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

Neurotechnologies for Human Cognitive Augmentation: Current State of the Art and Future Prospects

Recent advances in neuroscience have paved the way to innovative applications that cognitively augment and enhance humans in a variety of contexts. This paper aims at providing a snapshot of the current state of the art and a motivated forecast of the most likely developments in the next two decades. Firstly, we survey the main neuroscience technologies for both observing and influencing brain activity, which are necessary ingredients for human cognitive augmentation. We also compare and contrast such technologies, as their individual characteristics (e.g., spatio-temporal resolution, invasiveness, portability, energy requirements, and cost) influence their current and future role in human cognitive augmentation. Secondly, we chart the state of the art on neurotechnologies for human cognitive augmentation, keeping an eye both on the applications that already exist and those that are emerging or are likely to emerge in the next two decades. Particularly, we consider applications in the areas of communication, cognitive enhancement, memory, attention monitoring/enhancement, situation awareness and complex problem solving, and we look at what fraction of the population might benefit from such technologies and at the demands they impose in terms of user training. Thirdly, we briefly review the ethical issues associated with current neuroscience technologies. These are important because they may differentially influence both present and future research on (and adoption of) neurotechnologies for human cognitive augmentation: an inferior technology with no significant ethical issues may thrive while a superior technology causing widespread ethical concerns may end up being outlawed. Finally, based on the lessons learned in our analysis, using past trends and considering other related forecasts, we attempt to forecast the most likely future developments of neuroscience technology for human cognitive augmentation and provide informed recommendations for promising future research and exploitation avenues

Control of transcranial brain stimulation by a brain-computer interface based loop

Tese de mestrado integrado em Engenharia Biomédica e Biofísica, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2014A Estimulação Transcraniana com Corrente Alternada (tACS) é uma forma de estimulação que permite a influência direta das oscilações cerebrais. Entre outros efeitos, foi demonstrado que quando a aplicação de tACS se inicia em fase com o sinal cerebral de interesse, a perceção a estímulos visuais apresentados aumenta. Presentemente, a aplicação de tACS não obedece a nenhum tipo de sistema de feedback. A estimulação é simplesmente iniciada num qualquer momento, o que dificulta o controlo sobre o tipo de efeitos em estudo. Como tal, a presente tese propõe a utilização de ferramentas do foro de Brain-Computer Interfaces (BCIs) na criação de um sistema fechado de controlo dos parâmetros da estimulação, nomeadamente a fase inicial. Assim, três metodologias para deteção dos parâmetros a partir do electroencefalograma (EEG) espontâneo e ajuste dos parâmetros de estimulação são propostos e testados em conjuntos de dados artificiais. O melhor dos três métodos é aplicado a um conjunto de dados de EEG real. O resultado é supreendentemente instável (comparável ao obtido para um sinal de ruído Gaussiano). Assim, o foco da investigação muda para uma análise detalhada da estabilidade de sinais de EEG real, com base em metodologias inicialmente destinadas ao estudo de sincronia neuronal. Mostra-se que não há evidência para sustentar a assunção clássica de que a fase dos sinais cerebrais numa determinada banda de frequências é estável, pelo menos durante um curto intervalo de tempo. A presente tese representa um passo importante no sentido de compreender uma característica do EEG que muitas vezes se considera como bem conhecida e estudada, mas sobre a qual existem muitas dúvidas e lacunas de conhecimento. É ainda um avanço no problema de como aproximar um sistema de controlo fechado para tACS.Transcranial Alternating Current Stimulation (tACS) is a technique that enables the direct influence of ongoing brain oscillations. Among other after-effects, it has been shown to enhance perception to visual stimuli when started with the same phase as the ongoing brain oscillation of interest. Currently tACS is delivered using a feedforward paradigm, and thus it is very difficult to ensure that the parameters of stimulation meet the optimal requirements for studying a determined response. Thus, the present thesis proposes the use of Brain-Computer Interface (BCI) tools for devising a feedback loop to control the parameters of stimulation. Different methods are proposed and tested using artificial data. The best one is chosen, and tested on real Electroencephalogram (EEG) data. This yields surprisingly unstable results, that lead to a detailed investigation of the stability of the phase of real signals. Several datasets are analysed using systematic methodologies based on tools devised for the study of neuronal synchrony. The results for real signals are compared to artificially generated noise signals. It is shown that there is no evidence to support a claim of stability of phase behaviour along short time intervals, unlike what is assumed in classical EEG analysis. The present thesis presents an important step towards understanding a widely overlooked feature in EEG, and in tackling the problem of a feedback loop for tACS

