Alan D. Dorval

Real-Time Linux Dynamic Clamp: A Fast and Flexible Way to Construct Virtual Ion Channels in Living Cells

AbstractWe describe a system for real-time control of biological and other experiments. This device, based around the Real-Time Linux operating system, was tested specifically in the context of dynamic clamping, a demanding real-time task in which a computational system mimics the effects of nonlinear membrane conductances in living cells. The system is fast enough to represent dozens of nonlinear conductances in real time at clock rates well above 10 kHz. Conductances can be represented in deterministic form, or more accurately as discrete collections of stochastically gating ion channels. Tests were performed using a variety of complex models of nonlinear membrane mechanisms in excitable cells, including simulations of spatially extended excitable structures, and multiple interacting cells. Only in extreme cases does the computational load interfere with high-speed “hard” real-time processing (i.e., real-time processing that never falters). Freely available on the worldwide web, this experimental control system combines good performance, immense flexibility, low cost, and reasonable ease of use. It is easily adapted to any task involving real-time control, and excels in particular for applications requiring complex control algorithms that must operate at speeds over 1 kHz.

Deep Brain Stimulation Reduces Neuronal Entropy in the MPTP-Primate Model of Parkinson's Disease

High-frequency stimulation (HFS) of the subthalamic nucleus (STN) or internal segment of the globus pallidus is a clinically successful treatment for the motor symptoms of Parkinsons disease. However, the mechanisms by which HFS alleviates these symptoms are not understood. Whereas initial studies focused on HFS-induced changes in neuronal firing rates, recent studies suggest that changes in patterns of neuronal activity may correlate with symptom alleviation. We hypothesized that effective STN HFS reduces the disorder of neuronal firing patterns in the basal ganglia thalamic circuit, minimizing the pathological activity associated with parkinsonism. Stimulating leads were implanted in the STN of two rhesus monkeys rendered parkinsonian by 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP). Action potentials were recorded from neurons of the internal and external globus pallidus and the motor thalamus (ventralis anterior, ventralis lateralis pars oralis, and ventralis posterior lateralis pars oralis) during HFS that reduced motor symptoms and during clinically ineffective low-frequency stimulation (LFS). Firing pattern entropy was calculated from the recorded spike times to quantify the disorder of the neuronal activity. The firing pattern entropy of neurons within each region of the pallidum and motor thalamus decreased in response to HFS (n > or = 18 and P < or = 0.02 in each region), whereas firing rate changes were specific to pallidal neurons only. In response to LFS, firing rates were unchanged, but firing pattern entropy increased throughout the circuit (n > or = 24 and P < or = 10(-4) in each region). These data suggest that the clinical effectiveness of HFS is correlated with, and potentially mediated by, a regularization of the pattern of neuronal activity throughout the basal ganglia thalamic circuit.

Deep Brain Stimulation Alleviates Parkinsonian Bradykinesia by Regularizing Pallidal Activity

Deep brain stimulation (DBS) of the basal ganglia can alleviate the motor symptoms of Parkinsons disease although the therapeutic mechanisms are unclear. We hypothesize that DBS relieves symptoms by minimizing pathologically disordered neuronal activity in the basal ganglia. In human participants with parkinsonism and clinically effective deep brain leads, regular (i.e., periodic) high-frequency stimulation was replaced with irregular (i.e., aperiodic) stimulation at the same mean frequency (130 Hz). Bradykinesia, a symptomatic slowness of movement, was quantified via an objective finger tapping protocol in the absence and presence of regular and irregular DBS. Regular DBS relieved bradykinesia more effectively than irregular DBS. A computational model of the relevant neural structures revealed that output from the globus pallidus internus was more disordered and thalamic neurons made more transmission errors in the parkinsonian condition compared with the healthy condition. Clinically therapeutic, regular DBS reduced firing pattern disorder in the computational basal ganglia and minimized model thalamic transmission errors, consistent with symptom alleviation by clinical DBS. However, nontherapeutic, irregular DBS neither reduced disorder in the computational basal ganglia nor lowered model thalamic transmission errors. Thus we show that clinically useful DBS alleviates motor symptoms by regularizing basal ganglia activity and thereby improving thalamic relay fidelity. This work demonstrates that high-frequency stimulation alone is insufficient to alleviate motor symptoms: DBS must be highly regular. Descriptive models of pathophysiology that ignore the fine temporal resolution of neuronal spiking in favor of average neural activity cannot explain the mechanisms of DBS-induced symptom alleviation.

