Browsing by Author "Chen, You-Yin"

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A Data-driven Architecture for Remote Control of Sensors over a Wireless Sensor Network and the Internet
(2009)
Chang, Yuan-Jen
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Chen, Chin-Hsing
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Huang, Wen-Tzeng
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Chen, You-Yin
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Kuo, Chih-Yuan
This study revealed an applicable architecture in which a data-driven mechanism was designed to bridge a wireless sensor network (WSN) and the Internet. The system was divided into two independent parts. The first part is the data communication between the sensor network and the database. The other part is the data communication between the database and the user interface (UI). These two parts are connected by the database server. Asynchronous interoperation was introduced while exchanging data between these two parts. Users were not allowed to control the sensors through direct connection to the sensors and can only use the Web service to update the sensor profile built in the database. The sensors were triggered to start the action through a data-update event from the database. A sensor profile built in the database collected all sensor information and all user control command. For the information to be centralized and triggered by the database, regardless of whether sensors were measured in a periodic sampling or an event-driven environment, all sensor actions were triggered through a data-update event in the database. For the sake of improving user experience, a Web-based UI was implemented using scalable vector graphics (SVG) and Ajax technologies. All operations by users were conducted through the Hypertext Transfer Protocol (HTTP) standard method. Therefore, this system can be used via a browser and easily deployed. The proposed architecture is suitable for a healthcare system, a personal body area network, and the Intranet for control.
A Multitheragnostic Nanobubble System to Induce Blood-Brain Barrier Disruption with Magnetically Guided Focused Ultrasound
(2015-01-27)
Huang, Hsin-Yang
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Liu, Hao-Li
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Hsu, Po-Hung
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Chiang, Chih-Sheng
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Tsai, Chih-Hung
A New Scenario for Negative Functional Magnetic Resonance Imaging Signals: Endogenous Neurotransmission
(2009-03-11)
Shih, Yen-Yu I.
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Chen, Chiao-Chi V.
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Shyu, Bai-Chuang
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Lin, Zi-Jun
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Chiang, Yun-Chen
Functional magnetic resonance imaging (fMRI) has revolutionized investigations of brain functions. Increases in fMRI signals are usually correlated with neuronal activation, but diverse explanations have been proposed for negative fMRI responses, including decreases in neuronal activity, the vascular-steal effect, and large increases in oxygen consumption. These possible scenarios, although encompassing a wide range of potential neurovascular responses, cannot yet be used to interpret certain types of negative fMRI signals. Recent studies have found that intravenous injection of dopamine D(2) receptor (D2DR) agonist reduced the hemodynamic responses in the caudate-putamen (CPu); however, whether endogenous dopaminergic neurotransmission contributes to fMRI signals remains obscure. Since it has been suggested that the D2DR is involved in pain modulation, and the CPu shows equivocal fMRI signals during noxious stimulation, the present study established an animal model based on graded electrical stimulation to elicit different levels of nociception, and aimed to determine whether nociception-induced endogenous dopaminergic neurotransmission is sufficient to generate negative fMRI responses. Our results from cerebral blood volume (CBV)-weighted fMRI, Fos immunohistochemistry, and electro-physiological recording demonstrated a salient bilateral CBV decreases associated with heightened neuronal activity in the CPu induced by unilateral noxious electrical stimulation. In addition, preinjection of D2DR antagonist reduced the observed CBV decreases. Our findings reveal the role of the D2DR in regulating striatal vascular responses and suggest that endogenous neurotransmission-induced CBV decreases underlie negative fMRI signals. Hence, the influence of endogenous neurotransmission should be considered when interpreting fMRI data, especially in an area involved in strong vasoactive neurotransmission.
A novel multifunctional nano-platform with enhanced anti-cancer and photoacoustic imaging modalities using gold-nanorod-filled silica nanobeads
(2013)
Chen, Po-Jung
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Hu, Shang-Hsiu
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Fan, Chih-Tai
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Li, Meng-Lin
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Chen, You-Yin
The novel nano-seaurchin structure is characteristic of high-density and well-dispersed gold nanorods in one mesoporous silica nanobead. This nanoplatform provided increased photothermal stability, stable photoacoustic signal and highly efficient hyperthermia effect both in vitro and in vivo, indicating a powerful theranostic modality.
