Neuroscience and Biomedical Engineering (Discontinued) - Volume 4, Issue 2, 2016

Background: This article follows a theme of articles on the subject of ‘neuroregulation’ published by the author. The aim of this original article is to further elucidate that ‘Blood Glucose is Neurally Regulated’, in particular, that type 1 and type 2 diabetes are co-morbidities because the genetic expression of insulin is followed by the reaction of insulin with its substrate; and that any explanation for type 1 and type 2 diabetes must conform to the fundamental laws of chemistry which govern chemical reactions and influence (i) how the protein is expressed or activated (genotype) and to what extent, (ii) how the protein maintains its shape and reactivity (phenotype), (iii) how the protein reacts with its substrate, and (iv) that there is a complex network of organ systems which regulate functional parameters such as blood pressure, blood glucose, acidity, etc. It considers that the regulation of blood glucose, which we recognise as type 1 & 2 diabetes, is heavily influenced by the neural regulation of pH. In addition, recognition that the brain regulates the autonomic nervous system and physiological or functional systems leads us to consider that the onset and progression of type 1 diabetes, and hence the expression of pre-pro-insulin, are influenced by the many and various factors which alter gene conformation, the genetic expression of proteins, protein conformation, and/or by associated chemical reactions e.g. resulting from the influence of viruses, vaccines, drugs, bacteria and stress. Conclusions: Finally, and in support of the arguments presented, this article reports how Strannik Virtual Scanning – a cognitive technology which encompasses an understanding of the structural nature of the relationship between brain function, autonomic nervous system, physiological systems and pathological correlates - is able to distinguish between prediabetes and diabetes, between genotype and phenotype, determine the complex range of emergent pathologies influencing the health of each and every organ, and contribute to further advances regarding diabetes etiology.

Background: Doppler ultrasound is an important diagnostic tools used to view blood flow through the vessels. For the reconstruction we need to collect a large data to get a Doppler image with high performance. Objective: In this work, we propose an algorithm that combines compressed sensing (CS) and parallel computing to reconstruct the Doppler ultrasound signal and reduce the reconstruction time. Methods: Compressed sensing is a new sampling methods, appeared a few years ago, but it was used in different practical applications such as speeding up MRI scans by acquiring less data to achieve a given amount of resolution and Doppler ultrasound signal reconstruction using fewer measurements to achieve an image with high quality. The main idea of parallel signal/image reconstruction is to divide the main tasks into subtasks and solve them concurrently, in such way that total time can be divided between total tasks. Results: The reconstruction performed using Matlab on a personal computer running a Windows 7 operating system. Real and simulated Doppler data were used for the proposed algorithm validation. The result shows that as the number of cores increased the process time decreased. The image quality from serial programming is same as that a achieved by using parallel programming. The best quality of the image gain is when the 1-norm algorithm used. The lowest reconstruction time was obtained by combining the regularized orthogonal matching pursuit (ROMP) algorithm and parallel computing, with a reconstruction time less than 0.016 seconds when four cores were used and less than 0.034 seconds when two cores were used for 5% of the data. When 80% of the data used the reconstruction time for two cores was 0.109 and for four cores the time was 0.067. The best efficiency was achieved by parallelizing ROMP in two and four cores and the lowest efficiency achieved by parallelized CoSaMP algorithm. The excitation cost decreased with decreasing the numbers of cores. In all the reconstruction algorithms used to perform this work, four cores gave a lower excitation cost than those obtained with two cores. The lowest excitation cost was achieved via ROMP when four cores and fewer measurements were used (0.06) and higher cost gained by using 1-norm algorithm (12.42). The ROMP combined algorithm gives best result among all combined reconstruction algorithm used. Conclusion: We have demonstrated that it is possible to combine compressed sensing and parallel computing to reconstruct the Doppler ultrasound signal. The result of reconstructing the Doppler signal shows that combined algorithm leads to a reduction in reconstruction time and gives an image in realtime while reducing the computation complexity without affecting the image quality.