Channel noise is essential for perithreshold oscillations in entorhinal stellate neurons.

Previous experimental and computational work (for review, see White et al., 2000) has suggested that channel noise, generated by the stochastic flicker of voltage-gated ion channels, can be a major contributor to electrical membrane noise in neurons. In spiny stellate neurons of the entorhinal cortex, we remove the primary source of channel noise by pharmacologically blocking the native persistent Na+ conductance. Via the dynamic-clamp technique (Robinson and Kawai, 1993; Sharp et al., 1993), we then introduce virtual persistent Na+ channels into the membranes of the stellate neurons. By altering the mathematical properties of these virtual “knock-ins,” we demonstrate that stochastic flicker of persistent Na+ channels is necessary for the existence of slow perithreshold oscillations that characterize stellate neurons. Channel noise also alters the ability of stellate neurons to phase lock to weak sinusoidal stimuli. These results provide the first direct demonstration that physiological levels of channel noise can produce qualitative changes in the integrative properties of neurons.

Probability distributions of the logarithm of inter-spike intervals yield accurate entropy estimates from small datasets

The maximal information that the spike train of any neuron can pass on to subsequent neurons can be quantified as the neuronal firing pattern entropy. Difficulties associated with estimating entropy from small datasets have proven an obstacle to the widespread reporting of firing pattern entropies and more generally, the use of information theory within the neuroscience community. In the most accessible class of entropy estimation techniques, spike trains are partitioned linearly in time and entropy is estimated from the probability distribution of firing patterns within a partition. Ample previous work has focused on various techniques to minimize the finite dataset bias and standard deviation of entropy estimates from under-sampled probability distributions on spike timing events partitioned linearly in time. In this manuscript we present evidence that all distribution-based techniques would benefit from inter-spike intervals being partitioned in logarithmic time. We show that with logarithmic partitioning, firing rate changes become independent of firing pattern entropy. We delineate the entire entropy estimation process with two example neuronal models, demonstrating the robust improvements in bias and standard deviation that the logarithmic time method yields over two widely used linearly partitioned time approaches.

Stimulus features underlying reduced tremor suppression with temporally patterned deep brain stimulation

Deep brain stimulation (DBS) provides dramatic tremor relief when delivered at high-stimulation frequencies (more than ∼100 Hz), but its mechanisms of action are not well-understood. Previous studies indicate that high-frequency stimulation is less effective when the stimulation train is temporally irregular. The purpose of this study was to determine the specific characteristics of temporally irregular stimulus trains that reduce their effectiveness: long pauses, bursts, or irregularity per se. We isolated these characteristics in stimulus trains and conducted intraoperative measurements of postural tremor in eight volunteers. Tremor varied significantly across stimulus conditions (P < 0.015), and stimulus trains with pauses were significantly less effective than stimulus trains without (P < 0.002). There were no significant differences in tremor between trains with or without bursts or between trains that were irregular or periodic. Thus the decreased effectiveness of temporally irregular DBS trains is due to long pauses in the stimulus trains, not the degree of temporal irregularity alone. We also conducted computer simulations of neuronal responses to the experimental stimulus trains using a biophysical model of the thalamic network. Trains that suppressed tremor in volunteers also suppressed fluctuations in thalamic transmembrane potential at the frequency associated with cerebellar burst-driver inputs. Clinical and computational findings indicate that DBS suppresses tremor by masking burst-driver inputs to the thalamus and that pauses in stimulation prevent such masking. Although stimulation of other anatomic targets may provide tremor suppression, we propose that the most relevant neuronal targets for effective tremor suppression are the afferent cerebellar fibers that terminate in the thalamus.

Tremor varies as a function of the temporal regularity of deep brain stimulation

The frequency of stimulation is one of the primary factors determining the effectiveness of deep brain stimulation (DBS) in relieving tremor. DBS efficacy, however, may depend not only on the average frequency of stimulation, but also on the temporal pattern of stimulation. We conducted intraoperative measurements of the effect of temporally irregular DBS (nonconstant interpulse intervals) on tremor. As the coefficient of variation of irregular high frequency DBS trains increased, they became less effective at reducing tremor (mixed effects regression model, P<0.04). These data provide evidence that the effects of DBS are dependent not only on the average frequency of DBS, but also on the regularity of the temporal spacing of DBS pulses.

