Wireless deep brain stimulation of well-defined neuronal populations could facilitate the study of intact brain circuits and the treatment of neurological disorders. Here, we demonstrate minimally invasive and remote neural excitation through the activation of the heat-sensitive capsaicin receptor TRPV1 by magnetic nanoparticles. When exposed to alternating magnetic fields, the nanoparticles dissipate heat generated by hysteresis, triggering widespread and reversible firing of TRPV1(+) neurons. Wireless magnetothermal stimulation in the ventral tegmental area of mice evoked excitation in subpopulations of neurons in the targeted brain region and in structures receiving excitatory projections. The nanoparticles persisted in the brain for over a month, allowing for chronic stimulation without the need for implants and connectors.

Over the last two decades there has been an exponential rise in the number of patients receiving deep brain stimulation (DBS) to manage debilitating neurological symptoms in conditions such as Parkinson's disease, essential tremor, and dystonia. Novel applications of DBS continue to emerge including treatment of various psychiatric conditions (e.g. obsessive-compulsive disorder, major depression) and cognitive disorders such as Alzheimer's disease. Despite widening therapeutic applications, our understanding of the mechanisms underlying DBS remains limited. In addition to modulation of local and network-wide neuronal activity, growing evidence suggests that DBS may also have important neuroprotective effects in the brain by limiting synaptic dysfunction and neuronal loss in neurodegenerative disorders. In this review, we consider evidence from preclinical and clinical studies of DBS in Parkinson's disease, Alzheimer's disease, and epilepsy that suggest chronic stimulation has the potential to mitigate neuronal loss and disease progression.

No abstract available

The purpose of the present study was to evaluate deep learning-based image-guided surgical planning for deep brain stimulation (DBS). We developed deep learning semantic segmentation-based DBS targeting and prospectively applied the method clinically.

From a neuroimaging point of view, deep brain stimulation (DBS) in psychiatric disorders represents a unique source of information to probe results gained in functional, structural and molecular neuroimaging studies in vivo. However, the implementation has, up to now, been restricted by the heterogeneity of the data reported in DBS studies. The aim of the present study was therefore to provide a comprehensive and standardized database of currently used DBS targets in selected psychiatric disorders (obsessive-compulsive disorder (OCD), treatment-resistant depression (TRD), Gilles de la Tourette syndrome (GTS)) to enable topological comparisons between neuroimaging results and stimulation areas. A systematic literature research was performed and all peer-reviewed publications until the year 2012 were included. Literature research yielded a total of 84 peer-reviewed studies including about 296 psychiatric patients. The individual stimulation data of 37 of these studies meeting the inclusion criteria which included a total of 202 patients (63 OCD, 89 TRD, 50 GTS) was translated into MNI stereotactic space with respect to AC origin in order to identify key targets. The created database can be used to compare DBS target areas in MNI stereotactic coordinates with: 1) activation patterns in functional brain imaging (fMRI, phfMRI, PET, MET, EEG); 2) brain connectivity data (e.g., MR-based DTI/tractography, functional and effective connectivity); 3) quantitative molecular distribution data (e.g., neuroreceptor PET, post-mortem neuroreceptor mapping); 4) structural data (e.g., VBM for neuroplastic changes). Vice versa, the structural, functional and molecular data may provide a rationale to define new DBS targets and adjust/fine-tune currently used targets in DBS based on this overview in stereotactic coordinates. Furthermore, the availability of DBS data in stereotactic space may facilitate the investigation and interpretation of treatment effects and side effect of DBS by comparing these to neuroimaging results. The present study thus improves comparability between functional, structural and molecular data in standard stereotactic space gained in neuroimaging studies with surgical targets for DBS, which is among other possible implications of crucial importance for the definition of new targets for effective DBS.

Deep brain stimulation (DBS) is being used to treat movement, neurological, and psychiatric disorders; recently it has been successfully applied to patients with treatment-resistant depression or in minimally conscious state. In addition to its clinical importance, DBS presents a powerful approach to target specific neural circuits and determine the functional relationship between the components of these circuits. We examined the effect of high-frequency stimulation of a crucial component of the limbic circuitry, the anterior thalamic nuclei (ATN), on the generation of new neurons in the dentate gyrus (DG) of the hippocampus, another component of the same circuitry. Adult hippocampal neurogenesis emerges as a strong correlate of antidepressant treatments; however, in most cases, the progenitor cell population targeted by a specific treatment is not known. Using reporter mouse lines designed to quantify changes in selected classes of neural progenitors, we found that high-frequency stimulation of the ATN increases symmetric divisions of a defined class of neural progenitors in the DG; this effect is later manifested as an increased number of new neurons. The affected class of neural progenitors is also affected by the antidepressant fluoxetine (Prozac) and physical exercise (running). This indicates that neurogenic stimuli of different natures can converge on the same neurogenic target in the DG. Our results also suggest that hippocampal neurogenesis may be used as a sensitive indicator of the limbic circuitry activation induced by DBS.

