Browsing by Author "Su, Tung-Ping"

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Differences in white matter abnormalities between bipolar I and II disorders
(2010-12-01)
Liu, Jia-Xiu
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Chen, Yong-Sheng
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Hsieh, Jen-Chuen
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Su, Tung-Ping
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Yeh, Tzu-Chen
Background: Although patients with bipolar I and II disorders exhibit heterogeneous clinical presentations and cognitive functions, it remains unclear whether these two subtypes have distinct neural substrates. This study aimed to differentiate the fiber abnormalities between bipolar I and II patients using diffusion tensor images. Method: Fourteen bipolar I patients, thirteen bipolar II patients, and twenty-one healthy subjects were recruited. Fractional anisotropy (FA) values calculated from diffusion tensor images were compared among groups using two-sample t-test analysis in a voxel-wise manner. Correlations between the mean FA value of each survived area and the clinical characteristics as well as the scores of neuropsychological tests were further analyzed. Results: Patients of both subtypes manifested fiber impairments in the thalamus, anterior cingulate, and inferior frontal areas, whereas the bipolar II patients showed more fiber alterations in the temporal and inferior prefrontal regions. The FA values of the subgenual anterior cingulate cortices for both subtypes correlated with the performance of working memory. The FA values of the right inferior frontal area of bipolar land the left middle temporal area of bipolar II both correlated with executive function. For bipolar II patients, the left middle temporal and inferior prefrontal FA values correlated with the scores of YMRS and hypomanic episodes, respectively. Conclusions: Our findings suggest distinct neuropathological substrates between bipolar I and II subtypes. The fiber alterations observed in the bipolar I patients were majorly associated with cognitive dysfunction, whereas those in the bipolar II patients were related to both cognitive and emotional processing. (C) 2010 Elsevier B.V. All rights reserved.
Different patterns of abnormal gamma oscillatory activity in unipolar and bipolar disorder patients during an implicit emotion task
(2012-06-01)
Liu, Tai-Ying
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Hsieh, Jen-Chuen
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Chen, Yong-Sheng
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Tu, Pei-Chi
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Su, Tung-Ping
This study investigates the distinct patterns of local and long-range gamma oscillations between patients with bipolar disorder (BD) and major depressive disorder (MDD). Twenty BD patients, twenty MDD patients, and twenty normal controls participated in this study. For each participant, the event-related magnetoencephalographic responses while performing an implicit emotional task were recorded and processed with time-frequency analysis. Compared to normal controls, the BD patients exhibited the gamma power decease at the right frontal and prefrontal regions and yet gamma power increase at the right posterior temporal region. The abnormal long-range gamma oscillation between the right frontal and parietal-occipital region was also found. These results indicate that the BD patients may have hyperactivity in perceptual binding of emotional features and tend to be oversensitive to facial features. On the other hand, MOD patients displayed increased early gamma activity at the left anterior temporal region, which may imply their hyperactivated binding process of emotional features at corticolimbic regions. The distinct alterations of gamma patterns between the BD and MDD patients implicate that their impairments of binding processes are located at different regions. Gamma activity in the parietal and left posterior temporal regions may be a potential index to differentiate BD patients from MDD patients. (C) 2012 Elsevier Ltd. All rights reserved.
Distinct neuronal oscillatory responses between patients with bipolar and unipolar disorders: A magnetoencephalographic study
(2010-06-01)
Lee, Pin-Shiuan
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Chen, Yong-Sheng
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Hsieh, Jen-Chuen
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Su, Tung-Ping
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Chen, Li-Fen
Background: Bipolar disorder (BD) and major depressive disorder (MDD) have distinct pathophysiologies but similar depressive appearances. The present study aimed at the differentiation of the brain responses between BD and MDD patients. We hypothesized that different affective disorder patients may have distinct patterns of oscillatory cortical activities in response to negative emotional stimuli. Methods: Twenty BD patients, twenty MDD patients, and twenty age- and gender-matched healthy normal subjects were recruited. We adopted an implicit emotional task with facial image stimuli. The acquired event-related magnetoencephalographic signals were processed by the time-frequency analysis and beamformer-based source imaging techniques followed by statistical inference. Results: We found that there were gamma oscillation decreases in the frontal regions of both BD and MOD patients, gamma oscillation increases in the bilateral temporal regions of MDD, and alpha-beta rhythm increases in BD patients. Relative to the cortical activation in the control group, the BD patients displayed more widely increased oscillatory activities over the fronto-parieto-occipital regions than MOD patients. Conclusions: Our results demonstrate the distinct neuropathological patterns of emotional responses in BD and MDD patients. The findings suggest that the dysfunction of emotion regulation in BD may result from the increased sensitivity to emotionally salient information, implicating the potential cause of the emotion lability. The present study also suggests that the implicit emotional task is an effective approach to differentiate bipolar from unipolar disorders and their distinct neuropathological patterns to emotional stimuli may provide objective and quantitative measures for potential diagnostic significance. (C) 2009 Elsevier BM. All rights reserved.
