Papers by Sabato Santaniello
Dynamic modeling and statistical characterization of subthalamic nucleus neural activity in Parkinson's disease patients
2006 American Control Conference, 2006
The neural spiking activity of the subthalamic nucleus (STN) is devoted to modulate movement actu... more The neural spiking activity of the subthalamic nucleus (STN) is devoted to modulate movement actuation and correct movement disorders (i.e. tremor at rest, rigidity, akynesia and postural instability) in Parkinson's disease (PD) patients. Moreover, it has been recently revealed that an opportune electrical stimulation, called deep brain stimulation (DBS), can annihilate, if associated with the most common L-dopa based pharmacological
Basal Ganglia Modeling in Healthy and Parkinson's Disease State. I. Isolated Neurons Activity
2007 American Control Conference, 2007
Parkinson's disease (PD) is a neuro-degenerative pathology affecting the ... more Parkinson's disease (PD) is a neuro-degenerative pathology affecting the basal ganglia, a set of small subcortical nervous system nuclei. It induces a progressive necrosis of dopaminergic (i.e., releasing dopamine, a neurotransmitter) cells and, as a consequence, produces altered information patterns along movement-related ganglia-mediated pathways in the brain, thus inducing motor disorders like tremor at rest and postural instability. While pharmacological
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2000
Deep brain stimulation (DBS) is an effective therapy to treat movement disorders including essent... more Deep brain stimulation (DBS) is an effective therapy to treat movement disorders including essential tremor, dystonia, and Parkinson's disease. Despite over a decade of clinical experience the mechanisms of DBS are still unclear, and this lack of understanding makes the selection of stimulation parameters quite challenging. The objective of this work was to develop a closed-loop control system that automatically adjusted the stimulation amplitude to reduce oscillatory neuronal activity, based on feedback of electrical signals recorded from the brain using the same electrode as implanted for stimulation. We simulated a population of 100 intrinsically active model neurons in the Vim thalamus, and the local field potentials (LFPs) generated by the population were used as the feedback (control) variable for closed loop control of DBS amplitude. Based on the correlation between the spectral content of the thalamic activity and tremor (Hua et al., 1998), (Lenz et al., 1988), we implemented an adaptive minimum variance controller to regulate the power spectrum of the simulated LFPs and restore the LFP power spectrum present under tremor conditions to a reference profile derived under tremor free conditions. The controller was based on a recursively identified autoregressive model (ARX) of the relationship between stimulation input and LFP output, and showed excellent performances in tracking the reference spectral features through selective changes in the theta (2-7 Hz), alpha (7-13 Hz), and beta (13-35 Hz) frequency ranges. Such changes reflected modifications in the firing patterns of the model neuronal population, and, differently from open-loop DBS, replaced the tremor-related pathological patterns with patterns similar to those simulated in tremor-free conditions. The closed-loop controller generated a LFP spectrum that approximated more closely the spectrum present in the tremor-free condition than did open loop fixed intensity stimulation and adapted to match the spectrum after a change in the neuronal oscillation frequency. This computational study suggests the feasibility of closed-loop control of DBS amplitude to regulate the spectrum of the local field potentials and thereby normalize the aberrant pattern of neuronal activity present in tremor.
Biomedical Signal Processing and Control, 2008
Proceedings of the National Academy of Sciences of the United States of America, Jan 10, 2015
High-frequency deep brain stimulation (HFS) is clinically recognized to treat parkinsonian moveme... more High-frequency deep brain stimulation (HFS) is clinically recognized to treat parkinsonian movement disorders, but its mechanisms remain elusive. Current hypotheses suggest that the therapeutic merit of HFS stems from increasing the regularity of the firing patterns in the basal ganglia (BG). Although this is consistent with experiments in humans and animal models of Parkinsonism, it is unclear how the pattern regularization would originate from HFS. To address this question, we built a computational model of the cortico-BG-thalamo-cortical loop in normal and parkinsonian conditions. We simulated the effects of subthalamic deep brain stimulation both proximally to the stimulation site and distally through orthodromic and antidromic mechanisms for several stimulation frequencies (20-180 Hz) and, correspondingly, we studied the evolution of the firing patterns in the loop. The model closely reproduced experimental evidence for each structure in the loop and showed that neither the pro...
Analyzing Local Field Potentials in the healthy basal ganglia under Deep Brain Stimulation
49th IEEE Conference on Decision and Control (CDC), 2010
High frequency Deep Brain Stimulation in the Sub-Thalamic Nucleus is a clinically recognized ther... more High frequency Deep Brain Stimulation in the Sub-Thalamic Nucleus is a clinically recognized therapy for the treatment of motor disorders in Parkinson's Disease. Sub-thalamic Nucleus (STN) is a small lens-shaped nucleus in the brain where it is a part of the basal ganglia system and is currently thought to play a prominent role in Parkinson's Disease (Obeso et al., 1997).
An Optimal Control Approach to Seizure Detection in Drug-Resistant Epilepsy
A Systems Theoretic Approach to Systems and Synthetic Biology I: Models and System Characterizations, 2014
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2010
Deep Brain Stimulation (DBS) is an effective treatment for patients with Parkinsons disease, but ... more 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...
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2011
The early detection of epileptic seizures requires computing relevant statistics from multivariat... more 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 poste...