Background: A simulated functional magnetic resonance imaging (fMRI) environment, or "fMRI simulator", is helpful for prototyping behavioural tasks and acclimatizing individuals prior to actual fMRI of brain activity. When a position tracking system is integrated, such simulators can potentially be used to train individuals to perform behavioural tasks while suppressing their head motion. This is an important endeavor, as fMRI remains easily contaminated by unacceptable levels of motion artifact and no global strategy exists to eliminate the problem. New Method: In the present work, simulator-training procedures were developed that included visual feedback of head motion parameters. In two experiments, simulator training was applied to patients recovering from stroke, as well as cohorts of healthy young and elderly adults, who performed hand motor tasks. Results: Simulator training lead to statistically significant reductions in task-correlated motion for a subset of young healthy adults who had elevated motion levels at the outset (p<0.05). All stroke patients also showed large intra-individual reductions. Task-correlated motion was not reduced in healthy elderly adults, who exhibited motion with much slower trends. Comparison with existing methods: For young healthy adults with elevated task-correlated motion, simulator training reduced head motion to levels seen in a motion-screened, high compliant group of individuals with low head motion. Similarly, substantial reductions in task-correlated motion were observed in stroke patients after simulator training. Conclusion: These results support use of simulator training in cases where task-correlated head motion is expected to be problematic during fMRI experiments.

Background: When using integrative functional magnetic resonance imaging– magnetoencephalography (fMRI–MEG) methods, we have to construct a model based on prior information from an fMRI study. However, this predetermined model is unfortunately often insufficient because of a mismatch between the fMRI and MEG source-activated areas. Moreover, estimated signals from MEG data can be contaminated by sources that cannot be observed by fMRI (fMRI invisible sources). Methods: To address this problem, we propose a method to improve the normalized integrative fMRI–MEG method that enables the updating of the predetermined model using an iterative procedure, which is based on both model selection and an estimate of the fMRI invisible sources by beamforming. This updated model reduces the effect of fMRI invisible sources. Using the proposed method, we obtained an accurate model that accounts for the fMRI invisible sources, resulting in more accurate estimated source activities. To validate this proposed method, we performed simulations and assessed the accuracy of our estimates. Furthermore, we estimated the number and locations of the fMRI invisible sources. The simulation was based on an apparent motion perception experiment. White Gaussian noise and real MEG noise under open-eye conditions were used in the simulation. The accuracy of the estimated time course was assessed using the residual sum of squares. Results: The proposed method successfully estimated the fMRI invisible sources and provided a better estimation of the time course than that of predetermined model. Conclusion: These results demonstrate the feasibility of the proposed method.

Background: When we use our fingers to explore the fine surface (spatial features smaller than 200 μm) of an object, the relevant temporal features are encoded by cutaneous mechanoreceptor afferents that are widely believed to lead to the perception of roughness. However, whether visual input influences the haptic perception of fine surfaces and how the haptic and visual modalities interact with each other are questions that remain unanswered. Objective: In the present study, fifteen healthy volunteers participated in a series of unimodal (haptic-haptic (HH) task and visual-visual (VV) task) and bimodal (haptic- haptic & visual (HHv) task and visual-visual & haptic (VVh) task) fine surface roughness estimation tasks. Methods: The subjects were asked to estimate the roughness of a test surface that they compared to a standard surface in the HH and VV tasks. In the HHv and VVh tasks, the task procedures were the same as those in the unimodal tasks, but both haptic and visual surfaces were presented simultaneously. Results: Our results suggest that both the visual and haptic roughness estimations were influence by information from the other modality. Conclusion: In conclusion, we propose that humans store a modality-independent and dimensionless quantity in the brain when estimating the roughness of a fine surface.