Forehead EEG in support of Future Feasible Personal Healthcare Solutions: Sleep Management, Headache Prevention, and Depression Treatment
(2017-01-01)
Lin, Chin-Teng
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Chuang, Chun-Hsiang
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Cao, Zehong
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Singh, Avinash Kumar
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Hung, Chih-Sheng
There are current limitations in the recording technologies for measuring EEG activity in clinical and experimental applications. Acquisition systems involving wet electrodes are time-consuming and uncomfortable for the user. Furthermore, dehydration of the gel affects the quality of the acquired data and reliability of long-term monitoring. As a result, dry electrodes may be used to facilitate the transition from neuroscience research or clinical practice to real-life applications. EEG signals can be easily obtained using dry electrodes on the forehead, which provides extensive information concerning various cognitive dysfunctions and disorders. This paper presents the usefulness of the forehead EEG with advanced sensing technology and signal processing algorithms to support people with healthcare needs, such as monitoring sleep, predicting headaches, and treating depression. The proposed system for evaluating sleep quality is capable of identifying five sleep stages to track nightly sleep patterns. Additionally, people with episodic migraines can be notified of an imminent migraine headache hours in advance through monitoring forehead EEG dynamics. The depression treatment screening system can predict the efficacy of rapid antidepressant agents. It is evident that frontal EEG activity is critically involved in sleep management, headache prevention, and depression treatment. The use of dry electrodes on the forehead allows for easy and rapid monitoring on an everyday basis. The advances in EEG recording and analysis ensure a promising future in support of personal healthcare solutions.
Generalized EEG-Based Drowsiness Prediction System by Using a Self-Organizing Neural Fuzzy System
(2012-09-01)
Lin, Fu-Chang
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Ko, Li-Wei
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Chuang, Chun-Hsiang
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Su, Tung-Ping
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Lin, Chin-Teng
A generalized EEG-based Neural Fuzzy system to predict driver's drowsiness was proposed in this study. Driver's drowsy state monitoring system has been implicated as a causal factor for the safety driving issue, especially when the driver fell asleep or distracted in driving. However, the difficulties in developing such a system are lack of significant index for detecting the driver's drowsy state in real-time and the interference of the complicated noise in a realistic and dynamic driving environment. In our past studies, we found that the electroencephalogram (EEG) power spectrum changes were highly correlated with the driver's behavior performance especially the occipital component. Different from presented subject-dependent drowsy state monitor systems, whose system performance may decrease rapidly when different subject applies with the drowsiness detection model constructed by others, in this study, we proposed a generalized EEG-based Self-organizing Neural Fuzzy system to monitor and predict the driver's drowsy state with the occipital area. Two drowsiness prediction models, subject-dependent and generalized cross-subject predictors, were investigated in this study for system performance analysis. Correlation coefficients and root mean square errors are showed as the experimental results and interpreted the performances of the proposed system significantly better than using other traditional Neural Networks (p-value <0.038). Besides, the proposed EEG-based Self-organizing Neural Fuzzy system can be generalized and applied in the subjects' independent sessions. This unique advantage can be widely used in the real-life applications.
