Human brain distinctiveness based on EEG spectral coherence connectivity

The use of EEG biometrics, for the purpose of automatic people recognition, has received increasing attention in the recent years. Most of current analysis rely on the extraction of features characterizing the activity of single brain regions, like power-spectrum estimates, thus neglecting possible temporal dependencies between the generated EEG signals. However, important physiological information can be extracted from the way different brain regions are functionally coupled. In this study, we propose a novel approach that fuses spectral coherencebased connectivity between different brain regions as a possibly viable biometric feature. The proposed approach is tested on a large dataset of subjects (N=108) during eyes-closed (EC) and eyes-open (EO) resting state conditions. The obtained recognition performances show that using brain connectivity leads to higher distinctiveness with respect to power-spectrum measurements, in both the experimental conditions. Notably, a 100% recognition accuracy is obtained in EC and EO when integrating functional connectivity between regions in the frontal lobe, while a lower 97.41% is obtained in EC (96.26% in EO) when fusing power spectrum information from centro-parietal regions. Taken together, these results suggest that functional connectivity patterns represent effective features for improving EEG-based biometric systems.Comment: Key words: EEG, Resting state, Biometrics, Spectral coherence, Match score fusio

Emergence and interpretation of oscillatory behaviour similar to brain waves and rhythms

Electroencephalography (EEG) monitors -by either intrusive or noninvasive electrodes-time and frequency variations and spectral content of voltage fluctuations or waves, known as brain rhythms, which in some way uncover activity during both rest periods and specific events in which the subject is under stimulus. This is a useful tool to explore brain behavior, as it complements imaging techniques that have a poorer temporal resolution. We here approach the understanding of EEG data from first principles by numerical simulating and studying a networked model of excitatory and inhibitory neurons which generates a variety of comparable waves. In fact, we thus numerically reproduce oscillatory behavior similar to alpha, beta, gamma and other rhythms as observed by EEG recordings, and identify the details of the respectively involved complex phenomena, including a precise relationship between an input and the collective response to it. It ensues the potentiality of our model to better understand actual brain oscillatory activity in normal and pathological situations, and we also describe kind of stochastic resonance phenomena which could be useful to locate main qualitative changes of brain activity in (e.g.) humans. (C) 2019 Elsevier B.V. All rights reserved.We acknowledge the Spanish Ministry for Science and Technology and the "Agencia Espanola de Investigacion"(AEI) for financial support under grant FIS2017-84256-P (FEDER funds)

Electroencephalography (EEG) as a Research Tool in the Information Systems Discipline: Foundations, Measurement, and Applications

The concept of neuro-information systems (neuroIS) has emerged in the IS discipline recently. Since the neuroIS field’s genesis, several neuroIS papers have been published. Investigating empirical papers published in scientific journals and conference proceedings reveals that electroencephalography (EEG) is a widely used tool. Thus, considering its relevance in contemporary research and the fact that it will also play a major role in future neuroIS research, we describe EEG from a layman’s perspective. Because previous EEG descriptions in the neuroIS literature have only scantily outlined theoretical and methodological aspects related to this tool, we urgently need a more thorough one. As such, we inform IS scholars about the fundamentals of EEG in a compact way and discuss EEG’s potential for IS research. Based on the knowledge base provided in this paper, IS researchers can make an informed decision about whether EEG could, or should, become part of their toolbox

