Browsing by Author "Hsieh, Jen-Chuen"

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Altered Brain Complexity in Women with Primary Dysmenorrhea: A Resting-State Magneto-Encephalography Study Using Multiscale Entropy Analysis
(2017-12-01)
Low, Intan
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Kuo, Po-Chih
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Liu, Yu-Hsiang
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Tsai, Cheng-Lin
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Chao, Hsiang-Tai
How chronic pain affects brain functions remains unclear. As a potential indicator, brain complexity estimated by entropy-based methods may be helpful for revealing the underlying neurophysiological mechanism of chronic pain. In this study, complexity features with multiple time scales and spectral features were extracted from resting-state magnetoencephalographic signals of 156 female participants with/without primary dysmenorrhea (PDM) during pain-free state. Revealed by multiscale sample entropy (MSE), PDM patients (PDMs) exhibited loss of brain complexity in regions associated with sensory, affective, and evaluative components of pain, including sensorimotor, limbic, and salience networks. Significant correlations between MSE values and psychological states (depression and anxiety) were found in PDMs, which may indicate specific nonlinear disturbances in limbic and default mode network circuits after long-term menstrual pain. These findings suggest that MSE is an important measure of brain complexity and is potentially applicable to future diagnosis of chronic pain.
Asymmetric Engagement of Amygdala and Its Gamma Connectivity in Early Emotional Face Processing
(2015-01-28)
Liu, Tai-Ying
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Chen, Yong-Sheng
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Hsieh, Jen-Chuen
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Chen, Li-Fen
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資訊工程學系
The amygdala has been regarded as a key substrate for emotion processing. However, the engagement of the left and right amygdala during the early perceptual processing of different emotional faces remains unclear. We investigated the temporal profiles of oscillatory gamma activity in the amygdala and effective connectivity of the amygdala with the thalamus and cortical areas during implicit emotion-perceptual tasks using event-related magnetoencephalography (MEG). We found that within 100 ms after stimulus onset the right amygdala habituated to emotional faces rapidly (with duration around 20-30 ms), whereas activity in the left amygdala (with duration around 50-60 ms) sustained longer than that in the right. Our data suggest that the right amygdala could be linked to autonomic arousal generated by facial emotions and the left amygdala might be involved in decoding or evaluating expressive faces in the early perceptual emotion processing. The results of effective connectivity provide evidence that only negative emotional processing engages both cortical and subcortical pathways connected to the right amygdala, representing its evolutional significance (survival). These findings demonstrate the asymmetric engagement of bilateral amygdala in emotional face processing as well as the capability of MEG for assessing tha-lamo-cortico-limbic circuitry.
Brain morphological changes associated with cyclic menstrual pain
(2010-09-01)
Tu, Cheng-Hao
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Niddam, David M.
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Chao, Hsiang-Tai
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Chen, Li-Fen
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Chen, Yong-Sheng
Primary dysmenorrhea (PDM) is the most prevalent gynecological disorder for women in the reproductive age. PDM patients suffer from lower abdominal pain that starts with the onset of the menstrual flow. Prolonged nociceptive input to the central nervous system can induce functional and structural alterations throughout the nervous system. In PDM, a chronic viscero-nociceptive drive of cyclic nature, indications of central sensitization and altered brain metabolism suggest a substantial central reorganization. Previously, we hypothesized that disinhibition of orbitofrontal networks could be responsible for increased pain and negative affect in PDM. Here, we further tested this hypothesis. We used an optimized voxel-based morphometry (VBM) approach to compare total and regional gray matter (GM) increases and decreases in 32 PDM patients with 32 healthy age and menstrual cycle matched (peri-ovulatory phase) controls. Abnormal decreases were found in regions involved in pain transmission, higher level sensory processing, and affect regulation while increases were found in regions involved in pain modulation and in regulation of endocrine function. Moreover. GM changes in regions involved in top-down pain modulation and in generation of negative affect were related to the severity of the experienced PDM pain. Our results demonstrate that abnormal GM volume changes are present in PDM patients even in the absence of pain. These changes may underpin a combination of impaired pain inhibition, increased pain facilitation and increased affect. Our findings highlight that longer lasting central changes may occur not only in sustained chronic pain conditions but also in cyclic occurring pain conditions. (C) 2010 International Association for the Study of Pain. Published by Elsevier B.V. All rights reserved.
