Neuroengineering

This paper evaluates the patient-specific seizure prediction performance of pre-ictal changes in bivariate-synchrony between pairs of intracranial electroencephalographic (iEEG) signals within 15 minutes of a seizure in patients with pharmacoresistant focal epilepsy. Prediction horizons under 15 minutes reduce the durations of warning times and should provide adequate time for a seizure control device to intervene. Long-term continuous iEEG was obtained from six patients. The seizure prediction performance was evaluated for all possible channel pairs and for different prediction methods to find the best performing channel pairs and methods for both pre-ictal decreases and increases in synchrony. The different prediction methods involved changes in window duration, signal filtering, thresholding approach, and prediction horizon durations. Performance for each patient, for all seizures, was first compared with an analytical-Poisson-based random predictor. The performance of the top 5% of channel pairs for each patient closely matched the top 5% of analytical-Poisson-based random predictor performance indicating that patient-specific, bivariate-synchrony-based seizure prediction could be random in general (under the assumption that channel-pair prediction times are statistically independent). Analysis of the spatial patterns of performance showed no clear relationship to the seizure onset zone. For each patient the best channel pair showed better performance than Poisson-based random prediction for a selected subset of prediction thresholds. Given the caveats of comparing with this form of random prediction, alarm time surrogates were employed to assess statistical significance of a four-fold out-of-sample cross validation analysis applied to the best channel-pairs. The cross-validation analysis obtained reasonable testing performance for most patients when performance was compared to random prediction based on alarm time surrogates. The most significant case was a patient whose testing set sensitivity and false positive rate were 0.67+/-0.09 and 3.04+/-0.29 hr-1, respectively, for decreases in synchrony, an intervention time of 15 minutes and a seizure onset period of 5 minutes. For each testing set for this patient, performance was better than that obtained by random prediction at the significance level of 0.05 (average sensitivity of 0.47+/-0.05). Moreover, there were 9 seizures in each testing set which gives greater power to this cross-validation result, although the cross-validation was performed on the best channel pair selected by within-sample optimization for all seizures of the patient. Further validation with larger datasets from individual patients is needed. Improvements in prediction performance should be achievable through investigations of multivariate synchrony combined with nonlinear classification methods.

Background: Stair climbing up and down is an essential part of everyday's mobility. To enable wheelchair-dependent patients the repetitive practice of this task, a novel gait robot, G-EO-Systems (EO, Lat: I walk), based on the end-effector principle, has been designed. The trajectories of the foot plates are freely programmable enabling not only the practice of simulated floor walking but also stair climbing up and down. The article intended to compare lower limb muscle activation patterns of hemiparetic subjects during real floor walking and stairs climbing up, and during the corresponding simulated conditions on the machine, and secondly to demonstrate gait improvement on single case after training on the machine.

Electrical stimulation applied to the basolateral amygdala in the pentylenetetrazole animal model of seizures may result in either a proconvulsant or an anticonvulsant effect depending on the interpulse intervals used: periodic or nonperiodic, respectively. We tested the effect of this electrical stimulation temporal coding on the spontaneous and recurrent behavioral seizures produced in the chronic phase of the pilocarpine animal model of temporal lobe epilepsy, an experimental protocol that better mimics the human condition. After 45 days of the pilocarpine-induced status epilepticus, male Wistar rats were submitted to a surgical procedure for the implantation of a bipolar electrical stimulation electrode in the right basolateral amygdala and were allowed to recover for seven days. The animals were then placed in a glass box, and their behaviors were recorded daily on DVD for 6 h for 4 consecutive days (control period). Spontaneous recurrent behavioral seizures when showed in animals were further recorded for an extra 4-day period (treatment period), under periodic or nonperiodic electrical stimulation. The number, duration, and severity of seizures (according to the modified Racine's scale) during treatment were compared with those during the control period. The nonperiodically stimulated group displayed a significantly reduced total number and duration of seizures. There was no difference between control and treatment periods for the periodically stimulated group. Results corroborate previous findings from our group showing that nonperiodic electrical stimulation has a robust anticonvulsant property. In addition, results from the pilocarpine animal model further strengthen nonperiodic electrical stimulation as a valid therapeutic approach in current medical practice. Our working hypothesis is that temporally unstructured electrical stimulation may wield its effect by desynchronizing neural networks involved in the ictogenic process.

We developed an algorithm to extract and combine EEG spectral features, which effectively classifies cognitive states and is robust in the presence of sensor noise. The algorithm uses a partial-least squares (PLS) algorithm to decompose multi-sensor EEG spectra into a small set of components. These components are chosen such that they are linearly orthogonal to each other and maximize the covariance between the EEG input variables and discrete output variables, such as different cognitive states. A second stage of the algorithm uses robust cross-validation methods to select the optimal number of components for classification. The algorithm can process practically unlimited input channels and spectral resolutions. No a priori information about the spatial or spectral distributions of the sources is required. A final stage of the algorithm uses robust cross-validation methods to reduce the set of electrodes to the minimum set that does not sacrifice classification accuracy. We tested the algorithm with simulated EEG data in which mental fatigue was represented by increases frontal theta and occipital alpha band power. We synthesized EEG from bilateral pairs of frontal theta sources and occipital alpha sources generated by second-order autoregressive processes. We then excited the sources with white noise and mixed the source signals into a 19channel sensor array (10-20 system) with the three-sphere head model of the BESA Dipole Simulator. We generated synthetic EEG for 60 2-second long epochs. Separate EEG series represented the alert and fatigued states, between which alpha and theta amplitudes differed on average by a factor of two. We then corrupted the data with broadband white noise to yield signal-to-noise ratios (SNR) between 10 dB and -15 dB. We used half of the segments for training and cross-validation of the classifier and the other half for testing. Over this range of SNRs, classifier performance degraded smoothly, with test proportions correct (TPC) of 94%, 95%, 96%, 97%, 84%, and 53% for SNRs of 10 dB, 5 dB, 0 dB, -5 dB, -10 dB, and -15 dB, respectively. We will discuss the practical implications of this algorithm for real-time state classification and an off-line application to EEG data taken from pilots who performed cognitive and flight tests over a 37-hour period of extended wakefulness.