사람에서 점멸광자극을 이용한 성공적인 감마뇌파동조 유도의 결정 요인

학위논문(박사) -- 서울대학교대학원 : 자연과학대학 뇌인지과학과, 2023. 2. 김기웅.Background and Objectives: Although gamma entrainment using flickering light stimulus (FLS) of 40Hz was effective in reducing pathologies and enhancing cognitive function in mouse models of Alzheimers disease (AD), its efficacy was controversial in AD patients. The conflicting results in AD patients may be attributable to a couple of key factors. First, the optimal parameters of FLS for gamma entrainment may be different between diurnal humans and nocturnal mice. Second, the response to optimal FLS may be different between AD patients due to inter-individual difference in the microstructural integrity of white matter (WM) tracts. This study aimed to find the optimal parameters (color, luminal intensity and flickering frequency) of FLS for entraining gamma rhythms in diurnal humans and to examine the effect of fractional anisotropy (FA) of WM tracts on the entrainment and propagation of gamma rhythms. Methods: We first investigated the optimal color (white, red, green, and blue), luminal intensity (10 cd/m2, 100 cd/m2, 400 cd/m2, and 700 cd/m2), and frequency (32 - 50 Hz) of FLS for entraining gamma rhythms in visual cortex using event-related desynchronization/event-related synchronization (ERD/ERS) and for propagating gamma rhythm entrained in visual cortex to other brain regions using spectral Granger Causality (sGC) in 16 cognitively normal young adults (24.0 ± 3.7 yrs) and 35 cognitively normal older adults (70.0 ± 2.4 yrs). We also examined the adverse effects of FLS in both younger and older adults. Then we examined the effect of the FA of posterior thalamic radiations on the ERS of gamma rhythms entrained in visual cortex and that of and middle and superior longitudinal fasciculi on the sGC of the connectivity from visual cortex to temporal and frontal regions in 26 cognitively normal older adults using analysis of variance and linear regression analyses. Results: The FLSs using the lights of longer wavelengths such as white (p < 0.05) and red (p < 0.01) entrained and propagated gamma rhythms better than those of shorter wavelengths such as green and blue. The FLSs using stronger lights such as 700 cd/m2 (p < 0.001) and 400 cd/m2 (p < 0.01) entrained and propagated gamma rhythms better than weaker lights of 100 cd/m2 and 10 cd/m2. The FLSs flickering at 34-38 Hz were best for entraining and propagating gamma rhythm in younger adults (entrainment at Pz: p < 0.05, propagation: p < 0.05) while those flickering at 32-34 Hz were best for older adults (entrainment at Pz: p < 0.05, propagation: p < 0.001). In older adults, white FLSs of 700 cd/m2 flickering at 32–34 Hz entrained the gamma rhythms most strongly at visual cortex (p < 0.05) and propagated them most widely to other brain regions (p < 0.05). The FLSs of 700 cd/m2 flickering at 32 Hz entrained gamma rhythms worse in the visual cortex of the older adults whose FA of left posterior thalamic radiation was low than in those whose FA of left posterior thalamic radiation was not low (p 0.05), and their severity of adverse effects was milder than that in younger adults. Conclusion: In diurnal human, optimal flickering frequency for gamma entrainment was about 20% lower than that in nocturnal mice. Although the FLSs of stronger luminal intensity and the longer wavelength may entrain gamma rhythms better, they may result in more and severe adverse effects. In older adults, white or red FLSs of 700 cd/m2 flickering at 32-34 Hz may be optimal for entraining and propagating gamma rhythms. Since gamma rhythms were not properly entrained by optimal FLS in the older adults whose microstructural integrity of the white matter tracts was impaired, the integrity of the white matter tracts involved in the entrainment and propagation of gamma rhythm should be measured and considered in determining the indication of gamma entrainment using visual stimulation.