Failure of Delayed Feedback Deep Brain Stimulation for Intermittent Pathological Synchronization in Parkinson's Disease

Suppression of excessively synchronous beta-band oscillatory activity in the brain is believed to suppress hypokinetic motor symptoms of Parkinson's disease. Recently, a lot of interest has been devoted to desynchronizing delayed feedback deep brain stimulation (DBS). This type of synchrony control was shown to destabilize the synchronized state in networks of simple model oscillators as well as in networks of coupled model neurons. However, the dynamics of the neural activity in Parkinson's disease exhibits complex intermittent synchronous patterns, far from the idealized synchronous dynamics used to study the delayed feedback stimulation. This study explores the action of delayed feedback stimulation on partially synchronized oscillatory dynamics, similar to what one observes experimentally in parkinsonian patients. We employ a model of the basal ganglia networks which reproduces experimentally observed fine temporal structure of the synchronous dynamics. When the parameters of our model are such that the synchrony is unphysiologically strong, the feedback exerts a desynchronizing action. However, when the network is tuned to reproduce the highly variable temporal patterns observed experimentally, the same kind of delayed feedback may actually increase the synchrony. As network parameters are changed from the range which produces complete synchrony to those favoring less synchronous dynamics, desynchronizing delayed feedback may gradually turn into synchronizing stimulation. This suggests that delayed feedback DBS in Parkinson's disease may boost rather than suppress synchronization and is unlikely to be clinically successful. The study also indicates that delayed feedback stimulation may not necessarily exhibit a desynchronization effect when acting on a physiologically realistic partially synchronous dynamics, and provides an example of how to estimate the stimulation effect.Comment: 19 pages, 8 figure

Neural Dynamics in Parkinsonian Brain:The Boundary Between Synchronized and Nonsynchronized Dynamics

Synchronous oscillatory dynamics is frequently observed in the human brain. We analyze the fine temporal structure of phase-locking in a realistic network model and match it with the experimental data from parkinsonian patients. We show that the experimentally observed intermittent synchrony can be generated just by moderately increased coupling strength in the basal ganglia circuits due to the lack of dopamine. Comparison of the experimental and modeling data suggest that brain activity in Parkinson's disease resides in the large boundary region between synchronized and nonsynchronized dynamics. Being on the edge of synchrony may allow for easy formation of transient neuronal assemblies

Statistical Analysis for Hospital Length-of-Stay and Readmission Rate Study

Hospital readmission rate has become a major indicator of quality of care, with penalties given to hospitals that have high rates of readmission. At the same time, insurers are applying increasing pressure to improve efficiency and reduce costs, including decreasing hospital lengths of stay. We analyze these trends to determine if reducing lengths of stay (LOS) may actually worsen readmission rates. All records of patients admitted to the neurosurgical service at one hospital from October 2007 through June 2014 were aggregated and analyzed for several variables, including initial length of stay, readmission occurrence, and length of stay, admitting diagnosis, admission priority and discharge disposition. Any trends over time were also noted. 925 out of 9,409 patient encounters are readmissions. Readmission rate and average length of stay were found significantly negative correlated. Besides linear regression which directly connecting average length of stay and readmission rate, survival analysis methods with Cox proportional hazard ratio model were employed to determine which factors were associated with a higher risk of readmission. There was a clear increase in readmissions over the study period, but LOS remained relatively constant, suggesting that increasing medical complexity confounded efforts to decrease LOS and was responsible for increased readmission rates. This study can help providers avoid readmissions by focusing on effective management of comorbidities

Hospital Length of Stay and Readmission Rate for Neurosurgical Patients

BACKGROUND Hospital readmission rate has become a major indicator of quality of care, with penalties given to hospitals with high rates of readmission. At the same time, insurers are increasing pressure for greater efficiency and reduced costs, including decreasing hospital lengths of stay (LOS). OBJECTIVE To analyze the authors’ service to determine if there is a relationship between LOS and readmission rates. METHODS Records of patients admitted to the authors’ institution from October 2007 through June 2014 were analyzed for several data points, including initial LOS, readmission occurrence, admitting and secondary diagnoses, and discharge disposition. RESULTS Out of 9409 patient encounters, there were 925 readmissions. Average LOS was 6 d. Univariate analysis indicated a higher readmission rate with more diagnoses upon admission (P < .001) and an association between insurance type and readmission (P < .001), as well as decreasing average yearly LOS (P = .0045). Multivariate analysis indicated statistically significant associations between longer LOS (P = .03) and government insurance (P < .01). CONCLUSION A decreasing LOS over time has been associated with an increasing readmission rate at the population level. However, at the individual level, a prolonged LOS was associated with a higher risk of readmission. This was attributed to patient comorbidities. However, this increasing readmission rate may represent many factors including patients’ overall health status. Thus, the rate of readmission may represent a burden of illness rather than a valid metric for quality of care

