Correction: Data-driven analyses of motor impairments in animal models of neurological disorders.

[This corrects the article DOI: 10.1371/journal.pbio.3000516.].

A rodent obstacle course procedure controls delivery of enrichment and enhances complex cognitive functions.

Enrichment in rodents affects brain structure, improves behavioral performance, and is neuroprotective. Similarly, in humans, according to the cognitive reserve concept, enriched experience is functionally protective against neuropathology. Despite this parallel, the ability to translate rodent studies to human clinical situations is limited. This limitation is likely due to the simple cognitive processes probed in rodent studies and the inability to control, with existing methods, the degree of rodent engagement with enrichment material. We overcome these two difficulties with behavioral tasks that probe, in a fine-grained manner, aspects of higher-order cognition associated with deterioration with aging and dementia, and a new enrichment protocol, the 'Obstacle Course' (OC), which enables controlled enrichment delivery, respectively. Together, these two advancements will enable better specification (and comparisons) of the nature of impairments in animal models of complex mental disorders and the potential for remediation from various types of intervention (e.g., enrichment, drugs). We found that two months of OC enrichment produced substantial and sustained enhancements in categorization memory, perceptual object invariance, and cross-modal sensory integration in mice. We also tested mice on behavioral tasks previously shown to benefit from traditional enrichment: spontaneous object recognition, object location memory, and pairwise visual discrimination. OC enrichment improved performance relative to standard housing on all six tasks and was in most cases superior to conventional home-cage enrichment and exercise track groups.

Data-driven analyses of motor impairments in animal models of neurological disorders.

Behavior provides important insights into neuronal processes. For example, analysis of reaching movements can give a reliable indication of the degree of impairment in neurological disorders such as stroke, Parkinson disease, or Huntington disease. The analysis of such movement abnormalities is notoriously difficult and requires a trained evaluator. Here, we show that a deep neural network is able to score behavioral impairments with expert accuracy in rodent models of stroke. The same network was also trained to successfully score movements in a variety of other behavioral tasks. The neural network also uncovered novel movement alterations related to stroke, which had higher predictive power of stroke volume than the movement components defined by human experts. Moreover, when the regression network was trained only on categorical information (control = 0; stroke = 1), it generated predictions with intermediate values between 0 and 1 that matched the human expert scores of stroke severity. The network thus offers a new data-driven approach to automatically derive ratings of motor impairments. Altogether, this network can provide a reliable neurological assessment and can assist the design of behavioral indices to diagnose and monitor neurological disorders.

An effective method to identify various factors for denoising wrist pulse signal using wavelet denoising algorithm.

BACKGROUND: WPS is a non-invasive method to investigate human health. During signal acquisition, noises are also recorded along with WPS. OBJECTIVE: Clean WPS with high peak signal to noise ratio is a prerequisite before use in disease diagnosis. Wavelet Transform is a commonly used method in the filtration process. Apart from its extensive use, the appropriate factors for wavelet denoising algorithm is not yet clear in WPS application. The presented work gives an effective approach to select various factors for wavelet denoise algorithm. With the appropriate selection of wavelet and factors, it is possible to reduce noise in WPS. METHODS: In this work, all the factors of wavelet denoising are varied successively. Various evaluation parameters such as MSE, PSNR, PRD and Fit Coefficient are used to find out the performance of the wavelet denoised algorithm at every one step. RESULTS: The results obtained from computerized WPS illustrates that the presented approach can successfully select the mother wavelet and other factors for wavelet denoise algorithm. The selection of db9 as mother wavelet with sure threshold function and single rescaling function using UWT has been a better option for our database. CONCLUSION: The empirical results proves that the methodology discussed here could be effective in denoising WPS of any morphological pattern.

The identification of liver cirrhosis with modified LBP grayscaling and Otsu binarization.

Liver cirrhosis is considered as one of the most common diseases in healthcare. The widely accepted technology for the diagnosis of liver cirrhosis is via ultrasound imaging. This paper presents a technique for detecting the cirrhosis of liver through ultrasound images. The region of interest has been selected from these ultrasound images and endorsed from a radiologist. The identification of liver cirrhosis is finally detected through modified local binary pattern and OTSU methods. Experimental results from the proposed method demonstrated its feasibility and applicability for high performance cirrhotic liver identification.

SEMG signal analysis at acupressure points for elbow movement.

Surface electromyography signals (SEMG) are the most common form of non-invasive-measurement of muscle activities. To acquire proper SEMG for particular limb function, the placement of electrodes on the skin over respective active group of muscles becomes very important. Measurement of SEMG depends on a number of factors/parameters like amplitude, time and frequency domain properties. In the present investigation, analysis was carried firstly; to study the grip force vs. SEMG parameters at acupressure points on arm, using single channel approach. At all the selected acupressure points a linear increment of SEMG was observed. Secondly; to discriminate four elbow movements from different locations on arm using two channel approach with single parameter. The parameter for the analysis chosen was the root of mean of square (RMS) value of SEMG. Further; principal component analysis was used to verify the elbow movement discrimination. Extension and supination were the two operations which were observed to be easy to realize by prosthetic devices. The selection of these locations was done on the basis of acupressure points and anatomy of elbow. Matlab-softscope was used for acquiring the SEMG from line-in input port of PC-sound card. This study will also be helpful for the researchers in understanding the behavior of SEMG for elbow movement in development of prosthetic or exoskeleton devices.

Study of issues in the development of surface EMG controlled human hand.

In the process of improvement of prosthetic devices, there have been efforts to develop satisfactorily working artificial hands but still lots of work is to be done to meet the accuracy and requirements of the human hand movement. The EMG signal has been most promising signal in development of artificial limbs. The present review paper gives the historical developments in three main sections. First part describes the EMG signal properties. Second part deals with the mathematical models developed till now for EMG signal analysis. In the third part different design approaches have been reviewed for artificial hand. First approach discussed here is on the body-powered terminal devices which are controlled by the user's pull on the control cable to open the hand or hook and for the grip strength. Other being myoelectric controls type, an externally-powered system which uses electrical impulses, generated by contraction of the amputees own remaining muscles to operate a motor in a mechanical hand, hook or elbow. This paper presents a brief overview of above mentioned issues with regard to artificial hands.