This study provides Class III evidence that sirolimus does not significantly reduce seizure frequency in children with TSC and intractable epilepsy. The study lacked the precision to exclude a benefit from sirolimus.

Fully implantable wireless intra-cortical Brain Machine Interfaces (iBMI) is one of the most promising next frontiers in the nascent field of neurotechnology. However, scaling the number of channels in such systems by another 10X is difficult due to power and bandwidth requirements of the wireless transmitter. One promising solution for that is to include more processing, up to the decoder, in the implant so that transmission data rate is reduced drastically. Earlier work on neuromorphic decoders only showed classification of discrete states. We present results for continuous state decoding using a low power neuromorphic decoder chip termed Spike-input Extreme Learning Machine (SELMA). We compared SELMA against state of the art Steady State Kalman Filter (SSKF) across two different datasets involving a total of 4 non-human primates (NHPs). Results show at least a 10% or more increase in the fraction of variance accounted for by SELMA over SSKF across the datasets. Furthermore, estimated energy consumption comparison shows SELMA consuming ≈ 9 nJ/update against SSKF's ≈ 7.4 nJ/update for an iBMI with a 10 degree of freedom control. Thus, SELMA yields better performance against SSKF with a marginal increase in energy consumption paving the way for reducing transmission data rates in future scaled BMI systems.

This paper presents for the first time a real-time closed loop neuromorphic decoder chip-driven intra-cortical brain machine interface (iBMI) in a non-human primate (NHP) based experimental setup. Decoded results show trial success rates and mean times to target comparable to those obtained by hand-controlled joystick. Neural control trial success rates of ≈ 96% of those obtained by hand-controlled joystick have been demonstrated. Also, neural control has shown mean target reach speeds of ≈ 85% of those obtained by hand-controlled joystick . These results pave the way for fast and accurate, fully implantable neuromorphic neural decoders in iBMIs.

When arm and trunk segments are involved in reaching for objects within arm's reach, vestibulospinal pathways compensate for trunk motion influence on arm movement. This compensatory arm-trunk synergy is characterised by a gain coefficient of 0 to 1. Vestibular patients have less efficient arm-trunk synergies and lower gains. To assess the clinical usefulness of the gain measure, we used a portable ultrasound-based device to characterize arm-trunk coordination deficits in vestibular patients. Arm-trunk coordination without vision was measured in a Stationary Hand Task where hand position was maintained during trunk movement, and a Reaching Task with and without trunk motion. Twenty unilateral vestibular patients and 16 controls participated. For the Stationary Hand task, patient gains ranged from g = 0.94 (good compensation) to 0.31 (poor compensation) and, on average, were lower than in controls (patients: 0.67 ± 0.19; controls: 0.85 ± 0.07; p < 0.01). Gains were significantly correlated with clinical tests (Sensory Organization; r = 0.62, p < 0.01, Foam Romberg Eyes Closed; r = 0.65, p < 0.01). For the Reaching Task, blocking trunk movement during reaching modified hand position significantly more in patients (8.2 ± 4.3 cm) compared to controls (4.5 ± 1.7 cm, p < 0.02) and the amount of hand position deviation was correlated with the degree of vestibular loss in a sub-group (n = 14) of patients. Measurement of the Stationary Task arm-trunk gain and hand deviations during the Reaching Task can help characterize sensorimotor problems in vestibular-deficient patients and track recovery following therapeutic interventions. The ultrasound-based portable device is suitable for measuring vestibulospinal deficits in arm-trunk coordination in a clinical setting.

Fully implantable wireless intra-cortical Brain Machine Interfaces (iBMI) is one of the most promising next frontiers in the nascent field of neurotechnology. However, scaling the number of channels in such systems by another 10X is difficult due to power and bandwidth requirements of the wireless transmitter. One promising solution for that is to include more processing, up to the decoder, in the implant so that transmission data rate is reduced drastically. Earlier work on neuromorphic decoders only showed classification of discrete states. We present results for continuous state decoding using a low power neuromorphic decoder chip termed Spike-input Extreme Learning Machine (SELMA). We compared SELMA against state of the art Steady State Kalman Filter (SSKF) across two different datasets involving a total of 4 non-human primates (NHPs). Results show at least a 10% or more increase in the fraction of variance accounted for by SELMA over SSKF across the datasets. Furthermore, estimated energy consumption comparison shows SELMA consuming ≈ 9 nJ/update against SSKF's ≈ 7.4 nJ/update for an iBMI with a 10 degree of freedom control. Thus, SELMA yields better performance against SSKF with a marginal increase in energy consumption paving the way for reducing transmission data rates in future scaled BMI systems.

