Narrow Results

To effectively solve the problem of a small proportion of substantia nigra and midbrain regions in Magnetic Resolution Imaging (MRI) images of Parkinson’s disease (PD) patients, unclear boundaries with surrounding tissues, and difficulty in accurately delineating boundaries, an improved U²-Net algorithm for Parkinson’s substantia nigra midbrain image segmentation was proposed. This algorithm first improves the Residual U-blocks (RSU) and RSU-4F modules using the Shuffle Attention (SA) module, enhancing the network’s attention to Parkinson’s substantia nigra and midbrain blurry regions in the encoding and decoding layers. Next, replace some ordinary convolutions in RSU-4F with DynamicConv (DyConv), capture local features through dynamic convolution kernels, and improve the segmentation performance of Parkinson’s images. The experimental data used clinical data, and the improved algorithm achieved Dice, Precision, Recall, and mIou of 80.27%, 89.99%, 88.88%, and 82.38% in substantia nigra segmentation, respectively. The experimental results show that this algorithm can achieve more precise segmentation of Parkinson’s images.

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Bai Chunli to Lead CAS.

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CCMB Breakthrough in Seed Cloning.

First Asian-based Genome-Wide Association Study on Optic Disc Area.

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Singapore's First Cancer Drug Shows Promise.

Cancer Research Alliance.

A*STAR-Maccine Collaborate on Preclinical Imaging.

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New Drug Stops Parkinson's Disease in Mice.

TCM Plant Mechanism Elucidated.

'Good Cholesterol' Structure Identified.

First Genome Map of Rare, TCM Herb.

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Some Blind People 'See' With Ears.

Egg Woos Sperm with Hormone.

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Lethal Red Tape.

World's First Practical Artificial Leaf.

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Standing stability is controlled by musculoskeletal and neurological systems. Various methods have been used to evaluate the performance of subjects during standing including linear and non-linear methods. It is not clear which method has more sensitivity to represent the stability of subjects with various musculoskeletal disorders. Therefore, the aim of this study was to investigate the stability of the subjects with various musculoskeletal disorders by use of linear and non-linear methods. About 65 subjects including, normal and those with flatfoot, Parkinson and Perthes were recruited into this study. A Kistler forceplate was used to evaluate the stability. The difference between the linear (center of pressure excursion, velocity and path length) and non-linear (approximate entropy) parameters were evaluated using the independent t-test. The mean values of stability parameters (linear and non-linear) of flat arch subjects were more than that of normal subjects. Although there was no difference between linear stability parameters of normal and those with Parkinson disease, their mean value of non-linear parameter was less than that of normal subject (p-< 0.05). The results of stability analysis based on both linear and non-linear approaches showed that the subjects with Perthes disease were more unstable than normal subjects. It seems that non-linear method is more sensitive to represent the difference between stability of subjects with flatfoot, Parkinson and Perthes. However, if a combination of various parameters, based on linear method, is used to measure stability, the difference between stability can be enhanced. Depending on the disease condition increasing and decreasing the value of approximate entropy represent the unstability situations.

Magnetoencephalographic (MEG) recordings of patients with CNS disorders were obtained using a whole-head 122-channel magnetometer SQUID and analyzed using Fourier statistical analysis. External transcranial magnetic stimulation in the order of pico Tesla (pTMS) was applied to the above patients with proper characteristics (magnetic intensity 1–7.5 pT; frequency: 8–13 Hz) which were obtained with MEG recordings prior to pTMS. The MEG recordings after the application of pTMS showed a rapid attenuation of the high abnormal activity followed by an increase in the number of the low frequency components toward the patients α-rhythm. The possible mechanisms of the effects of external magnetic stimulation on the pineal gland in the brain are presented.

We propose hypothesis tests for detecting dopaminergic medication response in Parkinson disease patients, using longitudinal sensor data collected by smartphones. The processed data is composed of multiple features extracted from active tapping tasks performed by the participant on a daily basis, before and after medication, over several months. Each extracted feature corresponds to a time series of measurements annotated according to whether the measurement was taken before or after the patient has taken his/her medication. Even though the data is longitudinal in nature, we show that simple hypothesis tests for detecting medication response, which ignore the serial correlation structure of the data, are still statistically valid, showing type I error rates at the nominal level. We propose two distinct personalized testing approaches. In the first, we combine multiple feature-specific tests into a single union-intersection test. In the second, we construct personalized classifiers of the before/after medication labels using all the extracted features of a given participant, and test the null hypothesis that the area under the receiver operating characteristic curve of the classifier is equal to 1/2. We compare the statistical power of the personalized classifier tests and personalized union-intersection tests in a simulation study, and illustrate the performance of the proposed tests using data from mPower Parkinsons disease study, recently launched as part of Apples ResearchKit mobile platform. Our results suggest that the personalized tests, which ignore the longitudinal aspect of the data, can perform well in real data analyses, suggesting they might be used as a sound baseline approach, to which more sophisticated methods can be compared to.