Synchronization of Slow Cortical Rhythms During Motor Imagery-Based Brain–Machine Interface Control

Modulation of sensorimotor rhythm (SMR) power, a rhythmic brain oscillation physiologically linked to motor imagery, is a popular Brain–Machine Interface (BMI) paradigm, but its interplay with slower cortical rhythms, also involved in movement preparation and cognitive processing, is not entirely understood. In this study, we evaluated the changes in phase and power of slow cortical activity in delta and theta bands, during a motor imagery task controlled by an SMR-based BMI system. In Experiment I, EEG of 20 right-handed healthy volunteers was recorded performing a motor-imagery task using an SMR-based BMI controlling a visual animation, and during task-free intervals. In Experiment II, 10 subjects were evaluated along five daily sessions, while BMI-controlling same visual animation, a buzzer, and a robotic hand exoskeleton. In both experiments, feedback received from the controlled device was proportional to SMR power (11–14 Hz) detected by a real-time EEG-based system. Synchronization of slow EEG frequencies along the trials was evaluated using inter-trial-phase coherence (ITPC). Results: cortical oscillations of EEG in delta and theta frequencies synchronized at the onset and at the end of both active and task-free trials; ITPC was significantly modulated by feedback sensory modality received during the tasks; and ITPC synchronization progressively increased along the training. These findings suggest that phase-locking of slow rhythms and resetting by sensory afferences might be a functionally relevant mechanism in cortical control of motor function. We propose that analysis of phase synchronization of slow cortical rhythms might also improve identification of temporal edges in BMI tasks and might help to develop physiological markers for identification of context task switching and practice-related changes in brain function, with potentially important implications for design and monitoring of motor imagery-based BMI systems, an emerging tool in neurorehabilitation of stro

Usefulness of FDG PET/CT in the management of tuberculosis

Tuberculosis; Positron emission tomography (PET); FDGTuberculosis; Tomografía por emisión de positrones (PET); FDGTuberculosi; Tomografia per emissió de positrons (PET); FDGBACKGROUND: The aim of our study is to describe the FDG-PET/CT findings in patients with tuberculosis and to correlate them with the patient's prognosis. METHODS: We retrospectively collected data from patients with tuberculosis, who had an FDG-PET/CT performed prior to treatment initiation from 2010 to 2015. RESULTS: Forty-seven out of 504 patients with active tuberculosis diagnosis (9.33%) underwent an FDG-PET/CT. The reasons for performing the FDG-PET/CT were: characterization of a pulmonary nodule (24; 51.1%), study of fever of unknown origin (12; 25.5%), study of lymph node enlargement (5; 10.6%) and others (6; 12.8%). Median age was 64 (IQR 50-74) years and 31 (66%) patients were male. Twenty-six (55.3%) patients had an immunosuppressant condition. According to the FDG-PET/CT, 48.6% of the patients had more than 1 organ affected and 46.8% had lymph node involvement. Median SUVmax of the main lesion was 5 (IQR 0.28-11.85). We found an association between the FDG accumulation and the size of the main lesion with a correlation coefficient of 0.54 (p<0.002). Patients with an unsuccessful outcome had a higher ratio SUVmax main lesion / SUVmean liver (1.92 vs 7.67, p<0.02). CONCLUSIONS: In our cohort, almost half of the patients had more than 1 organ affected and 46.8% of them had lymph node involvement. FDG uptake was associated with the size of the main lesion and seems to be related to the treatment outcome. The extent of its potential to be used as an early predictor of treatment success still needs to be defined

A four-state Markov model of sleep-wakefulness dynamics along light/dark cycle in mice

<div><p>Behavioral states alternate between wakefulness (wk), rapid eye movement (rem) and non-rem (nrem) sleep at time scale of hours <i>i.e</i>., light and dark cycle rhythms and from several tens of minutes to seconds (<i>i.e</i>., brief awakenings during sleep). Using statistical analysis of bout duration, Markov chains of sleep-wk dynamics and quantitative EEG analysis, we evaluated the influence of light/dark (ld) changes on brain function along the sleep-wk cycle. Bout duration (bd) histograms and Kaplan-Meier (km) survival curves of wk showed a bimodal statistical distribution, suggesting that two types of wk do exist: brief-wk (wkb) and long-wk (wkl). Light changes modulated specifically wkl bouts, increasing its duration during active/dark period. In contrast, wkb, nrem and rem bd histograms and km curves did not change significantly along ld cycle. Hippocampal eeg of both types of wk were different: in comparison wkb showed a lower spectral power in fast gamma and fast theta bands and less emg tone. After fitting a four-states Markov chain to mice hypnograms, moreover in states transition probabilities matrix was found that: in dark/active period, state-maintenance probability of wkl increased, and probability of wkl to nrem transition decreased; the opposite was found in light period, favoring the hypothesis of the participation of brief wk into nrem-rem intrinsic sleep cycle, and the role of wkl in SWS homeostasis. In conclusion, we propose an extended Markov model of sleep using four stages (wkl, nrem, rem, wkb) as a fully adequate model accounting for both modulation of sleep-wake dynamics based on the differential regulation of long-wk (high gamma/theta) epochs during dark and light phases.</p></div

