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Ripples (80-250 Hz) are brief high-frequency oscillations that are often detected in intracranial EEG (iEEG) and are currently investigated as a potential biomarker to facilitate the Iocalization of the seizure onset zone (SOZ) in patients with drug-resistant epilepsy. While the rate and shape of these oscillations have been positively correlated with the SOZ, the temporal pattern of these oscillations in the epileptic brain still requires investigation. In this study, we investigate the temporal pattern of ripple events in five patients with temporal lobe epilepsy (TLE), which is one of the most common forms of epilepsy. The rate of ripple events is positively correlated with the SOZ in TLE but its diagnostic utility in localizing the SOZ remains unclear, which suggests that additional ripple-related features should be investigated. By combining point process modeling and cluster analysis, we show that a recurrent, non-stationary bursting pattern characterizes the SOZ channels consistently across patients, while the non-SOZ channels have poor between-channel similarity and no consistent pattern over time nor across patients. Furthermore, the degree of separation between SOZ and non-SOZ model parameter vectors is significantly higher (ANOVA test, ${P}$-value $P\lt 0.01$) than the degree of separation between the ripple rates, which suggests that the temporal pattern more than the rate may contribute to the pre- surgical Iocalization of the SOZ.

High frequency oscillations (HFOs) are potential biomarkers of epileptic areas. In patients with drug-resistant epilepsy, HFO rates tend to be higher in the seizure onset zone (SOZ) than in other brain regions and the resection of HFO-generating areas positively correlates with seizure-free surgery outcome. Nonetheless, the development of robust unsupervised HFO-based tools for SOZ localization remains challenging. Current approaches predict the SOZ by processing small samples of intracranial EEG (iEEG) data and applying patient-specific thresholds on the HFO rate. The HFO rate, though, varies largely over time with the patient's conditions (e.g., sleep versus wakefulness) and across patients. We propose a novel localization method for SOZ that uses a time-varying, HFO-based index to estimate the epileptic susceptibility of the iEEG channels. The method is insensitive to the average HFO rate across channels (which is both patient- and condition-specific), tracks the channel susceptibility over time, and predicts the SOZ based on the temporal evolution of the HFO rate. Tested on a preliminary dataset of continuous multi-day multichannel interictal iEEG recordings from two epileptic patients (117±97.6 h/per patient, mean ± S.D.), the reported SOZ prediction had an average 0.70±0.18 accuracy and 0.67±0.07 area under the ROC curve (mean ± S.D.) across patients.

Closed-loop modulation of deep brain stimulation (DBS) of the subthalamic nucleus (STN) in Parkinson's disease (PD) is investigated to automatically adjust the stimulation to the patients' conditions, optimize the clinical outcomes, and reduce the energy requirements. This study proposes a closed-loop control system for real-time adaptation of the STN-DBS amplitude based on the neural activity in the motor thalamus. Population-averaged post-stimulus time histograms are used to measure the average effects of STN-DBS on the thalamocortical neurons and a L 2 -norm minimization problem is solved to design the control algorithm, while the frequency of stimulation is kept constant. Applied on a large-scale, biophysically-based, anatomically-compliant model of the cortico-basal ganglia-thalamo-cortical motor loop under PD conditions, our adaptive DBS significantly (P-value P<;0.05) improved the relay fidelity of the thalamocortical neurons and restored the average power of the thalamocortical spike trains in the band [3, 100] Hz, two indicators of restored thalamocortical activity. Furthermore, adaptive-DBS significantly decreased the energy requirements when compared with non-adaptive-DBS at the same frequency. Finally, 30- and 60-Hz-adaptive-DBS determined the maximal restoration of thalamocortical activity and outperformed high-frequency, non-adaptive-DBS. Overall, results suggest that a feedback-controlled, low-frequency DBS pattern may result in significant restoration of the thalamocortical encoding while lowering the energy requirements.

Deep brain stimulation (DBS) is a surgical treatment for Parkinson's disease (PD) but, despite clinical efficacy, the mechanisms of DBS still require investigation. Recent evidence suggests that the temporal pattern of the electrical pulses may be critical to the therapeutic merit of DBS and carefully-designed, non-regular patterns could ameliorate some of the motor symptoms in PD. It is unclear, though, how different stimulation patterns affect the neural activity in the basal ganglia and whether this is related to the pathophysiology of PD. In this study, a non-human primate was treated with DBS of the subthalamic nucleus while single-unit recordings were collected in the animal's globus pallidus internus (GPi). Three stimulation patterns were applied (one regular, two non-regular) and the stimulation effects on the GPi spike trains were assessed via point process modeling. On a preliminary set of 23 GPi neurons, we show that regular DBS maximized the neuronal complexity, which is a measure of the amount of information that a single neuron can encode, and significantly increased the dependency of the neurons' spike trains on the background ensemble activity through an articulated balance of excitation and inhibition. Overall, regular DBS caused the largest modulation in the neurons' spiking pattern and the largest increment in encoding capabilities. Both results may be relevant to the mechanisms of therapeutic DBS.

