Browsing by Author "Lo, Yu-Chun"

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A novel patterned diffuser for high uniform and high bright LCD backlights
(2007)
Lo, Yu-Chun
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Fang, Jen-Yu
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Huang, Yi-Pai
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Shieh, Han-Ping D.
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Yu, Gu-Sheng
A novel patterned diffuser of which the diffusivity can be optimized spatially for the non-uniform luminance distribution backlights. was designed and realized. An 11% improvement in the brightness uniformity was successfully demonstrated in a 7-in side-edge backlight without sacrificing the brightness.
A Sliced Inverse Regression (SIR) Decoding the Forelimb Movement from Neuronal Spikes in the Rat Motor Cortex
(2016-12-09)
Yang, Shih-Hung
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Chen, You-Yin
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Lin, Sheng-Huang
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Liao, Lun-De
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Lu, Henry Horng-Shing
Several neural decoding algorithms have successfully converted brain signals into commands to control a computer cursor and prosthetic devices. A majority of decoding methods, such as population vector algorithms (PVA), optimal linear estimators (OLE), and neural networks (NN), are effective in predicting movement kinematics, including movement direction, speed and trajectory but usually require a large number of neurons to achieve desirable performance. This study proposed a novel decoding algorithm even with signals obtained from a smaller numbers of neurons. We adopted sliced inverse regression (SIR) to predict forelimb movement from single-unit activities recorded in the rat primary motor (M1) cortex in a water-reward lever-pressing task. SIR performed weighted principal component analysis (RCA) to achieve effective dimension reduction for nonlinear regression. To demonstrate the decoding performance, SIR was compared to PVA, OLE, and NN. Furthermore, RCA and sequential feature selection (SFS) which are popular feature selection techniques were implemented for comparison of feature selection effectiveness. Among SIR, PVA, OLE, PCA, SFS, and NN decoding methods, the trajectories predicted by SIR (with a root mean square error, RMSE, of 8.47 +/- 1.32 mm) was closer to the actual trajectories compared with those predicted by PVA (30.41 +/- 11.73 mm), OLE (20.17 +/- 6.43 mm), PCA (19.13 +/- 0.75 mm), SFS (22.75 +/- 2.01 mm), and NN (16.75 +/- 2.02 mm). The superiority of SIR was most obvious when the sample size of neurons was small. We concluded that SIR sorted the input data to obtain the effective transform matrices for movement prediction, making it a robust decoding method for conditions with sparse neuronal information.
A vision-based regression model to evaluate Parkinsonian gait from monocular image sequences
(2012-01-01)
Chen, You-Yin
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Cho, Chien-Wen
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Lin, Sheng-Huang
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Lai, Hsin-Yi
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Lo, Yu-Chun
Parkinson's Disease (PD) is a common neurodegenerative disorder with progressive loss of dopaminergic and other sub-cortical neurons. Among various approaches, gait analysis is commonly used to help identify the biometric features of PD. There have been some studies to date on both the classification of PD and estimation of gait parameters. However, it is also important to construct a regression system that can evaluate the degree of abnormality in PD patients. In this paper, we intended to develop a PD gait regression model that is capable of predicting the severity of motor dysfunction from given gait image sequences. We used a model-free strategy and thus avoided the critical demands of segmentation and parameter estimation. Furthermore, we used linear discriminant analysis (LDA) to increase the feature efficiency by maximizing and minimizing the between- and within-group variations. Regression was also achieved by assessing the spatial and temporal information through classification and finally by using these two new indices for linear regression. According to the experiments, the outcomes significantly correlated with the sum of sub-scores from the Unified Parkinson's Disease Rating Scale (UPDRS): motor examination section with r = 0.92 and 0.85 for training and testing, respectively, with p < 0.0001. Compared with conventional methods, our system provided a better evaluation of PD abnormality. (C) 2011 Elsevier Ltd. All rights reserved.
