Automated Quantification of Human Alpha Rhythm

This thesis seeks to quantify human alpha rhythm in order to obtain better measures to test theoretical models of neurodynamics, with potential clinical applications for the method of identification. An automated algorithm is developed in Chapter 2 to facilitate collection of objective data from an expanded alpha band (4–14 Hz) in a large number of subjects. This method avoids subjective bias inherent to traditional visual identification of the alpha activity, produced multiple peak information (if multiple peaks exist) that is absent in qEEG measures, and uses information from multiple electrode sites to eliminate spurious peaks. This method is tested and validated on 100 subjects. In addition to traditional measures of alpha activities such as the frequency and amplitude, further measures were devised to better quantify the alpha rhythm and its spatial characteristics. Background spectra in the alpha range are also characterized. In Chapter 3 the algorithm is applied to a large (1498 subjects) database of normal healthy subjects of approximately equal number in each sex, as well as a large span in age (6–86 years), in order to establish typical parameter ranges. Analysis is done comparing the age and the topological trends that are known variants in the alpha rhythm. Investigations are also performed to test for potential sex differences and/or lateralities. Key results are that double alpha peaks are resolved in a large proportion of the subjects ( 50%), while single alpha peak cases are likely to be double-peak cases in which the two peaks are not resolved. Age trends in measures of alpha activity show increase of alpha frequency from childhood to adolescence, a plateau in adulthood, and a slight decline in old age; a decrease in alpha amplitude in old age is also observed. These findings are consistent with previous literature and provide better statistics. Topological distribution of the alpha activity on the head is also explored, with little lateral asymmetry observed. No statistically significant differences between the sexes are found. The C++ code that was developed and utilized in this thesis is included in Appendix B. It is provided on disk and is available online. A study carried out on the same group of subjects to determine age-related trends of EEG parameters produced by model fitting is included in Appendixes C, to provide a comparison between the methods and highlights corresponding results

Integrated Central-Autonomic Multifractal Complexity in the Heart Rate Variability of Healthy Humans

Purpose of Study: The aim of this study was to characterize the central-autonomic interaction underlying the multifractality in heart rate variability (HRV) of healthy humans. Materials and Methods: Eleven young healthy subjects participated in two separate ~40 min experimental sessions, one in supine (SUP) and one in, head-up-tilt (HUT), upright (UPR) body positions. Surface scalp electroencephalography (EEG) and electrocardiogram (ECG) were collected and fractal correlation of brain and heart rate data was analyzed based on the idea of relative multifractality. The fractal correlation was further examined with the EEG, HRV spectral measures using linear regression of two variables and principal component analysis (PCA) to find clues for the physiological processing underlying the central influence in fractal HRV. Results: We report evidence of a central-autonomic fractal correlation (CAFC) where the HRV multifractal complexity varies significantly with the fractal correlation between the heart rate and brain data (P = 0.003). The linear regression shows significant correlation between CAFC measure and EEG Beta band spectral component (P = 0.01 for SUP and P = 0.002 for UPR positions). There is significant correlation between CAFC measure and HRV LF component in the SUP position (P = 0.04), whereas the correlation with the HRV HF component approaches significance (P = 0.07). The correlation between CAFC measure and HRV spectral measures in the UPR position is weak. The PCA results confirm these findings and further imply multiple physiological processes underlying CAFC, highlighting the importance of the EEG Alpha, Beta band, and the HRV LF, HF spectral measures in the supine position. Discussion and Conclusion: The findings of this work can be summarized into three points: (i) Similar fractal characteristics exist in the brain and heart rate fluctuation and the change toward stronger fractal correlation implies the change toward more complex HRV multifractality. (ii) CAFC is likely contributed by multiple physiological mechanisms, with its central elements mainly derived from the EEG Alpha, Beta band dynamics. (iii) The CAFC in SUP and UPR positions is qualitatively different, with a more predominant central influence in the fractal HRV of the UPR position

