Browsing by Author "Lai, Hsin-Yi"

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A Deep-Learning Approach to Determine the Dependency between Two Subject Types in the Web of Science
(2019-01-01)
Phoa, Frederick Kin Hing
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Lai, Hsin-Yi
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Chang, Livia Lin-Hsuan
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Honda, Keisuke
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統計學研究所
Traditional wisdom always suggests that some subjects have strong relationships, while others are almost mutual independent. However. there is a lack of quantitative approach to formulate these relationships in a systematic and unbiased way. In this work, we train a classification machine via deep learning to determine whether two subject types are independent based on the citation information from the Web of Science database. This machine not only achieves very high accuracy in estimating the dependency among subject types in the database, but also is able to predict the dependency when one or both subject types do not exist in the database.
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 two-step deep learning approach to data classification and modeling and a demonstration on subject type relationship analysis in the Web of Science
(1970-01-01)
Phoa, Frederick Kin Hing
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Lai, Hsin-Yi
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Chang, Livia Lin-Hsuan
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Honda, Keisuke
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統計學研究所
It is common sense that some subjects have strong relationships while others are perhaps almost mutually independent, but a quantitative and systematic approach to describe such sense is a deficiency. A technique called pointwise mutual information (PMI) from information science helps to fulfill the request, but the calculation through a large-scale database is computationally infeasible if one requires an instantaneous value. This work provides a two-step remedy via deep learning for estimating and predicting relationships among two subject types that are found in the large-scale citation database called the Web of Science. The resulting model successfully replicates existing PMI values among subject types, and it can be used for predicting PMI values of two subject types if one or both subject types does not exist in the database.
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.
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.
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 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.
Design, simulation and experimental validation of a novel flexible neural probe for deep brain stimulation and multichannel recording
(2012-06-01)
Lai, Hsin-Yi
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Liao, Lun-De
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Lin, Chin-Teng
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Hsu, Jui-Hsiang
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He, Xin
An implantable micromachined neural probe with multichannel electrode arrays for both neural signal recording and electrical stimulation was designed, simulated and experimentally validated for deep brain stimulation (DBS) applications. The developed probe has a rough three-dimensional microstructure on the electrode surface to maximize the electrode-tissue contact area. The flexible, polyimide-based microelectrode arrays were each composed of a long shaft (14.9 mm in length) and 16 electrodes (5 mu m thick and with a diameter of 16 mu m). The ability of these arrays to record and stimulate specific areas in a rat brain was evaluated. Moreover, we have developed a finite element model (FEM) applied to an electric field to evaluate the volume of tissue activated (VTA) by DBS as a function of the stimulation parameters. The signal-to-noise ratio ranged from 4.4 to 5 over a 50 day recording period, indicating that the laboratory-designed neural probe is reliable and may be used successfully for long-term recordings. The somatosensory evoked potential (SSEP) obtained by thalamic stimulations and in vivo electrode-electrolyte interface impedance measurements was stable for 50 days and demonstrated that the neural probe is feasible for long-term stimulation. A strongly linear (positive correlation) relationship was observed among the simulated VTA, the absolute value of the SSEP during the 200 ms post-stimulus period (Sigma SSEP) and c-Fos expression, indicating that the simulated VTA has perfect sensitivity to predict the evoked responses (c-Fos expression). This laboratory-designed neural probe and its FEM simulation represent a simple, functionally effective technique for studying DBS and neural recordings in animal models.
Dictionary-Based Classification Models. Applications for Multichannel Neural Activity Analysis
(2009)
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 advanced protocols for the discrimination and classification of neuronal spike waveforms within multichannel electrophysiological recordings. The programs are capable of detecting and classifying the spikes from multiple, simultaneously active neurons, even in situations where there is a high degree of spike waveform superposition on the recording channels. Sparse Decomposition (SD) approach was used to define the linearly independent signals underlying sensory information in cortical spike tiring patterns. 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. Ensembles of neurons were simultaneously recorded in this during long periods of spontaneous behaviour. Waveforms provided from the neural activity were then processed and classified. Typically, most information correlated across neurons in the ensemble were concentrated in a small number of signals. This showed that these encoding vectors functioned as a feature detector capable of selectively predicting significant sensory or behavioural events. Thus it encoded global magnitude of ensemble activity, caused either by combined sensory inputs or intrinsic network activity. SD on an overcomplete dictionary has recently attracted a lot of attention in the literature. because of its potential application in many different areas including Compressive Sensing (CS). SD approach is compared to the generative approach derived from the likelihood-based framework, in which each class is modeled by a known or unknown density function. The classification of electroencephalographic (EEG) waveforms present 2 main statistical issues: high dimensional data and signal representation.