Contributions of I h to feature selectivity in layer II stellate cells of the entorhinal cortex

Stellate cells (SCs) of the entorhinal cortex generate prominent subthreshold oscillations that are believed to be important contributors to the hippocampal theta rhythm. The slow inward rectifier Ih is expressed prominently in SCs and has been suggested to be a dominant factor in their integrative properties. We studied the input-output relationships in stellate cells (SCs) of the entorhinal cortex, both in control conditions and in the presence of the Ih antagonist ZD7288. Our results show that Ih is responsible for SCs’ subthreshold resonance, and contributes to enhanced spiking reliability to theta-rich stimuli. However, SCs still exhibit other traits of rhythmicity, such as subthreshold oscillations, under Ih blockade. To clarify the effects of Ih on SC spiking, we used a generalized form of principal component analysis to show that SCs select particular features with relevant temporal signatures from stimuli. The spike-selected mix of those features varies with the frequency content of the stimulus, emphasizing the inherent nonlinearity of SC responses. A number of controls confirmed that this selectivity represents a stimulus-induced change in the cellular input-output relationship rather than an artifact of the analysis technique. Sensitivity to slow features remained statistically significant in ZD7288. However, with Ih blocked, slow stimulus features were less predictive of spikes and spikes conveyed less information about the stimulus over long time scales. Together, these results suggest that Ih is an important contributor to the input-output relationships expressed by SCs, but that other factors in SCs also contribute to subthreshold oscillations and nonlinear selectivity to slow features.

Spike Phase Locking in CA1 Pyramidal Neurons Depends on Background Conductance and Firing Rate

Oscillatory activity in neuronal networks correlates with different behavioral states throughout the nervous system, and the frequency–response characteristics of individual neurons are believed to be critical for network oscillations. Recent in vivo studies suggest that neurons experience periods of high membrane conductance, and that action potentials are often driven by membrane potential fluctuations in the living animal. To investigate the frequency–response characteristics of CA1 pyramidal neurons in the presence of high conductance and voltage fluctuations, we performed dynamic-clamp experiments in rat hippocampal brain slices. We drove neurons with noisy stimuli that included a sinusoidal component ranging, in different trials, from 0.1 to 500 Hz. In subsequent data analysis, we determined action potential phase-locking profiles with respect to background conductance, average firing rate, and frequency of the sinusoidal component. We found that background conductance and firing rate qualitatively change the phase-locking profiles of CA1 pyramidal neurons versus frequency. In particular, higher average spiking rates promoted bandpass profiles, and the high-conductance state promoted phase-locking at frequencies well above what would be predicted from changes in the membrane time constant. Mechanistically, spike rate adaptation and frequency resonance in the spike-generating mechanism are implicated in shaping the different phase-locking profiles. Our results demonstrate that CA1 pyramidal cells can actively change their synchronization properties in response to global changes in activity associated with different behavioral states.

Deep brain stimulation of the subthalamic nucleus reestablishes neuronal information transmission in the 6-OHDA rat model of parkinsonism

Pathophysiological activity of basal ganglia neurons accompanies the motor symptoms of Parkinsons disease. High-frequency (>90 Hz) deep brain stimulation (DBS) reduces parkinsonian symptoms, but the mechanisms remain unclear. We hypothesize that parkinsonism-associated electrophysiological changes constitute an increase in neuronal firing pattern disorder and a concomitant decrease in information transmission through the ventral basal ganglia, and that effective DBS alleviates symptoms by decreasing neuronal disorder while simultaneously increasing information transfer through the same regions. We tested these hypotheses in the freely behaving, 6-hydroxydopamine-lesioned rat model of hemiparkinsonism. Following the onset of parkinsonism, mean neuronal firing rates were unchanged, despite a significant increase in firing pattern disorder (i.e., neuronal entropy), in both the globus pallidus and substantia nigra pars reticulata. This increase in neuronal entropy was reversed by symptom-alleviating DBS. Whereas increases in signal entropy are most commonly indicative of similar increases in information transmission, directed information through both regions was substantially reduced (>70%) following the onset of parkinsonism. Again, this decrease in information transmission was partially reversed by DBS. Together, these results suggest that the parkinsonian basal ganglia are rife with entropic activity and incapable of functional information transmission. Furthermore, they indicate that symptom-alleviating DBS works by lowering the entropic noise floor, enabling more information-rich signal propagation. In this view, the symptoms of parkinsonism may be more a default mode, normally overridden by healthy basal ganglia information. When that information is abolished by parkinsonian pathophysiology, hypokinetic symptoms emerge.