Neuroscience and Biomedical Engineering (Discontinued) - Volume 3, Issue 1, 2015

Humans are able to perceive symmetry information in the visual environment rapidly and efficiently. Although the psychophysical aspects of visual symmetry recognition have been investigated, relatively little attention has been paid to understanding their underlying neural mechanisms. This study aimed to investigate human visual-evoked potentials (VEPs) in response to complex object images of varying symmetry, and to find their neural correlates. The stimuli consisted of five gray-scale images of daily life objects (flower, butterfly, human face, bird, and car) with different levels of horizontal and vertical symmetry that were quantified using the images’ pixel values, and by subjective evaluation by the participants. VEPs were extracted from the electroencephalogram (EEG) data recorded using 128-channel scalp electrodes in 11 healthy human subjects. The results revealed a significant correlation between latency differences of the VEP component at occipito-temporal sites around 170 ms (N170) and the levels of stimulus symmetry.

The peptide poison binding mechanism shows how different types of potassium channels are affected or blocked by peptide poisons derived from disparate species of scorpions and snakes. The peptide poison binding mechanism to the extracellular domain of potassium channel called pore domain was examined. The binding affinity of a given poison molecule to a specific potassium channel is quantified on the basis of ZDock Score of Protein-Protein docking using Accelrys Discovery Studio. Interacting amino acid residues of peptide toxin and those of the extracellular pore domains are identified by a standalone application and mechanism behind the toxin’s binding severity to potassium channel is deduced. From ZDock score of pose with highest score obtained after docking, we found that peptide poisons have the highest binding affinity to the calcium-gated potassium channel, followed by inward rectifier potassium channel and then lastly to the voltage-gated potassium channel. Using Delphi, we calculated charges on the toxin molecule and found that Electrostatic charges on peptide poison and pore forming residues of Potassium channel are identified as key factors responsible for binding. Dendrotoxin, which is having overall and per amino acid higher positive charge, is found to be most potent toxin compared to charybdotoxin and agitoxin.

The goal of the present study is to explore the speed discrimination interaction between visual and tactile motion during unimodal tasks and crossmodal tasks. Fifteen participants performed four speed reproduction tasks that included a tactile-tactile task, a visual-visual task, a tactile-visual task and a visual-tactile task at various speeds. Participants were asked to remember the standard stimuli speed and match the stimuli speed with the standard stimuli speed. The present study showed that both visual discrimination speed and tactile visual discrimination are underestimated relative to the standard speed at relatively high speeds during unimodal tasks. The present study suggests that speed matching is processed similarly in both the visual and tactile sensory systems. Moreover, the present study demonstrated reciprocal interactions between the processing of visual speed matching and tactile speed matching. Visual speed stimuli positively affected tactile speed matching, whereas tactile speed stimuli disrupted visual speed matching at relatively high speeds. Interestingly, visual speed stimuli can calibrate the tactile speed discrimination, but not vice-versa. A possible explanation is that the perceptual system is organized such that speed stimuli input from visual modalities receive a higher weight in the interaction process than speed stimuli input from tactile modalities. The reciprocal interactions between visual and tactile speed matching modalities directly support the view that at some point in the motion processing stream, these two modalities have partially overlapping substrates.

Detection of breast cancer at early stages helps reducing the mortality rates in women. Mammography has proven to be very useful tool in the diagnosis of breast cancer, yet there are complications to separate diverse morphological features in mammographic images. This study investigates the potential use of morphological component analysis (MCA) for classifying normal and abnormal tissues in mammograms. Two different dictionaries i.e. Local discrete cosine transform (LDCT) and Curvelet transform via wrapping (CURVwrap) were used with varying iterations to obtain a morphological decomposition into two parts i.e. a piecewise smooth part and a texture part. The piecewise smooth parts for each iteration were kept and features were extracted using curvelet coefficients. Using the Simple logistic classifier with 10-folds cross validation, an accuracy of 86.50% with AUC value of 0.89 was achieved for 300 iterations.

The daytime nap sleep has positive relaxation function when the subject is waking up from about 20 minutes light sleep, but negative effect of sleep inertia when they fall and wake up from deep sleep. In this study, an automatic sleep level prediction method was developed for daytime short nap regulation. The ultimate purpose is to predict the tendency of sleep level from light to deep. Accordingly the subject can have mode refreshed by waking up from light sleep. The sleep data during nap in the afternoon was recorded. Totally, 8 subjects participated. The sleep level is described by two parameters of EEG (Electroencephalography) calculated for each 5-second segment data. ARMA (Auto-Regressive and Moving Average) model is adopted for sleep level prediction. In order to evaluate the effectiveness of prediction results, SVM (Supported Vector Machine) is utilized to make sleep stage classification. The obtained classification results were compared with the visual inspection. The accuracy was with an averaged value of 80%. The developed method was useful for the estimation and prediction of sleep level variation during one’s nap.

Clinical data collection in Parkinson’s disease (PD) patients is typically limited to twice or thrice yearly visits with the neurologist. This creates potential for recollection bias and difficulty assessing disease severity and response to medication. We sought to develop a patient-driven mobile app that optimizes PD patient-reported outcome collection. We observed what health topics dominated discussion during appointments between patients and their neurologists. We assessed how often specific health events were included in the neurological history form filled out before the patients’ appointments, how PD patients described symptom changes, and if PD patients kept a detailed account of their health events. We created an interactive prototype of an iPad app designed with specific patient physical and cognitive challenges in mind that would help PD patients keep detailed records of daily health status. PD patients, caregivers, and neurologists tested the prototype app, and iterative design changes were made based on their feedback. The app features a cognitively simple user interface that requires little instruction, an analog scale with graphical presentation for documenting slowness, tremor, walking/balance, dyskinesia, mood, speech and additional video/audio input that can provide neurologists with objective accounts of PD patients’ health condition. We interviewed PD patients who participated in the initial pilot to evaluate usability of this app in a PD population. The pilot demonstrated that an iPad-based app was easy to use. PD patient testing of the iPad app is ongoing. Once the study is completed, several validity measures will be assessed by independent interviews with patients and providers.

This paper proposes a method for construction of a clinical pathway based on attribute and sample clustering, called dual clustering. The method consists of the following four steps: first, histories of nursing orders are extracted from a hospital information system. Second, orders are classified into several groups by using clustering on the pricipal components (sample clustering). Third, attribute clustering is applied to the data. Finally, original temporal data are split into several sub-tables and the first step will be repeated in a recursive way. After the grouping results become stable, a new pathway will be constructed from all the induced results. The method was applied to datasets of a disease extracted from a hospital information system. The results show that the proposed method constructed a clinical pathway, which was not only similar to the pathway manually acquired from medical experts but also discovered nursing orders which they forget to include.