A Programmable Implantable Microstimulator SoC With Wireless Telemetry: Application in Closed-Loop Endocardial Stimulation for Cardiac Pacemaker
(2011-12-01)
Lee, Shuenn-Yuh
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Su, Mario YuCheng
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Liang, Ming-Chun
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Chen, You-Yin
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Hsieh, Cheng-Han
A low-power, wireless, and implantable microstimulator system on chip with smart powering management, immediate neural signal acquisition, and wireless rechargeable system is proposed. A system controller with parity checking handles the adjustable stimulus parameters for the stimulated objective. In the current paper, the rat's intra-cardiac electrogram is employed as the stimulated model in the animal study, and it is sensed by a low-voltage and low-power monitoring analog front end. The power management unit, which includes a rectifier, battery charging and detection, and a regulator, is used for the power control of the internal circuits. The stimulation data and required clock are extracted by a phase-locked-loop-based phase shift keying demodulator from an inductive AC signal. The full chip, which consumes 48 mu W only, is fabricated in a TSMC 0.35 mu m 2P4M standard CMOS process to perform the monitoring and pacing functions with inductively powered communication in the in vivo study.
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 analysis system for gait recognition in patients with Parkinson's disease
(2009-04-01)
Cho, Chien-Wen
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Chao, Wen-Hung
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Lin, Sheng-Huang
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Chen, You-Yin
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電控工程研究所
Recognition of specific Parkinsonian gait patterns is helpful in the diagnosis of Parkinson's disease (PD). However, there are few computer-aided methods to identify the specific gait patterns of PD. We propose a vision-based diagnostic system to aid in recognition of the gait patterns of Parkinson's disease. The proposed system utilizes an algorithm combining principal component analysis (PCA) with linear discriminant analysis (LDA). This scheme not only addresses the high data dimensionality problem during image processing but also distinguishes different gait categories simultaneously. The feasibility of the proposed system for the recognition of PD gait was tested by using gait videos of PD and normal subjects. The efficiency of feature extraction using PCA and LDA coefficients are also compared. Experimental results showed that LDA had a recognition rate for Parkinsonian gait of 95.49%, which is higher than the conventional PCA feature extraction method. The proposed system is a promising aid in identifying the gait of Parkinson's disease patients and can discriminate the gait patterns of PD patients and normal people with a very high classification rate. Crown Copyright (C) 2008 Published by Elsevier Ltd. All rights reserved.
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.
Accurate Analysis of IEEE 802.15.4 Slotted CSMA/CA over a Real-Time Wireless Sensor Network
(2010)
Huang, Wen-Tzeng
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Lin, Jing-Ting
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Chen, Chin-Hsing
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Chang, Yuan-Jen
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Chen, You-Yin
Here, we present the collision probability of the IEEE 802.15.4 slotted Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA), mechanism over a real-time wireless sensor network (RT-WSN), also called a synchronous network system. The "backoff," the random delay of CSMA/CA, can be used to create a probability model and to determine the average packet delay over RT-WSN. The proposed theoretical analysis model was nearly consistent with the simulation and with experimental results. This suggests that the slotted CSMA/CA mechanism cannot be applied effectively to RT-WSN because it cannot avoid a high collision rate with RT-WSN application requirements and, therefore, may waste a great deal of system bandwidth. We found that the packet collision rate increased up to 731% compared to an ideal model with more than 10 users within an RT-WSN. Without improvements in methodology, the high collision rate makes this slotted CSMA/CA mechanism unsuitable for RT-WSN applications.
An Integrated Service Model for Telecare System
(2008)
Chen, Chin-Hsing
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Huang, Wen-Tzeng
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Chen, You-Yin
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Chang, Yuan-Jen
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電控工程研究所
The medical costs for elderly people in Taiwan have increased largely due to the increasing number of aging people in the country. To meet the goals and requirements of the National Health Insurance (NHI), designing and providing a program which would reduce the unnecessary medical services demanded by elderly people is needed. In this research, a conceptual service model of the telecare system applied in Taiwan is discussed and presented. The service model is discussed in three dimensions, namely, service, delivery, and content. In the service dimension, the services provided by telecare service should be accessible and responsible. In the delivery dimension, telecare service should be highly interactive and with high usability. Finally, in the content dimension, the content of telecare service must be individualized and based on users\' needs. Most importantly, it must be comprehensive and authentic. Telecare service can be used as an important tool to assist nurses in nursing homes. It should be delivered by skilled nurses to increase the confidence of users. In district hospitals, doctors can substitute the weekly telecare service for traditional medical service. Therefore, the costs of NHI can be reduced. Moreover, the accessibility and interactivity of telecare service will give users greater satisfaction. The cooperation of nursing homes and district hospitals in the telecare service will not only improve the quality of long-term care but will also pave the way for the practice of telecare service in Taiwan.
