Establishing connectivity through microdissections of midbrain stimulation-related neural circuits.

Comprehensive understanding of the neural circuits involving the ventral tegmental area is essential for elucidating the anatomo-functional mechanisms governing human behaviour as well as the therapeutic and adverse effects of deep brain stimulation for neuropsychiatric diseases. While the ventral tegmental area has been successfully targeted with deep brain stimulation for different neuropsychiatric diseases, the axonal connectivity of the region has not been fully understood. Here using fiber micro-dissections in human cadaveric hemispheres, population-based high-definition fiber tractography, and previously reported deep brain stimulation hotspots, we find that the ventral tegmental area participates in an intricate network involving the serotonergic pontine nuclei, basal ganglia, limbic system, basal forebrain, and prefrontal cortex, which is implicated in the treatment of obsessive-compulsive disorder, major depressive disorder, Alzheimer's disease, cluster headaches, and aggressive behaviors.

Lesion network of oculogyric crises maps to brain dopaminergic transcriptomic signature.

Oculogyric crises are acute episodes of sustained, typically upward, conjugate deviation of the eyes. Oculogyric crises usually occur as the result of acute D2-dopamine receptor blockade, but the brain areas causally involved in generating this symptom remain elusive. Here, we used data from 14 previously reported cases of lesion-induced oculogyric crises and employed lesion network mapping to identify their shared connections throughout the brain. This analysis yielded a common network that included basal ganglia, thalamic, and brainstem nuclei, as well as the cerebellum. Comparison of this network with gene expression profiles associated with the dopamine system revealed spatial overlap specifically with the gene coding for dopamine receptor type 2 (DRD2) as defined by a large-scale transcriptomic database of the human brain. Furthermore, spatial overlap with DRD2 and DRD3 gene expression was specific to brain lesions associated with oculogyric crises when contrasted to lesions that led to other movement disorders. Our findings identify a common neural network causally involved in the occurrence of oculogyric crises and provide a pathophysiological link between lesion locations causing this syndrome and its most common pharmacological cause, namely DRD2 blockade.

Mapping dysfunctional circuits in the frontal cortex using deep brain stimulation.

Frontal circuits play a critical role in motor, cognitive and affective processing, and their dysfunction may result in a variety of brain disorders. However, exactly which frontal domains mediate which (dys)functions remains largely elusive. We studied 534 deep brain stimulation electrodes implanted to treat four different brain disorders. By analyzing which connections were modulated for optimal therapeutic response across these disorders, we segregated the frontal cortex into circuits that had become dysfunctional in each of them. Dysfunctional circuits were topographically arranged from occipital to frontal, ranging from interconnections with sensorimotor cortices in dystonia, the primary motor cortex in Tourette's syndrome, the supplementary motor area in Parkinson's disease, to ventromedial prefrontal and anterior cingulate cortices in obsessive-compulsive disorder. Our findings highlight the integration of deep brain stimulation with brain connectomics as a powerful tool to explore couplings between brain structure and functional impairments in the human brain.

The role of the motor thalamus in deep brain stimulation for essential tremor.

The advent of next-generation technology has significantly advanced the implementation and delivery of Deep Brain Stimulation (DBS) for Essential Tremor (ET), yet controversies persist regarding optimal targets and networks responsible for tremor genesis and suppression. This review consolidates key insights from anatomy, neurology, electrophysiology, and radiology to summarize the current state-of-the-art in DBS for ET. We explore the role of the thalamus in motor function and describe how differences in parcellations and nomenclature have shaped our understanding of the neuroanatomical substrates associated with optimal outcomes. Subsequently, we discuss how seminal studies have propagated the ventral intermediate nucleus (Vim)-centric view of DBS effects and shaped the ongoing debate over thalamic DBS versus stimulation in the posterior subthalamic area (PSA) in ET. We then describe probabilistic- and network-mapping studies instrumental in identifying the local and network substrates subserving tremor control, which suggest that the PSA is the optimal DBS target for tremor suppression in ET. Taken together, DBS offers promising outcomes for ET, with the PSA emerging as a better target for suppression of tremor symptoms. While advanced imaging techniques have substantially improved the identification of anatomical targets within this region, uncertainties persist regarding the distinct anatomical substrates involved in optimal tremor control. Inconsistent subdivisions and nomenclature of motor areas and other subdivisions in the thalamus further obfuscate the interpretation of stimulation results. While loss of benefit and habituation to DBS remain challenging in some patients, refined DBS techniques and closed-loop paradigms may eventually overcome these limitations.