Cognitive Assessment and Rehabilitation of subjects with Traumatic Brain Injury

This thesis regards the study and the development of new cognitive assessment and rehabilitation techniques of subjects with traumatic brain injury (TBI). In particular, this thesis i) provides an overview about the state of art of this new assessment and rehabilitation technologies, ii) suggests new methods for the assessment and rehabilitation and iii) contributes to the explanation of the neurophysiological mechanism that is involved in a rehabilitation treatment. Some chapters provide useful information to contextualize TBI and its outcome; they describe the methods used for its assessment/rehabilitation. The other chapters illustrate a series of experimental studies conducted in healthy subjects and TBI patients that suggest new approaches to assessment and rehabilitation. The new proposed approaches have in common the use of electroencefalografy (EEG). EEG was used in all the experimental studies with a different purpose, such as diagnostic tool, signal to command a BCI-system, outcome measure to evaluate the effects of a treatment, etc. The main achieved results are about: i) the study and the development of a system for the communication with patients with disorders of consciousness. It was possible to identify a paradigm of reliable activation during two imagery task using EEG signal or EEG and NIRS signal; ii) the study of the effects of a neuromodulation technique (tDCS) on EEG pattern. This topic is of great importance and interest. The emerged founding showed that the tDCS can manipulate the cortical network activity and through the research of optimal stimulation parameters, it is possible move the working point of a neural network and bring it in a condition of maximum learning. In this way could be possible improved the performance of a BCI system or to improve the efficacy of a rehabilitation treatment, like neurofeedback

A Multifaceted Approach to Covert Attention Brain-Computer Interfaces

Over the last years, brain-computer interfaces (BCIs) have shown their value for assistive technology and neurorehabilitation. Recently, a BCI-approach for the rehabilitation of hemispatial neglect has been proposed on the basis of covert visuospatial attention (CVSA). CVSA is an internal action which can be described as shifting one's attention to the visual periphery without moving the actual point of gaze. Such attention shifts induce a lateralization in parietooccipital blood flow and oscillations in the so-called alpha band (8-14 Hz), which can be detected via electroencephalography (EEG), magnetoencephalography (MEG) or functional magnetic resonance imaging (fMRI). Previous studies have proven the technical feasibility of using CVSA as a control signal for BCIs, but unfortunately, these BCIs could not provide every subject with sufficient control. The aim of this thesis was to investigate the possibility of amplifying the weak lateralization patterns in the alpha band - the main reason behind insufficient CVSA BCI performance. To this end, I have explored three different approaches that could lead to better performing and more inclusive CVSA BCI systems. The first approach illuminated the changes in the behavior and brain patterns by closing the loop between subject and system with continuous real-time feedback at the instructed locus of attention. I could observe that even short (20 minutes) stretches of real-time feedback have an effect on behavioral correlates of attention, even when the changes observed in the EEG remained less conclusive. The second approach attempted to complement the information extracted fromthe EEG signal with another sensing modality that could provide additional information about the state of CVSA. For this reason, I firstly combined functional functional near-infrared spectroscopy (fNIRS) with EEG measurements. The results showed that, while the EEG was able to pick up the expected lateralization in the alpha band, the fNIRS was not able to reliably image changes in blood circulation in the parietooccipital cortex. Secondly, I successfully combined data from the EEG with measures of pupil size changes, induced by a high illumination contrast between the covertly attended target regions, which resulted in an improved BCI decoding performance. The third approach examined the option of using noninvasive electrical brain stimulation to boost the power of the alpha band oscillations and therefore render the lateralization pattern in the alpha band more visible compared to the background activity. However, I could not observe any impact of the stimulation on the ongoing alpha band power, and thus results of the subsequent effect on the lateralization remain inconclusive. Overall, these studies helped to further understand CVSA and lay out a useful basis for further exploration of the connection between behavior and alpha power oscillations in CVSA tasks, as well as for potential directions to improve CVSA-based BCIs

Several inaccurate or erroneous conceptions and misleading propaganda about brain-computer interfaces

Brain-computer interface (BCI) is a revolutionizing human-computer interaction, which has potential applications for specific individuals or groups in specific scenarios. Extensive research has been conducted on the principles and implementation methods of BCI, and efforts are currently being made to bridge the gap from research to real-world applications. However, there are inaccurate or erroneous conceptions about BCI among some members of the public, and certain media outlets, as well as some BCI researchers, developers, manufacturers, and regulators, propagate misleading or overhyped claims about BCI technology. Therefore, this article summarizes the several misconceptions and misleading propaganda about BCI, including BCI being capable of “mind-controlled,” “controlling brain,” “mind reading,” and the ability to “download” or “upload” information from or to the brain using BCI, among others. Finally, the limitations (shortcomings) and limits (boundaries) of BCI, as well as the necessity of conducting research aimed at countering BCI systems are discussed, and several suggestions are offered to reduce misconceptions and misleading claims about BCI

