Neuroscience and Biomedical Engineering (Discontinued) - Volume 1, Issue 2, 2013

Recent quantitative EEG (qEEG) analyses in Parkinson’s disease (PD) are reviewed. We reported the first qEEG evaluation in PD not only employing multiple logistic regression analysis but also estimating the distribution of qEEG changes in 2008. We evaluated the spectral ratio, i.e. the total of the absolute power values in the α and β waves divided by the total of the δ and  waves values. The spectral ratios at all electrode locations excluded the frontal pole were presented to be the significant predictive variables in PD. PD presented diffuse slowing in the qEEG as compared to age-adjusted normal controls. We also evaluated the relation between the progression of PD and qEEG in 2009. The spectral ratio was significantly decreased according to the progression of PD at all the electrode locations. Moreover, we studied qEEG alterations in PD patients with executive dysfunction (ExD) in 2010 and cognitive impairment in 2011. An increase in slow wave was exhibited in frontal and frontal-pole locations in ExD and presented in all locations in dementia. It has been recently reported that qEEG finding has potential as a predictive biomarker for dementia incidence in PD in 2011.The qEEG analysis was thus not only a useful tool to study pathophysiological findings, but also a predictive biomarker for dementia.

Alzheimer’s disease (AD) is an aging-related and progressive neurodegenerative disease related with age. At the time of clinical manifestation of dementia AD, significant irreversible brain damage is already present, rendering the diagnosis of AD at early stages of the disease an urgent prerequisite for therapeutic treatment to halt, or at least slow, the onset or progression of the disease progression. Recent numerous studies using the state-of-art neuroimaging techniques that have identified AD-like structural and functional brain changes in elderly people who are cognitively within the normal range or who have mild cognitive impairment (MCI), a prodromal stage of the disease are discussed. In this review, we discussed various neuroimaging measures that are proving to have potential values as biomarkers of preclinical AD pathology for the its early detection and prediction as well as serving as a clinical trial outcome measures with additional validations.

In humans, visual information in the peripheral visual field is processed differently from such information in the central visual field. For example, peripheral vision prefers coarser information, while central vision prefers finer details. Recent advances in neuroimaging allowed us to non-invasively explore the neural substrates underlying the distinctiveness of peripheral and central vision. In the human visual cortex, there is a mosaic of orderly representations of the visual field, and this organization is called a visual field map. In this review, we summarize the various strands of research on visual field maps, which are crucial to understand human peripheral vision. We first describe the techniques that are used to measure visual field maps by means of functional magnetic resonance imaging (fMRI). We then review several studies that have tried to locate human visual areas using these techniques. We focused particularly on the findings of retinotopic organization for “far” peripheral visual field and tried to find the cortical regions that are crucial for peripheral vision.

pH is an important physiological index that alters in a host of disorders including inflammation, ischemic stroke and cancer. Therefore, it is necessary to quickly and accurately detect pH in vivo for diagnosing tissue damage and monitoring its response to treatment. Although magnetic resonance spectroscopy (MRS) can provide non-invasive measurement of pH, its spatiotemporal resolution is somewhat limited, hindering its routine use in clinic. Recently, chemical exchange saturation transfer (CEST) contrast mechanism has been developed as a novel approach to image pH at reasonable spatiotemporal resolution with promising in vivo applications. In this review, we discuss the principles of CEST phenomenon, quantify CEST effect and summarize its in vivo use.

Here, we studied how the intermodal selective attention between vision and audition affects the sensory information processing in the human auditory and visual cortical areas. Specifically, we investigated whether audio-visual intermodal orientation of attention modulates neural activity in the human visual and auditory cortices. We used MEG to assess the neural activity in both the primary auditory area and the extrastriate visual areas, where visual stimulus features such as color and shape are processed, while subjects performed spatial and non-spatial discrimination tasks, as presented by either visual or auditory modality, under three attentional conditions, namely a) selective attention to vision (V), b) selective attention to audition (A), and c) divided attention between vision and audition as neutral condition (N). MEG signals were analyzed by using linearly-constrained minimum-variance (LCMV) beamformer and the differences of the estimated activities in the visual cortical areas between the conditions were statistically tested. Neural activity in the contralateral occipitotemporal area (BA18) was significantly increased under the V condition in the spatial discrimination task but not in the non-spatial (color) discrimination task. On the other hand, attention to vision significantly enhanced activity in the posterior inferotemporal area (BA19, 37). The results support the hypothesis that the intermodal selective attention between the visual and auditory modalities modulates the neural activities in the ventral visual system in the stimulusfeature specific manner at different latencies.