Automatic spike sorting for extracellular electrophysiological recording using unsupervised single linkage clustering based on grey relational analysis
(2011-06-01)
Lai, Hsin-Yi
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Chen, You-Yin
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Lin, Sheng-Huang
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Lo, Yu-Chun
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Tsang, Siny
Automatic spike sorting is a prerequisite for neuroscience research on multichannel extracellular recordings of neuronal activity. A novel spike sorting framework, combining efficient feature extraction and an unsupervised clustering method, is described here. Wavelet transform (WT) is adopted to extract features from each detected spike, and the Kolmogorov-Smirnov test (KS test) is utilized to select discriminative wavelet coefficients from the extracted features. Next, an unsupervised single linkage clustering method based on grey relational analysis (GSLC) is applied for spike clustering. The GSLC uses the grey relational grade as the similarity measure, instead of the Euclidean distance for distance calculation; the number of clusters is automatically determined by the elbow criterion in the threshold-cumulative distribution. Four simulated data sets with four noise levels and electrophysiological data recorded from the subthalamic nucleus of eight patients with Parkinson's disease during deep brain stimulation surgery are used to evaluate the performance of GSLC. Feature extraction results from the use of WT with the KS test indicate a reduced number of feature coefficients, as well as good noise rejection, despite similar spike waveforms. Accordingly, the use of GSLC for spike sorting achieves high classification accuracy in all simulated data sets. Moreover, J-measure results in the electrophysiological data indicating that the quality of spike sorting is adequate with the use of GSLC.
Correction of inhomogeneous magnetic resonance images using multiscale retinex for segmentation accuracy improvement
(2012-03-01)
Chao, Wen-Hung
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Lai, Hsin-Yi
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Shih, Yen-Yu I.
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Chen, You-Yin
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Lo, Yu-Chun
The purpose of this study was to improve the accuracy of tissue segmentation on brain magnetic resonance (MR) images preprocessed by multiscale retinex (MSR), segmented with a combined boosted decision tree (BDT) and MSR algorithm (hereinafter referred to as the MSRBDT algorithm). Simulated brain MR (SBMR) T1-weighted images of different noise levels and RF inhomogeneities were adopted to evaluate the outcome of the proposed method; the MSRBDT algorithm was used to identify the gray matter (GM), white matter (WM), and cerebral-spinal fluid (CSF) in the brain tissues. The accuracy rates of GM, WM, and CSF segmentation, with spatial features (G, x, y, r, theta), were respectively greater than 0.9805, 0.9817, and 0.9871. In addition, images segmented with the MSRBDT algorithm were better than those obtained with the expectation maximization (EM) algorithm; brain tissue segmentation in MR images was significantly more precise. The proposed MSRBDT algorithm could be beneficial in clinical image segmentation. (C) 2011 Elsevier Ltd. All rights reserved.
Gene-Embedded Nanostructural Biotic-Abiotic Optoelectrode Arrays Applied for Synchronous Brain Optogenetics and Neural Signal Recording
(2019-03-27)
Huang, Wei-Chen
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Chi, Hui-Shang
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Lee, Yi-Chao
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Lo, Yu-Chun
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Liu, Ta-Chung
Optogenetics is a recently established neuromodulation technique in which photostimulation is used to manipulate neurons with high temporal and spatial precision. However, sequential genetic and optical insertion with double brain implantation tends to cause excessive tissue damage. In addition, the incorporation of light-sensitive genes requires the utilization of viral vectors, which remains a safety concern. Here, by combining device fabrication design, nanotechnology, and cell targeting technology, we developed a new gene-embedded optoelectrode array for neural implantation to enable spatiotemporal electroporation (EP) for gene delivery/transfection, photomodulation, and synchronous electrical monitoring of neural signals in the brain via one-time implantation. A biotic-abiotic neural interface (called PG) composed of reduced graphene oxide and conductive polyelectrolyte 3,4-ethylenedioxythiophene-modified amphiphilic chitosan was developed to form a nanostructural hydrogel with assembled nanodomains for encapsulating nonviral gene vectors (called PEI-NT-pDNA) formulated by neurotensin (NT) and polyethylenimine (PEI)-coupled plasmid DNA (pDNA). The PG can maintain high charge storage ability to respond to a minimal current of 125 mu A for controllable gene delivery. The in vitro analysis of PG-PEI-NT-pDNA on the microelectrode array chip showed that the microelectrodes provided electrically inductive electropermeabilization, which permitted gene transfection into localized rat adrenal pheochromocytoma cells with a strong green fluorescent protein expression that was up to 8-fold higher than that in nontreated cells. Furthermore, the in vivo implantation enabled on-demand spatiotemporal gene transfection to neurons with 10-fold enhancement of targeting ability compared with astrocytes. Finally, using the real optogenetic opsin channelrhodopsin-2, the flexible neural probe incorporated with an optical waveguide fiber displayed photoevoked extracellular spikes in the thalamic ventrobasal region after focal EP for only 7 days, which provided a proof of concept for the use of photomodulation to facilitate neural therapies.