Generalizing performance limitations of relay neurons: Application to Parkinson's disease
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2014
Relay cells are prevalent throughout sensory systems and receive two types of inputs: driving and... more 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 reliabi...
Computing network-based features from physiological time series: Application to sepsis detection
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2014
Sepsis is a systemic deleterious host response to infection. It is a major healthcare problem tha... more 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...
2011 5th International IEEE/EMBS Conference on Neural Engineering, 2011
Quickest detection is the problem of detecting a change in the probability distribution of a sequ... more Quickest detection is the problem of detecting a change in the probability distribution of a sequence of random observations with as little delay as possible and with low probability of false alarm. To date, algorithms for quickest detection exist mainly for cases where the random observations are independent, and linear or exponential cost functions of the delay are used. We propose a dynamic programming-based algorithm to solve the quickest detection problem when dependencies exist among the observations, and for any nondecreasing cost function of the detection delay. We implement the algorithm for a Bayesian formulation (i.e., the change time in the probability distribution of the observations is a random variable with a priori fixed geometric distribution) when the observations distribute according to two distinct point processes. We apply the algorithm to spiking activity observations from neurons recorded in the subthalamic nucleus of Parkinson's disease patients during the execution of a motor task. The algorithm exploits the point-process characterization of the spike trains before and during the movement (two states), and optimally detects the state transition at movement onset. Performances significantly (i.e., with a p-value p<0.05) improve over a chance level predictor.
Computing network-based features from intracranial EEG time series data: Application to seizure focus localization
2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014
The surgical resection of the epileptogenic zone (EZ) is the only effective treatment for many dr... more 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 &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot;arc&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot; signature, i.e., the EZ ranks first rose together shortly after seizure onset and then fell later during seizure.
Optimal Control-Based Bayesian Detection of Clinical and Behavioral State Transitions
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2000
Accurately detecting hidden clinical or behavioral states from sequential measurements is an emer... more Accurately detecting hidden clinical or behavioral states from sequential measurements is an emerging topic in neuroscience and medicine, which may dramatically impact neural prosthetics, brain-computer interface and drug delivery. For example, early detection of an epileptic seizure from sequential electroencephalographic (EEG) measurements would allow timely administration of anticonvulsant drugs or neurostimulation, thus reducing physical impairment and risks of overtreatment. We develop a Bayesian paradigm for state transition detection that combines optimal control and Markov processes. We define a hidden Markov model of the state evolution and develop a detection policy that minimizes a loss function of both probability of false positives and accuracy (i.e., lag between estimated and actual transition time). Our strategy automatically adapts to each newly acquired measurement based on the state evolution model and the relative loss for false positives and accuracy, thus resulting in a time varying threshold policy. The paradigm was used in two applications: 1) detection of movement onset (behavioral state) from subthalamic single unit recordings in Parkinson&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#39;s disease patients performing a motor task; 2) early detection of an approaching seizure (clinical state) from multichannel intracranial EEG recordings in rodents treated with pentylenetetrazol chemoconvulsant. Our paradigm performs significantly better than chance and improves over widely used detection algorithms.
Frontiers in Integrative Neuroscience, 2012
† These authors contributed equally to this work.
Quickest detection of drug-resistant seizures: An optimal control approach
Epilepsy & Behavior, 2011
Epilepsy affects 50 million people worldwide, and seizures in 30% of the cases remain drug resist... more 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 &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot;quickest detection&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot; (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.
Parkinson's Disease Workshop-May 22-23, 2012
SEIZURE DETECTION DEVICE AND SYSTEMS
Striatum (STR) is the major input stage of the basal ganglia (BG). It combines information from c... more Striatum (STR) is the major input stage of the basal ganglia (BG). It combines information from cortex, subthalamic nucleus (STN) and external globus pallidus (GPe), and projects to the output stages of the BG, where selection between concurrent motor programs is performed. Parkinson's disease (PD) reduces the concentration of dopamine (DA, a neurotransmitter) in STR and changes in the level of DA correlate with the onset of PD motor disorders. Though STR plays a pivotal role in BG, its behavior under PD and Deep Brain Stimulation (DBS) is still unclear. We develop pointprocess models of the STR neurons as a function of the activity in GPe, cortex, and DBS. We use single unit recordings from a monkey under STN DBS at different frequencies before and after treatment with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) to develop PD motor symptoms. The models suggest that STR neurons have prominent bursting activity in normal conditions, positive correlation with cortex (3-10 ms delay), and mild negative correlation with GPe (1-5 ms lag). DA depletion evokes 30-60 Hz oscillations, and increases the propensity of each neuron to be inhibited by surrounding neurons. DBS elicits antidromical activation, masks existent dynamics, reinforces dependencies between nuclei, and entrains at the stimulation frequency in both conditions.
Clinical Neurophysiology, 2014
The relationships between functional and structural networks provide insights into brain abnormal... more The relationships between functional and structural networks provide insights into brain abnormalities that are observed in epilepsy. Functional and effective connectivity methods have been used to identify the ictal onset zone as well as to characterize the onset, spread, and termination of seizures. Studies of the dynamics of epileptic networks suggest mechanisms that may explain the sudden onset and termination of seizures.
Uploads
Papers by Sabato Santaniello