Spatiotemporally-specific cortical-subthalamic coupling differentiates aspects of speech performance.

Speech provides a rich context for exploring human cortical-basal ganglia circuit function, but direct intracranial recordings are rare. We recorded electrocorticographic signals in the cortex synchronously with single units in the subthalamic nucleus (STN), a basal ganglia node that receives direct input from widespread cortical regions, while participants performed a syllable repetition task during deep brain stimulation (DBS) surgery. We discovered that STN neurons exhibited spike-phase coupling (SPC) events with distinct combinations of frequency, location, and timing that indexed specific aspects of speech. The strength of SPC to posterior perisylvian cortex predicted phoneme production accuracy, while that of SPC to perirolandic cortex predicted time taken for articulation Thus, STN-cortical interactions are coordinated via transient bursts of behavior-specific synchronization that involves multiple neuronal populations and timescales. These results both suggest mechanisms that support auditory-sensorimotor integration during speech and explain why firing-rate based models are insufficient for explaining basal ganglia circuit behavior.

WarpDrive: Improving spatial normalization using manual refinements.

Spatial normalization-the process of mapping subject brain images to an average template brain-has evolved over the last 20+ years into a reliable method that facilitates the comparison of brain imaging results across patients, centers & modalities. While overall successful, sometimes, this automatic process yields suboptimal results, especially when dealing with brains with extensive neurodegeneration and atrophy patterns, or when high accuracy in specific regions is needed. Here we introduce WarpDrive, a novel tool for manual refinements of image alignment after automated registration. We show that the tool applied in a cohort of patients with Alzheimer's disease who underwent deep brain stimulation surgery helps create more accurate representations of the data as well as meaningful models to explain patient outcomes. The tool is built to handle any type of 3D imaging data, also allowing refinements in high-resolution imaging, including histology and multiple modalities to precisely aggregate multiple data sources together.

Deep Brain Stimulation for Obsessive-Compulsive Disorder: Optimal Stimulation Sites.

BACKGROUND: Deep brain stimulation (DBS) is a promising treatment option for treatment-refractory obsessive-compulsive disorder (OCD). Several stimulation targets have been used, mostly in and around the anterior limb of the internal capsule and ventral striatum. However, the precise target within this region remains a matter of debate. METHODS: Here, we retrospectively studied a multicenter cohort of 82 patients with OCD who underwent DBS of the ventral capsule/ventral striatum and mapped optimal stimulation sites in this region. RESULTS: DBS sweet-spot mapping performed on a discovery set of 58 patients revealed 2 optimal stimulation sites associated with improvements on the Yale-Brown Obsessive Compulsive Scale, one in the anterior limb of the internal capsule that overlapped with a previously identified OCD-DBS response tract and one in the region of the inferior thalamic peduncle and bed nucleus of the stria terminalis. Critically, the nucleus accumbens proper and anterior commissure were associated with beneficial but suboptimal clinical improvements. Moreover, overlap with the resulting sweet- and sour-spots significantly estimated variance in outcomes in an independent cohort of 22 patients from 2 additional DBS centers. Finally, beyond obsessive-compulsive symptoms, stimulation of the anterior site was associated with optimal outcomes for both depression and anxiety, while the posterior site was only associated with improvements in depression. CONCLUSIONS: Our results suggest how to refine targeting of DBS in OCD and may be helpful in guiding DBS programming in existing patients.