Highly sensitive/selective 3D nanostructured immunoparticle-based interface on a multichannel sensor array for detecting amyloid-beta in Alzheimer's disease
(2018-01-01)
Liu, Ta-Chung
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Lee, Yi-Chao
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Ko, Chiung-Yuan
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Liu, Ren-Shyan
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Ke, Chien-Chih
Accumulation of beta-amyloid (A beta) peptides is highly associated with Alzheimer's disease (AD) progression in prevailing studies. The successful development of an ultrasensitive detection assay for A beta is a challenging task, especially from blood-based samples. Methods: We have developed a one-step electrophoresis/electropolymerization strategy for preparing a CSIP hierarchical immunoelectrochemical interface that is easily integrated into a PoCT device. The interface includes conductive silk fibroin-based immunoparticles (CSIPs) via electropolymerized Poly(3,4-ethylenedioxythiophene) (PEDOT) bridging to enable on-site electrochemical detection of serum amyloid-beta(42) (A beta(42)) and -beta(40) (A beta(40)) peptides from an AD blood test. In addition, micro-positron emission tomography (microPET) neuroimaging and behavioral tests were simultaneously performed. Results: This nanostructured conductive interface favors penetration of water-soluble biomolecules and catalyzes a redox reaction, providing limits of detection (LOD) of 6.63 pg/mL for A beta(40) and 3.74 pg/mL for A beta(42). Our proof-of-concept study confirms that the multi-sensing electrochemical immunosensor array (MEIA) platform enables simultaneous measurement of serum A beta(42) and A beta(40) peptide levels and is more informative in early stage AD animals than amyloid-labeling A beta plaque PET imaging and behavioral tests. Conclusion: We believe this study greatly expands the applications of silk fibroin-based materials, is an important contribution to the advancement of biomaterials, and would also be valuable in the design of new types of multichannel electrochemical immunosensor arrays for the detection of other diseases.
Imaging brain hemodynamic changes during rat forepaw electrical stimulation using functional photoacoustic microscopy
(2010-08-15)
Liao, Lun-De
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Li, Meng-Lin
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Lai, Hsin-Yi
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Shih, Yen-Yu I.
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Lo, Yu-Chun
The present study reported the development of a novel functional photoacoustic microscopy (fPAM) system for investigating hemodynamic changes in rat cortical vessels associated with electrical forepaw stimulation. Imaging of blood optical absorption by fPAM at multiple appropriately-selected and distinct wavelengths can be used to probe changes in total hemoglobin concentration (HbT, i.e., cerebral blood volume [CBV]) and hemoglobin oxygen saturation (SO(2)). Changes in CBV were measured by images acquired at a wavelength of 570 nm (lambda(570)), an isosbestic point of the molar extinction spectra of oxy- and deoxy-hemoglobin, whereas SO(2) changes were sensed by pixel-wise normalization of images acquired at lambda(560) or lambda(600) to those at lambda(570). We demonstrated the capacity of the fPAM system to image and quantify significant contralateral changes in both SO(2) and CBV driven by electrical forepaw stimulation. The fPAM system complements existing imaging techniques, with the potential to serve as a favorable tool for explicitly studying brain hemodynamics in animal models. (C) 2010 Elsevier Inc. All rights reserved.
MicroPET imaging of noxious thermal stimuli in the conscious rat brain
(2010)
Chen, You-Yin
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Shih, Yen-Yu I.