Decoding and encoding of visual patterns using magnetoencephalographic data represented in manifolds
(2014-11-15)
Kuo, Po-Chih
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Chen, Yong-Sheng
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Chen, Li-Fen
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Hsieh, Jen-Chuen
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分子醫學與生物工程研究所
Visual decoding and encoding are crucial aspects in investigating the representation of visual information in the human brain. This paper proposes a bidirectional model for decoding and encoding of visual stimulus based on manifold representation of the temporal and spatial information extracted from magnetoencephalographic data. In the proposed decoding process, principal component analysis is applied to extract temporal principal components (TPCs) from the visual cortical activity estimated by a beamforming method. The spatial distribution of each TPC is in a high-dimensional space and can be mapped to the corresponding spatiotemporal component (STC) on a low-dimensional manifold. Once the linear mapping between the STC and the wavelet coefficients of the stimulus image is determined, the decoding process can synthesize an image resembling the stimulus image. The encoding process is performed by reversing the mapping or transformation in the decoding model and can predict the spatiotemporal brain activity from a stimulus image. In our experiments using visual stimuli containing eleven combinations of checkerboard patches, the information of spatial layout in the stimulus image was revealed in the embedded manifold. The correlation between the reconstructed and original images was 0.71 and the correlation map between the predicted and original brain activity was highly correlated to the map between the original brain activity for different stimuli (r = 0.89). These results suggest that the temporal component is important in visual processing and manifolds can well represent the information related to visual perception. (c) 2014 Published by Elsevier Inc.
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.
Encoding of menstrual pain experience with theta oscillations in women with primary dysmenorrhea
(2017-11-22)
Lee, Pin-Shiuan
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Low, Intan
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Chen, Yong-Sheng
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Tu, Cheng-Hao
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Chao, Hsiang-Tai
Theta oscillation (4-7 Hz) is well documented for its association with neural processes of memory. Pronounced increase of theta activity is commonly observed in patients with chronic neurogenic pain. However, its association with encoding of pain experience in patients with chronic pain is still unclear. The goal of the present study is to investigate the theta encoding of sensory and emotional information of long-term menstrual pain in women with primary dysmenorrhea (PDM). Forty-six young women with PDM and 46 age-matched control subjects underwent resting-state magnetoencephalography study during menstrual and periovulatory phases. Our results revealed increased theta activity in brain regions of pain processing in women with PDM, including the right parahippocampal gyrus, right posterior insula, and left anterior/middle cingulate gyrus during the menstrual phase and the left anterior insula and the left middle/inferior temporal gyrus during the periovulatory phase. The correlations between theta activity and the psychological measures pertaining to pain experience (depression, state anxiety, and pain rating index) implicate the role of theta oscillations in emotional and sensory processing of pain. The present study provides evidence for the role of theta oscillations in encoding the immediate and sustained effects of pain experience in young women with PDM.
