Drug and Alcohol Rehabilitation: Brain State Evolution During Seizure and Under Anesthesia: A Network-Based Analysis of Stereotaxic Eeg Activity in Drug-Resistant Epilepsy Patients.
Brain state evolution during seizure and under anesthesia: A network-based analysis of stereotaxic eeg activity in drug-resistant epilepsy patients.
Filed under: Drug and Alcohol Rehabilitation
Conf Proc IEEE Eng Med Biol Soc. 2012 Aug; 2012: 5158-61
Yaffe R, Burns S, Gale J, Park HJ, Bulacio J, Gonzalez-Martinez J, Sarma SV
Epilepsy is a neurological condition with a prevalence of 1%, and 14-34% have medically refractory epilepsy (MRE). Seizures in focal MRE are generated by a single epileptogenic zone (or focus), thus there is potentially a curative procedure – surgical resection. This procedure depends significantly on correct identification of the focus, which is often uncertain in clinical practice. In this study, we analyzed intracranial stereotaxic EEG (sEEG) data recorded in two human patients with drug-resistant epilepsy prior to undergoing resection surgery. We view the sEEG data as samples from the brain network and hypothesize that seizure foci can be identified based on their network connectivity during seizure. Specifically, we computed a time sequence of connectivity matrices from EEG recordings that represent network structure over time. For each patient, connectivity between electrodes was measured using the coherence in a given frequency band. Matrix structure was analyzed using singular value decomposition and the leading singular vector was used to estimate each electrode’s time dependent centrality (importance to the network’s connectivity). Our preliminary study suggests that seizure foci may be the most weakly connected regions in the brain during the beginning of a seizure and the most strongly connected regions towards the end of a seizure. Additionally, in one of the patients analyzed, the network connectivity under anesthesia highlights seizure foci. Ultimately, network centrality computed from sEEG activity may be used to develop an automated, reliable, and computationally efficient algorithm for identifying seizure foci.
HubMed – drug
A network analysis of the dynamics of seizure.
Filed under: Drug and Alcohol Rehabilitation
Conf Proc IEEE Eng Med Biol Soc. 2012 Aug; 2012: 4684-7
Burns SP, Sritharan D, Jouny C, Bergey G, Crone N, Anderson WS, Sarma SV
Seizures are events that spread through the brain’s network of connections and create pathological activity. To understand what is occurring in the brain during seizure we investigated the time progression of the brain’s state from seizure onset to seizure suppression. Knowledge of a seizure’s dynamics and the associated spatial structure is important for localizing the seizure foci and determining the optimal location and timing of electrical stimulation to mitigate seizure development. In this study, we analyzed intracranial EEG data recorded in 2 human patients with drug-resistant epilepsy prior to undergoing resection surgery using network analyses. Specifically, we computed a time sequence of connectivity matrices from iEEG (intracranial electroencephalography) recordings that represent network structure over time. For each patient, connectivity between electrodes was measured using the coherence in the band of frequencies with the strongest modulation during seizure. The connectivity matrices’ structure was analyzed using an eigen-decomposition. The leading eigenvector was used to estimate each electrode’s time dependent centrality (importance to the network’s connectivity). The electrode centralities were clustered over the course of each seizure and the cluster centroids were compared across seizures. We found, for each patient, there was a consistent set of centroids that occurred during each seizure. Further, the brain reliably evolved through the same progression of states across multiple seizures including characteristic onset and suppression states.
HubMed – drug
A neural network-based design of an on-off adaptive control for Deep Brain Stimulation in movement disorders.
Filed under: Drug and Alcohol Rehabilitation
Conf Proc IEEE Eng Med Biol Soc. 2012 Aug; 2012: 4140-3
Shukla P, Basu I, Graupe D, Tuninetti D, Slavin KV
The current Food and Drug Administration approved system for the treatment of tremor disorders through Deep Brain Stimulation (DBS) of the area of the brain that controls movement, operates open-loop. It does not automatically adapt to the instantaneous patient’s needs or to the progression of the disease. This paper demonstrates an adaptive closed-loop controlled DBS that, after switching off stimulation, tracks few physiological signals to predict the reappearance of tremor before the patient experiences discomfort, at which point it instructs the DBS controller to switch on stimulation again. The core of the proposed approach is a Neural Network (NN) which effectively extracts tremor predictive information from non-invasively recorded surface-electromyogram(sEMG) and accelerometer signals measured at the symptomatic extremities. A simple feed-forward back-propagation NN architecture is shown to successfully predict tremor in 31 out of 33 trials in two Parkinson’s Disease patients with an overall accuracy of 75.8% and sensitivity of 92.3%. This work therefore shows that closed-loop DBS control is feasible in the near future and that it can be achieved without modifications of the electrodes implanted in the brain, i.e., is backward compatible with approved DBS systems.
HubMed – drug
An approach to controlled drug infusion via tracking of the time-varying dose-response.
Filed under: Drug and Alcohol Rehabilitation
Conf Proc IEEE Eng Med Biol Soc. 2012 Aug; 2012: 3539-42
Malaguttiy N, Dehghaniz A, Kennedyy RA
Automatic administration of medicinal drugs has the potential of delivering benefits over manual practices in terms of reduced costs and improved patient outcomes. Safe and successful substitution of a human operator with a computer algorithm relies, however, on the robustness of the control methodology, the design of which depends, in turn, on available knowledge about the underlying dose-response model. Real-time estimation of a patient’s actual response would ensure that the most suitable control algorithm is adopted, but the potentially time-varying nature of model parameters and the limited number of observation signals may cause the estimation problem to be ill-posed, posing a challenge to adaptive control methods. We propose the use of Bayesian inference through a particle filtering approach as a way to overcome these limitations and improve the robustness of automatic drug administration methods. We report on the results of a simulation study modeling the infusion of vasodepressor drug sodium nitroprusside for the control of mean arterial pressure in acute hypertensive patients. The proposed control architecture was able to meet the required performance objectives under challenging operating conditions.
HubMed – drug
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