Deep Brain Stimulation (DBS) is an efficacious treatment option for an increasing range of brain disorders. To enhance our knowledge about the mechanisms of action of DBS and to probe novel targets, basic research in animal models with DBS is an essential research base. Beyond nonhuman primate, pig, and mouse models, the rat is a widely used animal model for probing DBS effects in basic research. Reconstructing DBS electrode placement after surgery is crucial to associate observed effects with modulating a specific target structure. Post-mortem histology is a commonly used method for reconstructing the electrode location. In humans, however, neuroimaging-based electrode localizations have become established. For this reason, we adapt the open-source software pipeline Lead-DBS for DBS electrode localizations from humans to the rat model. We validate our localization results by inter-rater concordance and a comparison with the conventional histological method. Finally, using the open-source software pipeline OSS-DBS, we demonstrate the subject-specific simulation of the VTA and the activation of axon models aligned to pathways representing neuronal fibers, also known as the pathway activation model. Both activation models yield a characterization of the impact of DBS on the target area. Our results suggest that the proposed neuroimaging-based method can precisely localize DBS electrode placements that are essentially rater-independent and yield results comparable to the histological gold standard. The advantages of neuroimaging-based electrode localizations are the possibility of acquiring them in vivo and combining electrode reconstructions with advanced imaging metrics, such as those obtained from diffusion or functional magnetic resonance imaging (MRI). This paper introduces a freely available open-source pipeline for DBS electrode reconstructions in rats. The presented initial validation results are promising.

Treatment-resistant depression is a severely disabling disorder with no proven treatment options once multiple medications, psychotherapy, and electroconvulsive therapy have failed. Based on our preliminary observation that the subgenual cingulate region (Brodmann area 25) is metabolically overactive in treatment-resistant depression, we studied whether the application of chronic deep brain stimulation to modulate BA25 could reduce this elevated activity and produce clinical benefit in six patients with refractory depression. Chronic stimulation of white matter tracts adjacent to the subgenual cingulate gyrus was associated with a striking and sustained remission of depression in four of six patients. Antidepressant effects were associated with a marked reduction in local cerebral blood flow as well as changes in downstream limbic and cortical sites, measured using positron emission tomography. These results suggest that disrupting focal pathological activity in limbic-cortical circuits using electrical stimulation of the subgenual cingulate white matter can effectively reverse symptoms in otherwise treatment-resistant depression.

Successful deep brain stimulation (DBS) in Parkinson's disease (PD) requires optimal electrode placement. One technique of intraoperative electrode testing is determination of stimulation thresholds inducing corticospinal/corticobulbar tracts (CSBT) motor contractions.

Brain connectivity profiles seeding from deep brain stimulation (DBS) electrodes have emerged as informative tools to estimate outcome variability across DBS patients. Given the limitations of acquiring and processing patient-specific diffusion-weighted imaging data, a number of studies have employed normative atlases of the human connectome. To date, it remains unclear whether patient-specific connectivity information would strengthen the accuracy of such analyses. Here, we compared similarities and differences between patient-specific, disease-matched and normative structural connectivity data and their ability to predict clinical improvement. Data from 33 patients suffering from Parkinson's Disease who underwent surgery at three different centers were retrospectively collected. Stimulation-dependent connectivity profiles seeding from active contacts were estimated using three modalities, namely patient-specific diffusion-MRI data, age- and disease-matched or normative group connectome data (acquired in healthy young subjects). Based on these profiles, models of optimal connectivity were calculated and used to estimate clinical improvement in out of sample data. All three modalities resulted in highly similar optimal connectivity profiles that could largely reproduce findings from prior research based on this present novel multi-center cohort. In a data-driven approach that estimated optimal whole-brain connectivity profiles, out-of-sample predictions of clinical improvements were calculated. Using either patient-specific connectivity (R = 0.43 at p = 0.001), an age- and disease-matched group connectome (R = 0.25, p = 0.048) and a normative connectome based on healthy/young subjects (R = 0.31 at p = 0.028), significant predictions could be made. Our results of patient-specific connectivity and normative connectomes lead to similar main conclusions about which brain areas are associated with clinical improvement. Still, although results were not significantly different, they hint at the fact that patient-specific connectivity may bear the potential of explaining slightly more variance than group connectomes. Furthermore, use of normative connectomes involves datasets with high signal-to-noise acquired on specialized MRI hardware, while clinical datasets as the ones used here may not exactly match their quality. Our findings support the role of DBS electrode connectivity profiles as a promising method to investigate DBS effects and to potentially guide DBS programming.

