Neuroscience and Biomedical Engineering (Discontinued) - Volume 2, Issue 3, 2014

Subjective cognitive decline (SCD) is common in older people. It is an upsetting symptom in which seemingly healthy individuals report self-perceived decline in cognition function, usually memory, without objective evidence on formal neuropsychological tests. There is rapidly growing evidence that SCD may be an early symptomatic sign of the first effects of Alzheimer's disease (AD) pathology on cognitive functioning between complete compensation and incipient decline. In the future, performing effective treatment at this stage would likely preserve function at a high level. Thus, the valid application of SCD as an effective tool for the early detection of AD dementia would therefore be of significant benefit.

Deep brain stimulation (DBS) is an increasingly used surgical treatment for a range of neurological disorders, most commonly movement disorders. DBS provides durable electrical stimulation in different regions of the human brain according to a disorder specific manner. The mechanisms of action of DBS, particularly the interfaces between electrodes and brain are not well known and disputed. The interfaces have an impact on the outcome of the stimulation. This article presents an insight of electrochemical consideration at electrode-brain interface, the effects of electrode geometry, and the effect of current density distribution on the stimulation outcome. The understanding of the interface permits DBS to successfully treat a certain neurological disorder. The article also investigates the process and importance of charge balancing in neurons, and the hardware-related complications. Charge balancing technique enables DBS to yield a more predictable outcome and to be systematically developed as an effective treatment with minimum side effects. Moreover, the article synthesizes theoretical and empirical findings to provide a guidance for the selection of stimulus parameters. In this paper, experimental results, clinical studies, and human studies are reviewed. To spur on engineering and medical advances that will make DBS as a reliable and effective treatment for neurological disorders as cardiac pacemaker is now, the electrode-brain interfaces need to be continually re-assessed, the charge in brain might be balanced, and stimulation parameters need to be optimized.

In endovascular interventional surgery, the virtual-reality (VR) simulator is critical for robotic catheter operating system since it offers physicians visual information between catheter and vessel. In spite of most VR systems using mass-spring to formulate vascular physical model, the uniform deformation doesn’t satisfy the vascular characteristics. In order to simulate the vascular deformation more vividly, we introduce standard linear solid model to formulate its physical model and determine this model’s parameters based on vascular wall elasticity analysis. In this way, the vascular physical model deforms according to vascular radius and material properties. In addition, so as to provide the vascular radius information for parameter identification process, we adopt discrete curvature estimation based on meshed triangle method in VR system. Finally, the VR simulator equipped with the parameters determined stand linear solid model is used to describe vascular deformation which is related to its radius and material characteristics.

Working memory (WM) has been one of the central themes in the area of cognitive neuroscience research for the past decades. WM refers to the temporary storage of information and a memory system which can carry on the processing of information. Memory load plays an important role in WM, through varying numbers of stimulus to realize the change of memory load. Functional magnetic resonance imaging (fMRI) as a common method is used to research the brain neural mechanism in varying memory load. Previous studies have compared memory load in each phase of WM, including encoding, maintenance and probing. In contrast to the previous ones, this study focused on comparing the difference between accuracy- and error-related brain activities with variable memory load during maintenance phase in WM. Fourteen healthy subjects participated in a variable load verbal of the Sternberg Item Recognition Task (SIRT). The results showed that response times (RTs) increased and accuracy decreased with memory load becoming higher. In addition, the dorsolateral prefrontal cortex (DLPFC) activation increased with WM load increased. In accuracy- versus (vs.) errorrelated condition, right para-hippocampus involved in the pattern of lower memory load, and right cerebellum lobule played a significant role in the condition of higher memory load. Contrary to the above condition, left superior temporal gyrus was obviously activated. These results indicated that the neural activity patterns during working memory maintenance, and showed that these patterns depended on accuracy- and error-related condition of with varying memory load.

Previous studies have reported that insomniacs tend to have a discrepancy between nocturnal sleep length reported in a self-checked sleep diary and actigraphically recorded sleep length. However, a predictive marker for the presence of this phenomenon remains unclear. In order to clarify this issue, we calculated subjective-objective difference (SOD), using diary estimates of sleep period time (SPT) minus actigraphically measured total sleep time (TST), and investigated the relationship between SOD and descriptive measures, including the Pittsburgh Sleep Quality Index (PSQI). Participants of the present study consisted of 50 persons having subjective insomnia (mean age: 46.6 ± 11.9 years; 33 males, 17 females). All the participants were divided into three groups based on SOD values (positive group, intermediate group, and negative group). The negative group showed significantly worse PSQI scores for C1 (sleep quality), C2 (sleep onset latency), C4 (sleep efficiency), and C6 (use of sleep medications), compared to the other two groups, whereas no significant difference was found in C7 (daytime dysfunction) among the three groups. The results of the present study indicate that a negative value of SOD, a measure for evaluating the severity of paradoxical insomnia, increases along with aggravation of subjective sleep disturbance among insomniacs.

