High-frequency deep brain stimulation of the putamen improves bradykinesia in Parkinson's disease

Individual and group-level optimization of electric field in deep brain region during multichannel transcranial electrical stimulation

Electrode montage optimization for transcranial electric stimulation (tES) is a challenging topic for targeting a specific brain region. Targeting the deep brain region is difficult due to tissue inhomogeneity, resulting in complex current flow. In this study, a simplified protocol for montage optimization is proposed for multichannel tES (mc-tES). The purpose of this study was to reduce the computational cost for mc-tES optimization and to evaluate the mc-tES for deep brain regions. Optimization was performed using a simplified protocol for montages under safety constraints with 20 anatomical head models. The optimization procedure is simplified using the surface EF of the deep brain target region, considering its small volume and non-concentric distribution of the electrodes. Our proposal demonstrated that the computational cost was reduced by >90%. A total of six-ten electrodes were necessary for robust EF in the target region. The optimization with surface EF is comparable to or marginally better than using conventional volumetric EF for deep brain tissues. An electrode montage with a mean injection current amplitude derived from individual analysis was demonstrated to be useful for targeting the deep region at the group level. The optimized montage and injection current were derived at the group level. Our proposal at individual and group levels showed great potential for clinical application.

Dysconnectivity of the parafascicular nucleus in Parkinson's disease: A dynamic causal modeling analysis

BACKGROUND

Recent animal model studies have suggested that the parafascicular nucleus has the potential to be an effective deep brain stimulation target for Parkinson's disease. However, our knowledge on the role of the parafascicular nucleus in Parkinson's disease patients remains limited.

OBJECTIVE

We aimed to investigate the functional alterations of the parafascicular nucleus projections in Parkinson's disease patients.

METHODS

We enrolled 72 Parkinson's disease patients and 60 healthy controls, then utilized resting-state functional MRI and spectral dynamic causal modeling to explore the effective connectivity of the bilateral parafascicular nucleus to the dorsal putamen, nucleus accumbens, and subthalamic nucleus. The associations between the effective connectivity of the parafascicular nucleus projections and clinical features were measured with Pearson partial correlations.

RESULTS

Compared with controls, the effective connectivity from the parafascicular nucleus to dorsal putamen was significantly increased, while the connectivity to the nucleus accumbens and subthalamic nucleus was significantly reduced in Parkinson's disease patients. There was a significantly positive correlation between the connectivity of parafascicular nucleus-dorsal putamen projection and motor deficits. The connectivity from the parafascicular nucleus to the subthalamic nucleus was negatively correlated with motor deficits and apathy, while the connectivity from the parafascicular nucleus to the nucleus accumbens was negatively associated with depression.

CONCLUSION

The present study demonstrates that the parafascicular nucleus-related projections are damaged and associated with clinical symptoms of Parkinson's disease. Our findings provide new insights into the impaired basal ganglia-thalamocortical circuits and give support for the parafascicular nucleus as a potential effective neuromodulating target of the disease.

Structure-function similarities in deep brain stimulation targets cross-species

Deep Brain Stimulation (DBS) is an effective neurosurgical treatment to alleviate motor symptoms of advanced Parkinson's disease. Due to its potential, DBS usage is rapidly expanding to target a large number of brain regions to treat a wide range of diseases and neuropsychiatric disorders. The identification and validation of new target regions heavily rely on the insights gained from rodent and primate models. Here we present a large-scale automatic meta-analysis in which the structure-function associations within and between species are compared for 21 DBS targets in humans. The results indicate that the structure-function association for the majority of the 21 included subcortical areas were conserved cross-species. A subset of structures showed overlapping functional association. This can potentially be attributed to shared brain networks and might explain why multiple brain areas are targeted for the same disease or neuropsychiatric disorder.

Predicting Motor Responsiveness to Deep Brain Stimulation with Machine Learning

Deep brain stimulation is a complex movement disorder intervention that requires highly invasive brain surgery. Clinicians struggle to predict how patients will respond to this treatment. To address this problem, we are working toward developing a clinical tool to help neurologists predict deep brain stimulation response. We analyzed a cohort of 105 Parkinson's patients who underwent deep brain stimulation at Vanderbilt University Medical Center. We developed binary and multicategory models for predicting likelihood of motor symptom reduction after undergoing deep brain stimulation. We compared the performances of our best models to predictions made by neurologist experts in movement disorders. The strongest binary classification model achieved a 10-fold cross validation AUC of 0.90, outperforming the best neurologist predictions (0.56). These results are promising for future clinical applications, though more work is necessary to validate these findings in a larger cohort and taking into consideration broader quality of life outcome measures.

KTN1 Variants Underlying Putamen Gray Matter Volumes and Parkinson's Disease

Background

Selective loss of dopaminergic neurons and diminished putamen gray matter volume (GMV) represents a central feature of Parkinson’s disease (PD). Recent studies have reported specific effects of kinectin 1 gene (KTN1) variants on the putamen GMV.

