Early Prediction of Wound Healing Outcome Based on Chronic Wound Registry Database.

Chronic wounds cause a number of unnecessary amputations due to a delay in proper treatment. To expedite timely treatment, this paper presents an algorithm which uses a logistic regression classifier to predict whether the wound will heal or not within a specified time. The prediction is made at three time-points: one month, three months, and six months from the first visit of the patient to the healthcare facility. This prediction is made using a systematically collected chronic wound registry and is based entirely on data collected during patients' first visit. The algorithm achieves an area under the receiver operating characteristic curve (AUC) of 0.75, 0.72, and 0.71 for the prediction at the three time-points, respectively.Clinical relevance- Using the proposed prediction model, the clinicians will have an early estimate of the time taken to heal thereby providing appropriate treatments. We hope this will ensure timely treatments and reduce the number of unnecessary amputations.

Information sparseness in cortical microelectrode channels while decoding movement direction using an artificial neural network.

Implanted microelectrode arrays can directly pick up electrode signals from the primary motor cortex (M1) during movement, and brain-machine interfaces (BMIs) can decode these signals to predict the directions of contemporaneous movements. However, it is not well known how much each individual input is responsible for the overall performance of a BMI decoder. In this paper, we seek to quantify how much each channel contributes to an artificial neural network (ANN)-based decoder, by measuring how much the removal of each individual channel degrades the accuracy of the output. If information on movement direction was equally distributed among channels, then the removal of one would have a minimal effect on decoder accuracy. On the other hand, if that information was distributed sparsely, then the removal of specific information-rich channels would significantly lower decoder accuracy. We found that for most channels, their removal did not significantly affect decoder performance. However, for a subset of channels (16 out of 61), removing them significantly reduced the decoder accuracy. This suggests that information is not uniformly distributed among the recording channels. We propose examining these channels further to optimize BMIs more effectively, as well as understand how M1 functions at the neuronal level.

Intention Estimation Based Adaptive Unscented Kalman Filter for Online Neural Decoding.

The commonly used fixed discrete Kalman filters (DKF) in neural decoders do not generalize well to the actual relationship between neuronal firing rates and movement intention. This is due to the underlying assumption that the neural activity is linearly related to the output state. They also face the issues of requiring large amount of training datasets to achieve a robust model and a degradation of decoding performance over time. In this paper, an adaptive adjustment is made to the conventional unscented Kalman filter (UKF) via intention estimation. This is done by incorporating a history of newly collected state parameters to develop a new set of model parameters. At each time point, a comparative weighted sum of old and new model parameters using matrix squared sums is used to update the neural decoding model parameters. The effectiveness of the resulting adaptive unscented Kalman filter (AUKF) is compared against the discrete Kalman filter and unscented Kalman filter-based algorithms. The results show that the proposed new algorithm provides higher decoding accuracy and stability while requiring less training data.

Prefrontal Cortical Activation While Doing Mindfulness Task: a Pilot Functional Near-Infrared Spectroscopy Study.

Mindfulness interventions are increasingly used in clinical settings. Neurophysiological mechanisms underlying mindfulness offer objective evidence that can help us evaluate the efficacy of mindfulness. Recent advances in technology have facilitated the use of functional Near-Infrared Spectroscopy (fNIRS) as a light weight, portable, and relatively lower cost neuroimaging device as compared to functional Magnetic Resonance Imaging (fMRI). In contrast to numerous fMRI studies, there are scanty investigations using fNIRS to study mindfulness. Hence, this study was done to investigate the feasibility of using a continuous-wave multichannel fNIRS system to study cerebral cortex activations on a mindfulness task versus a baseline task. NIRS data from 14 healthy Asian subjects were collected. A statistical parametric mapping toolbox specific for statistical analysis of NIRS signal called NIRS_SPM was used to study the activations. The results from group analysis performed on the contrast of the mindfulness versus baseline tasks showed foci of activations on the left and central parts of the prefrontal cortex. The findings are consistent with prevailing fMRI studies and show promise of using fNIRS system for studying real-time neurophysiological cortical activations during mindfulness practice.

Decoding movement direction from cortical microelectrode recordings using an LSTM-based neural network.

