N. S. Dias

Neuron-specific proteotoxicity of mutant ataxin-3 in C. elegans: rescue by the DAF-16 and HSF-1 pathways

The risk of developing neurodegenerative diseases increases with age. Although many of the molecular pathways regulating proteotoxic stress and longevity are well characterized, their contribution to disease susceptibility remains unclear. In this study, we describe a new Caenorhabditis elegans model of Machado-Joseph disease pathogenesis. Pan-neuronal expression of mutant ATXN3 leads to a polyQ-length dependent, neuron subtype-specific aggregation and neuronal dysfunction. Analysis of different neurons revealed a pattern of dorsal nerve cord and sensory neuron susceptibility to mutant ataxin-3 that was distinct from the aggregation and toxicity profiles of polyQ-alone proteins. This reveals that the sequences flanking the polyQ-stretch in ATXN3 have a dominant influence on cell-intrinsic neuronal factors that modulate polyQ-mediated pathogenesis. Aging influences the ATXN3 phenotypes which can be suppressed by the downregulation of the insulin/insulin growth factor-1-like signaling pathway and activation of heat shock factor-1.

Chronic stress disrupts neural coherence between cortico-limbic structures

Chronic stress impairs cognitive function, namely on tasks that rely on the integrity of cortico-limbic networks. To unravel the functional impact of progressive stress in cortico-limbic networks we measured neural activity and spectral coherences between the ventral hippocampus (vHIP) and the medial prefrontal cortex (mPFC) in rats subjected to short term stress (STS) and chronic unpredictable stress (CUS). CUS exposure consistently disrupted the spectral coherence between both areas for a wide range of frequencies, whereas STS exposure failed to trigger such effect. The chronic stress-induced coherence decrease correlated inversely with the vHIP power spectrum, but not with the mPFC power spectrum, which supports the view that hippocampal dysfunction is the primary event after stress exposure. Importantly, we additionally show that the variations in vHIP-to-mPFC coherence and power spectrum in the vHIP correlated with stress-induced behavioral deficits in a spatial reference memory task. Altogether, these findings result in an innovative readout to measure, and follow, the functional events that underlie the stress-induced reference memory impairments.

Wireless instrumentation system based on dry electrodes for acquiring EEG signals.

This paper presents a complete non-invasive Wireless acquisition system based on dry electrodes for electroencephalograms (WiDE-EEG) with emphasis in the electronic system design. The WiDE-EEG is composed by a 2.4 GHz radio-frequency (RF) transceiver, biopotential acquisition electronics and dry electrodes. The WiDE-EEG can acquire electroencephalogram (EEG) signals from 5 unipolar channels, with a resolution of 16 bits and minimum analog amplitude of 9.98 μV(pp), at a sampling rate of 1000 samples/s/channel and sends them to a processing unit through RF in a 10 m range. The analog channels were optimized for EEG signals (with amplitudes in the range 70-100 μV) and present the following characteristics: a signal gain of 66 dB and a common mode rejection ratio of 56.5 dB. Each electrode is composed by 16 microtip structures that were fabricated through bulk micromachining of a <100>-type silicon substrate in a potassium hydroxide (KOH) solution. The microtips present solid angles of 54.7°, a height of 100-200 μm and 2 μm spaced apart. The electrodes have a thin layer (obtained by sputtering) of iridium oxide (IrO) to guaranty their biocompatibility and improve the contact with the skin. These dry electrodes are in direct contact with the electrolyte fluids of the inner skin layers, and avoid the use of conductive gels. The complete WiDE-EEG occupies a volume of 9 cm×8.5 cm×1 cm, which makes it suitable for true mobility of the subjects and at the same time allows high data transfer rates. Since the WiDE-EEG is battery-powered, it overcomes the need of galvanic isolation for ensuring patient safety observed on conventional EEG instrumentation systems. The WiDE-EEG presents a total power consumption of 107 mW, divided as follows: the acquisition system contributes with 10 mW per channel, whereas the commercial MICAz module contributes with 57 mW (e.g., 24 mW from the microcontroller and 33 mW from the RF chip). The WiDE-EEG also presents autonomy of about 25 h with two class AA 1.5 V batteries.