Connectivity Profile for Subthalamic Nucleus Deep Brain Stimulation in Early Stage Parkinson Disease.

OBJECTIVE: This study was undertaken to describe relationships between electrode localization and motor outcomes from the subthalamic nucleus (STN) deep brain stimulation (DBS) in early stage Parkinson disease (PD) pilot clinical trial. METHODS: To determine anatomical and network correlates associated with motor outcomes for subjects randomized to early DBS (n = 14), voxelwise sweet spot mapping and structural connectivity analyses were carried out using outcomes of motor progression (Unified Parkinson Disease Rating Scale Part III [UPDRS-III] 7-day OFF scores [∆baseline➔24 months, MedOFF/StimOFF]) and symptomatic motor improvement (UPDRS-III ON scores [%∆baseline➔24 months, MedON/StimON]). RESULTS: Sweet spot mapping revealed a location associated with slower motor progression in the dorsolateral STN (anterior/posterior commissure coordinates: 11.07 ± 0.82mm lateral, 1.83 ± 0.61mm posterior, 3.53 ± 0.38mm inferior to the midcommissural point; Montreal Neurological Institute coordinates: +11.25, -13.56, -7.44mm). Modulating fiber tracts from supplementary motor area (SMA) and primary motor cortex (M1) to the STN correlated with slower motor progression across STN DBS subjects, whereas fiber tracts originating from pre-SMA and cerebellum were negatively associated with motor progression. Robustness of the fiber tract model was demonstrated in leave-one-patient-out (R = 0.56, p = 0.02), 5-fold (R = 0.50, p = 0.03), and 10-fold (R = 0.53, p = 0.03) cross-validation paradigms. The sweet spot and fiber tracts associated with motor progression revealed strong similarities to symptomatic motor improvement sweet spot and connectivity in this early stage PD cohort. INTERPRETATION: These results suggest that stimulating the dorsolateral region of the STN receiving input from M1 and SMA (but not pre-SMA) is associated with slower motor progression across subjects receiving STN DBS in early stage PD. This finding is hypothesis-generating and must be prospectively tested in a larger study. ANN NEUROL 2023;94:271-284.

Mapping Dysfunctional Circuits in the Frontal Cortex Using Deep Brain Stimulation.

Frontal circuits play a critical role in motor, cognitive, and affective processing - and their dysfunction may result in a variety of brain disorders. However, exactly which frontal domains mediate which (dys)function remains largely elusive. Here, we study 534 deep brain stimulation electrodes implanted to treat four different brain disorders. By analyzing which connections were modulated for optimal therapeutic response across these disorders, we segregate the frontal cortex into circuits that became dysfunctional in each of them. Dysfunctional circuits were topographically arranged from occipital to rostral, ranging from interconnections with sensorimotor cortices in dystonia, with the primary motor cortex in Tourette's syndrome, the supplementary motor area in Parkinson's disease, to ventromedial prefrontal and anterior cingulate cortices in obsessive-compulsive disorder. Our findings highlight the integration of deep brain stimulation with brain connectomics as a powerful tool to explore couplings between brain structure and functional impairment in the human brain.

Lead-DBS v3.0: Mapping deep brain stimulation effects to local anatomy and global networks.