Background: Peripheral facial nerve palsy (FNP) is sometimes managed in rehabilitation medicine. Bell’s palsy is the most common cause of FNP. Approximately 31% of patients with Bell’s palsy who did not receive appropriate treatment may suffer from incomplete recovery. In severe cases, the facial muscles shorten due to frequent co-contraction with the synkinesis in the chronic phase. Since intra-oral massage (IOM) can be easily applied to outpatients without using any special devices, we applied it to the complications following severe FNP in the chronic stage for an outpatient. It is designed to stretch the shortened facial muscles directly from the oral cavity. Objective: The aim of the present study was to investigate the effect of IOM on muscle stiffness with the facial nerve palsy patients in the chronic stage that could not be relieved by the conventional superficial massage. Methods: To objectively assess the change in muscle stiffness, the findings were compared with sonoelastography, which is widely used for diagnosing breast cancer, before and after training. Results: At pre-IOM, the cheek of the affected side was significantly harder than that of the unaffected side. The strain increased significantly on the affected side after IOM. The cheek of the affected side became softer to the same degree as the unaffected side. Conclusion: Structural changes after IOM were objectively evaluated using sono-elastography.

Background: Our brain can collaborate useful information from different sensory stimuli automatically through multisensory integration. However, we cannot understand visual and auditory information clearly from the TV when the sound and graphics are out of synchronization. This is because our brain is not being able to integrate the visual and auditory information automatically. Objective and Method: To determine whether patients with Parkinson’s disease (PD) have the same audiovisual integration as individuals without PD, we designed an experiment using three groups of subjects: 17 normal younger individuals (the NY group), 21 normal aged control individuals (the NC group) and 16 individuals with Parkinson’s disease (the PD group). All subjects were required to press the response key when the auditory, visual or audiovisual stimuli were presented. Results and Conclusion: We recorded the accuracy (AC) and reaction time (RT) for each task. The results suggest that the mean RT of the PD group was significantly longer than that of the NY and NC groups. Interestingly, we found that patients with PD exhibited inadequate audiovisual integration and significantly lower enhancement than the NY and NC groups, which suggests the presence of basic cognition errors in PD patients.

This work presents results from a project using smart phones as a data collection tool for medical record keeping in low-resource settings. Those devices were used to collect medical data and store it as an Electronic Health Record (EHR). Background: In community care, EHR could be a bridge from untrained Community Health Workers (CHWs) to healthcare providers with timely and relevant data. Although CHWs are the backbone of health care delivery in developing countries, they often have little formal education and training. Providing CHWs with appropriate training and workplace tools could improve their ability to provide quality community based care. The field work was carried out on site in Rwanda, a country with one of the world’s lowest doctor to patient ratios, where CHWs play an important role in healthcare delivery. Objective: The study evaluates the feasibility and usability of a specific mobile health application, and compares two different methods for health data collection, electronic and paper based. Methods: The usability is measured in terms of three attributes: effectiveness, efficiency and satisfaction. Electronic data is compared with paper-based data using two quantitative measures: Mean Absolute Error and Mean Absolute Percentage Error. Results: CHWs were found effective in data collection, collecting close to 2000 records from boys and girls under the age of five. Data analysis demonstrates the evidence that these new electronic records are more accurate, consistent and accessible than the currently paper-based records. Conclusion: This study demonstrates that using modern electronic tools for health data collection is allowing better tracking of health indicators.

Background: In functional brain imaging, intersubject brain registration is widely used to describe the loci of brain activation or lesions and to normalize functional data between individual brains based on anatomical similarities. However, such registration necessarily has limits because brain structure varies among individuals and is not always closely correlated with brain function. Objective: This study quantitatively compared three registration algorithms—linear volume-based, nonlinear volume-based, and surface-based methods—using probability and entropy maps of human visual areas. Methods: fMRI retinotopic mapping was performed in 16 subjects to construct a model for 12 visual areas. The surface and volumetric models of each visual area were registered to the standard brain template using the three registration methods. Results: After surface-based registration, the probability of visual areas being present in the common space was increased approximately 3-fold compared with the volume-based method, but the average probability was relatively small at approximately 0.3. On the other hand, average entropy was around 1 bit, revealing no significant difference between the two methods. Conclusion: Our results indicate that the current technology has room for improvement and thus should be used carefully with consideration of its limits. We suggest that the information-theoretic approach can be naturally extended to the analysis of brain structure-function relationships by taking advantage of mutual information.