Interactions of BDNF Val66Met Polymorphism and Menstrual Pain on Brain Complexity
(2018-11-20)
Low, Intan
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Kuo, Po-Chih
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Tsai, Cheng-Lin
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Liu, Yu-Hsiang
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Lin, Ming-Wei
The irregularity and uncertainty of neurophysiologic signals across different time scales can be regarded as neural complexity, which is related to the adaptability of the nervous system and the information processing between neurons. We recently reported general loss of brain complexity, as measured by multiscale sample entropy (MSE), at pain-related regions in females with primary dysmenorrhea (PDM). However, it is unclear whether this loss of brain complexity is associated with inter-subject genetic variations. Brain-derived neurotrophic factor (BDNF) is a widely expressed neurotrophin in the brain and is crucial to neural plasticity. The BDNF Val66Met single-nucleotide polymorphism (SNP) is associated with mood, stress, and pain conditions. Therefore, we aimed to examine the interactions of BDNF Val66Met polymorphism and long-term menstrual pain experience on brain complexity. We genotyped BDNF Val66Met SNP in 80 PDM females (20 Val/Val, 31 Val/Met, 29 Met/Met) and 76 healthy female controls (25 Val/Val, 36 Val/Met, 15 Met/Met). MSE analysis was applied to neural source activity estimated from resting-state magnetoencephalography (MEG) signals during pain-free state. We found that brain complexity alterations were associated with the interactions of BDNF Val66Met polymorphism and menstrual pain experience. In healthy female controls, Met carriers (Val/Met and Met/Met) demonstrated lower brain complexity than Val/Val homozygotes in extensive brain regions, suggesting a possible protective role of Val/Val homozygosity in brain complexity. However, after experiencing long-term menstrual pain, the complexity differences between different genotypes in healthy controls were greatly diminished in PDM females, especially in the limbic system, including the hippocampus and amygdala. Our results suggest that pain experience preponderantly affects the effect of BDNF Val66Met polymorphism on brain complexity. The results of the present study also highlight the potential utilization of resting-state brain complexity for the development of new therapeutic strategies in patients with chronic pain.
Maximum contrast beamformer for electromagnetic mapping of brain activity
(2006-09-01)
Chen, Yong-Sheng
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Cheng, Chih-Yu
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Hsieh, Jen-Chuen
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Chen, Li-Fen
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資訊工程學系
Beamforming technique can be applied to map the neuronal activities from magnetoencephalographic/electroencephalographic (MEG/EEG) recordings. One of the major difficulties of the scalar-type MEG/EEG beamformer is the determination of accurate dipole orientation, which is essential to an effective spatial filter. This paper presents a new beamforming technique which exploits a maximum contrast criterion to maximize the ratio of the neuronal activity estimated in a specified active state to the activity estimated in a control state. This criterion leads to a closed-form solution of the dipole orientation. Experiments with simulation, phantom, and finger-lifting data clearly demonstrate the effectiveness, efficiency, and accuracy of the proposed method.
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.
Stabilized linear model for neuromagnetic source localization
(2007)
Chen, Yong-Sheng
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Cheng, Chih-Yu
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Lin, Tzu-Jung
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Hsieh, Jen-Chuen
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Chen, Li-Fen
In MEG studies, head movement during data recording often causes problems in event-related averaging as well as source localization accuracy. We propose in this work a stabilized linear model which can solve this head movement problem by conceptually increasing the sensor number, under the condition that the head movement is known. This method is particularly beneficial for MEG studies in which head movement commonly Occurs, such as those studies requiring long experimental duration or those when patients/children are involved. Phantom studies and studies of somatosensory evoked field clearly show the effectiveness and accuracy of the proposed method.
Unravelling the Spatio-Temporal Neurodynamics of Rhythm Encoding-reproduction Networks by a Novel fMRI Autoencoder
(2019-01-01)
Kao, Chia-Hsiang
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Yang, Ching-Ju
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Cheng, Li-Kai
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Yu, Hsin-Yen
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Chen, Yong-Sheng
Visualization of how the external stimuli are processed dynamically in the brain would help understanding the neural mechanisms of functional segregation and integration. The present study proposed a novel temporal autoencoder to estimate the neurodynamics of functional networks involved in rhythm encoding and reproduction. A fully-connected two-layer autoencoder was proposed to estimate the temporal dynamics in functional magnetic resonance image recordings. By minimizing the reconstruction error between the predicted next time sample and the corresponding ground truth next time sample, the system was trained to extract spatial patterns of functional network dynamics without any supervision effort. The results showed that the proposed model was able to extract the spatial patterns of task-related functional dynamics as well as the interactions between them. Our findings suggest that artificial neural networks would provide a useful tool to resolve temporal dynamics of neural processing in the human brain.