Following its introduction in 2014 and with support of a broad international community, the open-source toolbox Lead-DBS has evolved into a comprehensive neuroimaging platform dedicated to localizing, reconstructing, and visualizing electrodes implanted in the human brain, in the context of deep brain stimulation (DBS) and epilepsy monitoring. Expanding clinical indications for DBS, increasing availability of related research tools, and a growing community of clinician-scientist researchers, however, have led to an ongoing need to maintain, update, and standardize the codebase of Lead-DBS. Major development efforts of the platform in recent years have now yielded an end-to-end solution for DBS-based neuroimaging analysis allowing comprehensive image preprocessing, lead localization, stimulation volume modeling, and statistical analysis within a single tool. The aim of the present manuscript is to introduce fundamental additions to the Lead-DBS pipeline including a deformation warpfield editor and novel algorithms for electrode localization. Furthermore, we introduce a total of three comprehensive tools to map DBS effects to local, tract- and brain network-levels. These updates are demonstrated using a single patient example (for subject-level analysis), as well as a retrospective cohort of 51 Parkinson's disease patients who underwent DBS of the subthalamic nucleus (for group-level analysis). Their applicability is further demonstrated by comparing the various methodological choices and the amount of explained variance in clinical outcomes across analysis streams. Finally, based on an increasing need to standardize folder and file naming specifications across research groups in neuroscience, we introduce the brain imaging data structure (BIDS) derivative standard for Lead-DBS. Thus, this multi-institutional collaborative effort represents an important stage in the evolution of a comprehensive, open-source pipeline for DBS imaging and connectomics.

Optimal deep brain stimulation sites and networks for stimulation of the fornix in Alzheimer's disease.

Deep brain stimulation (DBS) to the fornix is an investigational treatment for patients with mild Alzheimer's Disease. Outcomes from randomized clinical trials have shown that cognitive function improved in some patients but deteriorated in others. This could be explained by variance in electrode placement leading to differential engagement of neural circuits. To investigate this, we performed a post-hoc analysis on a multi-center cohort of 46 patients with DBS to the fornix (NCT00658125, NCT01608061). Using normative structural and functional connectivity data, we found that stimulation of the circuit of Papez and stria terminalis robustly associated with cognitive improvement (R = 0.53, p < 0.001). On a local level, the optimal stimulation site resided at the direct interface between these structures (R = 0.48, p < 0.001). Finally, modulating specific distributed brain networks related to memory accounted for optimal outcomes (R = 0.48, p < 0.001). Findings were robust to multiple cross-validation designs and may define an optimal network target that could refine DBS surgery and programming.

Linking profiles of pathway activation with clinical motor improvements - A retrospective computational study.

BACKGROUND: Deep brain stimulation (DBS) is an established therapy for patients with Parkinson's disease. In silico computer models for DBS hold the potential to inform a selection of stimulation parameters. In recent years, the focus has shifted towards DBS-induced firing in myelinated axons, deemed particularly relevant for the external modulation of neural activity. OBJECTIVE: The aim of this project was to investigate correlations between patient-specific pathway activation profiles and clinical motor improvement. METHODS: We used the concept of pathway activation modeling, which incorporates advanced volume conductor models and anatomically authentic fiber trajectories to estimate DBS-induced action potential initiation in anatomically plausible pathways that traverse in close proximity to targeted nuclei. We applied the method on two retrospective datasets of DBS patients, whose clinical improvement had been evaluated according to the motor part of the Unified Parkinson's Disease Rating Scale. Based on differences in outcome and activation levels for intrapatient DBS protocols in a training cohort, we derived a pathway activation profile that theoretically induces a complete alleviation of symptoms described by UPDRS-III. The profile was further enhanced by analyzing the importance of matching activation levels for individual pathways. RESULTS: The obtained profile emphasized the importance of activation in pathways descending from the motor-relevant cortical regions as well as the pallidothalamic pathways. The degree of similarity of patient-specific profiles to the optimal profile significantly correlated with clinical motor improvement in a test cohort. CONCLUSION: Pathway activation modeling has a translational utility in the context of motor symptom alleviation in Parkinson's patients treated with DBS.

Lead-OR: A multimodal platform for deep brain stimulation surgery.

