I currently have an active laboratory working on computational and applied neuroscience. I develop and apply various Adaptive Data Analysis methods [e.g., Holo-Hilbert Spectral Analysis (HHSA, recently developed by Norden E. Huang, see the 2016 special issue on Adaptive Data Analysis in the Philosophical Transactions of the Royal Society of London), Hilbert-Huang Transformation (HHT), Event-Related Mode (ERM) analysis, and intrinsic Multiscale Entropy (iMSE)] for studying neural mechanisms underlying EEG/MEG signals in cognitive neuroscience. In addition, I used neural modulating techniques such as noninvasive brain stimulation (NIBS, e.g., Transcranial Magnetic Stimulation, Transcranial Direct/Alternating Current Stimulation) and mindfulness meditation, to probe the neural mechanisms of attention, inhibitory control, and visual working memory for adults, elders, preschoolers, and patients. I currently have a long-term collaboration with Professor Norden E. Huang and Professor Chung-Kang Peng (Harvard Medical School), to apply or adapt their innovative methods (e.g. HHSA, and MSE) to cognitive neuroscience. In the fundamental aspect of these methods, for example, I customized the original HHSA for EEG/MEG multi-electrode data and extended it to a source-level representation to produce a 3D (XYZ) + 3D (AM, Frequency, and Time) dynamic brain tomographic image to explore issues in cognitive neuroscience. I also add new members to the family of Adaptive Data Analysis methods. For instance, I developed a connectivity analysis based on HHSA, to better understand the intersite cross-frequency coupling in brain networks during cognitive processes (under review). In the aspect of application, I have an ongoing collaboration with the Neurological Institute of Taipei Veterans General Hospital (collaborative laboratories led by Dr. Shuu-Jiun Wang, and Dr. I-Hui Lee), to develop a new HHSA-based diagnostic method for migraine, Alzheimer’s disease, Parkinson's disease, major depression, and stroke. In addition, I have a special interest in studying individual differences in personality traits and mindfulness meditation by using these Adaptive Data Analysis methods.
My current research areas are
(i) Investigating the neural correlates of visual working memory and individual differences in working memory capacity by HHSA/HHT. Our current findings indicate that both the anterior cingulate cortex (ACC) and the bilateral parietal brain regions have strong interactive amplitude modulation in brainwaves for memory retention (under revision). These results have verified several working memory findings revealed by invasive techniques such as electrocorticogram (e.g. Canolty et al., 2006; Fell and Axmacher, 2011). This is also the very first time a detailed demonstration of amplitude modulation mechanisms with non-invasive human EEG in a whole-brain level. One set of results that used Adaptive Data Analysis methods to investigate individual differences in personality traits and mindfulness meditation has been published in the Social Cognitive and Affective Neuroscience 2019, Frontiers in Integrative Neuroscience, and Frontiers in Psychology 2018. All the HHSA/HHT results for cognitive functions are under review or in preparation and will appear in 2021.
(ii) Developing an efficient HHSA-based biomarker for migraine, Alzheimer’s disease, Parkinson's disease, major depression, and stroke. Our preliminary results show that AD can be characterized by deficits in amplitude modulation in the gamma frequency band, whereas PD can be categorized by excessive amplitude modulation in the gamma rhythms. For migraine, we found several biomarkers that can be used to diagnose migraine with and without aura, and predict the efficiency of medication for chronic migraine by pre-med EEG. In major depression, excessive amplitude modulation in the beta rhythms was observed. Some of these results are in preparation and will appear in 2021.
(iii) Using Multiscale Entropy (MSE) to quantify the degree of complexity and adaptability of the brain during cognitive processes. This application of MSE for cognitive neuroscience has produced several important issues: 1) For inhibitory control & tDCS, we found that neural processes behind successful impulse control are usually accompanied by higher MSE values, and tDCS can efficiently elevate MSE during such process, thereby improving the ability of inhibitory control (a hypothesis and brief literature review of this relationship between tDCS and MSE has been published in Journal of Neuroscience and Neuroengineering; the concrete results have been published in NeuroImage 2014 and Entropy 2015); 2) For sport and exercise, we found a close relationship between aerobic exercise modulates transfer and brain signal complexity following cognitive training (the result has been published in Biological Psychology 2019 and Neuroscience 2020); 3) In the elderly population, we observed a beneficial effect of physical activity on the aging brain in terms of brain signal complexity (these results have been published in Brain and Cognition 2014).
(iv) Using time-frequency analysis (e.g. Fast Fourier Transform, Wavelet Analysis, and Hilbert-Huang Transformation (HHT)) to investigate oscillatory features in EEG/MEG data. I employ these techniques to analyze brain signals from healthy subjects, elders, children, athletes, and patients with Alzheimer's disease, depression, migraine, etc. Published results that employed these techniques: 1) A study that revealed the electrophysiological correlates of working memory-load effects in a symmetry span task with HHT method (Frontiers in Physiology 2019); 2) A series of athlete’s studies that demonstrated the relationship between theta oscillatory power and athletes’ performance in exercise (Psychology of Sport & Exercise 2015; Psychophysiology 2015); 3) A developmental study that investigated the importance of beta oscillatory power in the neural development of preschoolers (Developmental Neuropsychology 2012).
(v) Using stochastic optimal control theory to model TMS saccade data (including the effect of visual target change and visual distractors, one result has been published in the Journal of Neurophysiology 2012);
(vii) Using “Amplitude Modulated” Transcranial Alternative Current Stimulation (AM-tACS) to selectively and precisely facilitate/inhibit brain functions by generating stimulation current according to the brain oscillatory pattern obtained by HHSA. This method of brain stimulation is a revolutionary protocol that can be used to efficiently change the state of the brain.
Taken together, my aim of research is to build a family of Adaptive Data Analysis methods tailored to cognitive neuroscience, as well as the know-how of applying these methods to understand neural mechanisms of cognitive functions.
博士生 (PhD students):
蔡忠志 Chong-Chih Tsai
碩士生 (Master students):
顏廷安 Ting-An Yen
博士級研究助理 (Postdoctoral fellow):
黃時霖 Shih-Lin Huang