Stress affects theta activity in limbic networks and impairs novelty-induced exploration and familiarization

Exposure to a novel environment triggers the response of several brain areas that regulate emotional behaviors. Here, we studied theta oscillations within the hippocampus (HPC)-amygdala (AMY)-medial prefrontal cortex (mPFC) network in exploration of a novel environment and subsequent familiarization through repeated exposures to that same environment; in addition, we assessed how concomitant stress exposure could disrupt this activity and impair both behavioral processes. Local field potentials (LFP) were simultaneously recorded from dorsal and ventral hippocampus (dHPC and vHPC, respectively), basolateral amygdala (BLA) and mPFC in freely behaving rats while they were exposed to a novel environment, then repeatedly re-exposed over the course of 3 weeks to that same environment and, finally, on re-exposure to a novel unfamiliar environment. A longitudinal analysis of theta activity within this circuit revealed a reduction of vHPC and BLA theta power and vHPC-BLA theta coherence through familiarization which was correlated with a return to normal exploratory behavior in control rats. In contrast, a persistent over-activation of the same brain regions was observed in stressed rats that displayed impairments in novel exploration and familiarization processes. Importantly, we show that stress also affected intra-hippocampal synchrony and heightened the coherence between vHPC and BLA. In summary, we demonstrate that modulatory theta activity in the aforementioned circuit, namely in the vHPC and BLA, is correlated with the expression of anxiety in novelty-induced exploration and familiarization in both normal and pathological conditions.

Hydrogel-based photonic sensor for a biopotential wearable recording system.

Wearable devices are used to record several physiological signals, providing unobtrusive and continuous monitoring. These systems are of particular interest for applications such as ambient-assisted living (AAL), which deals with the use of technologies, like brain-computer interface (BCI). The main challenge in these applications is to develop new wearable solutions for acquisition of electroenchephalogram (EEG) signals. Conventional solutions based on brain caps, are difficult and uncomfortable to wear. This work presents a new optical fiber biosensor based on electro-active gel - polyacrylamide (PAAM) hydrogel - with the ability to measure the required EEG signals and whose technology principle leads to contactless electrodes. Experiments were performed in order to evaluate the electro-active properties of the hydrogel and its frequency response, using an electric and optical setup. A sinusoidal electric field was applied to the hydrogel while the light passes through the sample. An optical detector was used to collect the resultant modulated light. The results have shown an adequate sensitivity in the range of μV, as well as a good frequency response, pointing the PAAM hydrogel sensor as an eligible sensing component for wearable biopotential recording applications.

A 2.4-GHz Low-Power/Low-Voltage Wireless Plug-and-Play Module for EEG Applications

This paper presents a plug-and-play module for wireless electroencephalogram (EEG) applications. The wireless module is composed by an electrode, processing electronics, a radio-frequency (RF) transceiver, and an associated antenna. The RF transceiver was fabricated in the UMC RF 0.18 mum CMOS process, and operates in the 2.4-GHz ISM band. The receiver has a sensitivity of -60 dBm and a power consumption of 6.3 mW from a 1.8 V supply. The transmitter delivers an output power of 0 dBm with a power consumption of 11.2 mW, for a range of 10 m. It is also presented the electrical performance and comparison between different electrodes for EEG applications, namely sputtered titanium nitride (TiN) electrodes, standard sintered silver/silver chloride (Ag/AgCl) ring electrodes and sputtered iridium oxide (IrO2) electrodes. The experimental results show a better performance of the sputtered IrO2 electrodes compared with the standard sintered Ag/AgCl ring electrodes. These results promise a new opportunity for the application of a dry IrO2 electrodes in wireless modules for using in a wearable EEG braincap. These wireless EEG modules will allow patients to wear a brain cap and maintain their mobility, while simultaneously having their electrical brain activity monitored.