A supervised machine-learning method for detecting steady-state visually evoked potentials for use in brain computer interfaces: A comparative assessment

It is hypothesised that supervised machine learning on the estimated parameters output by a model for visually evoked potentials (VEPs), created by Kremlácek et al. (2002), could be used to classify steady-state visually evoked potentials (SSVEP) by frequency of stimulation. Classification of SSVEPs by stimulus frequency has application in SSVEP-based brain computer interfaces (BCI), where users are presented with flashing stimuli and user intent is decoded by identifying which stimulus the subject is attending to. We investigate the ability of the model of VEPs to fit the initial portions of SSVEPs, which are not yet in a steady state and contain characteristic features of VEPs superimposed with those of a steady state response. In this process the estimated parameters, as a function of the model for a given SSVEP response, were found. These estimated parameters were used to train several support vector machines (SVM) to classify the SSVEPs. Three initialisation conditions for the model are examined for their contribution to the goodness of fit and the subsequent classification accuracy, of the SVMs. It was found that the model was able to fit SSVEPs with a normalised root mean square error (NRMSE) of 27%, this performance did not match the expected NRMSE values of 13% reported by Kremlácek et al. (2002) for fits on VEPs. The fit data was assessed by the machine learning scheme and generated parameters which were classifiable by SVM above a random chance of 14% (Reang 9% to 28%). It was also shown that the selection of initial parameters had no distinct effect on the classification accuracy. Traditional classification approaches using spectral techniques such as Power Spectral Density Analysis (PSDA) and canonical correlation analysis (CCA) require a window period of data above 1 s to perform accurately enough for use in BCIs. The larger the window period of SSVEP data used the more the Information transfer rate (ITR) decreases. Undertaking a successful classification on only the initial 250 ms portions of SSVEP data would lead to an improved ITR and a BCI which is faster to use. Classification of each method was assessed at three SSVEP window periods (0.25, 0.5 and 1 s). Comparison of the three methods revealed that, on a whole CCA outperformed both the PSDA and SVM methods. While PSDA performance was in-line with that of the SVM method. All methods performed poorly at the window period of 0.25 s with an average accuracy converging on random chance - 14%. At the window period of 0.5 s the CCA only marginally outperformed the SVM method and at a time of 1 s the CCA method significantly (p<0.05) outperformed the SVM method. While the SVMs tended to improve with window period the results were not generally significant. It was found that certain SVMs (Representing a unique combination of subject, initial conditions and window period) achieved an accuracy as high as 30%. For a few instances the accuracy was comparable to the CCA method with a significance of 5%. While we were unable to predict which SVM would perform well for a given subject, it was demonstrated that with further refinement this novel method may produce results similar to or better than that of CCA

Enhanced Plasticity of Human Evoked Potentials by Visual Noise During the Intervention of Steady-State Stimulation Based Brain-Computer Interface

Neuroplasticity, also known as brain plasticity, is an inclusive term that covers the permanent changes in the brain during the course of an individual's life, and neuroplasticity can be broadly defined as the changes in function or structure of the brain in response to the external and/or internal influences. Long-term potentiation (LTP), a well-characterized form of functional synaptic plasticity, could be influenced by rapid-frequency stimulation (or “tetanus”) within in vivo human sensory pathways. Also, stochastic resonance (SR) has brought new insight into the field of visual processing for the study of neuroplasticity. In the present study, a brain-computer interface (BCI) intervention based on rapid and repetitive motion-reversal visual stimulation (i.e., a “tetanizing” stimulation) associated with spatiotemporal visual noise was implemented. The goal was to explore the possibility that the induction of LTP-like plasticity in the visual cortex may be enhanced by the SR formalism via changes in the amplitude of visual evoked potentials (VEPs) measured non-invasively from the scalp of healthy subjects. Changes in the absolute amplitude of P1 and N1 components of the transient VEPs during the initial presentation of the steady-state stimulation were used to evaluate the LTP-like plasticity between the non-noise and noise-tagged BCI interventions. We have shown that after adding a moderate visual noise to the rapid-frequency visual stimulation, the degree of the N1 negativity was potentiated following an ~40-min noise-tagged visual tetani. This finding demonstrated that the SR mechanism could enhance the plasticity-like changes in the human visual cortex