Previous studies have used the cue-target paradigm to study the effect of the endogenous temporal attention on only visual or auditory stimuli. Furthermore, some studies found that the visual and auditory stimuli is not processed in isolation but produce coherent cognition in the brain when the visual and auditory stimuli are simultaneously presented. However, the effect of endogenous temporal attention on audiovisual (AV) stimuli processing is unclear. Utilizing the high temporal resolution of event-related potentials (ERPs), we used a central cue that can predict the time point (600 ms or 1800 ms) of audiovisual target to investigate whether endogenous temporal attention could modulate AV stimuli processing. The results showed that the endogenous temporal attention could not change the amplitude of the early ERP component in conditions of either short (600 ms) or long (1800 ms) cue-target intervals, indicating that the endogenous temporal attention had no effect on the early-stage of AV stimuli processing. However, the late ERP component showed differences between the short (600 ms) and long (1800 ms) cue-target intervals, supporting a model in which endogenous temporal attention might determine the late stage of AV stimuli processing.

Bayesian Network (BN) modeling has recently been proposed and utilized in resting-state functional magnetic resonance imaging (fMRI) studies to derive directed connectivity among brain regions in an integrated network. This report focuses on three issues related to the resting-state fMRI BN approach: 1) How the disturbance variable and noise affect BN inference and whether it is reasonable to use extracted fMRI time series averaged over multiple subjects to characterize the effective connectivity common to all of these subjects, 2) the effects of the fMRI time series length (duration) for a given TR(repetition time) on the BN results and 3) the effects of varying the time point at which the data acquisition starts (starting point). Both synthetic dataset and real resting-state fMRI dataset were employed. It is found that the averaged group BN inference is more robust than individual BN inference under most conditions according to the results of synthetic dataset, and the BN structure and parameter estimation are robust to both the variation of the time length (with a minimum) and starting point. Our results might demonstrate the stability of averaged group BN inference on resting fMRI dataset under most conditions, and justify its use to estimate the effective connectivity, which is independent of the start point and the length of resting-state fMRI time series.

Endovascular intervention is expected to become increasingly popular in medical practice, both for diagnosis and for surgery. Accordingly, researches of robotic systems for endovascular surgery assistant have been carried out widely. Robotic catheter navigation systems are with advantages of higher precision, can be controlled remotely etc. However, the haptic feelings, the important function for propelling robotic catheter navigation system sre immature. In the paper, a robotic catheter navigation system is proposed. The navigation system is designed to simulate the surgeon’s operating procedure. And the haptic feedback issue is concerned. A clamping force measurring method is developed. Therefore, surgeons can carry out operation with their own skills. System implement and performance are presented.

Many studies have reported that blood oxygen level–dependent (BOLD) activity in object-selective extrastriate areas is enhanced by visual attention. However, only a few studies have compared the effects of attention between these areas. In this study, we examined the influence of small and big noise level on BOLD responses in the word-selective region (visual word form area, VWFA) and house-selective region (parahippocampal place area, PPA) during Chinese character and house pictograph discrimination task. Our results showed that the BOLD response in the VWFA and PPA are modulated differently by visual attention. More specifically, the selective response in the VWFA is more easily affected by attentional demand than that in the PPA. These results may suggest that visual attention is distributed differently along the extrastiate cortex.

Neural activations can be measured based on modulation of EEG rhythmic activities within specific frequency bands. Previous studies have typically extracted EEG rhythmic activities using Fourier based methods such as short time Fourier and wavelet transforms. But the methods tend to obscure intrawave frequency fluctuations and smear the energy over a much wider frequency range. In this study, we propose a method that combines multivariate empirical mode decomposition (MEMD) with the Hilbert transform, rather than a wavelet transform, to extract rhythmic activities more precisely and to visualize them more clearly. The performance of the method was validated using measured EEG data obtained by a wrist movement experiment. The results demonstrated that the proposed method can extract and visualize multi- channel EEG rhythmic activities with higher resolution than methods employing short time Fourier and wavelet transforms.