Márcio Flávio Dutra Moraes, Daniel de Castro Medeiros, Flávio Afonso Gonçalves Mourao, Sergio Augusto Vieira Cancado, Vinicius Rosa Cota - The idea of the epileptic brain being highly excitable and facilitated to synchronic activity has guided pharmacological treatment since the early twentieth century. Although tackling epilepsy's seizure-prone feature, by tonically modifying overall circuit excitability and/or connectivity, the last 50 years of drug development has not seen a substantial improvement in seizure suppression of refractory epilepsies. This review presents a new conceptual framework for epilepsy in which the temporal dynamics of the disease plays a more critical role in both its understanding and therapeutic strategies. The repetitive epileptiform pattern (characteristic during ictal activity) and other well-defined electrographic signatures (i.e., present during the interictal period) are discussed in terms of the sequential activation of the circuit motifs. Lessons learned from the physiological activation of neural circuitry are used to further corroborate the argument and explore the transition from proper function to a state of instability. Furthermore, the review explores how interfering in the temporally dependent abnormal connectivity between circuits maywork as a therapeutic approach. We also reviewthe use of probing stimulation to access network connectivity and evaluate its power to determine transitional states of the dynamical system as it moves towards regions of instability, especially when conventional electrographic monitoring is proven inefficient. Unorthodox cases, with little or no scalp electrographic correlate, in which ictogenic circuitry and/or seizure spread is temporally restricted to neurovegetative, cognitive, and motivational areas are shown as possible explanations for sudden death in epilepsy (SUDEP) and other psychiatric comorbidities. In short, this review presents a paradigm shift in the way that we address the disease and is aimed to encourage debate rather than narrow the rationale epilepsy is currently engaged in.

Standard methods for seizure prediction involve passive monitoring of intracranial electroencephalography (iEEG) in order to track the 'state' of the brain. This paper introduces a new method for measuring cortical excitability using an electrical probing stimulus. Electrical probing enables feature extraction in a more robust and controlled manner compared to passively tracking features of iEEG signals. The probing stimuli consist of 100 bi-phasic pulses, delivered every 10 min. Features representing neural excitability are estimated from the iEEG responses to the stimuli. These features include the amplitude of the electrically evoked potential, the mean phase variance (univariate), and the phase-locking value (bivariate). In one patient, it is shown how the features vary over time in relation to the sleep-wake cycle and an epileptic seizure. For a second patient, it is demonstrated how the features vary with the rate of interictal discharges. In addition, the spatial pattern of increases and decreases in phase synchrony is explored when comparing periods of low and high interictal discharge rates, or sleep and awake states. The results demonstrate a proof-of-principle for the method to be applied in a seizure anticipation framework.

Background: Approximately 60% of stroke survivors experience hand dysfunction limiting execution of daily activities. Several methods have been proposed to objectively quantify fingers' joints range of motion (ROM), while few studies exist about multi-finger coordination during hand movements. The present work analysed this aspect, by providing a complete characterization of spatial and temporal aspects of hand movement, through the mathematical modelling of multi-joint finger motion in healthy subjects and stroke patients. Methods: Hand opening and closing movements were examined in 12 healthy volunteers and 14 hemiplegic stroke survivors by means of optoelectronic kinematic analysis. The flexion/extension angles of metacarpophalangeal (MCPJ) and proximal interphalangeal joints (IPJ) of all fingers were computed and mathematically characterized by a four-parameter hyperbolic tangent function. Accuracy of the selected model was analysed by means of coefficient of determination (R 2 ) and root mean square error (RMSE). Test-retest reliability was quantified by intraclass correlation coefficient (ICC) and test-retest errors. Comparison between performances of healthy controls and stroke subjects were performed by analysing possible differences in parameters describing angular and temporal aspects of hand kinematics and inter-joint, inter-digit coordination. Results: The angular profiles of hand opening and closing were accurately characterized by the selected model, both in healthy controls and in stroke subjects (R 2 > 0.973, RMSE < 2.0°). Test-retest reliability was found to be excellent, with ICC > 0.75 and remarking errors comparable to those obtained with other methods. Comparison with healthy controls revealed that hemiparetic hand movement was impaired not only in joints ROM but also in the temporal aspects of motion: peak velocities were significantly decreased, inter-digit coordination was reduced of more than 50% and inter-joint coordination patterns were highly disrupted. In particular, the stereotypical proximal-to-distal opening sequence (reversed during hand closing) found in healthy subjects, was altered in stroke subjects who showed abnormally high delay between IPJ and MCPJ movement or reversed moving sequences.

Background: Body weight support (BWS) systems on treadmill have been proposed as a strategy for gait training of subjects with stroke. Considering that ground level is the most common locomotion surface and that there is little information about individuals with stroke walking with BWS on ground level, it is important to investigate the use of BWS on ground level in these individuals as a possible alternative strategy for gait training.