Sabato Santaniello

Closed-Loop Control of Deep Brain Stimulation: A Simulation Study

Deep brain stimulation (DBS) is an effective therapy to treat movement disorders including essential tremor, dystonia, and Parkinsons 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 , 1998), (Lenz , 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.

Network dynamics of the brain and influence of the epileptic seizure onset zone

Significance In epilepsy, seizures elicit changes in the functional connectivity of the brain that shed insight into the seizures’ nature and onset zone. We investigated the brain connectivity of patients with partial epileptic seizures from continuous multiday recordings and found that (i) the connectivity defines a finite set of brain states, (ii) seizures are characterized by a consistent progression of states, and (iii) the seizure onset zone is isolated from the surrounding regions at seizure onset but becomes most connected toward seizure termination. Our results suggest that a finite-dimensional state space model may characterize the dynamics of the epileptic brain and ultimately help localize the seizure onset zone, which is currently done by clinicians through visual inspection of electrocorticographic recordings. The human brain is a dynamic networked system. Patients with partial epileptic seizures have focal regions that periodically diverge from normal brain network dynamics during seizures. We studied the evolution of brain connectivity before, during, and after seizures with graph-theoretic techniques on continuous electrocorticographic (ECoG) recordings (5.4 ± 1.7 d per patient, mean ± SD) from 12 patients with temporal, occipital, or frontal lobe partial onset seizures. Each electrode was considered a node in a graph, and edges between pairs of nodes were weighted by their coherence within a frequency band. The leading eigenvector of the connectivity matrix, which captures network structure, was tracked over time and clustered to uncover a finite set of brain network states. Across patients, we found that (i) the network connectivity is structured and defines a finite set of brain states, (ii) seizures are characterized by a consistent sequence of states, (iii) a subset of nodes is isolated from the network at seizure onset and becomes more connected with the network toward seizure termination, and (iv) the isolated nodes may identify the seizure onset zone with high specificity and sensitivity. To localize a seizure, clinicians visually inspect seizures recorded from multiple intracranial electrode contacts, a time-consuming process that may not always result in definitive localization. We show that network metrics computed from all ECoG channels capture the dynamics of the seizure onset zone as it diverges from normal overall network structure. This suggests that a state space model can be used to help localize the seizure onset zone in ECoG recordings.

Physiology of functional and effective networks in epilepsy.

Epilepsy is a network phenomenon characterized by atypical activity during seizure both at the level of single neurons and neural populations. The etiology of epilepsy is not completely understood but a common theme among proposed mechanisms is abnormal synchronization between neuronal populations. Recent advances in novel imaging and recording technologies have enabled the inference of comprehensive maps of both the anatomical and physiological inter-relationships between brain regions. Clinical protocols established for diagnosis and treatment of epilepsy utilize both advanced neuroimaging techniques and neurophysiological data. These growing clinical datasets can be further exploited to better understand the complex connectivity patterns in the epileptic brain. In this article, we review results and insights gained from the growing body of research focused on epilepsy from a network perspective. In particular, we put an emphasis on two different notions of network connectivity: functional and effective; and studies investigating these notions in epilepsy are highlighted. We also discuss limitations and opportunities in data collection and analyses that will further our understanding of epileptic networks and the mechanisms of seizures.

Quickest detection of drug-resistant seizures: an optimal control approach.

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.

Therapeutic mechanisms of high-frequency stimulation in Parkinson’s disease and neural restoration via loop-based reinforcement

Significance We investigated the therapeutic mechanisms of high-frequency stimulation (HFS) in Parkinson’s disease by developing a computational model of the cortico-basal ganglia-thalamo-cortical loop in normal and parkinsonian conditions under the effects of stimulation at several frequencies. We found that the stimulation injected in the loop elicits neural perturbations that travel along multiple pathways with different latencies and rendezvous in striatum (one of the basal ganglia). If the stimulation frequency is high enough, these perturbations overlap (reinforcement) and cause more regular, stimulus-locked firing patterns in striatum. Overlap is maximal at clinically relevant HFS and restores more normal activity in the remaining structures of the loop. This suggests that neural restoration and striatal reinforcement may be a therapeutic merit and mechanism of HFS, respectively. 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 proximal effects nor the distal effects individually account for the observed pattern changes, whereas the combined impact of these effects increases with the stimulation frequency and becomes significant for HFS. Perturbations evoked proximally and distally propagate along the loop, rendezvous in the striatum, and, for HFS, positively overlap (reinforcement), thus causing larger poststimulus activation and more regular patterns in striatum. Reinforcement is maximal for the clinically relevant 130-Hz stimulation and restores a more normal activity in the nuclei downstream. These results suggest that reinforcement may be pivotal to achieve pattern regularization and restore the neural activity in the nuclei downstream and may stem from frequency-selective resonant properties of the loop.

Optimal Control-Based Bayesian Detection of Clinical and Behavioral State Transitions

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 Parkinsons 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.

Non-stationary discharge patterns in motor cortex under subthalamic nucleus deep brain stimulation

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 Parkinsons 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.

Basal Ganglia Modeling in Healthy and Parkinson's Disease State. I. Isolated Neurons Activity

Parkinsons 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 and electrical therapies are currently available for PD treatment, the mechanisms according to which such disease operates are still partly unclear, due to the lack of knowledge about the basal ganglia role in motor tasks execution. For that reason, in order to shed some light on the inner dynamics of the basal ganglia and investigate how PD alters their electric patterns, we develop a two-stages modeling study: in the present paper we focus on those nuclei involved in the genesis of PD motor symptoms and, for them, develop a conductance-based electrical model able to mimic quantitative data from different in vitro physiological analyses. Such models show how several highly nonlinear electrical behaviors can stem from the interaction between specific ionic currents as particular parameters change. In (S. Santaniello et al., 2007), then, cellular models are inserted in a network scheme to reproduce the main actual anatomical connections and the resulting macroscopic behaviors reported in literature for normal and Parkinsonian conditions.

Modeling the effects of Deep Brain Stimulation on sensorimotor cortex in normal and MPTP conditions

Deep Brain Stimulation (DBS) is an effective surgical therapy for the treatment of movement disorders in Parkinsons disease (PD) and other neurological pathologies. DBS is known to modulate the spiking activity of the neurons within the basal ganglia, but how such modulation impacts the primary sensorimotor cortex is still uncertain. In this study a monkey was stimulated with DBS at several frequencies in the subthalamic nucleus (STN) before and after treatment with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) to develop PD symptoms, while single unit recordings are simultaneously obtained from the sensorimotor cortex. We exploit such data to develop point-process input-output models of the cortical neurons. Our models describe the effects of stimulation in normal and MPTP conditions and investigate the influence of the stimulation frequency on the neuronal activity. Our models show increased synchronization of the cortical neurons in MPTP vs. normal conditions before stimulation, suggest that STN DBS impacts the cortical activity by antidromically eliciting spikes at the stimulation frequency, and support the hypothesis that high frequency DBS partially masks the effects of thalamo-cortical input.

Modeling the motor striatum under Deep Brain Stimulation in normal and MPTP conditions

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. Parkinsons 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 point-process 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-tetrahydro-pyridine (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.