연구배경 및 목적: 40Hz 점멸광자극 (flickering light stimulation, FLS)을 사용한 감마뇌파동조는 알츠하이머병 (Alzheimers disease, AD) 모델 쥐에서 병리를 감소시키고 인지 기능을 향상시키는 데 효과적이었지만 알츠하이머병 환자에서는 그 효능에 대해 논란이 있다. 알츠하이머병 환자의 상충되는 결과는 몇 가지 주요 요인에 기인할 수 있다. 첫째, 감마뇌파동조를 위한 FLS의 최적 매개변수는 일주 동물인 인간과 야행성 동물인 쥐 간에 다를 수 있다. 둘째, 최적의 FLS에 대한 반응은 백질 (white matter, WM) 섬유 다발 미세 구조적 무결성의 개인 간 차이로 인해 알츠하이머병 환자 간에 다를 수 있다. 이 연구는 일주 동물인 인간에서 감마뇌파를 동반하기 위한 FLS의 최적 매개변수 (색상, 밝기 및 점멸 주파수)를 찾고 감마뇌파의 동반 및 전파에 대한 백질 섬유 다발의 확산비등방성 (fractional anisotropy, FA)의 영향을 조사하는 것을 목표로 했다. 연구방법: 인지기능이 정상인 젊은 성인 16명과 노인 35명을 대상으로, 시각피질에 감마뇌파동조를 유도하고, 동조 된 시각피질의 감마뇌파를 다른 뇌 영역으로의 전파시킬 수 있는 FLS의 최적 색상 (백색, 적색, 녹색 및 청색), 밝기 (10 cd/m2, 100 cd/m2, 400 cd/m2 및 700 cd/m2) 및 점멸 주파수 (32-50 Hz)를 사건 관련 비 동기화/사건 관련 동기화 (event-related desynchronization/event-related synchronization, ERD/ERS)와 스펙트럼 그랜저 인과성 (spectral Granger Causality, sGC) 분석을 이용하여 조사했다. 아울러 젊은 성인과 노인에서 FLS의 부작용을 조사했다. 이어서 감마뇌파가 FLS에 의해 시각피질에 적절하게 동조 된 인지기능이 정상인 노인 26명을 대상으로, 시각피질에서 동조 된 감마뇌파의 ERS와 시각피질과 측두 및 전두 영역들 간 연결성인 sGC에 후방시상방사와 중간 및 상부 세로다발들의 확산비등방성이 미치는 영향을 회귀분석과 분산분석을 이용하여 조사했다. 연구결과: 사람에서는 백색 (p < 0.05) 및 적색 (p < 0.01)과 같은 장파장 FLS가 녹색 및 청색과 같은 단파장 FLS보다 감마뇌파동조를 더 강하게 유발하고, 동조 된 감마뇌파를 더 넓은 뇌 영역으로 전파시켰다. 또 700 cd/m2 (p < 0.001) 및 400 cd/m2 (p < 0.01)와 같은 강한 휘도 FLS는 100 cd/m2 및 10 cd/m2와 같은 약한 휘도 FLS보다 감마뇌파동조를 더 강하게 유발하고, 동조 된 감마뇌파를 더 넓은 뇌 영역으로 전파시켰다. 34-38 Hz에서 점멸하는 FLS는 젊은 성인에서 감마뇌파를 동반하고 전파하는 데 가장 효과적이었고 (Pz에서 동반: p < 0.05, 전파: p < 0.05) 32-34 Hz에서 점멸하는 FLS는 노인에게 가장 효과적이었다 (Pz에서 동반: p < 0.05, 전파: p < 0.001). 노인에서 32-34 Hz에서 점멸하는 700 cd/m2의 백색 FLS는 시각 피질에서 가장 강하게 감마뇌파를 동반하고 (p < 0.05) 다른 뇌 영역으로 가장 널리 전파했다 (p < 0.05). 32 Hz에서 점멸하는 700 cd/m2의 FLS는 좌후시상방사선의 FA가 낮지 않은 노인보다 낮은 노인에서 감마뇌파가 시각피질에 덜 동반된다 (p 0.05), 부작용의 심각성은 젊은 성인보다 경미했다. 결론: 주행성인 인간에서 감마 동조를 위한 최적의 점멸 주파수는 야행성 쥐보다 약 20% 낮았다. 더 강한 휘도와 더 긴 파장의 FLS가 감마뇌파를 더 잘 동조 시킬 수 있지만 더 크고 심각한 부작용을 초래할 수 있다. 노인의 경우 32-34 Hz에서 점멸하는 700 cd/m2의 백색 또는 적색 FLS가 감마뇌파를 동조하고 전파하는 데 최적일 수 있다. 감마뇌파는 백질 섬유 다발의 미세 구조적 무결성이 손상된 노인에서는 최적의 FLS에 의해 적절하게 동조 되지 않았기 때문에, 감마뇌파의 동조 및 전파와 관련된 백질 영역의 무결성은 시각적 자극을 사용하여 감마뇌파동조 적용을 결정할 때 측정되고 고려되어야 한다.1. Introduction 1 1.1. Background 1 1.2. Purpose 4 2. Methods 6 2.1. Study design 6 2.1.1. Study 1. Investigation on the optimal parameters of FLS for gamma entrainment in humans 6 2.1.2. Study 2. Investigation on the effect of WM microstructural integrity on the gamma entrainment by FLS in humans 7 2.2. Participants 7 2.2.1. Study 1. Investigation on the optimal parameters of FLS for gamma entrainment in humans 7 2.2.2. Study 2. Investigation on the effect of WM microstructural integrity on the gamma entrainment by FLS in humans 8 2.2.3. Clinical evaluation of the participants 8 2.3. Research ethics 9 2.4. FLS 9 2.5. Recording, preprocessing and analysis of EEG 10 2.6. Acquisition, preprocessing and analysis of DTI 13 2.7. Statistical analyses 14 3. Results 16 3.1. Effects of the rsEEG spectral band power on cognitive function 16 3.2. Entrainment and propagation of the gamma rhythms by FLS 16 3.3. Effects of the FLS color on gamma entrainment and propagation 17 3.4. Effects of the FLS intensity on gamma entrainment and propagation 18 3.5. Effects of the FLS frequency on gamma entrainment and propagation 18 3.6. Effects of the microstructural integrity of WM tracts on the gamma entrainment and propagation 20 3.7. Adverse effects 21 4. Discussions 23 5. Conclusions 35 Bibliography 66 국문초록 81박