Objective

To examine the relationship of KTN1 variants, KTN1 mRNA expression in the putamen and substantia nigra pars compacta (SNc), putamen GMV, and PD.

Methods

We examined the associations between PD and a total of 1847 imputed KTN1 single nucleotide polymorphisms (SNPs) in one discovery sample [2,000 subjects with PD vs. 1,986 healthy controls (HC)], and confirmed the nominally significant associations (p < 0.05) in two replication samples (900 PD vs. 867 HC, and 940 PD vs. 801 HC, respectively). The regulatory effects of risk variants on the KTN1 mRNA expression in putamen and SNc and the putamen GMV were tested. We also quantified the expression levels of KTN1 mRNA in the putamen and/or SNc for comparison between PD and HC in five independent cohorts.

Results

Six replicable and two non-replicable KTN1-PD associations were identified (0.009 ≤ p ≤ 0.049). The major alleles of five SNPs, including rs12880292, rs8017172, rs17253792, rs945270, and rs4144657, significantly increased risk for PD (0.020 ≤ p ≤ 0.049) and putamen GMVs (19.08 ≤ β ≤ 60.38; 2.82 ≤ Z ≤ 15.03; 5.0 × 10^–51 ≤ p ≤ 0.018). The risk alleles of five SNPs, including rs8017172, rs17253792, rs945270, rs4144657, and rs1188184 also significantly increased the KTN1 mRNA expression in the putamen or SNc (0.021 ≤ p ≤ 0.046). The KTN1 mRNA was abundant in the putamen and/or SNc across five independent cohorts and differentially expressed in the SNc between PD and HC in one cohort (p = 0.047).

Conclusion

There was a consistent, significant, replicable, and robust positive relationship among the KTN1 variants, PD risk, KTN1 mRNA expression in putamen, and putamen volumes, and a modest relation between PD risk and KTN1 mRNA expression in SNc, suggesting that KTN1 may play a functional role in the development of PD.

Signal recovery from stimulation artifacts in intracranial recordings with dictionary learning

OBJECTIVE

Electrical stimulation of the human brain is commonly used for eliciting and inhibiting neural activity for clinical diagnostics, modifying abnormal neural circuit function for therapeutics, and interrogating cortical connectivity. However, recording electrical signals with concurrent stimulation results in dominant electrical artifacts that mask the neural signals of interest. Here we develop a method to reproducibly and robustly recover neural activity during concurrent stimulation. We concentrate on signal recovery across an array of electrodes without channel-wise fine-tuning of the algorithm. Our goal includes signal recovery with trains of stimulation pulses, since repeated, high-frequency pulses are often required to induce desired effects in both therapeutic and research domains. We have made all of our code and data publicly available.

APPROACH

We developed an algorithm that automatically detects templates of artifacts across many channels of recording, creating a dictionary of learned templates using unsupervised clustering. The artifact template that best matches each individual artifact pulse is subtracted to recover the underlying activity. To assess the success of our method, we focus on whether it extracts physiologically interpretable signals from real recordings.

MAIN RESULTS

We demonstrate our signal recovery approach on invasive electrophysiologic recordings from human subjects during stimulation. We show the recovery of meaningful neural signatures in both electrocorticographic (ECoG) arrays and deep brain stimulation (DBS) recordings. In addition, we compared cortical responses induced by the stimulation of primary somatosensory (S1) by natural peripheral touch, as well as motor cortex activity with and without concurrent S1 stimulation.

SIGNIFICANCE

Our work will enable future advances in neural engineering with simultaneous stimulation and recording.

Optimal Trajectories of Brain State Transitions Indicate Motor Function Changes Associated with Aging

Healthy aging is associated with structural and functional changes in sensorimotor systems, leading to a deterioration in motor function. However, most of the previous studies focused on the descriptive measures of alteration, little is known about how structural network facilitate functional dynamics during aging. Based on the structural brain network constructed by diffusion-weighted imaging, we employed recent network control theory to evaluate the control energy necessary to drive the state transition from baseline condition with default mode network (DMN) activation to motor network activation in a large cohort (n = 625; 18-88 years). We found at the whole, the control energy required to activate the motor network declined with aging, in which the motor network contributed most of the control energy. The control energy of nodes within motor network showed both positive and negative age effects, reflecting an aberrant functional integration associated with aging. Interestingly, the control energy of subcortical network most significantly increased with aging, suggesting an altered motor-subcortical circuit, thus requiring more energy for the optimal control. Moreover, the control energy of bilateral putamen showed the largest positive age effect, and this pattern was also supported by the energetic impact of nodes, implying a key role for motor modulation associated with aging. Taken together, our results offer insights of control energy cost necessary for the age-dependent decline in motor task, and provide new clues for brain optimal control of neuromodulation in older adults.

