Maxwell D. Murphy

Objective. An accurate forecast of longitudinal brain activity is essential for monitoring disease progression. While time-series forecasting has been applied to short-term electroencephalography (EEG) dynamics, the modeling of continuous, longitudinal signals remains largely unexplored. This study addresses this gap by validating an individualized time series forecasting framework based on a trigonometric, box-cox transformation with autoregressive moving average errors and trend and seasonal/periodic components (TBATS) model to predict slow-wave activity (SWA, 0.5–4 Hz) power in EEG collected from cynomolgus monkeys. Approach. We recorded continuous wireless telemetry EEG data from three cynomolgus monkeys for 12 consecutive days. The SWA power was aggregated into hourly bins. We implemented a TBATS model to forecast SWA dynamics and compared its root mean squared errors (RMSE) against a naïve model, Holt-Winters (HW) model, and seasonal autoregressive integrated moving average (SARIMA) model. We then assessed the model validity using residual autocorrelation to evaluate the independence of model residuals. We also applied an expanding window approach to calculate the minimum amount of training data required for accurate model performance. Main results. The TBATS model demonstrated high data efficiency by achieving stable forecasts with as few as 2 d of training data. TBATS demonstrated comparable out-of-sample RMSE values in comparison to SARIMA and HW benchmarks. In contrast to SARIMA and HW, TBATS was the only framework to consistently produce white-noise residuals, which indicated that it captured the latent temporal dynamics of the time series data. Significance. This study demonstrated the TBATS model as a robust and efficient tool for forecasting longitudinal EEG signals. By accurately modeling subject-specific temporal structure with minimal data requirements, this approach provided a practical method for establishing a personalized neural baseline, critical for tracking neurodegeneration or optimizing closed-loop therapies.

Most individuals with tetraplegia retain some myoelectric function in their forearms, which offers the possibility of using surface electromyographic (sEMG) control for human-computer interaction (HCI). We demonstrate the potential of this approach by showing that people with motor-complete (n=5) and motor-incomplete (n=2) tetraplegia can accurately control myoelectric activity in their forearm to perform discrete button-click and continuous positioning tasks. These control inputs were mapped to the firing rate of motor units detected by a wireless wristband sensor designed for everyday use. Participants completed four testing sessions to assess their speed and accuracy. Motor units that displayed a wide dynamic range in their firing rate performed best during tasks requiring continuous, single-axis control. Interestingly, the level of impairment did not affect performance on the clicking and 1D cursor control tasks. However, those with motor-incomplete injuries showed greater independent control over two motor units than participants with motor-complete injuries, who exhibited stronger coupling between units. Participants also confirmed the practical utility of the device, successfully placing and removing the sEMG wristband on their own and consistently rating it as comfortable and easy to manage. These findings are significant because they offer the first demonstration of motor unit-based control in individuals with cervical spinal cord injury (SCI) using a fully wearable wristband interface, highlighting the feasibility of moving these systems out of the lab and into daily life.

Studies have shown that most individuals with motor complete spinal cord injuries (SCIs) can activate motor units (MUs) below the injury level, potentially enabling the use of myoelectric controllers for computer tasks. We present a novel wearable neuromotor interface driven by surface electromyography (sEMG). This wearable sEMG interface detects motor unit action potentials (MUAPs) from an array of sensors on the forearm. The MU event rate is translated (i.e. decoded) into discrete or continuous inputs for emulating button presses or joystick inputs. Our study demonstrates real-time detection and decoding of MUAPs in two individuals with tetraplegia, enabling computer task control via a non-invasive interface. Participants calibrated a spike sorting model through periods of rest and tonic activation, mapping MUAP firing rates to various degrees of freedom (DOF) for cursor or character control in tasks and 2D games. Each MUAP was mapped to a separate DOF axis, which we utilized in a Fitts’ Law target acquisition task to evaluate device throughput rate and other performance metrics such as completion rate, initiation time, time to target acquisition, and dial-in time. Our findings highlight MU firing as an effective control input, enabling gaming and social interaction for individuals with tetraplegia. This device’s wearability and ease of use offer an innovative human-computer interaction solution that may enable people with SCI to interact freely with computers and other digital devices.

Activity dependent stimulation (ADS) is a closed loop stimulation technique whose neurophysiological effects have not been deeply investigated. Here we explored how Local field Potentials (LFP) are impacted by a focal ischemic lesion and, subsequently, by ADS treatment. Intracortical microelectrode arrays were implanted in the rostral forelimb area (RFA) and in the primary somatosensory area (S1) of anaesthetized rats. An ischemic injury was induced in the caudal forelimb area through microinjections of Endothelin-1. The lesion induced an acute depressive trend in LFP power in RFA (evaluated in 6 bands of interest: Delta (1–4Hz), Theta (4–8Hz), Alpha (8-11Hz), Beta (11–30Hz), LowGamma (30–55Hz) and HighGamma (55-80)) followed by a noticeable significant rebound in both areas. Applying ADS induced an overall decrease of power. The lesion impacted the connectivity in a frequency specific manner, resulting in widespread increase in connectivity in Delta both between and within areas. Two hours after the lesion, without stimulation, correlated activity between areas increased in Beta and Gamma. After stimulation, inter-area connectivity increased in Delta, Theta and Alpha, while considerably dropping within RFA in highGamma. By computing phase-amplitude coupling, we found that the lesion produced an incremental increase in the coupling between (Theta) Alpha phase and (lowGamma) highGamma amplitude within RFA, while S1 had a more generalized increase. Likewise, coupling between Theta phase and lowGamma/highGamma amplitudes increased between areas after lesion. ADS induced a similar increase, but greater in magnitude both within and between RFA and S1. These results have important implications on the emerging field of closed-loop adaptive stimulation promoting ADS as an innovative tool for the treatment of neurological disorders.