Brain-machine interfaces (BMIs) allow individuals to communicate with computers using neural signals, and Kalman Filter (KF) are prevailingly used to decode movement directions from these neural signals. In this paper, we implemented a multi-layer long short-term memory (LSTM)based artificial neural network (ANN) for decoding BMI neural signals. We collected motor cortical neural signals from a nonhuman primate (NHP), implanted with microelectrode array (MEA) while performing a directional joystick task. Next, we compared the LSTM model in decoding the joystick trajectories from the neural signals against the prevailing KF model. The results showed that the LSTM model yielded significantly improved decoding accuracy measured by mean correlation coefficient (0.84, p < 10-7) than the KF model (0.72). In addition, using a principal component analysis (PCA)-based dimensionality reduction technique yielded slightly deteriorated accuracies for both the LSTM (0.80) and KF (0.70) models, but greatly reduced the computational complexity. The results showed that the LSTM decoding model holds promise to improve decoding in BMIs for paralyzed humans.

Robust Local Field Potential-based Neural Decoding by Actively Selecting Discriminative Channels.

Local field potentials (LFPs) have been proposed as a neural decoding signal to compensate for spike signal deterioration in invasive brain-machine interface applications. However, the presence of redundancy among LFP signals at different frequency bands across multiple channels may affect the decoding performance. In order to remove redundant LFP channels, we proposed a novel Fisher-distance ratio-based method to actively batch select discriminative channels to maximize the separation between classes. Experimental evaluation was conducted on 5 non-consecutive days of data from a non-human primate. For data from each day, the first experimental session was used to generate the training model, which was then used to perform 4-class decoding of signals from other sessions. Decoding achieved an average accuracy of 79.55%, 79.02% and 79.40% using selected LFP channels for beta, low gamma and high gamma frequency bands, respectively. Compared with decoding using full LFP channels, decoding using selected LFP channels in high gamma band resulted in an increase of 8.67% in accuracy, even if this accuracy was still 7.26% lower than that of spike-based decoding. These results demonstrate the effectiveness of the proposed method in selecting discriminative LFP channels for neural decoding.

EEG Channel Selection Based on Correlation Coefficient for Motor Imagery Classification: A Study on Healthy Subjects and ALS Patient.

Brain-Computer Interface (BCI) provides an alternate channel of interaction for people with severe motor disabilities. The Common Spatial Pattern (CSP) algorithm is effective in extracting discriminative features from EEG data for motor imagery-based Brain-Computer Interface (BCI). CSP yields signal from various locations for better performance. In this study, we selected a subset of EEG channels using correlation coefficient of spectral entropy and compared the classification performance using the Filter Bank Common Spatial Pattern (FBCSP) algorithm. We conducted experiments on 4 healthy subjects and one Amyotrophic Lateral Sclerosis (ALS) patient. The results showed that the proposed channel selection method increased classification accuracy of all subjects from 1.25% to 8.22%. Optimal performance was obtained using between 13 to 24 channels, and channels located over the motor cortex zone possess higher probabilities of being selected. Comparing with the channels manually selected to over the motor cortex area, the correlation coefficient method is able to identify the optimal channel combination and improve the motor imagery decoding accuracy of Healthy and ALS subjects.

Boosting performance in brain-machine interface by classifier-level fusion based on accumulative training models from multi-day data.

The nonstationarity of neural signal is still an unresolved issue despite the rapid progress made in brain-machine interface (BMI). This paper investigates how to utilize the rich information and dynamics in multi-day data to address the variability in day-to-day signal quality and neural tuning properties. For this purpose, we propose a classifier-level fusion technique to build a robust decoding model by jointly considering the classifier outputs from multiple base-training models using multi-day data collected prior to test day. The data set used in this study consisted of recordings of 8 days from a non-human primate (NHP) during control of a mobile robot using a joystick. Offline analysis demonstrates the superior performance of the proposed method which results in 4.4% and 13.10% improvements in decoding (significant by one-way ANOVA and post hoc t-test) compared with the two baseline methods: 1) concatenating data from multiple days based on common effective channels, and 2) averaging accuracies across all base-training models. These results further validate the effectiveness of proposed method without recalibration of the model.

Stop state classification in intracortical brain-machine-interface.

Invasive brain-machine-interface (BMI) has the prospect to empower tetraplegic patients with independent mobility through the use of brain-controlled wheelchairs. For the practical and long-term use of such control systems, the system has to distinguish between stop and movement states and has to be robust to overcome non-stationarity in the brain signals. In this work, we investigates the non-stationarity of the stop state on neural data collected from a macaque trained to control a robotic platform to stop and move in left, right, forward directions We then propose a hybrid approach that employs both random forest and linear discriminant analysis (LDA). Using this approach, we performed offline decoding on 8 days of data collected over the course of three months during joystick control of the robotic platform. We compared the results of using the proposed approach with the use of LDA alone to perform direct classifications of stop, left, right and forward. The results showed an average performance increment of 22.7% using the proposed hybrid approach. The results yielded significant improvements during sessions where LDA showed a heavy bias towards the stop state. This suggests that the proposed hybrid approach addresses the non-stationarity in the stop state and subsequently facilitates a more accurate decoding of the movement states.