Interaction of synchronized dynamics in cortical and subcortical circuits in Parkinson’s disease

poster abstractParkinson’s disease pathophysiology is marked by increased oscillatory and synchronous activity in the beta frequency band in cortical and basal ganglia circuits. This study explores the functional connections between synchronized dynamics of cortical areas and dynamics of subcortical areas in Parkinson’s disease. We simultaneously recorded neuronal units (spikes) and local field potentials (LFP) from subthalamic nucleus (STN), and electroencephalograms (EEGs) from the scalp in parkinsonian patients and analyzed the correlation between the time-courses of the spike-LFP synchronization and inter-electrode EEG synchronization. We found the (noninvasively obtained) time-course of the synchrony strength between EEG electrodes and the (invasively obtained) time-course of the synchrony between spiking unit and LFP in STN to be weakly, but significantly correlated with each other. This correlation is largest for the bilateral motor EEG synchronization followed by bilateral frontal EEG synchronization. Our observations suggest that there may be multiple functional modes by which the cortical and basal ganglia circuits interact with each other in Parkinson’s disease: not only synchronization may be observed between some areas in cortex and the basal ganglia, but also synchronization within cortex and within basal ganglia may be related, suggesting potentially more global way of functional interaction. More coherent dynamics in one brain region may modulate or activate the dynamics of another brain region in a more powerful way causing correlations between changes in synchrony strength in both regions

Cortex – basal ganglia synchronization in Parkinson’s disease

poster abstractIncreased synchrony in the beta band in cortico-basal ganglia circuits is well described in patients with PD. Less is known, however, about how these abnormal firing patterns are correlated across these brain regions. In this study we investigated how this intra-operative data recorded from STN correlates with scalp recorded EEG. Intraoperative single unit recordings and LFPs were obtained from STN and scalp EEG recordings were collected from four electrodes positioned over prefrontal and motor areas. We computed the STN spike-LFP (Local Filed Potential) phase synchrony over short temporal windows as it fluctuates in time. We also computed the EEG phase synchrony index time series for all 6 pairs of EEG electrodes. Next we explored cross-correlation between the two synchrony level time-series of the spike-LFP vs. EEG pairs. EEG synchrony was found to be correlated with spike-LFP synchrony. Correlation between surface EEG and STN was strongest for ipsilateral EEG and STN recordings. Spike-LFP synchronization is believed to characterize the input-output characteristics of STN dynamics and to be strongly relevant to the expression of motor symptoms. Our results indicate that non-invasive and relatively simple EEG recordings retain some information about synchronous dynamics in the subcortical regions, which can be access only in an invasive manner during functional neurosurgical procedures

A Flexible Platform for Biofeedback-driven Control and Personalization of Electrical Nerve Stimulation Therapy

Electrical vagus nerve stimulation is a treatment alternative for many epileptic and depressed patients whose symptoms are not well managed with pharmaceutical therapy. However, the fixed stimulus, open loop dosing mechanism limits its efficacy and precludes major advances in the quality of therapy. A real-time, responsive form of vagus nerve stimulation is needed to control nerve activation according to therapeutic need. This personalized approach to therapy will improve efficacy and reduce the number and severity of side effects. We present autonomous neural control, a responsive, biofeedback-driven approach that uses the degree of measured nerve activation to control stimulus delivery. We demonstrate autonomous neural control in rats, showing that it rapidly learns how to most efficiently activate any desired proportion of vagal A, B, and/or C fibers over time. This system will maximize efficacy by minimizing patient response variability and by minimizing therapeutic failures resulting from longitudinal decreases in nerve activation with increasing durations of treatment. The value of autonomous neural control equally applies to other applications of electrical nerve stimulation