Virtual environment control throught BCI-hybrid interface using EOG/EEG signals during motor imagery

[Resumen] La discapacidad producida por las enfermedades neurológicas es uno de los primeros problemas de salud y calidad de vida en las sociedades avanzadas. Las tecnologías asistenciales han demostrado poder aumentar la independencia de los pacientes y mejorar su capacidad de controlar su entorno. Los sistemas híbridos interfaces cerebro-computadora (BCI), que utilizan una fusión de señales no invasivas basadas en EOG y EEG, son sistemas prometedores para el desarrollo de este tipo de aplicaciones. En este estudio participaron ocho sujetos con patología neurológica (accidente cerebro-vascular, lesión medular o enfermedad neurodegenerativa). Los pacientes a través de BCIEEG y EOG navegaban por el entorno virtual realizando diferentes actividades de la vida diaria (AVD). El objetivo principal de esta experimentación fue evaluar el rendimiento de un grupo de sujetos con discapacidad física severa para la ejecución de un conjunto de tareas implementadas en un entorno de navegación virtual, utilizando un interfaz de control mediante BCI híbrido (EOG y EEG basado en imaginería motora).[Abstract] Dissabilities produced by neurological diseases are the first problem of health and life quality in this society. Assistance technology has demostrated increase the patient's independence and improve the capability to facilitate their environmental control. BCI hybrid systems that use a signal non-invasive fusion based in EOG and EEG are promising systems. In this study eight subjects with neurological pathology (stroke, spinal cord injury or neurodegenerative disease) were participed. The patients navegated along the virtual environment through EEG and EOG performed several ADLs. The main aim of this experimentation was evaluated the subject's performance with several physic disability for the execution of tasks implemented in a virtual environment using a control interface throught BCI-hybrid system (EOG and EEG based in motor imagery)

Representative data illustrating dark/light modulation of sleep-wake behavior in C57Bl/6 mouse.

<p>A i-ii) Hypnogram was generated by automatic sleep scoring of wk, nrem and rem sleep states. B) Long (-) and brief (*) wk bouts were identified based on the frequency pattern of cortex and hippocampal eeg activity (i-ii). iii) raw eeg during brief wk.</p

Diagram of four-state Markov model accounting for the wk-sleep dynamics across dark/light cycle.

<p>Circular arrows correspond to the probability of maintaining a state (i.e., the time spent in the corresponding state or bout duration), and straight arrows to transitions between states; arrows thickness are proportional to the corresponding probabilities (from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0189931#pone.0189931.t001" target="_blank">Table 1</a>), and the dark and light periods are represented in black and grey. The sleep-wake model of four states comprises of two wk states (with spectral differences, see text): a long-wk (wkl) and a brief-wk (wkb); and of nrem and rem sleep states. States of wkl and nrem were more stable than rem and wkb, while state transitions wkb to nrem and rem to nrem were the most probable. Circadian modulation increased the stability of wkl mainly by reducing the transitions from wkl to nrem during dark active period (*, <i>p</i> < 0.05; and **, <i>p</i> < 0.01).</p

Statistics of wk, nrem and rem bout duration as a function of dark/light periods.

<p>A) Bouts duration histogram showed a clear bimodal density distribution of wake (brief- and long-wk) while nrem and rem distribution was unimodal; only the density of long-wk varied during circadian rhythm, lasting longer in the dark (active) than light (inactive) phase. B) Cumulative wk showed by Kaplan-Meyer survival curves exhibited a biexponential distribution (wkb and wkl, vertical bar) with a significant increment of wkl duration in dark phase. C) Mean bout durations of wkl nrem rem and wkb; duration increased in wkl during dark in comparison with light phase. Clear differences in duration distribution during light and dark period are shown (*, Anova and Log-Rank Test for Kaplan-Meier analysis <i>p</i> < 0.05).</p

Spectral analysis of sleep stages normalized to the total spectrum of the signal in 24 hours.

<p>A) Hippocampal spectral content of long and brief wake is different (in red; cortex in blue); i and ii, long wake epochs had an augmented theta band with a characteristic 8 Hz peak (<i>θ</i>₂ band; arrow a), whereas in the brief wake a lower frequency theta peak activity was at 6 Hz (<i>θ</i>₁ band; arrow b); in addition, the power of fast gamma hippocampal frequencies in long wake epochs is significantly increased compared to brief wake. During nrem sleep a predominance of delta and beta power, and a reduction of gamma band were observed (iii) and in rem there was a predominance of theta (iv; arrow a) with low delta and beta, and an increased gamma band with respect to nrem. B) Long vs. brief wake spectrums; hippocampal, but not cortical, fast gamma and theta power (<i>θ</i>₂) and EMG tone were decreased in brief-wk (<i>p</i> < 0.001).</p

Additional file 1 of Human arm joints reconstruction algorithm in rehabilitation therapies assisted by end-effector robotic devices

Solution of Îł angle. (PDF 104 kb

Additional file 2 of Human arm joints reconstruction algorithm in rehabilitation therapies assisted by end-effector robotic devices

Estimation of the initial conditions. (PDF 107 kb