PURPOSE. Compensatory eye movements (CEM) maintain a stable image on the retina by minimizing retinal slip. The optokinetic reflex (OKR) and vestibulo-ocular reflex (VOR) compensate for low and high velocity stimuli, respectively. The OKR system is known to be highly nonlinear. The VOR is generally modeled as a linear system and assumed to satisfy the superposition and homogeneity principles. To probe CEM violation of the superposition principle, we recorded eye movement responses in C57BL/6 mice to sum of sine (SoS) stimulation, a combination of multiple nonharmonic inputs. METHODS.We tested the VOR, OKR, VVOR (visually enhanced VOR), and SVOR (suppressed VOR). We used stimuli containing 0.6 Hz, 0.8 Hz, 1.0 Hz, and 1.9 Hz. Power spectra of SoS stimuli did not yield distortion products. Gains and delays of SoS and single sine (SS) responses were compared to yield relative gains and delays.RESULTS. We find the superposition principle is violated primarily in the OKR, VOR, and SVOR conditions. In OKR, we observed relative gain suppression of the lower SoS stimulus frequency component irrespective of the absolute frequency. Conversely, SVOR and VOR results showed gain enhancement of the lower frequency component and overall decrease in lead. Visually enhanced VOR results showed trends for overall gain suppression and delay decrease. CONCLUSIONS.Compensatory eye movements arguably depend on predictive signals. These results may reflect better prediction for SS stimuli. Natural CEM system stimulation generally involves complex frequency spectra. Use of SoS stimuli is a step toward unravelling the signals that really drive CEM and the predictive algorithms they depend on.

This paper presents a novel sparse ensemble based machine learning approach to enhance robustness of intracortical Brain Machine Interfaces (iBMIs) in the face of non-stationary distribution of input neural data across time. Each classifier in the ensemble is trained on a randomly sampled (with replacement) set of input channels. These sparse connections ensure that with a high chance, few of the base classifiers should be less affected by the variations in some of the recording channels. We have tested the generality of this technique on different base classifiers -linear discriminant analysis (LDA), support vector machine (SVM), extreme learning machine (ELM) and multilayer perceptron (MLP). Results show decoding accuracy improvements of up to ≈ 21%, 13%, 19%, 10% in non-human primate (NHP) A and 7%, 9%, 7%, 9% in NHP B across test days while using the sparse ensemble approach over a single classifier model for LDA, SVM, ELM and MLP algorithms respectively. The technique also holds ground when the most informative electrode on the test day is dropped. Accordingly, improvements of up to ≈ 24%, 11%, 22%, 9% in NHP A and 14%, 19%, 7%, 28% in NHP B are obtained for LDA, SVM, ELM and MLP respectively.

We present a working model of the compensatory eye movement system in mice. We challenge the model with a data set of eye movements in mice (n =34) recorded in 4 different sinusoidal stimulus conditions with 36 different combinations of frequency (0.1-3.2 Hz) and amplitude (0.5-8 •) in each condition. The conditions included vestibular stimulation in the dark (vestibular-ocular reflex, VOR), optokinetic stimulation (optokinetic reflex, OKR), and two combined visual/vestibular conditions (the visual-vestibular ocular reflex, vVOR, and visual suppression of the VOR, sVOR). The model successfully reproduced the eye movements in all conditions, except for minor failures to predict phase when gain was very low. Most importantly, it could explain the interaction of VOR and OKR when the two reflexes are activated simultaneously during vVOR stimulation. In addition to our own data, we also reproduced the behavior of the compensatory eye movement system found in the existing literature. These include its response to sum-of-sines stimuli, its response after lesions of the nucleus prepositus hypoglossi or the flocculus, characteristics of VOR adaptation, and characteristics of drift in the dark. Our model is based on ideas of state prediction and forward modeling that have been widely used in the study of motor control. However, it represents one of the first quantitative efforts to simulate the full range of behaviors of a specific system. The model has two separate processing loops, one for vestibular stimulation and one for visual stimulation. Importantly, state prediction in the visual processing loop depends on a forward model of residual retinal slip after vestibular processing. In addition, we hypothesize that adaptation in the system is primarily adaptation of this model. In other words, VOR adaptation happens primarily in the OKR loop.