line denotes the sec of the seizure onset

Brain connectivity patterns
Rhythmicity is a useful characteristic examined in brain electrical activity recordings because it reflects the synchronization of neuronal activity, which in turn reflects changes of neuronal membrane potential.  If interdependence of temporal relations of brain rhythms does exist, it is possible that rhythms observed in various brain regions are reciprocally related, and this rhythmicity is important for the integration of mental processes. The degree of synchronization between brain structures is analyzed by coherence techniques revealing the causal relation (direction of influence) between electrophysiological signals. It is considered that propagation of brain activity is related to the information flow and cortical connectivity of human brain and can be understood as the causal influence of one structure on the other.
Methods for the estimation of the causal influence are developed for both direct and indirect interactions among EEG/ERP signals.  The findings can be visualized in brain maps showing the connectivity among brain structures. The study of information flow can give insights for upper cognitive functions and mental state and it is examined for understanding neurophysiological disorders in obsessive compulsive disorder, schizophrenia and dyslexia. This information is also important in the treatment of neurological diseases, because it can reveal the spatial propagation of an epileptic seizure along time or how brain structures in motor cortex communicate to each other in Parkinson's disease.

Intracranial source localization
The determination of intracranial sources of brain electrical activity based on EEG/ERP surface recordings, is of great importance since it allows to improve our understanding of the basic mechanisms of cognitive processes and a better characterization of pathologies. To this end, various techniques are used that follow different approaches and assumptions. The choice of the most suitable technique is better-aimed if there is a priori information on the sources and the shape of head.
In addition, methods for the integration of source imaging results with anatomical data are developed, to define the coordinates of the active sources in terms of Broadmann areas or in Talairach coordinates and to draw conclusions about the anatomical/functional structures being active. Clinical applications of noninvasive source imaging include improved understanding and diagnosis of disorders such as depression, schizophrenia, Parkinson's and Alzheimer's disease. Moreover, source localization combined with personalized imaging data constitutes a powerful tool for neurotherapeutic tasks, such as surgery-based therapy in Epilepsy.

Computational Neuroscience - Modeling of the basal ganglia

The underlying pathophysiology of Parkinson's disease is almost undoubtedly attributed to the structural and functional characteristics of the basal ganglia, a group of nuclei in the center of the brain. Modeling of the brain, in general, and the basal ganglia, in specific, has begun since the early 20th century; however, the flourishing of a new therapeutic technology has rendered the entitlement with models of the basal ganglia even more challenging. Deep Brain Stimulation (DBS) has sprung from the family of lesioning therapeutic interventions just ten years ago and has ever since dominated the operating theatres for refractory types of Parkinson's disease. In the case of DBS, the approach was firstly proved to be successful in practice, while in theory, elementary concepts are still subtle and need clarification.
During functional interventions and DBS electrode placing procedures, microelectrode recordings from the basal ganglia are routinely acquired. Practically, they are useful in deciding the target for the permanent placement of the DBS electrode, but their value goes way beyond that. The information included in such recordings is manifold, combining both fast spiking activity and slow activities such as local field potentials. Because of the hierarchical structure of the brain, the models can be designed to target mechanisms that belong to functional categories spanning from the subcellular to whole system levels of description. Today, it remains unclear which level of single-cell modeling is appropriate to understand the dynamics and computations carried out by such large systems. A thorough understanding of single-neuron function together with network stability and system processing effectiveness can be obtained only by relating different levels of abstraction. Trying to incorporate every biological detail of the investigated neuron is likely to obscure the focus on the essential dynamics, whereas limiting investigations to highly abstract processing schemes casts doubt on the biological relevance of specific findings. Thus, we are focused on multi-level hierarchical modelling approaches and the development of efficient transition techniques between different modelling levels. Our group's efforts on network-level models of the basal ganglia concentrate on the functioning of the subthalamic nucleus, both because of its hypothetical central role in the functioning of the basal ganglia and because it is nowadays the established target of the DBS intervention. The study of the functioning of this nucleus is performed via biologically plausible models that mainly follow the Hodgkin-Huxley formulation and are appropriately integrated to represent neuronal assemblies.
On the system-level, various hypothetical mechanisms have been proposed for basal ganglia functioning and their participation in action selection, working memory representation, sequence production and in processing of cognitive tasks. The objective of our team, through computational models on a high level of abstraction, is to thoroughly study the major computational hypothesis characterizing the role of basal ganglia as a central switch and decision making mechanism of the brain, in both motor and cognitive tasks. Particular emphasis is given to the examination of the dopamine effects in the smooth functioning of the basal ganglia network and in the generation of characteristic Parkinsonian patterns of activity.
Central to our team's efforts is to combine modeling approaches with real intra-operative microelectrode recordings (acquired by the Neurosurgical Clinic of the University of Athens). Also, since modeling of the basal ganglia together with DBS data and control can supply information about the underlying facts, the models of the basal ganglia are designed by our group in a way that can support simulations of the DBS interaction with the tissues, thus providing indications for the validity of the approach. Other goals include the determination of the functional characteristics parameters as well as the exact placement of the brain stimulating electrode, during the DBS intervention.  

An abstract scheme of the Basal Ganglia Network depicting the main nuclei interconnections

Efficient management and analysis of epileptic patients' data

The EPILDA (EPILepsy DAtabase) system is a wide-scale web-based data management and analysis tool. The main purpose of the system is to assist the efficient management and analysis of epidemiological, clinical, paraclinical, pre-surgical evaluation and therapeutic data concerning epilepsy.
The EPILDA system is designed in a patient-oriented manner, enabling the user to easily navigate through the data management options and facilitating handling of tools for data analysis. The design of data management interfaces and underlying storing organization has adopted well-established medical approaches and was guided by expert neurosurgeons and neurologists. An important part of the system is a web-based environment for viewing and handling crucial electrophysiological recordings and neuro-imaging data. EEG analysis outcomes, measures and statistics are summarized in charts and, once calculated, are stored in the database for subsequent any-time access. EPILDA supports DICOM data management and provides automatic routines to extract and store header data such as patient, examination and scanner information. Imaging data acquired in various modalities can be loaded, and tools for image enhancement, filtering, edge detection and segmentation can be used to assist their interpretation.
Efficient data management and analysis tools provided by EPILDA system, enable the evaluation of therapy in epilepsy, since valuable conclusions for the treatment outcome can be extracted. In addition to this, the results of patient's data analysis (both electrophysiological and neuro-imaging), can be used for the surgical candidacy determination in epilepsy.

Innovative aspects and advantages

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