While substantial task-related neural activity has been observed during motor tasks in rodent primary motor cortex and premotor cortex, the long-term stability of these responses in healthy rats is uncertain, limiting the interpretability of longitudinal changes in the specific patterns of neural activity associated with learning or motor recovery following injury. This study examined the stability of task-related neural activity associated with execution of two distinct reaching tasks in healthy rodents. A novel automated rodent behavioral apparatus was constructed and rats were trained to perform a reaching task combining a ‘gross’ lever press and a ‘fine’ pellet retrieval. In each animal, two chronic microelectrode arrays were implanted in motor cortex spanning the caudal forelimb area (rodent primary motor cortex) and the rostral forelimb area (rodent premotor cortex). We recorded multiunit spiking and local field potential activity from 10 days to 7–10 weeks post-implantation to characterize the patterns of neural activity observed during each task component and analyzed the consistency of channel-specific task-related neural activity. Task-related changes in neural activity were observed on the majority of channels. While the task-related changes in multi-unit spiking and local field potential spectral power were consistent over several weeks, spectral power changes were more stable, despite the trade-off of decreased spatial and temporal resolution. These results show that neural activity in rodent primary and premotor cortex is associated with specific phases of reaching movements with stable patterns of task-related activity across time, establishing the relevance of the rodent for future studies designed to examine changes in task-related neural activity during recovery from focal cortical lesions.

Objective . Activity-dependent stimulation (ADS) is designed to strengthen the connections between neuronal circuits and therefore may be a promising tool for promoting neurophysiological reorganization following a brain injury. To successfully perform this technique, two criteria must be met: (1) spikes in the extracellular electrical field potential must be detected accurately at one site of interest, and (2) stimulation pulses generated at fixed (Approach . We implemented a spike detection state machine on a field-programmable gate array (FPGA). Because the accuracy of spike detection can be heavily reduced in awake and behaving animals due to biological artifacts such as movement and chewing, the state machine tracks candidate spike waveforms, checking them against multiple programmable thresholds and rejecting any spikes that fail to meet a programmed threshold criterion. Main Results . A series of offline analyses showed that our implementation was able to appropriately trigger stimulation during epochs of biological artifacts with an overall accuracy between 72% and 97%, fixed computational latency of 167 µ s, and an algorithmic latency of 300 µ s to 800 µ s. Significance . Our improvements have been made open-source and are freely available to all scientists working on closed-loop neuroprosthetic devices. Importantly, the improvements are easily incorporated into existing workflows that utilize the Intan Stimulation and Recording Controller.

Following injury to motor cortex, reorganization occurs throughout spared brain regions and is thought to underlie motor recovery. Unfortunately, the standard neurophysiological and neuroanatomical measures of post-lesion plasticity are only indirectly related to observed changes in motor execution. While substantial task-related neural activity has been observed during motor tasks in rodent primary motor cortex and premotor cortex, the long-term stability of these responses in healthy rats is uncertain, limiting the interpretability of longitudinal changes in the specific patterns of neural activity during motor recovery following injury. This study examined the stability of task-related neural activity associated with execution of reaching movements in healthy rodents. Rats were trained to perform a novel reaching task combining a ‘gross’ lever press and a ‘fine’ pellet retrieval. In each animal, two chronic microelectrode arrays were implanted in motor cortex spanning the caudal forelimb area (rodent primary motor cortex) and the rostral forelimb area (rodent premotor cortex). We recorded multiunit spiking and local field potential activity from 10 days to 7-10 weeks post-implantation to characterize the patterns of neural activity observed during each task component and analyzed the consistency of channel-specific task-related neural activity. Task-related changes in neural activity were observed on the majority of channels. While the task-related changes in multi-unit spiking and local field potential spectral power were consistent over several weeks, spectral power changes were more stable, despite the trade-off of decreased spatial and temporal resolution. These results show that rodent primary and premotor cortex are both involved in reaching movements with stable patterns of task-related activity across time, establishing the relevance of the rodent for future studies designed to examine changes in task-related neural activity during recovery from focal cortical lesions.

This study assesses the feasibility of latent factor analysis via dynamical systems (LFADS) for evaluating differences in the observed spiking response dynamics imposed by two electrical microstimulation regimes in awake rats. LFADS is a recently-developed deep learning method that uses stimulus-aligned neural spiking data to determine the initial neural state of each trial, as well as infer a set of time-dependent perturbations to the learned neural dynamics within trials. We show that time-dependent perturbations inferred by an LFADS model trained on spikes from trials on a single session can distinguish between different stimulation conditions. Furthermore, we use these data to exemplify how LFADS inferences track the evolution of stimulus-related spiking responses during chronic microstimulation experiments.

Current research in brain computer interface (BCI) technology is advancing beyond preclinical studies, with trials beginning in human patients. To date, these trials have been carried out with several different types of recording interfaces. The success of these devices has varied widely, but different factors such as the level of invasiveness, timescale of recorded information, and ability to maintain stable functionality of the device over a long period of time all must be considered in addition to accuracy in decoding intent when assessing the most practical type of device moving forward. Here, we discuss various approaches to BCIs, distinguishing between devices focusing on control of operations extrinsic to the subject (e.g., prosthetic limbs, computer cursors) and those focusing on control of operations intrinsic to the brain (e.g. using stimulation or external feedback), including closed-loop or adaptive devices. In this discussion, we consider the current challenges facing the translation of various types of BCI technology to eventual human application.