Circadian Rhythms in Fractal Features of EEG Signals

Time-of-day modulations affect both performance on a wide range of cognitive tasks and electrical activity of the brain, as recorded by electroencephalography (EEG). The aim of this work was to identify fluctuations of fractal properties of EEG time series due to circadian rhythms. In twenty-one healthy volunteers (all males, age between 20 and 30 years, chronotype: neutral type) high density EEG recordings at rest in open and closed eyes conditions were acquired in 4 times of the day (8.00 a.m., 11.30 a.m., 2.30 p.m., 7.00 p.m.). A vigilance task (Psychomotor Vigilance Test, PVT) was also performed. Detrended fluctuation Analysis (DFA) of envelope of alpha, beta and theta rhythms was performed, as well as Highuchi fractal dimension (HFD) of the whole band EEG. Our results evidenced circadian fluctuations of fractal features of EEG at rest in both eyes closed and eyes open conditions. Lower values of DFA exponent were found in the time T1 in closed eyes condition, likely effect of the sleep inertia. An alpha DFA exponent reduction was found also in central sensory-motor areas at time T3, the day time in which the sleepiness can be present. In eyes open condition, HFD lowered during the day. In eyes closed condition, an HFD increase was observed in central and frontal regions at time T2, the time in which alertness reaches its maximum and homeostatic sleep pressure is low. Complexity and the persistence of temporal correlations of brain rhythms change during daytime, parallel to changes in alertness and performance

The effect of ageing on fMRI: Correction for the confounding effects of vascular reactivity evaluated by joint fMRI and MEG in 335 adults.

In functional magnetic resonance imaging (fMRI) research one is typically interested in neural activity. However, the blood-oxygenation level-dependent (BOLD) signal is a composite of both neural and vascular activity. As factors such as age or medication may alter vascular function, it is essential to account for changes in neurovascular coupling when investigating neurocognitive functioning with fMRI. The resting-state fluctuation amplitude (RSFA) in the fMRI signal (rsfMRI) has been proposed as an index of vascular reactivity. The RSFA compares favourably with other techniques such as breath-hold and hypercapnia, but the latter are more difficult to perform in some populations, such as older adults. The RSFA is therefore a candidate for use in adjusting for age-related changes in vascular reactivity in fMRI studies. The use of RSFA is predicated on its sensitivity to vascular rather than neural factors; however, the extent to which each of these factors contributes to RSFA remains to be characterized. The present work addressed these issues by comparing RSFA (i.e., rsfMRI variability) to proxy measures of (i) cardiovascular function in terms of heart rate (HR) and heart rate variability (HRV) and (ii) neural activity in terms of resting state magnetoencephalography (rsMEG). We derived summary scores of RSFA, a sensorimotor task BOLD activation, cardiovascular function and rsMEG variability for 335 healthy older adults in the population-based Cambridge Centre for Ageing and Neuroscience cohort (Cam-CAN; www.cam-can.com). Mediation analysis revealed that the effects of ageing on RSFA were significantly mediated by vascular factors, but importantly not by the variability in neuronal activity. Furthermore, the converse effects of ageing on the rsMEG variability were not mediated by vascular factors. We then examined the effect of RSFA scaling of task-based BOLD in the sensorimotor task. The scaling analysis revealed that much of the effects of age on task-based activation studies with fMRI do not survive correction for changes in vascular reactivity, and are likely to have been overestimated in previous fMRI studies of ageing. The results from the mediation analysis demonstrate that RSFA is modulated by measures of vascular function and is not driven solely by changes in the variance of neural activity. Based on these findings we propose that the RSFA scaling method is articularly useful in large scale and longitudinal neuroimaging studies of ageing, or with frail participants, where alternative measures of vascular reactivity are impractical.The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) research was supported by the Biotechnology and Biological Sciences Research Council (grant number BB/H008217/1). We are grateful to the Cam-CAN respondents and their primary care teams in Cambridge for their participation in this study. We also thank col- leagues at the MRC Cognition and Brain Sciences Unit MEG and MRI facilities for their assistance.This is the final version of the article. It first appeared at http://onlinelibrary.wiley.com/doi/10.1002/hbm.22768/ful

Unveiling age-independent spectral markers of propofol-induced loss of consciousness by decomposing the electroencephalographic spectrum into its periodic and aperiodic components