Arrayed rGOSH/PMASH Microcapsule Platform Integrating Surface Topography, Chemical Cues, and Electrical Stimulation for Three-Dimensional Neuron-Like Cell Growth and Neurite Sprouting
(2014-06-01)
Liu, Heng-Wen
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Huang, Wei-Chen
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Chiang, Chih-Sheng
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Hu, Shang-Hsiu
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Liao, Chia-Hsin
The biocompatible thiol-functionalized rGOSH/PMASH microcapsules encapsulating nerve growth factor (NGF) are arrayed onto a transparent and conductive substrate, i.e., indium tin oxide (ITO), to integrate electrically stimulated cellular differentiation, electrically controlled NGF release, and topographically rough nano-surfaces into a 3-D platform for nerve regeneration. The rGOSH/PMASH microcapsules with microscale topography function not only as an adhesive coating to promote the adhesion of PC12 cells but also as electroactive NGF-releasing electrodes that stimulate NGF release and accelerate the differentiation of PC12 cells during electrical stimulation. Once electrical treatment is applied, NGF release and electrically enhanced cellular differentiation lead to an obvious increase both in the percentage of cells with neurites and in the neurite length. This length can reach nearly 90 m within 2 days of cell culture. The average neurite length is significantly increased (four-fold) after culture on the rGOSH/PMASH microcapsule substrate for 2 days compared with culture on a substrate without an rGOSH/PMASH coating. These multifunctional rGOSH/PMASH microcapsules may be used as potential 3-D patterned substrates for neural regeneration and neural prosthetics in tissue engineering applications.
Automatic segmentation of magnetic resonance images using a decision tree with spatial information
(2009-03-01)
Chao, Wen-Hung
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Chen, You-Yin
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Lin, Sheng-Huang
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Shih, Yen-Yu I.
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Tsang, Siny
Here we proposed an automatic segmentation method based on a decision tree to classify the brain tissues in magnetic resonance (MR) images. Two types of data - phantom MR images obtained from IBSR (http://www.cma.mgh.harvard.edu/ibsr) and simulated brain MR images obtained from BrainWeb (http://www.bic.mni.mcgill.ca/brainweb) - were segmented using an automatic decision tree algorithm to obtain images with improved visual rendition. Spatial information on the general gray level (G), spatial gray level (S), and two-dimensional wavelet transform (M was combined in-plane in two coordinate systems (Euclidean coordinates (x,y) or polar coordinates (r,theta)). The decision tree was constructed based on a binary tree with nodes created by splitting the distribution of input features of the tree. The spatial information obtained from MR images with different noise levels and inhomogeneities were segmented to compare whether the use of a decision tree improved the identification of human anatomical structures in a neuroimage. The average accuracy rates of segmentation for phantom images with a noise variation of 15 gray levels were 0.9999 and 0.9973 with spatial information (G, x,y, r, theta) and (S, x, y, r, theta), respectively, and 0.9999 and 0.9819 with spatial information (G, x, y, S, r, theta) and (W, x, y, G, r, theta). The average accuracy rates of segmentation for simulated MR images with a noise level of 5% were 0.9532 and 0.9439 with spatial information (G, x, y, r, theta) and (S, x, y, r, theta), respectively, and 0.9446 and 0.9287 with spatial information (G, x, y, S, r, theta) and (W, x, y, G, r, theta). The accuracy rates of segmentation were highest for both simulated phantom and brain MR images, having the lowest noise levels, from a reduction of overlapping gray levels in the images. The accuracies of segmentation were higher when the spatial information included the general gray level than when it included the spatial gray level, which in turn were higher than when it included the wavelet transform. Furthermore, the performance of segmentation was also evaluated with a boundary detection methodology that is based on the Hausdorff distance to compare with the mean computer to observer difference (COD) and mean interobserver difference (IOD) for gray matter (GM), white matter (WM), and all areas (ALL) from images segmented using the decision tree. The values of mean COD are similar and around 12 mm for GM segmented using the decision tree. Our segmentation method based on a decision tree algorithm presented an easy way to perform automatic segmentation for both phantom and tissue regions in brain MR images. (C) 2008 Published by Elsevier Ltd.
