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

Deep Brain Stimulation Modified Autism-Like Deficits via the Serotonin System in a Valproic Acid-Induced Rat Model

Deep brain stimulation (DBS) is known to be a promising treatment for resistant depression, which acts via the serotonin (5-hydroxytryptamine, 5-HT) system in the infralimbic prefrontal cortex (ILPFC). Previous study revealed that dysfunction of brain 5-HT homeostasis is related to a valproate (VPA)-induced rat autism spectrum disorder (ASD) model. Whether ILPFC DBS rescues deficits in VPA-induced offspring through the 5-HT system is not known. Using VPA-induced offspring, we therefore explored the effect of DBS in autistic phenotypes and further investigated the underlying mechanism. Using combined behavioral and molecular approaches, we observed that applying DBS and 5-HT_1A receptor agonist treatment with 8-hydroxy-2-(di-n-propylamino)tetralin (8-OH-DPAT) reversed sociability deficits, anxiety and hyperactivity in the VPA-exposed offspring. We then administered the selective 5-HT_1A receptor antagonist N-[2-[4-(2-Methoxyphenyl)-1-piperazinyl]ethyl]-N-2-pyridinylcyclohexanecarboxamide maleate (WAY 100635), following which the effect of DBS in terms of improving autistic behaviors was blocked in the VPA-exposed offspring. Furthermore, we found that both 8-OH-DPAT and DBS treatment rescued autistic behaviors by decreasing the expressions of NR2B subunit of N-methyl-D-aspartate receptors (NMDARs) and the β₃ subunit of γ-aminobutyric acid type A receptors (GABA_AR) in the PFC region. These results provided the first evidence of characteristic behavioral changes in VPA-induced offspring caused by DBS via the 5-HT system in the ILPFC.

Systems approaches to optimizing deep brain stimulation therapies in Parkinson's disease

Over the last 30 years, deep brain stimulation (DBS) has been used to treat chronic neurological diseases like dystonia, obsessive-compulsive disorders, essential tremor, Parkinson's disease, and more recently, dementias, depression, cognitive disorders, and epilepsy. Despite its wide use, DBS presents numerous challenges for both clinicians and engineers. One challenge is the design of novel, more efficient DBS therapies, which are hampered by the lack of complete understanding about the cellular mechanisms of therapeutic DBS. Another challenge is the existence of redundancy in clinical outcomes, that is, different DBS programs can result in similar clinical benefits but very little information (e.g., predictive models, longitudinal data, metrics, etc.) is available to select one program over another. Finally, there is high variability in patients' responses to DBS, which forces clinicians to carefully adjust the stimulation settings to each patient via lengthy programming sessions. Researchers in neural engineering and systems biology have been tackling these challenges over the past few years with the specific goal of developing novel DBS therapies, design methodologies, and computational tools that optimize the therapeutic effects of DBS in each patient. Furthermore, efforts are being made to automatically adapt the DBS treatment to the fluctuations of disease symptoms. A review of the quantitative approaches currently available for the treatment of Parkinson's disease is presented here with an emphasis on the contributions that systems theoretical approaches have provided to understand the global dynamics of complex neuronal circuits in the brain under DBS. This article is categorized under: Translational, Genomic, and Systems Medicine > Therapeutic Methods Analytical and Computational Methods > Computational Methods Analytical and Computational Methods > Dynamical Methods Physiology > Mammalian Physiology in Health and Disease.

Neuronal Complexity in Subthalamic Nucleus is Reduced in Parkinson's Disease

Several theories posit increased Subthalamic Nucleus (STN) activity is causal to Parkinsonism, yet in our previous study we showed that activity from 113 STN neurons from two epilepsy patients and 103 neurons from nine Parkinson's disease (PD) patients demonstrated no significant differences in frequencies or in the coefficients of variation of mean discharge frequencies per 1-s epochs. We continued our analysis using point process modeling to capture higher order temporal dynamics; in particular, bursting, beta-band oscillations, excitatory and inhibitory ensemble interactions, and neuronal complexity. We used this analysis as input to a logistic regression classifier and were able to differentiate between PD and epilepsy neurons with an accuracy of 92%. We also found neuronal complexity, i.e., the number of states in a neuron's point process model, and inhibitory ensemble dynamics, which can be interpreted as a reduction in complexity, to be the most important features with respect to classification accuracy. Even in a dataset with no significant differences in firing rate, we observed differences between PD and epilepsy for other single-neuron measures. Our results suggest PD comes with a reduction in neuronal "complexity," which translates to a neuron's ability to encode information; the more complexity, the more information the neuron can encode. This is also consistent with studies correlating disease to loss of variability in neuronal activity, as the lower the complexity, the less variability.

A systematic approach to selecting task relevant neurons

BACKGROUND

Since task related neurons cannot be specifically targeted during surgery, a critical decision to make is to select which neurons are task-related when performing data analysis. Including neurons unrelated to the task degrade decoding accuracy and confound neurophysiological results. Traditionally, task-related neurons are selected as those with significant changes in firing rate when a stimulus is applied. However, this assumes that neurons' encoding of stimuli are dominated by their firing rate with little regard to temporal dynamics.