Feature selection on movement imagery discrimination and attention detection

Noninvasive brain–computer interfaces (BCI) translate subject’s electroencephalogram (EEG) features into device commands. Large feature sets should be down-selected for efficient feature translation. This work proposes two different feature down-selection algorithms for BCI: (a) a sequential forward selection; and (b) an across-group variance. Power rar ratios (PRs) were extracted from the EEG data for movement imagery discrimination. Event-related potentials (ERPs) were employed in the discrimination of cue-evoked responses. While center-out arrows, commonly used in calibration sessions, cued the subjects in the first experiment (for both PR and ERP analyses), less stimulating arrows that were centered in the visual field were employed in the second experiment (for ERP analysis). The proposed algorithms outperformed other three popular feature selection algorithms in movement imagery discrimination. In the first experiment, both algorithms achieved classification errors as low as 12.5% reducing the feature set dimensionality by more than 90%. The classification accuracy of ERPs dropped in the second experiment since centered cues reduced the amplitude of cue-evoked ERPs. The two proposed algorithms effectively reduced feature dimensionality while increasing movement imagery discrimination and detected cue-evoked ERPs that reflect subject attention.

Age effects on EEG correlates of the Wisconsin Card Sorting Test

Body and brain undergo several changes with aging. One of the domains in which these changes are more remarkable relates with cognitive performance. In the present work, electroencephalogram (EEG) markers (power spectral density and spectral coherence) of age‐related cognitive decline were sought whilst the subjects performed the Wisconsin Card Sorting Test (WCST). Considering the expected age‐related cognitive deficits, WCST was applied to young, mid‐age and elderly participants, and the theta and alpha frequency bands were analyzed. From the results herein presented, higher theta and alpha power were found to be associated with a good performance in the WCST of younger subjects. Additionally, higher theta and alpha coherence were also associated with good performance and were shown to decline with age and a decrease in alpha peak frequency seems to be associated with aging. Additionally, inter‐hemispheric long‐range coherences and parietal theta power were identified as age‐independent EEG correlates of cognitive performance. In summary, these data reveals age‐dependent as well as age‐independent EEG correlates of cognitive performance that contribute to the understanding of brain aging and related cognitive deficits.

Comparison of EEG Pattern Classification Methods for Brain-Computer Interfaces

The aim of this study is to compare 2 EEG pattern classification methods towards the development of BCI. The methods are: (1) discriminant stepwise, and (2) principal component analysis (PCA) - linear discriminant analysis (LDA) joint method. Both methods use Fishers LDA approach, but differ in the data dimensionality reduction procedure. Data were recorded from 3 male subjects 20-30 years old. Three runs per subject took place. The classification methods were tested in 240 trials per subject after merging all runs for the same subject. The mental tasks performed were feet, tongue, left hand and right hand movement imagery. In order to avoid previous assumptions on preferable channel locations and frequency ranges, 105 (21 electrodestimes5 frequency ranges) electroencephalogram (EEG) features were extracted from the data. The best performance for each classification method was taken into account. The discriminant stepwise method showed better performance than the PCA based method. The classification error by the stepwise method varied between 31.73% and 38.5% for all subjects whereas the error range using the PCA based method was 39.42% to 54%.

Optimization of electrode channels in brain computer interfaces

What is the optimal number of electrodes one can use in discrimination of tasks for a Brain Computer Interface (BCI)? To address this question, the number and location of scalp electrodes in the acquisition of human electroencephalography (EEG) and discrimination of motor imagery tasks were optimized by using a systematic optimization approach. The systematic analysis results in the most reliable procedure in electrode optimization as well as a validating means for the other feature selection techniques. We acquired human scalp EEG in response to cue-based motor imagery tasks. We employed a systematic analysis by using all possible combinations of the channels and calculating task discrimination errors for each of these combinations by using linear discriminant analysis (LDA) for feature classification. Channel combination that resulted in the smallest discrimination error was selected as the optimum number of channels to be used in BCI applications. Results from the systematic analysis were compared with another feature selection algorithm: forward stepwise feature selection combined with LDA feature classification. Our results demonstrate the usefulness of the fully optimized technique for a reliable selection of scalp electrodes in BCI applications.