[Show abstract][Hide abstract] ABSTRACT: 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 (30-130Hz) 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 3-7ms and were mildly changed by low frequency (LF, i.e., <100Hz) DBS (i.e., <20% of neurons affected). With high frequency (HF, i.e., 100-130Hz) 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.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 08/2012; 2012:1214-7.
[Show abstract][Hide abstract] ABSTRACT: 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.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 08/2012; 2012:2539-42.
[Show abstract][Hide abstract] ABSTRACT: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) directly modulates the basal ganglia (BG), but how such stimulation impacts the cortex upstream is largely unknown. There is evidence of cortical activation in 6-hydroxydopamine (OHDA)-lesioned rodents and facilitation of motor evoked potentials in Parkinson's disease (PD) patients, but the impact of the DBS settings on the cortical activity in normal vs. Parkinsonian conditions is still debated. We use point process models to analyze non-stationary activation patterns and inter-neuronal dependencies in the motor and sensory cortices of two non-human primates during STN DBS. These features are enhanced after treatment with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP), which causes a consistent PD-like motor impairment, while high-frequency (HF) DBS (i.e., ≥100 Hz) strongly reduces the short-term patterns (period: 3-7 ms) both before and after MPTP treatment, and elicits a short-latency post-stimulus activation. Low-frequency DBS (i.e., ≤50 Hz), instead, has negligible effects on the non-stationary features. Finally, by using tools from the information theory [i.e., receiver operating characteristic (ROC) curve and information rate (IR)], we show that the predictive power of these models is dependent on the DBS settings, i.e., the probability of spiking of the cortical neurons (which is captured by the point process models) is significantly conditioned on the timely delivery of the DBS input. This dependency increases with the DBS frequency and is significantly larger for high- vs. low-frequency DBS. Overall, the selective suppression of non-stationary features and the increased modulation of the spike probability suggest that HF STN DBS enhances the neuronal activation in motor and sensory cortices, presumably because of reinforcement mechanisms, which perhaps involve the overlap between feedback antidromic and feed-forward orthodromic responses along the BG-thalamo-cortical loop.
Frontiers in Integrative Neuroscience 01/2012; 6:35.
[Show abstract][Hide abstract] ABSTRACT: Epilepsy affects 50 million people worldwide, and seizures in 30% of the cases remain drug resistant. This has increased interest in responsive neurostimulation, which is most effective when administered during seizure onset. We propose a novel framework for seizure onset detection that involves (i) constructing statistics from multichannel intracranial EEG (iEEG) to distinguish nonictal versus ictal states; (ii) modeling the dynamics of these statistics in each state and the state transitions; you can remove this word if there is no room. (iii) developing an optimal control-based "quickest detection" (QD) strategy to estimate the transition times from nonictal to ictal states from sequential iEEG measurements. The QD strategy minimizes a cost function of detection delay and false positive probability. The solution is a threshold that non-monotonically decreases over time and avoids responding to rare events that normally trigger false positives. We applied QD to four drug resistant epileptic patients (168 hour continuous recordings, 26-44 electrodes, 33 seizures) and achieved 100% sensitivity with low false positive rates (0.16 false positive/hour). This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.
[Show abstract][Hide abstract] ABSTRACT: 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.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 08/2011; 2011:1435-8.
[Show abstract][Hide abstract] ABSTRACT: Deep brain stimulation (DBS) is an effective electric therapy to treat movement disorders associated with chronical neural diseases like essential tremor, dystonia and Parkinsonpsilas disease. In spite of a long clinical experience, the cellular effects of the DBS are still partially unknown because of the lack of information about the target sites. Recent studies, however, have proposed the local field potentials (LFPs) in the targets as a useful tool to study the behavior before and after stimulation . Through a previously described  simulator of the electric activity in the nucleus ventralis intermediate of thalamus (Vim, one of the preferred stimulation targets) under tremor conditions, our work investigates the relationship between DBS settings and LFPs. A least-square approach is adopted to identify a functional, input-output ARMAX model structure for the Vim and evaluate the effects of the stimulation on its electric patterns. Based on it, a feedback control scheme is then proposed to restore the spectral features of the Vimpsilas LFPs to reference values, derived from subjects not affected by movement disorders. Results indicate good performances in tracking the reference spectral features through selective changes in the low (2-7 Hz), alpha (7-13 Hz) and beta (13-35 Hz) ranges.
Control Applications, 2008. CCA 2008. IEEE International Conference on; 10/2008
[Show abstract][Hide abstract] ABSTRACT: The subthalamic nucleus (STN) plays a central role in movement actuation and manifestation of movement disorders (i.e., tremor, rigidity, akynesia and postural instability) in Parkinson's disease (PD) patients. Moreover, it has been recently revealed that an opportune electrical stimulation of the STN, called deep brain stimulation (DBS), can strongly contribute to the annihilation of the PD-related motor disorders. Currently, a great effort is made both in Medicine, Neurosciences and Engineering for understanding and modeling in details how the STN works, how PD determines its pathological behavior and DBS restores the correct motor function.The paper is organized in two parts. Firstly some stochastic properties of the STN electrical activity are obtained by analyzing a preliminary set of experimental data coming from microelectrode recordings (MERs) in two PD patients who underwent the surgical implantation of DBS electrodes. Then, a nonlinear, stochastic, continuous-state model describing the global electrical behavior of the STN in PD patients is proposed. It is inspired by the fundamental physiologic features of the subthalamic cells and a fictitious vector state is introduced to represent the main dynamics. Its numerical parameters and stochastic properties are chosen by fitting the available data.
Biomedical Signal Processing and Control 01/2008; 3(3):203-211. · 1.07 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Deep brain stimulation (DBS) is an effective electric therapy to treat movement dis-orders associated with chronical neural diseases like essential tremor, dystonia and Parkinson's disease. In spite of a long clinical experience, the cellular effects of the DBS are still partially unknown because of the lack of information about the target sites. Recent studies, however, have proposed the local field potentials (LFPs) in the targets as a useful tool to study the behavior before and after stimulation [Priori et al., 2006]. Our work investigates the relationship between DBS settings and LFPs in a detailed simulator of the electric activity in the Vim (one of the preferred surgical targets) under tremor conditions. A least-square approach is adopted to identify a functional, input-output ARX model structure for the Vim and evaluate the effects of the stimulation on its electric patterns. Based on it, an adaptive minimum variance control scheme is then proposed to restore the spectral features of the Vim's LFPs to reference values, i.e., as in subjects not affected by movement disorders. Results indicate good performances in tracking the reference spectral features through selective changes in the low (2-7 Hz), α (7-13 Hz) and β (13-35 Hz) ranges.