Functional Connectivity-Based Modelling Simulates Subject-Specific Network Spreading Effects of Focal Brain Stimulation

Neurostimulation remarkably alleviates the symptoms in a variety of brain disorders by modulating the brain-wide network. However, how brain-wide effects on the direct and indirect pathways evoked by focal neurostimulation elicit therapeutic effects in an individual patient is unknown. Understanding this remains crucial for advancing neural circuit-based guidance to optimize candidate patient screening, pre-surgical target selection, and post-surgical parameter tuning. To address this issue, we propose a functional brain connectome-based modeling approach that simulates the spreading effects of stimulating different brain regions and quantifies the rectification of abnormal network topology in silico. We validated these analyses by pinpointing nuclei in the basal ganglia circuits as top-ranked targets for 43 local patients with Parkinson’s disease and 90 patients from a public database. Individual connectome-based analysis demonstrated that the globus pallidus was the best choice for 21.1% and the subthalamic nucleus for 19.5% of patients. Down-regulation of functional connectivity (up to 12%) at these prioritized targets optimally maximized the therapeutic effects. Notably, the priority rank of the subthalamic nucleus significantly correlated with motor symptom severity (Unified Parkinson’s Disease Rating Scale III) in the local cohort. These findings underscore the potential of neural network modeling for advancing personalized brain stimulation therapy, and warrant future experimental investigation to validate its clinical utility.

Uncovering the underlying mechanisms and whole-brain dynamics of deep brain stimulation for Parkinson's disease

Deep brain stimulation (DBS) for Parkinson's disease is a highly effective treatment in controlling otherwise debilitating symptoms. Yet the underlying brain mechanisms are currently not well understood. Whole-brain computational modeling was used to disclose the effects of DBS during resting-state functional Magnetic Resonance Imaging in ten patients with Parkinson's disease. Specifically, we explored the local and global impact that DBS has in creating asynchronous, stable or critical oscillatory conditions using a supercritical bifurcation model. We found that DBS shifts global brain dynamics of patients towards a Healthy regime. This effect was more pronounced in very specific brain areas such as the thalamus, globus pallidus and orbitofrontal regions of the right hemisphere (with the left hemisphere not analyzed given artifacts arising from the electrode lead). Global aspects of integration and synchronization were also rebalanced. Empirically, we found higher communicability and coherence brain measures during DBS-ON compared to DBS-OFF. Finally, using our model as a framework, artificial in silico DBS was applied to find potential alternative target areas for stimulation and whole-brain rebalancing. These results offer important insights into the underlying large-scale effects of DBS as well as in finding novel stimulation targets, which may offer a route to more efficacious treatments.

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.

A Point Process Model-based Framework Reveals Reinforcement Mechanisms in Striatum during High Frequency STN DBS

Striatum is a major stage of the motor loop but, despite a pivotal role in the execution of movements, it has been poorly studied thus far under Parkinsonian conditions and Deep Brain Stimulation (DBS). We propose a computational framework to analyze the spiking activity of striatal neurons under several conditions. This framework combines point process models and single unit recordings, and separately evaluates the effects of the spiking history, DBS frequency, and other cells on the neuronal discharge pattern, thus giving a full characterization of non-stationary neuronal dynamics and inter-neuronal dependencies. We applied this framework to 166 striatal neurons collected in a monkey both at rest and during DBS (30-130 Hz). Our analysis was conducted both before and after treatment of the animal with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP), which evoked Parkinsonian-like motor disorders. We reported that high frequency (≥100 Hz) DBS reduces non-stationary dynamics and inter-neuronal dependencies by regularizing the discharge patterns both in MPTP and normal striatum, while the combination of MPTP and low frequency (30-80 Hz) DBS enhances these features, thus suggesting that pattern regularization in striatum might contribute to the therapeutic effect of high frequency DBS and presumably results from the overlap of feed-forward and feedback activation along the motor loop (reinforcement).

The epistemology of Deep Brain Stimulation and neuronal pathophysiology

Deep Brain Stimulation (DBS) is a remarkable therapy succeeding where all manner of pharmacological manipulations and brain transplants fail. The success of DBS has resurrected the relevance of electrophysiology and dynamics on the order of milliseconds. Despite the remarkable effects of DBS, its mechanisms of action are largely unknown. There has been an expanding catalogue of various neuronal and neural responses to DBS or DBS-like stimulation but no clear conceptual encompassing explanatory scheme has emerged despite the technological prowess and intellectual sophistication of the scientists involved. Something is amiss. If the scientific observations are sound, then why has there not been more progress? The alternative is that it may be the hypotheses that frame the questions are at fault as well as the methods of inference (logic) used to validate the hypotheses. An analysis of the past and current notions of the DBS mechanisms of action is the subject in order to identify the presuppositions (premises) and logical fallacies that may be at fault. The hope is that these problems will be avoided in the future so the DBS can realize its full potential quickly. In this regard, the discussion of the methods of inference and presuppositions that underlie many current notions is no different then a critique of experimental methods common in scientific discussions and consequently, examinations of the epistemology and logic are appropriate. This analysis is in keeping with the growing appreciation among scientists and philosophers of science, the scientific observations (data) to not “speak for themselves” nor is the scientific method self-evidently true and that consideration of the underlying inferential methods is necessary.