Neural synchrony within the motor system: what have we learned so far?

Synchronization of neural activity is considered essential for information processing in the nervous system. Both local and inter-regional synchronization are omnipresent in different frequency regimes and relate to a variety of behavioral and cognitive functions. Over the years, many studies have sought to elucidate the question how alpha/mu, beta, and gamma synchronization contribute to motor control. Here, we review these studies with the purpose to delineate what they have added to our understanding of the neural control of movement. We highlight important findings regarding oscillations in primary motor cortex, synchronization between cortex and spinal cord, synchronization between cortical regions, as well as abnormal synchronization patterns in a selection of motor dysfunctions. The interpretation of synchronization patterns benefits from combining results of invasive and non-invasive recordings, different data analysis tools, and modeling work. Importantly, although synchronization is deemed to play a vital role, it is not the only mechanism for neural communication. Spike timing and rate coding act together during motor control and should therefore both be accounted for when interpreting movement-related activity

Dynamic BOLD functional connectivity in humans and its electrophysiological correlates

Neural oscillations subserve many human perceptual and cognitive operations. Accordingly, brain functional connectivity is not static in time, but fluctuates dynamically following the synchronization and desynchronization of neural populations. This dynamic functional connectivity has recently been demonstrated in spontaneous fluctuations of the Blood Oxygen Level-Dependent (BOLD) signal, measured with functional Magnetic Resonance Imaging (fMRI). We analyzed temporal fluctuations in BOLD connectivity and their electrophysiological correlates, by means of long (≈50 min) joint electroencephalographic (EEG) and fMRI recordings obtained from two populations: 15 awake subjects and 13 subjects undergoing vigilance transitions. We identified positive and negative correlations between EEG spectral power (extracted from electrodes covering different scalp regions) and fMRI BOLD connectivity in a network of 90 cortical and subcortical regions (with millimeter spatial resolution). In particular, increased alpha (8-12 Hz) and beta (15-30 Hz) power were related to decreased functional connectivity, whereas gamma (30-60 Hz) power correlated positively with BOLD connectivity between specific brain regions. These patterns were altered for subjects undergoing vigilance changes, with slower oscillations being correlated with functional connectivity increases. Dynamic BOLD functional connectivity was reflected in the fluctuations of graph theoretical indices of network structure, with changes in frontal and central alpha power correlating with average path length. Our results strongly suggest that fluctuations of BOLD functional connectivity have a neurophysiological origin. Positive correlations with gamma can be interpreted as facilitating increased BOLD connectivity needed to integrate brain regions for cognitive performance. Negative correlations with alpha suggest a temporary functional weakening of local and long-range connectivity, associated with an idling state

