Frequency-domain source localization shows state-dependent diazepam effects in 47-channel EEG

The topic of this study was to evaluate state-dependent effects of diazepam on the frequency characteristics of 47-channel spontaneous EEG maps. A novel method, the FFT-Dipole-Approximation (Lehmann and Michel, 1990), was used to study effects on the strength and the topography of the maps in the different frequency bands. Map topography was characterized by the 3-dimensional location of the equivalent dipole source and map strength was defined as the spatial standard deviation (the Global Field Power) of the maps of each frequency point. The Global Field Power can be considered as a measure of the amount of energy produced by the system, while the source location gives an estimate of the center of gravity of all sources in the brain that were active at a certain frequency. State-dependency was studied by evaluating the drug effects before and after a continuous performance task of 25 min duration. Clear interactions between drug (diazepam vs. placebo) and time after drug intake (before and after the task) were found, especially in the inferior-superior location of the dipole sources. It supports the hypothesis that diazepam, like other drugs, has different effects on brain functions depending on the momentary functional state of the brain. In addition to the drug effects, clearly different source locations and Global Field Power were found for the different frequency bands, replicating earlier reports (Michel et al., 1992).

Zazen meditation and no-task resting EEG compared with LORETA intracortical source localization

Meditation is a self-induced and willfully initiated practice that alters the state of consciousness. The meditation practice of Zazen, like many other meditation practices, aims at disregarding intrusive thoughts while controlling body posture. It is an open monitoring meditation characterized by detached moment-to-moment awareness and reduced conceptual thinking and self-reference. Which brain areas differ in electric activity during Zazen compared to task-free resting? Since scalp electroencephalography (EEG) waveforms are reference-dependent, conclusions about the localization of active brain areas are ambiguous. Computing intracerebral source models from the scalp EEG data solves this problem. In the present study, we applied source modeling using low resolution brain electromagnetic tomography (LORETA) to 58-channel scalp EEG data recorded from 15 experienced Zen meditators during Zazen and no-task resting. Zazen compared to no-task resting showed increased alpha-1 and alpha-2 frequency activity in an exclusively right-lateralized cluster extending from prefrontal areas including the insula to parts of the somatosensory and motor cortices and temporal areas. Zazen also showed decreased alpha and beta-2 activity in the left angular gyrus and decreased beta-1 and beta-2 activity in a large bilateral posterior cluster comprising the visual cortex, the posterior cingulate cortex and the parietal cortex. The results include parts of the default mode network and suggest enhanced automatic memory and emotion processing, reduced conceptual thinking and self-reference on a less judgmental, i.e., more detached moment-to-moment basis during Zazen compared to no-task resting.

A Time-based Passive Source Localization System for Narrow-band Signal

Time-based indoor localization has been investigated for several years but the accuracy of existing solutions is limited by several factors, e.g., imperfect synchronization, signal bandwidth and indoor environment. In this paper, we compare two time-based localization algorithms for narrow-band signals, i.e., multilateration and fingerprinting. First, we develop a new Linear Least Square (LLS) algorithm for Differential Time Difference Of Arrival (DTDOA). Second, fingerprinting is among the most successful approaches used for indoor localization and typically relies on the collection of measurements on signal strength over the area of interest. We propose an alternative by constructing fingerprints of fine-grained time information of the radio signal. We offer comprehensive analytical discussions on the feasibility of the approaches, which are backed up by evaluations in a software defined radio based IEEE 802.15.4 testbed. Our work contributes to research on localization with narrow-band signals. The results show that our proposed DTDOA-based LLS algorithm obviously improves the localization accuracy compared to traditional TDOA-based LLS algorithm but the accuracy is still limited because of the complex indoor environment. Furthermore, we show that time-based fingerprinting is a promising alternative to power-based fingerprinting.

A Passive Source Localization System for IEEE 802.15.4 Signal

In this work, we provide a passive location monitoring system for IEEE 802.15.4 signal emitters. The system adopts software defined radio techniques to passively overhear IEEE 802.15.4 packets and to extract power information from baseband signals. In our system, we provide a new model based on the nonlinear regression for ranging. After obtaining distance information, a Weighted Centroid (WC) algorithm is adopted to locate users. In WC, each weight is inversely proportional to the nth power of propagation distance, and the degree n is obtained from some initial measurements. We evaluate our system in a 16m-18m area with complex indoor propagation conditions. We are able to achieve a median error of 2:1m with only 4 anchor nodes.

A Passive WiFi Source Localization System based on Fine-grained Power-based Trilateration

Indoor localization systems become more interesting for researchers because of the attractiveness of business cases in various application fields. A WiFi-based passive localization system can provide user location information to third-party providers of positioning services. However, indoor localization techniques are prone to multipath and Non-Line Of Sight (NLOS) propagation, which lead to significant performance degradation. To overcome these problems, we provide a passive localization system for WiFi targets with several improved algorithms for localization. Through Software Defined Radio (SDR) techniques, we extract Channel Impulse Response (CIR) information at the physical layer. CIR is later adopted to mitigate the multipath fading problem. We propose to use a Nonlinear Regression (NLR) method to relate the filtered power information to propagation distances, which significantly improves the ranging accuracy compared to the commonly used log-distance path loss model. To mitigate the influence of ranging errors, a new trilateration algorithm is designed as well by combining Weighted Centroid and Constrained Weighted Least Square (WC-CWLS) algorithms. Experiment results show that our algorithm is robust against ranging errors and outperforms the linear least square algorithm and weighted centroid algorithm.