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.
Characterization of a Fiber Bundle-Based Real-Time Ultrasound/Photoacoustic Imaging System and Its In Vivo Functional Imaging Applications
(2019-12-01)
Leng, He
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Wang, Yuhling
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Jhang, De-Fu
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Chu, Tsung-Sheng
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Tsao, Chia-Hui
Photoacoustic (PA) imaging is an attractive technology for imaging biological tissues because it can capture both functional and structural information with satisfactory spatial resolution. Current commercially available PA imaging systems are limited by their bulky size or inflexible user interface. We present a new handheld real-time ultrasound/photoacoustic imaging system (HARP) consisting of a detachable, high-numerical-aperture (NA) fiber bundle-based illumination system integrated with an array-based ultrasound (US) transducer and a data acquisition platform. In this system, different PA probes can be used for different imaging applications by switching the transducers and the corresponding jackets to combine the fiber pads and transducer into a single probe. The intuitive user interface is a completely programmable MATLAB-based platform. In vitro phantom experiments were conducted to test the imaging performance of the developed PA system. Furthermore, we demonstrated (1) in vivo brain vasculature imaging, (2) in vivo imaging of real-time stimulus-evoked cortical hemodynamic changes during forepaw electrical stimulation, and (3) in vivo imaging of real-time cerebral pharmacokinetics in rats using the developed PA system. The overall purpose of this design concept for a customizable US/PA imaging system is to help overcome the diverse challenges faced by medical researchers performing both preclinical and clinical PA studies.
Conductive nanogel-interfaced neural microelectrode arrays with electrically controlled in-situ delivery of manganese ions enabling high-resolution MEMRI for synchronous neural tracing with deep brain stimulation
(2017-04)
Huang, Wei-Chen
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Lo, Yu-Chih
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Chu, Chao-Yi
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Lai, Hsin-Yi
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Chen, You-Yin
Chronic brain stimulation has become a promising physical therapy with increased efficacy and efficiency in the treatment of neurodegenerative diseases. The application of deep brain electrical stimulation (DBS) combined with manganese-enhanced magnetic resonance imaging (MEMRI) provides an unbiased representation of the functional anatomy, which shows the communication between areas of the brain responding to the therapy. However, it is challenging for the current system to provide a real-time high-resolution image because the incorporated MnCl2 solution through microinjection usually results in image blurring or toxicity due to the uncontrollable diffusion of Mn2+. In this study, we developed a new type of conductive nanogel-based neural interface composed of amphiphilic chitosan-modified poly(3,4 -ethylenedioxythiophene) (PMSDT) that can exhibit biomimic structural/mechanical properties and ionic/electrical conductivity comparable to that of Au. More importantly, the PMSDT enables metal-ligand bonding with Mn2+ ions, so that the system can release Mn2+ ions rather than MnCl2 solution directly and precisely controlled by electrical stimulation (ES) to achieve real-time high resolution MEMRI. With the integration of PMSDT nanogel-based coating in polyimide-based micro electrode arrays, the post-implantation DBS enables frequency-dependent MR imaging in vivo, as well as small focal imaging in response to channel site-specific stimulation on the implant. The MR imaging of the implanted brain treated with 5-min electrical stimulation showed a thalamocortical neuronal pathway after 36 h, confirming the effective activation of a downstream neuronal circuit following DBS. By eliminating the susceptibility to artifact and toxicity, this system, in combination with a MR-compatible implant and a bio-compliant neural interface, provides a harmless and synchronic functional anatomy for DBS. The study demonstrates a model of MEMRI-functionalized DBS based on functional neural interface engineering and controllable delivery technology, which can be utilized in more detailed exploration of the functional anatomy in the treatment of neurodegenerative diseases. (C) 2017 Elsevier Ltd. All rights reserved.