Sepsis, a systemic inflammatory response to infection, is a major health care problem that affects millions of patients every year in the intensive care units (ICUs) worldwide. Despite the fact that ICU patients are heavily instrumented with physiological sensors, early sepsis detection remains challenging, perhaps because clinicians identify sepsis by (i) using static scores derived from bed-side measurements individually, and (ii) deriving these scores at a much slower rate than the rate for which patient data is collected. In this study, we construct a generalized linear model (GLM) for the probability that an ICU patient has sepsis as a function of demographics and bedside measurements. Specifically, models were trained on 29 patient recordings from the MIMIC II database and evaluated on a different test set including 8 patient recordings. A classification accuracy of 62.5% was achieved using demographic measures as features. Adding physiological time series features to the model increased the classification accuracy to 75%. Although very preliminary, these results suggest that using generalized linear models incorporating real time physiological signals may be useful for an early detection of sepsis, thereby improving the chances of a successful treatment.

Relay cells are prevalent throughout sensory systems and receive two types of inputs: driving and modulating. The driving input contains receptive field properties that must be transmitted while the modulating input alters the specifics of transmission. Relay reliability of a relay cell is defined as the fraction of pulses in the driving input that generate action potentials at the neuron's output, and is in general a complicated function of the driving input, the modulating input and the cell's properties. In a recent study, we computed analytic bounds on the reliability of relay neurons for a class of Poisson driving inputs and sinusoidal modulating inputs. Here, we generalize our analysis and compute bounds on the relay reliability for any modulating input. Furthermore, we show that if the modulating input is generated by a colored Gaussian process, closed form expressions for bounds on relay reliability can be derived. We applied our analysis to investigate relay reliability of thalamic cells in health and in Parkinson's disease (PD). It is hypothesized that in health, neurons in the motor thalamus relay information only when needed and this capability is compromised in PD due to exaggerated beta-band oscillations in the modulating input from the basal ganglia (BG). To test this hypothesis, we used modulating and driving inputs simulated from a detailed computational model of the cortico-BG-thalamo-cortical motor loop and computed our theoretical bounds in both PD and healthy conditions. Our bounds match well with our empirically computed reliability and show that the relay reliability is larger in the healthy condition across the population of thalamic neurons. Furthermore, we show that the increase in power in the beta-band of the modulating input (output of BG) is causally related with the decrease in relay reliability in the PD condition.

Sepsis is a systemic deleterious host response to infection. It is a major healthcare problem that affects millions of patients every year in the intensive care units (ICUs) worldwide. Despite the fact that ICU patients are heavily instrumented with physiological sensors, early sepsis detection remains challenging, perhaps because clinicians identify sepsis by using static scores derived from bed-side measurements individually, i.e., without systematically accounting for potential interactions between these signals and their dynamics. In this study, we apply network-based data analysis to take into account interactions between bed-side physiological time series (PTS) data collected in ICU patients, and we investigate features to distinguish between sepsis and non-sepsis conditions. We treated each PTS source as a node on a graph and we retrieved the graph connectivity matrix over time by tracking the correlation between each pair of sources' signals over consecutive time windows. Then, for each connectivity matrix, we computed the eigenvalue decomposition. We found that, even though raw PTS measurements may have indistinguishable distributions in non-sepsis and early sepsis states, the median /I of the eigenvalues computed from the same data is statistically different (p <; 0.001) in the two states and the evolution of /I may reflect the disease progression. Although preliminary, these findings suggest that network-based features computed from continuous PTS data may be useful for early sepsis detection.

The surgical resection of the epileptogenic zone (EZ) is the only effective treatment for many drug-resistant epilepsy (DRE) patients, but the pre-surgical identification of the EZ is challenging. This study investigates whether the EZ exhibits a computationally identifiable signature during seizures. In particular, we compute statistics of the brain network from intracranial EEG (iEEG) recordings and track the evolution of network connectivity before, during, and after seizures. We define each node in the network as an electrode and weight each edge connecting a pair of nodes by the gamma band cross power of the corresponding iEEG signals. The eigenvector centrality (EVC) of each node is tracked over two seizures per patient and the electrodes are ranked according to the corresponding EVC value. We hypothesize that electrodes covering the EZ have a signature EVC rank evolution during seizure that differs from electrodes outside the EZ. We tested this hypothesis on multi-channel iEEG recordings from 2 DRE patients who had successful surgery (i.e., seizures were under control with or without medications) and 1 patient who had unsuccessful surgery. In the successful cases, we assumed that the resected region contained the EZ and found that the EVC rank evolution of the electrodes within the resected region had a distinct "arc" signature, i.e., the EZ ranks first rose together shortly after seizure onset and then fell later during seizure.

Deep brain stimulation (DBS) is a highly promising therapy for Parkinson's disease (PD). However, most patients do not get full therapeutic benefit from DBS, due to its critical dependence on electrode location in the Subthalamic Nucleus (STN). For this reason, we believe that the development of a novel surgical tool for DBS placement, i.e., an automated intraoperative closed-loop DBS localization system, is essential. In this paper, we analyze single unit spiking activity of 120 neurons in different STN locations collected from 4 PD patients. Specifically, for each neuron, we estimate a point process model (PPM) of the spiking activity for different depths within the STN by which we are able to detect pathological bursting and oscillations. Our results suggest that these signatures are more prominent in the dorsolateral part of the STN. Therefore, accurately placing the DBS electrode in this target may result in maximal therapeutic benefit with less power effort required by DBS. Furthermore, PPMs might be an effective tool for modeling of the STN neuronal activities as a function of location within the STN, which may pave the way towards developing a closed-loop navigation tool for optimal DBS electrode placement.