From episodic to habitual prospective memory: ERP-evidence for a linear transition

Performing a prospective memory task repeatedly changes the nature of the task from episodic to habitual. The goal of the present study was to investigate the neural basis of this transition. In two experiments, we contrasted event-related potentials (ERPs) evoked by correct responses to prospective memory targets in the first, more episodic part of the experiment with those of the second, more habitual part of the experiment. Specifically, we tested whether the early, middle, or late ERP-components, which are thought to reflect cue detection, retrieval of the intention, and post-retrieval processes, respectively, would be changed by routinely performing the prospective memory task. The results showed a differential ERP effect in the middle time window (450 - 650 ms post-stimulus). Source localization using low resolution brain electromagnetic tomography analysis (LORETA) suggests that the transition was accompanied by an increase of activation in the posterior parietal and occipital cortex. These findings indicate that habitual prospective memory involves retrieval processes guided more strongly by parietal brain structures. In brief, the study demonstrates that episodic and habitual prospective memory tasks recruit different brain areas.

Individual differences in inhibitory control - relationship between baseline activation in lateral PFC and an electrophysiological index of response inhibition

The capacity to inhibit inappropriate responses is crucial for goal-directed behavior. Inhibiting such responses seems to come more easily to some of us than others, however. From where do these individual differences originate? Here, we measured 263 participants' neural baseline activation using resting electroencephalogram. Then, we used this stable neural marker to predict a reliable electrophysiological index of response inhibition capacity in the cued Continuous Performance Test, the NoGo-Anteriorization (NGA). Using a source-localization technique, we found that resting delta, theta, and alpha1 activity in the left middle frontal gyrus and resting alpha1 activity in the right inferior frontal gyrus were negatively correlated with the NGA. As a larger NGA is thought to represent better response inhibition capacity, our findings demonstrate that lower levels of resting slow-wave oscillations in the lateral prefrontal cortex, bilaterally, are associated with a better response inhibition capacity.

Tonic activity level in the right prefrontal cortex predicts individuals' risk taking

Human risk taking is characterized by a large amount of individual heterogeneity. In this study, we applied resting-state electroencephalography, which captures stable individual differences in neural activity, before subjects performed a risk-taking task. Using a source-localization technique, we found that the baseline cortical activity in the right prefrontal cortex predicts individual risk-taking behavior. Individuals with higher baseline cortical activity in this brain area display more risk aversion than do other individuals. This finding demonstrates that neural characteristics that are stable over time can predict a highly complex behavior such as risk-taking behavior and furthermore suggests that hypoactivity in the right prefrontal cortex might serve as a dispositional indicator of lower regulatory abilities, which is expressed in greater risk-taking behavior.

Evaluating the Impact of the Number of Access Points in Mobile Robots Localization Using Artificial Neural Networks

Localization is information of fundamental importance to carry out various tasks in the mobile robotic area. The exact degree of precision required in the localization depends on the nature of the task. The GPS provides global position estimation but is restricted to outdoor environments and has an inherent imprecision of a few meters. In indoor spaces, other sensors like lasers and cameras are commonly used for position estimation, but these require landmarks (or maps) in the environment and a fair amount of computation to process complex algorithms. These sensors also have a limited field of vision. Currently, Wireless Networks (WN) are widely available in indoor environments and can allow efficient global localization that requires relatively low computing resources. However, the inherent instability in the wireless signal prevents it from being used for very accurate position estimation. The growth in the number of Access Points (AP) increases the overlap signals areas and this could be a useful means of improving the precision of the localization. In this paper we evaluate the impact of the number of Access Points in mobile nodes localization using Artificial Neural Networks (ANN). We use three to eight APs as a source signal and show how the ANNs learn and generalize the data. Added to this, we evaluate the robustness of the ANNs and evaluate a heuristic to try to decrease the error in the localization. In order to validate our approach several ANNs topologies have been evaluated in experimental tests that were conducted with a mobile node in an indoor space.

Angiotensinergic innervation of the kidney: Localization and relationship with catecholaminergic postganglionic and sensory nerve fibers

We describe an angiotensin (Ang) II-containing innervation of the kidney. Cryosections of rat, pig and human kidneys were investigated for the presence of Ang II-containing nerve fibers using a mouse monoclonal antibody against Ang II (4B3). Co-staining was performed with antibodies against synaptophysin, tyrosine 3-hydroxylase, and dopamine beta-hydroxylase to detect catecholaminergic efferent fibers and against calcitonin gene-related peptide to detect sensory fibers. Tagged secondary antibodies and confocal light or laser scanning microscopy were used for immunofluorescence detection. Ang II-containing nerve fibers were densely present in the renal pelvis, the subepithelial layer of the urothelium, the arterial nervous plexus, and the peritubular interstitium of the cortex and outer medulla. They were infrequent in central veins and the renal capsule and absent within glomeruli and the renal papilla. Ang II-positive fibers represented phenotypic subgroups of catecholaminergic postganglionic or sensory fibers with different morphology and intrarenal distribution compared to their Ang II-negative counterparts. The Ang II-positive postganglionic fibers were thicker, produced typically fusiform varicosities and preferentially innervated the outer medulla and periglomerular arterioles. Ang II-negative sensory fibers were highly varicose, prevailing in the pelvis and scarce in the renal periphery compared to the rarely varicose Ang II-positive fibers. Neurons within renal microganglia displayed angiotensinergic, catecholaminergic, or combined phenotypes. Our results suggest that autonomic fibers may be an independent source of intrarenal Ang II acting as a neuropeptide co-transmitter or neuromodulator. The angiotensinergic renal innervation may play a distinct role in the neuronal control of renal sodium reabsorption, vasomotion and renin secretion.