Reject option to reduce false prediction rates for EEG-motor imagery based BCI.

The Filter Bank Common Spatial Pattern (FBCSP) algorithm had been shown to be effective in performing multi-class Electroencephalogram (EEG) decoding of motor imagery using the one-versus-the-rest approach on the BCI Competition IV Dataset IIa. In this paper, we propose a method to reduce false detection rates of decoding through a rejection option based on the difference in the posterior probability computed by the Naïve Bayesian classifier. We applied the proposed approach on the BCI Competition IV Dataset IIa, and the results showed a decrease in the false detection rates from 34.6 % to 6.9%, while average decoded trials decreased from 93.2% to 34.2% using a rejection threshold between 0.1 and 0.9. We subsequently formulated a method to optimize the rejection threshold based on the maximum F0.5 score. The optimal rejection threshold yielded an average decrease in false detection rate to 19.1% with an average of 67.5% of trials decoded. The results showed the feasibility of decreasing false detection rates at a cost of rejection. Nevertheless, the results suggest that the use of reject option (RO) may be used as a training feedback system to train subjects' overt and covert EEG control strategies for better (dexterity and safety) continuous control of external device.

Optimized temporal pattern of brain stimulation designed by computational evolution.

Brain stimulation is a promising therapy for several neurological disorders, including Parkinson's disease. Stimulation parameters are selected empirically and are limited to the frequency and intensity of stimulation. We varied the temporal pattern of deep brain stimulation to ameliorate symptoms in a parkinsonian animal model and in humans with Parkinson's disease. We used model-based computational evolution to optimize the stimulation pattern. The optimized pattern produced symptom relief comparable to that from standard high-frequency stimulation (a constant rate of 130 or 185 Hz) and outperformed frequency-matched standard stimulation in a parkinsonian rat model and in patients. Both optimized and standard high-frequency stimulation suppressed abnormal oscillatory activity in the basal ganglia of rats and humans. The results illustrate the utility of model-based computational evolution of temporal patterns to increase the efficiency of brain stimulation in treating Parkinson's disease and thereby reduce the energy required for successful treatment below that of current brain stimulation paradigms.

Frequency-dependent, transient effects of subthalamic nucleus deep brain stimulation on methamphetamine-induced circling and neuronal activity in the hemiparkinsonian rat.

Methamphetamine-induced circling is used to quantify the behavioral effects of subthalamic nucleus (STN) deep brain stimulation (DBS) in hemiparkinsonian rats. We observed a frequency-dependent transient effect of DBS on circling, and quantified this effect to determine its neuronal basis. High frequency STN DBS (75-260Hz) resulted in transient circling contralateral to the lesion at the onset of stimulation, which was not sustained after the first several seconds of stimulation. Following the transient behavioral change, DBS resulted in a frequency-dependent steady-state reduction in pathological ipsilateral circling, but no change in overall movement. Recordings from single neurons in globus pallidus externa (GPe) and substantia nigra pars reticulata (SNr) revealed that high frequency, but not low frequency, STN DBS elicited transient changes in both firing rate and neuronal oscillatory power at the stimulation frequency in a subpopulation of GPe and SNr neurons. These transient changes were not sustained, and most neurons exhibited a different response during the steady-state phase of DBS. During the steady-state, DBS produced elevated neuronal oscillatory power at the stimulus frequency in a majority of GPe and SNr neurons, and the increase was more pronounced during high frequency DBS than during low frequency DBS. Changes in oscillatory power during both transient and steady-state DBS were highly correlated with changes in firing rates. These results suggest that distinct neural mechanisms were responsible for transient and sustained behavioral responses to STN DBS. The transient contralateral turning behavior following the onset of high frequency DBS was paralleled by transient changes in firing rate and oscillatory power in the GPe and SNr, while steady-state suppression of ipsilateral turning was paralleled by sustained increased synchronization of basal ganglia neurons to the stimulus pulses. Our analysis of distinct frequency-dependent transient and steady-state responses to DBS lays the foundation for future mechanistic studies of the immediate and persistent effects of DBS.

Prediction and detection of seizures from simultaneous thalamic and scalp electroencephalography recordings.

