Elzbieta Olejarczyk

Fractal Dimension of EEG Activity Senses Neuronal Impairment in Acute Stroke

The brain is a self-organizing system which displays self-similarities at different spatial and temporal scales. Thus, the complexity of its dynamics, associated to efficient processing and functional advantages, is expected to be captured by a measure of its scale-free (fractal) properties. Under the hypothesis that the fractal dimension (FD) of the electroencephalographic signal (EEG) is optimally sensitive to the neuronal dysfunction secondary to a brain lesion, we tested the FD’s ability in assessing two key processes in acute stroke: the clinical impairment and the recovery prognosis. Resting EEG was collected in 36 patients 4–10 days after a unilateral ischemic stroke in the middle cerebral artery territory and 19 healthy controls. National Health Institute Stroke Scale (NIHss) was collected at T0 and 6 months later. Highuchi FD, its inter-hemispheric asymmetry (FDasy) and spectral band powers were calculated for EEG signals. FD was smaller in patients than in controls (1.447±0.092 vs 1.525±0.105) and its reduction was paired to a worse acute clinical status. FD decrease was associated to alpha increase and beta decrease of oscillatory activity power. Larger FDasy in acute phase was paired to a worse clinical recovery at six months. FD in our patients captured the loss of complexity reflecting the global system dysfunction resulting from the structural damage. This decrease seems to reveal the intimate nature of structure-function unity, where the regional neural multi-scale self-similar activity is impaired by the anatomical lesion. This picture is coherent with neuronal activity complexity decrease paired to a reduced repertoire of functional abilities. FDasy result highlights the functional relevance of the balance between homologous brain structures’ activities in stroke recovery.

Age-Related Changes in Electroencephalographic Signal Complexity

The study of active and healthy aging is a primary focus for social and neuroscientific communities. Here, we move a step forward in assessing electrophysiological neuronal activity changes in the brain with healthy aging. To this end, electroencephalographic (EEG) resting state activity was acquired in 40 healthy subjects (age 16–85). We evaluated Fractal Dimension (FD) according to the Higuchi algorithm, a measure which quantifies the presence of statistical similarity at different scales in temporal fluctuations of EEG signals. Our results showed that FD increases from age twenty to age fifty and then decreases. The curve that best fits the changes in FD values across age over the whole sample is a parabola, with the vertex located around age fifty. Moreover, FD changes are site specific, with interhemispheric FD asymmetry being pronounced in elderly individuals in the frontal and central regions. The present results indicate that fractal dimension well describes the modulations of brain activity with age. Since fractal dimension has been proposed to be related to the complexity of the signal dynamics, our data demonstrate that the complexity of neuronal electric activity changes across the life span of an individual, with a steady increase during young adulthood and a decrease in the elderly population.

Nonlinear dynamics of EEG-signal reveals influence of magnetic field on the brain

Proposes new methods adapted from nonlinear dynamics, based on pattern entropy and fractal analysis of EEG signals, to assess the influence of magnetic field (used e.g. in physiotherapy) on the human brain.

Complexity of EEG‐signal in Time Domain ‐ Possible Biomedical Application

Human brain is a highly complex nonlinear system. So it is not surprising that in analysis of EEG‐signal, which represents overall activity of the brain, the methods of Nonlinear Dynamics (or Chaos Theory as it is commonly called) can be used. Even if the signal is not chaotic these methods are a motivating tool to explore changes in brain activity due to different functional activation states, e.g. different sleep stages, or to applied therapy, e.g. exposure to chemical agents (drugs) and physical factors (light, magnetic field). The methods supplied by Nonlinear Dynamics reveal signal characteristics that are not revealed by linear methods like FFT. Better understanding of principles that govern dynamics and complexity of EEG‐signal can help to find ‘the signatures’ of different physiological and pathological states of human brain, quantitative characteristics that may find applications in medical diagnostics.

Application of fractal dimension method of functional MRI time-series to limbic dysregulation in anxiety study

Functional magnetic resonance imaging (fMRI) allows to investigate the amplitude of activation in neural networks of brain. In this work we present the results of fMRI time-series analysis performed to identify the process of dysregulation of dynamic interaction between different limbic system regions in healthy adults in state of increased anxiety. The results obtain for 65 healthy adults using nonlinear dynamics methods like fractal dimension confirm the key roles of the bilateral amygdala, bilateral hippocampus, BA9 (dorsolateral prefrontal cortex), and BA45 (ventromedial prefrontal cortex) in modulating emotional response in healthy adults. For different regions of interest (ROIs) significant correlations were found not only for the neutral respective rest but also for fear and angry contrasts.