Despite a pivotal role in the motor loop, dorsolateral striatum (putamen) has been poorly studied thus far under Parkinsonian conditions and Deep Brain Stimulation (DBS). We analyze the activity of the putamen in a monkey by combining single unit recordings and point process models. The animal received DBS (30-130 Hz) in the subthalamic nucleus (STN) while at rest and recordings were acquired both before and after treatment with 1-methyl-4-phenyl-1,2,3,6- tetrahydropyridine (MPTP), which induced Parkinsonian-like motor disorders. 141 neurons were collected and, for each neuron, a point process model captured DBS-evoked discharge patterns. In the normal animal, spike trains at rest had Poisson like distribution with non-stationary recurrent patterns (RPs) of period 3-7 ms and were mildly changed by low frequency (LF, i.e., < 100 Hz) DBS (i.e., < 20% of neurons affected). With high frequency (HF, i.e., 100-130 Hz) DBS, instead, up to 59% of neurons were affected, the DBS history significantly impacted the neuronal spiking propensity, and the RPs and the post-stimulus activation latency decreased. MPTP evoked inter-neuronal dependencies (INDs) at rest and, compared to normal, LF DBS of the MPTP animal increased RPs and INDs, while HF DBS elicited a faster and wider post-stimulus activation. Overall, HF DBS reduced ongoing non-stationary dynamics by regularizing the discharge patterns both in MPTP and normal putamen, while the combination of MPTP and LF DBS enhanced such dynamics.

The early detection of epileptic seizures requires computing relevant statistics from multivariate data and defining a robust decision strategy as a function of these statistics that accurately detects the transition from the normal to the peri-ictal (problematic) state. We model the afflicted brain as a hidden Markov model (HMM) with two hidden clinical states (normal and peri-ictal). The output of the HMM is a statistic computed from multivariate neural measurements. A Bayesian framework is developed to analyze the a posteriori conditional probability of being in peri-ictal state given current and past output measurements. We apply this method to multichannel intracortical EEGs (iEEGs) from the thalamo-cortical ictal pathway in an epilepsy rat model. We first define the output statistic as the max singular value of a connectivity matrix computed on the EEG channels with spectral techniques Then, we estimate the HMM transition probabilities from this statistic and track the a posteriori probability of being in peri-ictal state (the "information state variable"). We show how the information state variable changes as a function of time and we predict a seizure when this variable becomes greater than 0.5. This Bayesian strategy significantly improves over chance level and heuristically-chosen threshold-based predictors.

Deep Brain Stimulation (DBS) is an effective treatment for patients with Parkinsons disease, but its impact on basal ganglia nuclei is not fully understood. DBS applied to the subthalamic nucleus (STN) affects neurons in the Globus Pallidus pars interna (GPi) through direct projections, as well as indirectly through the Globus Pallidus pars externa (GPe). Since traditional statistical analyses of electrophysiological data provide too coarse a view of circuit dynamics, and mesoscopic biophysical dynamic models contain an intractable number of state variables for small populations of neurons, we apply a modular approach and treat each region in the STN-GPe-GPi circuit as a multi-input multi-output point process system. We use microelectrode recordings of a normal primate with DBS applied to STN at 100 and 130 Hz to estimate point process models (PPMs) for recorded regions in GPi. Our PPMs uncovered distinct dependencies between regions of GPe and GPi neurons, separated by the position of the GPi neurons, and showed normal refractory periods, inhibition from projecting neurons in the GPe, and DBS-induced oscillatory effects. The PPMs also showed the relative impact of the above factors, which traditional statistics fail to capture. Our PPM framework suggests a useful approach for understanding dynamics of complex neural circuits.

High frequency (HF) Deep Brain Stimulation (DBS) in the Sub-Thalamic Nucleus (STN) is a clinically recognized therapy for the treatment of motor disorders in Parkinson Disease (PD). The underlying mechanisms of DBS and how it impacts neighboring nuclei, however, are not yet completely understood. Electrophysiological data has been collected in PD patients and primates to better understand the impact of DBS on STN and the entire Basal Ganglia (BG) motor circuit. We use single unit recordings from Globus Pallidus, both pars interna and externa segments (GPi and GPe) in the BG, in a normal primate before and after DBS to reconstruct Local Field Potentials (LFPs) in the region. We then use system identification techniques to understand how GPe LFP activity and the DBS signal applied to STN influence GPi LFP activity. Our models suggest that when no stimulation is applied, the GPe LFPs have an inhibitory effect on GPi LFPs with a 2-3 ms delay, as is the case for single unit neuronal activity. On the other hand, when DBS is ON the models suggest that stimulation has a dominant effect on GPi LFPs which mask the inhibitory effects of GPe.