State-Dependent Cortical Network Dynamics

Neuropsychiatric illness represents a major health burden in the United States with a paucity of effective treatment. Many neuropsychiatric illnesses are network disorders, exhibiting aberrant organization of coordinated activity within and between brain areas. Cortical oscillations, arising from the synchronized activity of groups of neurons, are important in mediating both local and long-range communication in the brain and are particularly affected in neuropsychiatric diseases. A promising treatment approach for such network disorders entails ‘correcting’ abnormal oscillatory activity through non-invasive brain stimulation. However, we lack a clear understanding of the functional role of oscillatory activity in both health and disease. Thus, basic science and translational work is needed to elucidate the role of oscillatory activity and other network dynamics in neuronal processing and behavior. Organized activity in the brain occurs at many spatial and temporal scales, ranging from the millisecond duration of individual action potentials to the daily circadian rhythm. The studies comprising this dissertation focused on organization in cortex at the time scale of milliseconds, assessing local field potential and spiking activity, and contribute to understanding (1) the effects of non-invasive brain stimulation on behavioral responses, (2) network dynamics within and across cortical areas during different states, and (3) how oscillatory activity organizes spiking activity locally and long-range during sustained attention. Taken together, this work provides insight into the physiological organization of network dynamics and can provide the basis for future rational design of non-invasive brain stimulation treatments.Doctor of Philosoph

A New Theory of Consciousness: The Missing Link - Organization

What is consciousness and what is the missing link between the sensory input and the cortical centre in the brain for consciousness? In the literature there are more than a million pages written about consciousness. The perspectives range from the field of metaphysics to those of quantum mechanics. However, no one today has produced a theory which is universally accepted. Consciousness is “something” which the majority of humans know that they posses, they use it when they want to understand their environment. However, no individual human knows whether other humans also posses consciousness. unless some tests such as she is looking at me, he is talking etc., are performed. We are caught in an intellectual sort of recursive carousel – we need consciousness to understand consciousness. To understand consciousness we have to understand the mechanism of its function, which is to effectively organize sensory inputs from our environment. Consciousness is the outcome of the process of organizing these sensory inputs. This implies that organization is an act which precedes consciousness. Since every activity in nature is to organize/disorganize, what is the element which compels this action? I am proposing that just like energy is the physical element that causes action, there is another physical element I have called it NASCIUM which has the capacity to cause organization. This is the missing link. Understanding the nature of organization, i.e. nascium, will enhance our capability to understand consciousness

Independent Component Analysis of Event-Related Electroencephalography During Speech and Non-Speech Discrimination: : Implications for the Sensorimotor ∆∞ Rhythm in Speech Processing