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Lo, Yu-Chun
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Lu, Pen-Li
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Tsang, Siny
Small animal positron emission tomography (microPET) has been utilized in the investigation of nociception. However, a possible drawback from previous studies is the reduced activation pattern due to the application of anesthesia. The purpose of the present study was to demonstrate a potential means of avoiding anesthesia during stimulation, as well as minimizing the confounding anesthetic effect. Sodium pentobarbital and ketamine were first evaluated to determine their effect on microPET images in the current study. [(18)F]-Fluorodeoxyglucose ((18)F-FDG) was an appropriate radiotracer to reveal activated regions in rat brains. Pentobarbital anesthesia significantly reduced (18)F-FDG uptake in neural tissues, blurrier to lower contrast; therefore, ketamine was used to anesthetize animals during microPET. After the rats were anesthetized and secured in a laboratory-made stereotaxic frame, a simple, noninvasive stereotaxic technique was used to position their heads in the microPET scanner and to roughly conform the images in the stereotaxic atlas. For functional imaging, conscious rats were restrained in cages with minimal ambient noise; short repetitive thermal stimuli were applied to each rat's tail subsequently. The rats were adequately anesthetized with ketamine following 30 min of scanning without stimulation. An activation index (AI) was calculated from microPET data to quantify the local metabolic activity changes according to the normalized (18)F-FDG dosage. The average AI indicated a side-to-side difference for all innocuous stimulations in the thalamus. However, such side-to-side difference was only observed for noxious heat and cold stimulations in primary somatosensory cortex (SI), secondary somatosensory cortex (SII), and agranular insular cortex (AIC). The present study demonstrated the feasibility of the microPET technique to image metabolic functions of the conscious rat brain, offering better rationale and protocol designs for future pain studies.
Multi-performance film (MPF) for highly efficient LCD backlights
(2008)
Hsieh, Jen-Chieh
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Huang, Yi-Pai
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Chen, Chun-Ho
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Lo, Yu-Chun
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Fang, Jen-Yu
A multi-performance film (MPF) which has concave-parabolic cylinder micro prism structures was proposed to improve light coupling efficiency and to reduce lamp-Mura defect. A MPF combined light diffusion and light collecting performance as a multiple functional film. By applying the MPF wider FWHM angle, higher light output coupling efficiency, and slighter lamp-Mura were demonstrated on a 32-inch direct-type backlight.
Quantification and recognition of parkinsonian gait from monocular video imaging using kernel-based principal component analysis
(2011-11-10)
Chen, Shih-Wei
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Lin, Sheng-Huang
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Liao, Lun-De
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Lai, Hsin-Yi
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Pei, Yu-Cheng
Background: The computer-aided identification of specific gait patterns is an important issue in the assessment of Parkinson's disease (PD). In this study, a computer vision-based gait analysis approach is developed to assist the clinical assessments of PD with kernel-based principal component analysis (KPCA). Method: Twelve PD patients and twelve healthy adults with no neurological history or motor disorders within the past six months were recruited and separated according to their "Non-PD", "Drug-On", and "Drug-Off" states. The participants were asked to wear light-colored clothing and perform three walking trials through a corridor decorated with a navy curtain at their natural pace. The participants' gait performance during the steady-state walking period was captured by a digital camera for gait analysis. The collected walking image frames were then transformed into binary silhouettes for noise reduction and compression. Using the developed KPCA-based method, the features within the binary silhouettes can be extracted to quantitatively determine the gait cycle time, stride length, walking velocity, and cadence. Results and Discussion: The KPCA-based method uses a feature-extraction approach, which was verified to be more effective than traditional image area and principal component analysis (PCA) approaches in classifying "Non-PD" controls and "Drug-Off/On" PD patients. Encouragingly, this method has a high accuracy rate, 80.51%, for recognizing different gaits. Quantitative gait parameters are obtained, and the power spectrums of the patients' gaits are analyzed. We show that that the slow and irregular actions of PD patients during walking tend to transfer some of the power from the main lobe frequency to a lower frequency band. Our results indicate the feasibility of using gait performance to evaluate the motor function of patients with PD. Conclusion: This KPCA-based method requires only a digital camera and a decorated corridor setup. The ease of use and installation of the current method provides clinicians and researchers a low cost solution to monitor the progression of and the treatment to PD. In summary, the proposed method provides an alternative to perform gait analysis for patients with PD.