Robot-aided rehabilitation training allows patients to receive a more effective and stable rehabilitation process. Exoskeleton devices are superior to the endpoint manipulators and cable suspension devices on the aspect that they can train and measure the angle and torque on each joint of impaired limbs. For robotic devices, physical safety should be guaranteed since the robot-assisted training relies on high human-robot interaction especially for exoskeletons. Traditional robotic devices mainly introduce the stiffness actuator, while the high levels of kinetic energy of robots will induce unsafe. For guaranteeing the safety of patients, compliant actuator such as the series elastic actuator (SEA) and variable stiffness actuator (VSA) design has been involved into these devices. The added compliance can make robots intrinsically safe and realize the energy-efficient actuation. The VSA used a variable stiffness elastic component instead of a constant stiffness elastic component, and VSAs is deemed to a kind of SEAs. A closed-loop interaction control method was used for SEAs to generate low impedance. By comparison, the VSA realizes adaptable compliance properties with inherent mechanical design. Thus, for SEAs, an additional mechanism is needed to adjust the output stiffness. In this paper, two kinds of compliant exoskeleton devices designed with the SEA and VSA respectively are introduced. The mechanical design and control method for each device are introduced; especially the design for guaranteeing patients’ safety.

Transcranial direct current stimulation (tDCS) involves passing low currents through the brain and is a promising tool for inducing alteration of cortical excitability. However, tDCS presents challenges in terms of optimization of the electrode placement and stimulation parameters especially in cases of heterogeneously damaged cortical structures. The analysis of a simplified multi-shell computational head-model indicated that the neuronal fibers lying perpendicular to the electrode-scalp interface in the white matter (WM) beneath the stimulating anode are subjected to a depolarizing drive while those lying parallel to the electrode-scalp interface are exposed to a hyperpolarizing drive, but in the gray matter (GM) the opposite was observed. Moreover, parameter sensitivity analysis revealed that the neuronal fibers lying perpendicular to the electrode-scalp interface in the GM beneath the anode were subjected to a depolarizing drive while those lying parallel to the electrode-scalp interface were exposed to a hyperpolarizing drive when the WM and GM conductivity were 0.13S/m and 0.2S/m respectively. However, when the WM conductivity was increased to 1.1 S/m (GM at 0.2 S/m), the results were reversed. This highlighted the importance of accurate conductivity values in computational head-model in the cases of diseased brain tissue. Here, anisotropic conductivity and fiber trajectories can be estimated from diffusion magnetic resonance imaging (dMRI) which provides excellent anatomical details, and can be used to differentiate between healthy, ischemic, and perilesional brain tissue. Such dMRI-guided patient-specific head-models can be used to optimize electrode placement and stimulation parameters that is based on the distributions of the generalized activating function. Here, a Magnetic Resonance Current Density Imaging (MRCDI) based method is proposed for dMRI-guided subject-specific parameter estimation and model validation that is a novel approach in our opinion.

Effective diagnosis of Alzheimer’s disease (AD) and Mild Cognitive Impairment (MCI), a transitional state between normal and AD, is of great importance in dementia research. Different neuroimaging biomarkers for AD may be potentially different and complementary in the diagnosis of AD. It is necessary to combine multimodal neuroimaging biomarkers simultaneously for the higher classification accuracy and more effective diagnosis. In this study, Magnetic Resonance Imaging (MRI) and Fluorine-18 deoxyglucose (FDG) positron emission tomography (PET) were firstly analysed separately, then biomarkers from both MRI and FDG-PET were combined using logistic regression and fisher linear discrimination, widely used approaches to combine multiple variables for the simultaneous analysis. The results showed that MRI and FDG-PET biomarkers were all sensitive to the AD diagnosis, although some differences existed. Combining information from multiple brain regions or from multimodal neuroimaging data sets could increase the accuracy of distinguishing AD or MCI patients from NC group, instead of causing information redundancy.