Background: The functional significance of sensorimotor integration in acoustic speech processing is unclear despite more than three decades of neuroimaging research. Constructivist theories have long speculated that listeners make predictions about articulatory goals functioning to weight sensory analysis toward expected acoustic features (e.g. analysis-by-synthesis; internal models). Direct-realist accounts posit that sensorimotor integration is achieved via a direct match between incoming acoustic cues and articulatory gestures. A method capable of favoring one account over the other requires an ongoing, high-temporal resolution measure of sensorimotor cortical activity prior to and following acoustic input. Although scalp-recorded electroencephalography (EEG) provides a measure of cortical activity on a millisecond time scale, it has low-spatial resolution due to the blurring or mixing of cortical signals on the scalp surface. Recently proposed solutions to the low-spatial resolution of EEG known as blind source separation algorithms (BSS) have made the identification of distinct cortical signals possible. The µ rhythm of the EEG is known to briefly suppress (i.e., decrease in spectral power) over the sensorimotor cortex during the performance, imagination, and observation of biological movements, suggesting that it may provide a sensitive index of sensorimotor integration during speech processing. Neuroimaging studies have traditionally investigated speech perception in two-forced choice designs in which participants discriminate between pairs of speech and nonspeech control stimuli. As such, this classical design was employed in the current dissertation work to address the following specific aims to: 1) isolate independent components with traditional EEG signatures within the dorsal sensorimotor stream network; 2) identify components with features of the sensorimotor µ rhythm and; 3) investigate changes in timefrequency activation of the µ rhythm relative to stimulus type, onset, and discriminability (i.e., perceptual performance). In light of constructivist predictions, it was hypothesized that the µ rhythm would show significant suppression for syllable stimuli prior to and following stimulus onset, with significant differences between correct discrimination trials and those discriminated at chance levels. Methods: The current study employed millisecond temporal resolution EEG to measure ongoing decreases and increases in spectral power (event-related spectral perturbations; ERSPs) prior to, during, and after the onset of acoustic speech and tone-sweep stimuli embedded in white-noise. Sixteen participants were asked to passively listen to or actively identify speech and tone signals in a two-force choice same/different discrimination task. To investigate the role of ERSPs in perceptual identification performance, high signal-to-noise ratios (SNRs) in which speech and tone identification was significantly better than chance (+4dB) and low SNRs in which performance was below chance (-6dB and -18dB) were compared to a baseline of passive noise. Independent component analysis (ICA) of the EEG was used to reduce artifact and source mixing due to volume conduction. Independent components were clustered using measure product methods and cortical source modeling, including spectra, scalp distribution, equivalent current dipole estimation (ECD), and standardized low-resolution tomography (sLORETA). Results: Data analysis revealed six component clusters consistent with a bilateral dorsal-stream sensorimotor network, including component clusters localized to the precentral and postcentral gyrus, cingulate cortex, supplemental motor area, and posterior temporal regions. Timefrequency analysis of the left and right lateralized µ component clusters revealed significant (pFDR\u3c.05) suppression in the traditional beta frequency range (13-30Hz) prior to, during, and following stimulus onset. No significant differences from baseline were found for passive listening conditions. Tone discrimination was different from passive noise in the time period following stimulus onset only. No significant differences were found for correct relative to chance tone stimuli. For both left and right lateralized clusters, early suppression (i.e., prior to stimulus onset) compared to the passive noise baseline was found for the syllable discrimination task only. Significant differences between correct trials and trials identified at chance level were found for the time period following stimulus offset for the syllable discrimination task in left lateralized cluster. Conclusions: As this is the first study to employ BSS methods to isolate components of the EEG during acoustic speech and non-speech discrimination, findings have important implications for the functional role of sensorimotor integration in speech processing. Consistent with expectations, the current study revealed component clusters associated with source models within the sensorimotor dorsal stream network. Beta suppression of the µ component clusters in both the left and right hemispheres is consistent with activity in the precentral gyrus prior to and following acoustic input. As early suppression of the µ was found prior the syllable discrimination task, the present findings favor internal model concepts of speech processing over mechanisms proposed by direct-realists. Significant differences between correct and chance syllable discrimination trials are also consistent with internal model concepts suggesting that sensorimotor integration is related to perceptual performance at the point in time when initial articulatory hypotheses are compared with acoustic input. The relatively inexpensive, noninvasive EEG methodology used in this study may have translational value in the future as a brain computer interface (BCI) approach. As deficits in sensorimotor integration are thought to underlie cognitive-communication impairments in a number of communication disorders, the development of neuromodulatory feedback approaches may provide a novel avenue for augmenting current therapeutic protocols