OBJECTIVE The authors explored the feasibility of seizure detection and prediction using signals recorded from the anterior thalamic nucleus, a major target for deep brain stimulation (DBS) in the treatment of epilepsy. METHODS Using data from 5 patients (13 seizures in total), the authors performed a feasibility study and analyzed the performance of a seizure prediction and detection algorithm applied to simultaneously acquired scalp and thalamic electroencephalography (EEG). The thalamic signal was obtained from DBS electrodes. The applied algorithm used the similarity index as a nonlinear measure for seizure identification, with patient-specific channel and threshold selection. Receiver operating characteristic (ROC) curves were calculated using data from all patients and channels to compare the performance between DBS and EEG recordings. RESULTS Thalamic DBS recordings were associated with a mean prediction rate of 84%, detection rate of 97%, and false-alarm rate of 0.79/hr. In comparison, scalp EEG recordings were associated with a mean prediction rate of 71%, detection rate of 100%, and false-alarm rate of 1.01/hr. From the ROC curves, when considering all channels, DBS outperformed EEG for both detection and prediction of seizures. CONCLUSIONS This is the first study to compare automated seizure detection and prediction from simultaneous thalamic and scalp EEG recordings. The authors have demonstrated that signals recorded from DBS leads are more robust than EEG recordings and can be used to predict and detect seizures. These results indicate feasibility for future designs of closed-loop anterior nucleus DBS systems for the treatment of epilepsy.

Failure to suppress low-frequency neuronal oscillatory activity underlies the reduced effectiveness of random patterns of deep brain stimulation.

Subthalamic nucleus (STN) deep brain stimulation (DBS) is an established treatment for the motor symptoms of Parkinson's disease (PD). However, the mechanisms of action of DBS are unknown. Random temporal patterns of DBS are less effective than regular DBS, but the neuronal basis for this dependence on temporal pattern of stimulation is unclear. Using a rat model of PD, we quantified the changes in behavior and single-unit activity in globus pallidus externa and substantia nigra pars reticulata during high-frequency STN DBS with different degrees of irregularity. Although all stimulus trains had the same average rate, 130-Hz regular DBS more effectively reversed motor symptoms, including circling and akinesia, than 130-Hz irregular DBS. A mixture of excitatory and inhibitory neuronal responses was present during all stimulation patterns, and mean firing rate did not change during DBS. Low-frequency (7-10 Hz) oscillations of single-unit firing times present in hemiparkinsonian rats were suppressed by regular DBS, and neuronal firing patterns were entrained to 130 Hz. Irregular patterns of DBS less effectively suppressed 7- to 10-Hz oscillations and did not regularize firing patterns. Random DBS resulted in a larger proportion of neuron pairs with increased coherence at 7-10 Hz compared with regular 130-Hz DBS, which suggested that long pauses (interpulse interval >50 ms) during random DBS facilitated abnormal low-frequency oscillations in the basal ganglia. These results suggest that the efficacy of high-frequency DBS stems from its ability to regularize patterns of neuronal firing and thereby suppress abnormal oscillatory neural activity within the basal ganglia.

Combining firing rate and spike-train synchrony features in the decoding of motor cortical activity.

Decoding of directional information in the motor cortex traditionally utilizes only firing rate information. However, information from other features could be extracted and combined with firing rate in order to increase classification accuracy. This study proposes the combination of firing rate and spike-train synchrony information in the decoding of motor cortical activity. Synchrony measures used are Event Synchronization (ES), SPIKE-Distance, and ISI-Distance. All data used for analyses were obtained from implanted electrode recordings of the primary motor cortex of a monkey that was trained to manipulate a motorized vehicle with 4 degrees of freedom (left, right, front and stop) via joystick control. Firstly, synchrony features could decode time periods, which were otherwise incorrectly decoded by firing rate alone, above chance levels. Secondly, using an ensemble classifier design for offline analysis, combining firing rate and ISI-distance information increases overall decoding accuracy by 1.1%. These results show that synchrony features in spike-trains do contain information not carried in firing rate. In addition, these results also demonstrate the feasibility of combining synchrony and firing rate for improving the classification accuracy of invasive brain-machine interface (BMI) in the control of neural prosthetics.

On the asynchronously continuous control of mobile robot movement by motor cortical spiking activity.

This paper presents an asynchronously intracortical brain-computer interface (BCI) which allows the subject to continuously drive a mobile robot. This system has a great implication for disabled patients to move around. By carefully designing a multiclass support vector machine (SVM), the subject's self-paced instantaneous movement intents are continuously decoded to control the mobile robot. In particular, we studied the stability of the neural representation of the movement directions. Experimental results on the nonhuman primate showed that the overt movement directions were stably represented in ensemble of recorded units, and our SVM classifier could successfully decode such movements continuously along the desired movement path. However, the neural representation of the stop state for the self-paced control was not stably represented and could drift.