Background: Induction of general anesthesia with propofol induces radical changes in cortical network organization, leading to unconsciousness. While perioperative frontal electroencephalography (EEG) has been widely implemented in the past decades, validated and age-independent EEG markers for the timepoint of loss of consciousness (LOC) are lacking. Especially the appearance of spatially coherent frontal alpha oscillations (8-12 Hz) marks the transition to unconsciousness.Here we explored whether decomposing the EEG spectrum into its periodic and aperiodic components unveiled markers of LOC and investigated their age-dependency. We further characterized the LOC-associated alpha oscillations by parametrizing the adjusted power over the aperiodic component, the center frequency, and the bandwidth of the peak in the alpha range. Methods: In this prospective observational trial, EEG were recorded in a young (18-30 years) and an elderly age-cohort (>= 70 years) over the transition to propofol-induced unconsciousness. An event marker was set in the EEG recordings at the timepoint of LOC, defined with the suppression of the lid closure reflex. Spectral analysis was conducted with the multitaper method. Aperiodic and periodic components were parametrized with the FOOOF toolbox. Aperiodic parametrization comprised the exponent and the offset. The periodic parametrization consisted in the characterization of the peak in the alpha range with its adjusted power, center frequency and bandwidth. Three time-segments were defined: preLOC (105 - 75 s before LOC), LOC (15 s before to 15 s after LOC), postLOC (190 - 220 s after LOC). Statistical significance was determined with a repeated-measures ANOVA. Results: Loss of consciousness was associated with an increase in the aperiodic exponent (young: p = 0.004, elderly: p = 0.007) and offset (young: p = 0.020, elderly: p = 0.004) as well as an increase in the adjusted power (young: p < 0.001, elderly p = 0.011) and center frequency (young: p = 0.008, elderly: p < 0.001) of the periodic alpha peak. We saw age-related differences in the aperiodic exponent and offset after LOC as well as in the power and bandwidth of the periodic alpha peak during LOC. Conclusion: Decomposing the EEG spectrum over induction of anesthesia into its periodic and aperiodic components unveiled novel age-independent EEG markers of propofol-induced LOC: the aperiodic exponent and offset as well as the center frequency and adjusted power of the power peak in the alpha range