Knowledge-based identification of sleep stages based on two forehead electroencephalogram channels
(2014-09-04)
Huang, Chih-Sheng
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Lin, Chun-Ling
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Ko, Li-Wei
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Liu, Shen-Yi
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Su, Tung-Ping
Sleep quality is important, especially given the considerable number of sleep-related pathologies. The distribution of sleep stages is a highly effective and objective way of quantifying sleep quality. As a standard multi-channel recording used in the study of sleep, polysomnography (PSG) is a widely used diagnostic scheme in sleep medicine. However, the standard process of sleep clinical test, including PSG recording and manual scoring, is complex, uncomfortable, and time-consuming. This process is difficult to implement when taking the whole PSG measurements at home for general healthcare purposes. This work presents a novel sleep stage classification system, based on features from the two forehead EEG channels FP1 and FP2. By recording EEG from forehead, where there is no hair, the proposed system can monitor physiological changes during sleep in a more practical way than previous systems. Through a headband or self-adhesive technology, the necessary sensors can be applied easily by users at home. Analysis results demonstrate that classification performance of the proposed system overcomes the individual differences between different participants in terms of automatically classifying sleep stages. Additionally, the proposed sleep stage classification system can identify kernel sleep features extracted from forehead EEG, which are closely related with sleep clinician\'s expert knowledge. Moreover, forehead EEG features are classified into five sleep stages by using the relevance vector machine. In a leave-one-subject-out cross validation analysis, we found our system to correctly classify five sleep stages at an average accuracy of 76.7 +/- 4.0 (SD) % [average kappa 0.68 +/- 0.06 (SD)]. Importantly, the proposed sleep stage classification system using forehead EEG features is a viable alternative for measuring EEG signals at home easily and conveniently to evaluate sleep quality reliably, ultimately improving public healthcare.
Quantitative Evaluation of Brain Magnetic Resonance Images Using Voxel-based Morphometry and Bayesian Theorem for Patients with Bipolar Disorder
(2008)
Chen, Yong-Sheng
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Chen, Li-Fen
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Chang, Ya-Ting
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Huang, Yung-Tien
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Su, Tung-Ping
The present diagnosis of bipolar disorder (BD), which mainly depends on patients' symptoms and self reports of past history and mood status, may encounter difficulty of distinguishing from unipolar disorder when patients behave depressively in clinic. This work proposes a novel computer-aided evaluation system for bipolar disorder using anatomic magnetic resonance images (MRI) to provide a second opinion for clinician's diagnosis. First we adopt the voxel-based morphometry method to identify brain regions with significant difference between patient and normal control groups as regions of interest (ROIs). Then the MRI data within these ROIs are processed with principal component analysis (PCA) in order to reduce feature dimensionality. Finally, a classification model based on Bayesian theorem together with Parzen-window density estimation in PCA space is constructed to provide the possibility of an individual belonging to the BD patient group. The proposed system reaches 86.8% accuracy in classification. In our experiment, the misdetection rate was zero, and the false alarm was 15.8%. Through appropriate feature analysis and selection method, this computer-aided system can detect the disease of BD and obtain high classification accuracy.
Review of Wireless and Wearable Electroencephalogram Systems and Brain-Computer Interfaces - A Mini-Review
(2010)
Lin, Chin-Teng
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Ko, Li-Wei
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Chang, Meng-Hsiu
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Duann, Jeng-Ren
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Chen, Jing-Ying
Biomedical signal monitoring systems have rapidly advanced in recent years, propelled by significant advances in electronic and information technologies. Brain-computer interface (BCI) is one of the important research branches and has become a hot topic in the study of neural engineering, rehabilitation, and brain science. Traditionally, most BCI systems use bulky, wired laboratory-oriented sensing equipments to measure brain activity under well-controlled conditions within a confined space. Using bulky sensing equipments not only is uncomfortable and inconvenient for users, but also impedes their ability to perform routine tasks in daily operational environments. Furthermore, owing to large data volumes, signal processing of BCI systems is often performed off-line using high-end personal computers, hindering the applications of BCI in real-world environments. To be practical for routine use by unconstrained, freely-moving users, BCI systems must be noninvasive, nonintrusive, light-weight and capable of online signal processing. This work reviews recent online BCI systems, focusing especially on wearable, wireless and real-time systems. Copyright (C) 2009 S. Karger AG, Basel