Effective deep brain stimulation suppresses low-frequency network oscillations in the basal ganglia by regularizing neural firing patterns.

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for the motor symptoms of Parkinson's disease (PD). The effects of DBS depend strongly on stimulation frequency: high frequencies (>90 Hz) improve motor symptoms, while low frequencies (<50 Hz) are either ineffective or exacerbate symptoms. The neuronal basis for these frequency-dependent effects of DBS is unclear. The effects of different frequencies of STN-DBS on behavior and single-unit neuronal activity in the basal ganglia were studied in the unilateral 6-hydroxydopamine lesioned rat model of PD. Only high-frequency DBS reversed motor symptoms, and the effectiveness of DBS depended strongly on stimulation frequency in a manner reminiscent of its clinical effects in persons with PD. Quantification of single-unit activity in the globus pallidus externa (GPe) and substantia nigra reticulata (SNr) revealed that high-frequency DBS, but not low-frequency DBS, reduced pathological low-frequency oscillations (∼9 Hz) and entrained neurons to fire at the stimulation frequency. Similarly, the coherence between simultaneously recorded pairs of neurons within and across GPe and SNr shifted from the pathological low-frequency band to the stimulation frequency during high-frequency DBS, but not during low-frequency DBS. The changes in firing patterns in basal ganglia neurons were not correlated with changes in firing rate. These results indicate that high-frequency DBS is more effective than low-frequency DBS, not as a result of changes in firing rate, but rather due to its ability to replace pathological low-frequency network oscillations with a regularized pattern of neuronal firing.

Characterizing effects of subthalamic nucleus deep brain stimulation on methamphetamine-induced circling behavior in hemi-Parkinsonian rats.

The unilateral 6-hydroxydopamine (6-OHDA) lesioned rat model is frequently used to study the effects of subthalamic nucleus (STN) deep brain stimulation (DBS) for the treatment of Parkinson's disease. However, systematic knowledge of the effects of DBS parameters on behavior in this animal model is lacking. The goal of this study was to characterize the effects of DBS on methamphetamine-induced circling in the unilateral 6-OHDA lesioned rat. DBS parameters tested include stimulation amplitude, stimulation frequency, methamphetamine dose, stimulation polarity, and anatomical location of the electrode. When an appropriate stimulation amplitude and dose of methamphetamine were applied, high-frequency stimulation (> 130 Hz), but not low frequency stimulation (< 10 Hz), reversed the bias in ipsilateral circling without inhibiting movement. This characteristic frequency tuning profile was only generated when at least one electrode used during bipolar stimulation was located within the STN. No difference was found between bipolar stimulation and monopolar stimulation when the most effective electrode contact was selected, indicating that monopolar stimulation could be used in future experiments. Methamphetamine-induced circling is a simple, reliable, and sensitive behavioral test and holds potential for high-throughput study of the effects of STN DBS in unilaterally lesioned rats.

Relative contributions of local cell and passing fiber activation and silencing to changes in thalamic fidelity during deep brain stimulation and lesioning: a computational modeling study.

Deep brain stimulation (DBS) and lesioning are two surgical techniques used in the treatment of advanced Parkinson's disease (PD) in patients whose symptoms are not well controlled by drugs, or who experience dyskinesias as a side effect of medications. Although these treatments have been widely practiced, the mechanisms behind DBS and lesioning are still not well understood. The subthalamic nucleus (STN) and globus pallidus pars interna (GPi) are two common targets for both DBS and lesioning. Previous studies have indicated that DBS not only affects local cells within the target, but also passing axons within neighboring regions. Using a computational model of the basal ganglia-thalamic network, we studied the relative contributions of activation and silencing of local cells (LCs) and fibers of passage (FOPs) to changes in the accuracy of information transmission through the thalamus (thalamic fidelity), which is correlated with the effectiveness of DBS. Activation of both LCs and FOPs during STN and GPi-DBS were beneficial to the outcome of stimulation. During STN and GPi lesioning, effects of silencing LCs and FOPs were different between the two types of lesioning. For STN lesioning, silencing GPi FOPs mainly contributed to its effectiveness, while silencing only STN LCs did not improve thalamic fidelity. In contrast, silencing both GPi LCs and GPe FOPs during GPi lesioning contributed to improvements in thalamic fidelity. Thus, two distinct mechanisms produced comparable improvements in thalamic function: driving the output of the basal ganglia to produce tonic inhibition and silencing the output of the basal ganglia to produce tonic disinhibition. These results show the importance of considering effects of activating or silencing fibers passing close to the nucleus when deciding upon a target location for DBS or lesioning.