Deep brain stimulation (DBS) has emerged as a novel therapy for the treatment of several movement and neuropsychiatric disorders, and may also be suitable for the treatment of Tourette syndrome (TS). The main DBS targets used to date in patients with TS are located within the basal ganglia-thalamo-cortical circuit involved in the pathophysiology of this syndrome. They include the ventralis oralis/centromedian-parafascicular (Vo/CM-Pf) nucleus of the thalamus and the nucleus accumbens. Current DBS treatments deliver continuous electrical stimulation and are not designed to adapt to the patient's symptoms, thereby contributing to unwanted side effects. Moreover, continuous DBS can lead to rapid battery depletion, which necessitates frequent battery replacement surgeries. Adaptive deep brain stimulation (aDBS), which is controlled based on neurophysiological biomarkers, is considered one of the most promising approaches to optimize clinical benefits and to limit the side effects of DBS. aDBS consists of a closed-loop system designed to measure and analyse a control variable reflecting the patient's clinical condition and to modify on-line stimulation settings to improve treatment efficacy. Local field potentials (LFPs), which are sums of pre- and post-synaptic activity arising from large neuronal populations, directly recorded from electrodes implanted for DBS can theoretically represent a reliable correlate of clinical status in patients with TS. The well-established LFP-clinical correlations in patients with Parkinson's disease reported in the last few years provide the rationale for developing and implementing new aDBS devices whose efficacies are under evaluation in humans. Only a few studies have investigated LFP activity recorded from DBS target structures and the relationship of this activity to clinical symptoms in TS. Here, we review the available literature supporting the feasibility of an LFP-based aDBS approach in patients with TS. In addition, to increase such knowledge, we report explorative findings regarding LFP data recently acquired and analysed in patients with TS after DBS electrode implantation at rest, during voluntary and involuntary movements (tics), and during ongoing DBS. Data available up to now suggest that patients with TS have oscillatory patterns specifically associated with the part of the brain they are recorded from, and thereby with clinical manifestations. The Vo/CM-Pf nucleus of the thalamus is involved in movement execution and the pathophysiology of TS. Moreover, the oscillatory patterns in TS are specifically modulated by DBS treatment, as reflected by improvements in TS symptoms. These findings suggest that LFPs recorded from DBS targets may be used to control new aDBS devices capable of adaptive stimulation responsive to the symptoms of TS.

The frontal cortex is involved in motor, cognitive, and affective brain functions. In humans, however, neuroanatomy-function mappings are predominantly derived from correlative neuroimaging studies. Hence, exactly which frontal domains causally mediate which function remains largely elusive. Herein, we leverage a strategy that allows for causal inference using invasive neuromodulation. Studying 394 subthalamic deep brain stimulation electrodes in patients suffering from one of four brain disorders, we segregated the frontal cortex into cortical projection sites of modulated circuits by their involvement in specific functions. Modulating projections from sensory and motor cortices in dystonia, from primary motor cortex in Tourette's syndrome, from supplementary motor cortex in Parkinson's disease, and from ventromedial prefrontal, anterior cingulate, dorsolateral prefrontal and orbitofrontal cortices in obsessive-compulsive disorder linked to respective symptom improvements. Our findings showcase the combination of deep brain stimulation and brain connectomics as a tool for causal inference on structure-function mappings within the human brain.

Deep brain stimulation of the pedunculopontine nucleus is a promising surgical procedure for the treatment of Parkinsonian gait and balance dysfunction. It has, however, produced mixed clinical results that are poorly understood. We used tractography with the aim to rationalise this heterogeneity. A cohort of eight patients with postural instability and gait disturbance (Parkinson's disease subtype) underwent pre-operative structural and diffusion MRI, then progressed to deep brain stimulation targeting the pedunculopontine nucleus. Pre-operative and follow-up assessments were carried out using the Gait and Falls Questionnaire, and Freezing of Gait Questionnaire. Probabilistic diffusion tensor tractography was carried out between the stimulating electrodes and both cortical and cerebellar regions of a priori interest. Cortical surface reconstructions were carried out to measure cortical thickness in relevant areas. Structural connectivity between stimulating electrode and precentral gyrus (r = 0.81, p = 0.01), Brodmann areas 1 (r = 0.78, p = 0.02) and 2 (r = 0.76, p = 0.03) were correlated with clinical improvement. A negative correlation was also observed for the superior cerebellar peduncle (r = -0.76, p = 0.03). Lower cortical thickness of the left parietal lobe and bilateral premotor cortices were associated with greater pre-operative severity of symptoms. Both motor and sensory structural connectivity of the stimulated surgical target characterises the clinical benefit, or lack thereof, from surgery. In what is a challenging region of brainstem to effectively target, these results provide insights into how this can be better achieved. The mechanisms of action are likely to have both motor and sensory components, commensurate with the probable nature of the underlying dysfunction.