SEM Image Analysis for Roughness Assessment of Implant Materials

We propose a new very simple method to determine roughness of a surface of an implant material from its scanning electron microscopy (SEM) image. For this purpose we have combined a preprocessing method that has been used in histopathology with fractal method used in nonlinear time series analysis. In the pre-processing step the image is transformed into 1-D signals (‘landscapes’) that are subsequently analyzed. Our method draws from multiple disciplines and may find multidisciplinary applications.

Comparison of connectivity analyses for resting state EEG data

OBJECTIVE In the present work, a nonlinear measure (transfer entropy, TE) was used in a multivariate approach for the analysis of effective connectivity in high density resting state EEG data in eyes open and eyes closed. Advantages of the multivariate approach in comparison to the bivariate one were tested. Moreover, the multivariate TE was compared to an effective linear measure, i.e. directed transfer function (DTF). Finally, the existence of a relationship between the information transfer and the level of brain synchronization as measured by phase synchronization value (PLV) was investigated. APPROACH The comparison between the connectivity measures, i.e. bivariate versus multivariate TE, TE versus DTF, TE versus PLV, was performed by means of statistical analysis of indexes based on graph theory. MAIN RESULTS The multivariate approach is less sensitive to false indirect connections with respect to the bivariate estimates. The multivariate TE differentiated better between eyes closed and eyes open conditions compared to DTF. Moreover, the multivariate TE evidenced non-linear phenomena in information transfer, which are not evidenced by the use of DTF. We also showed that the target of information flow, in particular the frontal region, is an area of greater brain synchronization. SIGNIFICANCE Comparison of different connectivity analysis methods pointed to the advantages of nonlinear methods, and indicated a relationship existing between the flow of information and the level of synchronization of the brain.

Graph-based analysis of brain connectivity in schizophrenia

The present study evaluated brain connectivity using electroencephalography (EEG) data from 14 patients with schizophrenia and 14 healthy controls. Phase-Locking Value (PLV), Phase-Lag Index (PLI) and Directed Transfer Function (DTF) were calculated for the original EEG data and following current source density (CSD) transformation, re-referencing using the average reference electrode (AVERAGE) and reference electrode standardization techniques (REST). The statistical analysis of adjacency matrices was carried out using indices based on graph theory. Both CSD and REST reduced the influence of volume conducted currents. The largest group differences in connectivity were observed for the alpha band. Schizophrenic patients showed reduced connectivity strength, as well as a lower clustering coefficient and shorter characteristic path length for both measures of phase synchronization following CSD transformation or REST re-referencing. Reduced synchronization was accompanied by increased directional flow from the occipital region for the alpha band. Following the REST re-referencing, the sources of alpha activity were located at parietal rather than occipital derivations. The results of PLV and DTF demonstrated group differences in fronto-posterior asymmetry following CSD transformation, while for PLI the differences were significant only using REST. The only analysis that identified group differences in inter-hemispheric asymmetry was DTF calculated for REST. Our results suggest that a comparison of different connectivity measures using graph-based indices for each frequency band, separately, may be a useful tool in the study of disconnectivity disorders such as schizophrenia.

The EEG Split Alpha Peak: Phenomenological Origins and Methodological Aspects of Detection and Evaluation

Electroencephalographic (EEG) patterns were analyzed in a group of ambulatory patients who ranged in age and sex using spectral analysis as well as Directed Transfer Function, a method used to evaluate functional brain connectivity. We tested the impact of window size and choice of reference electrode on the identification of two or more peaks with close frequencies in the spectral power distribution, so called “split alpha.” Together with the connectivity analysis, examination of spatiotemporal maps showing the distribution of amplitudes of EEG patterns allowed for better explanation of the mechanisms underlying the generation of split alpha peaks. It was demonstrated that the split alpha spectrum can be generated by two or more independent and interconnected alpha wave generators located in different regions of the cerebral cortex, but not necessarily in the occipital cortex. We also demonstrated the importance of appropriate reference electrode choice during signal recording. In addition, results obtained using the original data were compared with results obtained using re-referenced data, using average reference electrode and reference electrode standardization techniques.

Classification of EEG bursts in deep sevoflurane, desflurane and isoflurane anesthesia using AR-modeling and entropy measures

A study relating signal patterns of burst onsets in burst suppression EEG to the anesthetic agent or anesthesia induction protocol is presented. A dataset of 82 recordings of sevoflurane, isoflurane and desflurane anesthesia underlies the study. 3 second segments from the onset of altogether 3214 bursts are described using AR model parameters, spectral entropy and sample entropy as features. The features are clustered using the K-means algorithm. The results indicate that no clear cut distinction can be made between the burst patterns induced by the mentioned anesthetics although bursts of certain properties are more common in certain patient groups. Several directions for further investigations are proposed based on visual inspection of the recordings.