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.
Implantable Graphene-based Neural Electrode Interfaces for Electrophysiology and Neurochemistry in In Vivo Hyperacute Stroke Model
(2016-01-13)
Liu, Ta-Chung
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Chuang, Min-Chieh
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Chu, Chao-Yi
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Huang, Wei-Chen
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Lai, Hsin-Yi
Implantable microelectrode arrays have attracted considerable interest due to their high temporal and spatial resolution recording of neuronal activity in tissues. We herein presented an implantable multichannel neural probe with multiple real-time monitoring of neural-chemical and neural-electrical signals by a nonenzymatic neural-chemical interface, which was designed by creating the newly developed reduced graphene oxide-gold oxide (rGO/Au2O3) nanocomposite electrode. The modified electrode on the neural probe was prepared by a facile one-step cyclic voltammetry (CV) electrochemical method with simultaneous occurrence of gold oxidation and GOs reduction to induce the intimate attachment by electrostatic interaction using chloride ions (Cl-). The rGO/Au2O3-modified electrode at a low deposition scan rate of 10 mVs(-1) displayed significantly improved electrocatalytic activity due to large active areas and well-dispersive attached rGO sheets. The in vitro amperometric response to H2O2 demonstrated a fast response of less than 5 s and a very low detection limit of 0.63 mu M. In in vivo hyperacute stroke model, the concentration of H2O2 was measured as 100.48 +/- 4.52 mu M for rGO/Au2O3 electrode within 1 h photothrombotic stroke, which was much higher than that (71.92 mu M +/- 2.52 mu M) for noncoated electrode via in vitro calibration. Simultaneously, the somatosensory-evoked potentials (SSEPs) test provided reliable and precise validation for detecting functional changes of neuronal activities. This newly developed implantable probe with localized rGO/Au2O3 nanocomposite electrode can serve as a rapid and reliable sensing platform for practical H2O2 detection in the brain or for other neural-chemical molecules in vivo.
In vivo functional photoacoustic micro-imaging of the electrically stimulated rat brain with multi-wavelengths
(2010)
Liao, Lun-De
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Li, Meng-Lin
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Lai, Hsin-Yi
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Chen, You-Yin
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Chao, Paul C. -P.
In this study, we report on using multi-wavelength photoacoustic microscopy to image hemodynamic changes of total hemoglobin concentration (HbT) (i.e., blood volume) and oxygenation (SO(2)) in rat brain cortex vessels with electrical stimulation. Electrical stimulation of the rat left forelimb was applied to evoke changes in vascular dynamics of the rat somatosensory cortex. The applied current pulses were with a pulse frequency of 3 Hz, pulse duration of 0.2 ms, and pulse amplitude of 5 mA, respectively. The imaging target of rat brains was demarcated at AP 0 - -2.5 mm and ML +/-6 mm with respect to bregma. HbT changes were probed by images acquired at 570 nm, a hemoglobin isosbestic point while SO(2) changes were imaged by those acquired at 560 nm or 600 nm and their derivatives, which were normalized to those with 570 nm wavelengths. Correlation between the electrical stimulation paradigm and images acquired at 570, 560, and 600 nm in contralateral and ipsilateral vasculature was statistically analyzed, showing that the HbT and SO(2) changes revealed by multi-wavelength photoacoustic images spatially correlated with contralateral vasculature.
Investigation of the cerebral hemodynamic response function in single blood vessels by functional photoacoustic microscopy
(2012-06-01)
Liao, Lun-De
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Lin, Chin-Teng
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Shih, Yen-Yu I.