CONSERVING BANDWIDTH IN A WIRELESS SENSOR NETWORK FOR TELEMEDICINE APPLICATION
(2010)
Hsu, Shuo-Jen
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Chen, Chin-Hsing
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Chen, Show-Hong
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Huang, Wen-Tzeng
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Chang, Yuan-Jen
Telemedicine aided by wireless sensor networks (WSN) has recently become a healthcare trend. Many previous studies have adopted the ZigBee-based WSN to implement a platform and/or a telemedicine system. However, the low data rate and bandwidth have limited the maximum number of nodes in a WSN during continuous and simultaneous transmission. The issue of low data throughput has not been addressed in previous research. In this study, we propose an arrhythmia-aware system, and a new DSP-based WSN platform is developed for the high compression performance of physiological data in a ZigBee-based WSN. Proven by simulations and several real tests, the combination of the proposed platform and the sensor nodes can lead to more bandwidth conservation and extend the WSN scale for the next generation of telemedicine system.
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.
Decomposition of EEG Signals for Multichannel Neural Activity Analysis in Animal Experiments
(2010)
Vigneron, Vincent
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Chen, Hsin
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Chen, Yen-Tai
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Lai, Hsin-Yi
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Chen, You-Yin
We describe in this paper some advanced protocols for the discrimination and classification of neuronal spike waveforms within multichannel electrophysiological recordings. Sparse decomposition was used to serarate the linearly independent signals underlying sensory information in cortical spike firing patterns. We introduce some modifications in the the IDE algorithm to take into account prior knowledge on the spike waveforms. We have investigated motor cortex responses recorded during movement in freely moving rats to provide evidence for the relationship between these patterns and special behavioral task.
Design and characterization of a novel amphiphilic chitosan nanocapsule-based thermo-gelling biogel with sustained in vivo release of the hydrophilic anti-epilepsy drug ethosuximide
(2012-08-10)
Hsiao, Meng-Hsuan
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Larsson, Mikael
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Larsson, Anette
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Evenbratt, Hanne
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Chen, Ying-Yu
Thermo-gelling injectable nanogels, with no burst release of loaded drug, were prepared by a simple route by combining self assembled nanocapsules of amphiphilically modified chitosan with glycerophosphate di-sodium salt and glycerol. The potential as a depot drug delivery system was demonstrated in vivo through the therapeutic effect of ethosuximide (ESM) loaded nanogels, suppressing spike wave discharges (SWDs) in Long Evan rat model. Simultaneously clearance of gels from the site of administration was monitored non-invasively using MRI. The gel structure was characterized using TEM and SEM, confirming the gels to be an assembly of nanocapsules and using two-photon microscopy to visualize the network structure. In vitro drug release studies using ESM revealed that the nanogels exhibited extended, mostly Fickian release. Finally, all investigated formulations displayed excellent cytotoxicity data determined by MTT assay using human retinal pigmented epithelium cells. All presented properties are highly desirable for injectable depot gels for drug delivery. (c) 2012 Elsevier B.V. All rights reserved.
Design and fabrication of a polyimide-based microelectrode array: Application in neural recording and repeatable electrolytic lesion in rat brain
(2009-08-30)
Chen, You-Yin
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Lai, Hsin-Yi
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Lin, Sheng-Huang
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Cho, Chien-Wen
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Chao, Wen-Hung
The design and testing of a new microelectrode array, the NCTU (National Chiao Tung University) probe, was presented. Evaluation results showed it has good biocompatibility, high signal-to-noise ratio (SNR: the root mean square of background noise to the average peak-to-peak amplitude of spikes) during chronic neural recordings, and high reusability for electrolytic lesions. The probe was a flexible, polyimide-based microelectrode array with a long shaft (14.9 mm in length) and 16 electrodes (5 mu m-thick and 16 mu m in radius); its performance in chronic in vivo recordings was examined in rodents. To improve the precision of implantation, a metallic, impact-resistant layer was sandwiched between the polyimide layers to strengthen the probe. The three-dimensional (3D) structure of electrodes fabricated by electroplating produced rough textures that increased the effective surface area. The in vitro impedance of electrodes on the NCTU probe was 2.4 +/- 0.52 M Omega at 1 kHz. In addition, post-surgical neural recordings of implanted NCTU probes were conducted for up to 40 days in awake, normally behaving rats. The electrodes on the NCTU probe functioned well and had a high SNR (range: 4-5) with reliable in vivo impedance (<0.7 M Omega). The electrodes were also robust enough to functionally record events, even after the anodal current (30 mu A, 10 s) was repeatedly applied for 60 times. With good biocompatibility, high and stable SNR for chronic recording, and high tolerance for electrolytic lesion, the NCTU probe would serve as a useful device in future neuroscience research. (C) 2009 Elsevier B.V. All rights reserved.