Deep Brain Stimulation (DBS) is an established treatment option for movement disorders and is under investigation for treatment in a growing number of other brain diseases. It has been shown that exact electrode placement crucially affects the efficacy of DBS and this should be considered when investigating novel indications or DBS targets. To measure clinical improvement as a function of electrode placement, neuroscientific methodology and specialized software tools are needed. Such tools should have the goal to make electrode placement comparable across patients and DBS centers, and include statistical analysis options to validate and define optimal targets. Moreover, to allow for comparability across different centers, these need to be performed within an algorithmically and anatomically standardized and openly available group space. With the publication of Lead-DBS software in 2014, an open-source tool was introduced that allowed for precise electrode reconstructions based on pre- and postoperative neuroimaging data. Here, we introduce Lead Group, implemented within the Lead-DBS environment and specifically designed to meet aforementioned demands. In the present article, we showcase the various processing streams of Lead Group in a retrospective cohort of 51 patients suffering from Parkinson's disease, who were implanted with DBS electrodes to the subthalamic nucleus (STN). Specifically, we demonstrate various ways to visualize placement of all electrodes in the group and map clinical improvement values to subcortical space. We do so by using active coordinates and volumes of tissue activated, showing converging evidence of an optimal DBS target in the dorsolateral STN. Second, we relate DBS outcome to the impact of each electrode on local structures by measuring overlap of stimulation volumes with the STN. Finally, we explore the software functions for connectomic mapping, which may be used to relate DBS outcomes to connectivity estimates with remote brain areas. The manuscript is accompanied by a walkthrough tutorial which allows users to reproduce all main results presented here. All data and code needed to reproduce results are openly available.

Over the course of the development of deep brain stimulation (DBS) into a well-established therapy for Parkinson's disease, essential tremor, and dystonia, its utility as a potential treatment for autonomic dysfunction has emerged. Dysfunction of autonomic processes is common in neurological diseases. Depending on the specific target in the brain, DBS has been shown to raise or lower blood pressure, normalize the baroreflex, to alter the caliber of bronchioles, and eliminate hyperhidrosis, all through modulation of the sympathetic nervous system. It has also been shown to improve cortical control of the bladder, directly induce or inhibit the micturition reflex, and to improve deglutition and gastric emptying. In this review, we will attempt to summarize the relevant available studies describing these effects of DBS on autonomic function, which vary greatly in character and magnitude with respect to stimulation target.

Patients with advanced Parkinson's disease (PD) often receive deep brain stimulation (DBS) treatment, in which conductive leads are surgically implanted in the brain. While DBS treats tremor and rigidity, patients often continue to suffer from speech and swallowing impairments. There is preliminary evidence that transcranial magnetic stimulation (TMS) of the cortex may be beneficial for these symptoms. However, the potential electromagnetic interactions of the strong magnetic fields from TMS on the conductive leads is unknown, and the combination therapy has not been approved for use. In this article, we report an experimental study of the safety of combining DBS and TMS. We fabricated an anatomically accurate head and brain phantom with electrical conductivities matching cerebrospinal fluid and averaged conductivity of gray and white matter. Induced current on an implanted DBS probe in the brain phantom was measured. Our results show that TMS will induce current values in the range or higher than typical DBS stimulation current. Thus, the combination of TMS/DBS treatment might cause over-stimulation in the brain when stimulated directly over the DBS lead with 100% TMS current intensity.

Dementia is among the leading causes of severe and long-term disability worldwide, decreasing the quality of life of individuals and families. Moreover, it induces an enormous economic burden on societies. The most prevalent cause of dementia is Alzheimer's disease (AD). Because current treatment options for AD are limited, deep brain stimulation (DBS) has been considered.