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Lai, Hsin-Yi
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Zhao, Wan-Ting
The specificity of the hemodynamic response function (HRF) is determined spatially by the vascular architecture and temporally by the evolution of hemodynamic changes. Here, we used functional photoacoustic microscopy (fPAM) to investigate single cerebral blood vessels of rats after left forepaw stimulation. In this system, we analyzed the spatiotemporal evolution of the HRFs of the total hemoglobin concentration (HbT), cerebral blood volume (CBV), and hemoglobin oxygen saturation (SO2). Changes in specific cerebral vessels corresponding to various electrical stimulation intensities and durations were bilaterally imaged with 36 x 65-mu m(2) spatial resolution. Stimulation intensities of 1, 2, 6, and 10 mA were applied for periods of 5 or 15 s. Our results show that the relative functional changes in HbT, CBV, and SO2 are highly dependent not only on the intensity of the stimulation, but also on its duration. Additionally, the duration of the stimulation has a strong influence on the spatiotemporal characteristics of the HRF as shorter stimuli elicit responses only in the local vasculature (smaller arterioles), whereas longer stimuli lead to greater vascular supply and drainage. This study suggests that the current fPAM system is reliable for studying relative cerebral hemodynamic changes, as well as for offering new insights into the dynamics of functional cerebral hemodynamic changes in small animals. (C) 2012 Society of Photo-Optical Instrumentation Engineers (SPIE). [ DOI: 10.1117/1.JBO.17.6.061210]
Multifunctional 3D Patternable Drug-Embedded Nanocarrier-Based Interfaces to Enhance Signal Recording and Reduce Neuron Degeneration in Neural Implantation
(2015-07-22)
Huang, Wei-Chen
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Lai, Hsin-Yi
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Kuo, Li-Wei
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Liao, Chia-Hsin
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Chang, Po-Hsieh
Nonlinear Analysis of Movement-related Changes in Human Subthalamic Local Field Potentials
(2010)
Wei, Chun-Shu
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Tsai, Chon-Haw
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Chiou, Shang-Ming
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Hseu, Ya-Ting
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Liu, Ta-Wei
Movement-related changes such as event-related desynchronizationcan (ERD) and event-related synchronization (ERS) can be found in human subthalamic nucleus (STN) with analysis on local field potentials (LFP) recorded from Parkinson's disease (PD) patients. Besides traditional time-frequency (TF) analysis, we introduced nonlinear analysis, bispectral and approximate entropy (ApEn), to measure the signal nonlinear correlation and regularity in neural activities. Movement-related changes were found in the beta band, bicoherence and ApEn, and variation during stationary movement is more available by nonlinear methods. Therefore, we suggest nonlinear analysis for further related studies.
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.
Self-assembling PVA-F127 thermosensitive nanocarriers with highly sensitive magnetically-triggered drug release for epilepsy therapy in vivo
(2012)
Huang, Hsin-Yang
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Hu, Shang-Hsiu
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Chian, Chih-Shang
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Chen, San-Yuan
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Lai, Hsin-Yi
Self-assembling, crosslinker-free, highly thermosensitive nanocarriers (TSNCs) were synthesized by the incorporation of iron oxide nanoparticles and hydrophobic drug molecules into a thermosensitive matrix composed of PEO-PPO-PEO (F127) triblock-copolymer and polyvinyl alcohol (PVA) using a mini-emulsion process. The addition of PVA significantly contributes to the stability of the thermosensitive PVA-F127 nanocarriers and reduces drug leakage because it provides hydrogen bonds to react with the PEO segments in the shell. The TSNCs exhibit a remarkable triggered size contraction and shrinkage as a result of opposing polymer thermal effects. These effects include thermal expansion of the PVA and thermal shrinkage of the F127 when the magnetic field induced a temperature change that reached 40 to 50 degrees C through heat generation of the magnetic nanoparticles. Depending on the PVA/F127 ratios, the TSNCs can act as a remotely triggered drug delivery platform with a tunable burst drug release profile through the structure deformation by an external magnetic field. Furthermore, the TSNCs also presented ultrasensitive magnetic resonance imaging (MRI), as demonstrated by a relatively high r(2)/r(1) ratio (430). A preliminary in vivo study using the Long-Evans rat model has demonstrated a significant reduction in the spike-wave discharge after the anti-epilepsy drug, ethosuximide (ETX), was burst released from the TSNCs. These results were compared to PVA nanocarriers under the same magnetic induction as the in vitro carriers. Using a well-controlled burst release, these nanocarriers may provide significant advantages as highly temperature-responsive nanocarriers for the treatment of acute diseases.