Fractal analysis of the EEG and clinical applications

2010/2011Most of the knowledge about physiological systems has been learned using linear system theory. The randomness of many biomedical signals has been traditionally ascribed to a noise-like behavior. An alternative explanation for the irregular behavior observed in systems which do not seem to be inherently stochastic is provided by one of the most striking mathematical developments of the past few decades, i.e., chaos theory. Chaos theory suggests that random-like behavior can arise in some deterministic nonlinear systems with just a few degrees of freedom. One of the most evocative aspects of deterministic chaos is the concept of fractal geometry. Fractal structure, characterized by self-similarity and noninteger dimension, is displayed in chaotic systems by a subset of the phase space known as strange attractor. However, fractal properties are observed also in the unpredictable time evolution and in the 1/f^β power-law of many biomedical signals. The research activities carried out by the Author during the PhD program are concerned with the analysis of the fractal-like behavior of the EEG. The focus was set on those methods which evaluate the fractal geometry of the EEG in the time domain, in the hope of providing physicians and researchers with new valuable tools of low computational cost for the EEG analysis. The performances of three widely used techniques for the direct estimation of the fractal dimension of the EEG were compared and the accuracy of the fBm scaling relationship, often used to obtain indirect estimates from the slope of the spectral density, was assessed. Direct estimation with Higuchi's algorithm turned out to be the most suitable methodology, producing correct estimates of the fractal dimension of the electroencephalogram also on short traces, provided that minimum sampling rate required to avoid aliasing is used. Based on this result, Higuchi's fractal dimension was used to address three clinical issues which could involve abnormal complexity of neuronal brain activity: 1) the monitoring of carotid endarterectomy for the prevention of intraoperative stroke, 2) the assessment of the depth of anesthesia to monitor unconsciousness during surgery and 3) the analysis of the macro-structural organization of the EEG in autism with respect to mental retardation. The results of the clinical studies suggest that, although linear spectral analysis still represents a valuable tool for the investigation of the EEG, time domain fractal analysis provides additional information on brain functioning which traditional analysis cannot achieve, making use of techniques of low computational cost.La maggior parte delle conoscenze acquisite sui sistemi fisiologici si deve alla teoria dei sistemi lineari. Il comportamento pseudo stocastico di molti segnali biomedici è stato tradizionalmente attribuito al concetto di rumore. Un'interpretazione alternativa del comportamento irregolare rilevato in sistemi che non sembrano essere intrinsecamente stocastici è fornita da uno dei più sorprendenti sviluppi matematici degli ultimi decenni: la teoria del caos. Tale teoria suggerisce che una certa componente casuale può sorgere in alcuni sistemi deterministici non lineari con pochi gradi di libertà. Uno degli aspetti più suggestivi del caos deterministico è il concetto di geometria frattale. Strutture frattali, caratterizzate da auto-somiglianza e dimensione non intera, sono rilevate nei sistemi caotici in un sottoinsieme dello spazio delle fasi noto con il nome di attrattore strano. Tuttavia, caratteristiche frattali possono manifestarsi anche nella non prevedibile evoluzione temporale e nella legge di potenza 1/f^β tipiche di molti segnali biomedici. Le attività di ricerca svolte dall'Autore nel corso del dottorato hanno riguardato l'analisi del comportamento frattale dell'EEG. L'attenzione è stata rivolta a quei metodi che affrontano lo studio della geometria frattale dell'EEG nel dominio del tempo, nella speranza di fornire a medici e ricercatori nuovi strumenti utili all'analisi del segnale EEG e caratterizzati da bassa complessità computazionale. Sono state messe a confronto le prestazioni di tre tecniche largamente utilizzate per la stima diretta della dimensione frattale dell'EEG e si è valutata l'accuratezza della relazione di scaling del modello fBm, spesso utilizzata per ottenere stime indirette a partire dalla pendenza della densità spettrale di potenza. Il metodo più adatto alla stima della dimensione frattale dell'elettroencefalogramma è risultato essere l'algoritmo di Higuchi, che produce stime accurate anche su segmenti di breve durata a patto che il segnale sia campionato alla minima frequenza di campionamento necessaria ad evitare il fenomeno dell'aliasing. Sulla base di questo risultato, la dimensione frattale di Higuchi è stata utilizzata per esaminare tre questioni cliniche che potrebbero coinvolgere una variazione della complessità dell'attività neuronale: 1) il monitoraggio dell'endoarterectomia carotidea per la prevenzione dell'ictus intraoperatorio, 2) la valutazione della profondità dell'anestesia per monitorare il livello di incoscienza durante l'intervento chirurgico e 3) l'analisi dell'organizzazione macro-strutturale del EEG nell'autismo rispetto alla condizione di ritardo mentale. I risultati degli studi clinici suggeriscono che, sebbene l'analisi spettrale rappresenti ancora uno strumento prezioso per l'indagine dell'EEG, l'analisi frattale nel dominio del tempo fornisce informazioni aggiuntive sul funzionamento del cervello che l'analisi tradizionale non è in grado di rilevare, con il vantaggio di impiegare tecniche a basso costo computazionale.XXIV Ciclo198

EEG resting asymmetries and frequency oscillations in approach/avoidance personality traits: a systematic review

Background: Brain cortical activity in resting electroencephalogram (EEG) recordings can be considered as measures of latent individual disposition to approach/avoidance behavior. This systematic review aims to provide an updated overview of the relationship between resting EEG cortical activity and approach/avoidance motivation personality traits. Methods: The review process was conducted according to the PRISMA-Statement, using PsycArticles, MEDLINE, Scopus, Science Citation Index, and Research Gate database. Restrictions were made by selecting EEG studies conducted in resting idling conditions, which included approach/avoidance personality traits or parallel measures, and an index of EEG brain activity. In the review 50 studies were selected, wherein 7120 healthy adult individuals participated. Results: The study of the relationship between resting EEG cortical activity and approach/avoidance personality traits provides controversial and unclear results. Therefore, the validity of resting asymmetry or frequency oscillations as a potential marker for approach/avoidance personality traits is not supported. Conclusions: There are important contextual and interactional factors not taken into account by researchers that could mediate or moderate this relationship or prove it scarcely replicable. Further, it would be necessary to conduct more sessions of EEG recordings in different seasons of the year to test the validity and the reliability of the neurobiological measures