Deep brain stimulation is a widely used therapy for Parkinson's disease (PD) but currently lacks dynamic responsiveness to changing clinical and neural states. Feedback control has the potential to improve therapeutic effectiveness, but optimal control strategy and additional benefits of "adaptive" neurostimulation are unclear. We implemented adaptive subthalamic nucleus stimulation, controlled by subthalamic or cortical signals, in three PD patients (five hemispheres) during normal daily life. We identified neurophysiological biomarkers of residual motor fluctuations using data-driven analyses of field potentials over a wide frequency range and varying stimulation amplitudes. Narrowband gamma oscillations (65-70 Hz) at either site emerged as the best control signal for sensing during stimulation. A blinded, randomized trial demonstrated improved motor symptoms and quality of life compared to clinically optimized standard stimulation. Our approach highlights the promise of personalized adaptive neurostimulation based on data-driven selection of control signals and may be applied to other neurological disorders.

Deep brain stimulation is an effective treatment for Parkinson's disease but can be complicated by side-effects such as cognitive decline. There is often a delay before this side-effect is apparent and the mechanism is unknown, making it difficult to identify patients at risk or select appropriate deep brain stimulation settings. Here, we test whether connectivity between the stimulation site and other brain regions is associated with cognitive decline following deep brain stimulation. First, we studied a unique patient cohort with cognitive decline following subthalamic deep brain stimulation for Parkinson's disease (n = 10) where reprogramming relieved the side-effect without loss of motor benefit. Using resting state functional connectivity data from a large normative cohort (n = 1000), we computed connectivity between each stimulation site and the subiculum, an a priori brain region functionally connected to brain lesions causing memory impairment. Connectivity between deep brain stimulation sites and this same subiculum region was significantly associated with deep brain stimulation induced cognitive decline (P < 0.02). We next performed a data-driven analysis to identify connections most associated with deep brain stimulation induced cognitive decline. Deep brain stimulation sites causing cognitive decline (versus those that did not) were more connected to the anterior cingulate, caudate nucleus, hippocampus, and cognitive regions of the cerebellum (PFWE < 0.05). The spatial topography of this deep brain stimulation-based circuit for cognitive decline aligned with an a priori lesion-based circuit for memory impairment (P = 0.017). To begin translating these results into a clinical tool that might be used for deep brain stimulation programming, we generated a 'heat map' in which the intensity of each voxel reflects the connectivity to our cognitive decline circuit. We then validated this heat map using an independent dataset of Parkinson's disease patients in which cognitive performance was measured following subthalamic deep brain stimulation (n = 33). Intersection of deep brain stimulation sites with our heat map was correlated with changes in the Mattis dementia rating scale 1 year after lead implantation (r = 0.39; P = 0.028). Finally, to illustrate how this heat map might be used in clinical practice, we present a case that was flagged as 'high risk' for cognitive decline based on intersection of the patient's deep brain stimulation site with our heat map. This patient had indeed experienced cognitive decline and our heat map was used to select alternative deep brain stimulation parameters. At 14 days follow-up the patient's cognition improved without loss of motor benefit. These results lend insight into the mechanism of deep brain stimulation induced cognitive decline and suggest that connectivity-based heat maps may help identify patients at risk and who might benefit from deep brain stimulation reprogramming.

Deep brain stimulation (DBS) therapies have shown clinical success in the treatment of a number of neurological illnesses, including obsessive-compulsive disorder, epilepsy, and Parkinson's disease. An emerging strategy for increasing the efficacy of DBS therapies is to develop closed-loop, adaptive DBS systems that can sense biomarkers associated with particular symptoms and in response, adjust DBS parameters in real-time. The development of such systems requires extensive analysis of the underlying neural signals while DBS is on, so that candidate biomarkers can be identified and the effects of varying the DBS parameters can be better understood. However, DBS creates high amplitude, high frequency stimulation artifacts that prevent the underlying neural signals and thus the biological mechanisms underlying DBS from being analyzed. Additionally, DBS devices often require low sampling rates, which alias the artifact frequency, and rely on wireless data transmission methods that can create signal recordings with missing data of unknown length. Thus, traditional artifact removal methods cannot be applied to this setting. We present a novel periodic artifact removal algorithm for DBS applications that can accurately remove stimulation artifacts in the presence of missing data and in some cases where the stimulation frequency exceeds the Nyquist frequency. The numerical examples suggest that, if implemented on dedicated hardware, this algorithm has the potential to be used in embedded closed-loop DBS therapies to remove DBS stimulation artifacts and hence, to aid in the discovery of candidate biomarkers in real-time. Code for our proposed algorithm is publicly available on Github.