NEW METHOD

This paper proposes a systematic approach for neuron selection, which uses a likelihood ratio test to capture the contribution of stimulus to spiking activity while taking into account task-irrelevant intrinsic dynamics that affect firing rates. This approach is denoted as the model deterioration excluding stimulus (MDES) test.

RESULTS

MDES is compared to firing rate selection in four case studies: a simulation, a decoding example, and two neurophysiology examples.

COMPARISON WITH EXISTING METHODS

The MDES rankings in the simulation match closely with ideal rankings, while firing rate rankings are skewed by task-irrelevant parameters. For decoding, 95% accuracy is achieved using the top 8 MDES-ranked neurons, while the top 12 firing-rate ranked neurons are needed. In the neurophysiological examples, MDES matches published results when firing rates do encode salient stimulus information, and uncovers oscillatory modulations in task-related neurons that are not captured when neurons are selected using firing rates.

CONCLUSIONS

These case studies illustrate the importance of accounting for intrinsic dynamics when selecting task-related neurons and following the MDES approach accomplishes that. MDES selects neurons that encode task-related information irrespective of these intrinsic dynamics which can bias firing rate based selection.

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

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.

Reinforcement mechanisms in putamen during high frequency STN DBS: A point process study

Despite a pivotal role in the motor loop, dorsolateral striatum (putamen) has been poorly studied thus far under Parkinsonian conditions and Deep Brain Stimulation (DBS). We analyze the activity of the putamen in a monkey by combining single unit recordings and point process models. The animal received DBS (30-130 Hz) in the subthalamic nucleus (STN) while at rest and recordings were acquired both before and after treatment with 1-methyl-4-phenyl-1,2,3,6- tetrahydropyridine (MPTP), which induced Parkinsonian-like motor disorders. 141 neurons were collected and, for each neuron, a point process model captured DBS-evoked discharge patterns. In the normal animal, spike trains at rest had Poisson like distribution with non-stationary recurrent patterns (RPs) of period 3-7 ms and were mildly changed by low frequency (LF, i.e., < 100 Hz) DBS (i.e., < 20% of neurons affected). With high frequency (HF, i.e., 100-130 Hz) DBS, instead, up to 59% of neurons were affected, the DBS history significantly impacted the neuronal spiking propensity, and the RPs and the post-stimulus activation latency decreased. MPTP evoked inter-neuronal dependencies (INDs) at rest and, compared to normal, LF DBS of the MPTP animal increased RPs and INDs, while HF DBS elicited a faster and wider post-stimulus activation. Overall, HF DBS reduced ongoing non-stationary dynamics by regularizing the discharge patterns both in MPTP and normal putamen, while the combination of MPTP and LF DBS enhanced such dynamics.

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 Parkinson's 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.

Adult human olfactory epithelial-derived progenitors: a potential autologous source for cell-based treatment for Parkinson's disease

Human adult olfactory epithelial-derived neural progenitors (hONPs) can differentiate along several neural lineages in response to morphogenic signals in vitro. A previous study optimized the transfection paradigm for the differentiation of hONPs to dopaminergic neurons. This study engrafted cells modified by the most efficient transfection paradigm for dopaminergic neural restriction and pretransfected controls into a unilateral neurotoxin, 6-hydroxydopamine-induced parkinsonian rat model. Approximately 35% of the animals engrafted with hONPs had improved behavioral recovery as demonstrated by the amphetamine-induced rotation test, as well as a corner preference and cylinder paw preference, over a period of 24 weeks. The pre- and post-transfected groups produced equivalent responses, indicating that the toxic host environment supported hONP dopaminergic differentiation in situ. Human fibroblasts used as a cellular control did not diminish the parkinsonian rotational deficits at any point during the study. Increased numbers of tyrosine hydroxylase (TH)-positive cells were detected in the engrafted brains compared with the fibroblast-implanted and medium-only controls. Engrafted TH-positive hONPs were detected for a minimum of 6 months in vivo; they were multipolar, had long processes, and migrated beyond their initial injection sites. Higher dopamine levels were detected in the striatum of behaviorally improved animals than in equivalent regions of their nonrecovered counterparts. Throughout these experiments, no evidence of tumorigenicity was observed. These results support our hypothesis that human adult olfactory epithelial-derived progenitors represent a unique autologous cell type with promising potential for future use in a cell-based therapy for patients with Parkinson's disease.

Aggregate input-output models of neuronal populations

An extraordinary amount of electrophysiological data has been collected from various brain nuclei to help us understand how neural activity in one region influences another region. In this paper, we exploit the point process modeling (PPM) framework and describe a method for constructing aggregate input-output (IO) stochastic models that predict spiking activity of a population of neurons in the "output" region as a function of the spiking activity of a population of neurons in the "input" region. We first build PPMs of each output neuron as a function of all input neurons, and then cluster the output neurons using the model parameters. Output neurons that lie within the same cluster have the same functional dependence on the input neurons. We first applied our method to simulated data, and successfully uncovered the predetermined relationship between the two regions. We then applied our method to experimental data to understand the input-output relationship between motor cortical neurons and 1) somatosensory and 2) premotor cortical neurons during a behavioral task. Our aggregate IO models highlighted interesting physiological dependences including relative effects of inhibition/excitation from input neurons and extrinsic factors on output neurons.