Background: Deep brain stimulation (DBS) electrode implant trajectories are stereotactically defined using preoperative neuroimaging. To validate the correct trajectory, microelectrode recordings (MERs) or local field potential recordings can be used to extend neuroanatomical information (defined by MRI) with neurophysiological activity patterns recorded from micro- and macroelectrodes probing the surgical target site. Currently, these two sources of information (imaging vs. electrophysiology) are analyzed separately, while means to fuse both data streams have not been introduced. Methods: Here, we present a tool that integrates resources from stereotactic planning, neuroimaging, MER, and high-resolution atlas data to create a real-time visualization of the implant trajectory. We validate the tool based on a retrospective cohort of DBS patients (N = 52) offline and present single-use cases of the real-time platform. Results: We establish an open-source software tool for multimodal data visualization and analysis during DBS surgery. We show a general correspondence between features derived from neuroimaging and electrophysiological recordings and present examples that demonstrate the functionality of the tool. Conclusions: This novel software platform for multimodal data visualization and analysis bears translational potential to improve accuracy of DBS surgery. The toolbox is made openly available and is extendable to integrate with additional software packages. Funding: Deutsche Forschungsgesellschaft (410169619, 424778381), Deutsches Zentrum für Luft- und Raumfahrt (DynaSti), National Institutes of Health (2R01 MH113929), and Foundation for OCD Research (FFOR).

Deep brain stimulation is an established therapy for patients with Parkinson's disease and an emerging option for other neurological conditions. Electrodes are implanted deep in the brain to stimulate precise brain regions and control abnormal brain activity in those areas. The most common target for Parkinson's disease, for instance, is a structure called the subthalamic nucleus, which sits at the base of the brain, just above the brain stem. To ensure electrodes are placed correctly, surgeons use various sources of information to characterize the patient's brain anatomy and decide on an implant site. These data include brain scans taken before surgery and recordings of brain activity taken during surgery to confirm the intended implant site. Sometimes, the brain activity signals from this last confirmation step may slightly alter surgical plans. It represents one of many challenges for clinical teams: to analyse, assimilate, and communicate data as it is collected during the procedure. Oxenford et al. developed a software pipeline to aggregate the data surgeons use to implant electrodes. The open-source platform, dubbed Lead-OR, visualises imaging data and brain activity recordings (termed electrophysiology data) in real time. The current set-up integrates with commercial tools and existing software for surgical planning. Oxenford et al. tested Lead-OR on data gathered retrospectively from 32 patients with Parkinson's who had electrodes implanted in their subthalamic nucleus. The platform showed good agreement between imaging and electrophysiology data, although there were some unavoidable discrepancies, arising from limitations in the imaging pipeline and from the surgical procedure. Lead-OR was also able to correct for brain shift, which is where the brain moves ever so slightly in the skull. With further validation, this proof-of-concept software could serve as a useful decision-making tool for surgical teams implanting electrodes for deep brain stimulation. In time, if implemented, its use could improve the accuracy of electrode placement, translating into better surgical outcomes for patients. It also has the potential to integrate forthcoming ultra-high-resolution data from current brain mapping projects, and other commercial surgical planning tools.

Personalizing Deep Brain Stimulation Using Advanced Imaging Sequences.

OBJECTIVE: With a growing appreciation for interindividual anatomical variability and patient-specific brain connectivity, advanced imaging sequences offer the opportunity to directly visualize anatomical targets for deep brain stimulation (DBS). The lack of quantitative evidence demonstrating their clinical utility, however, has hindered their broad implementation in clinical practice. METHODS: Using fast gray matter acquisition T1 inversion recovery (FGATIR) sequences, the present study identified a thalamic hypointensity that holds promise as a visual marker in DBS. To validate the clinical utility of the identified hypointensity, we retrospectively analyzed 65 patients (26 female, mean age = 69.1 ± 12.7 years) who underwent DBS in the treatment of essential tremor. We characterized its neuroanatomical substrates and evaluated the hypointensity's ability to predict clinical outcome using stimulation volume modeling and voxelwise mapping. Finally, we determined whether the hypointensity marker could predict symptom improvement on a patient-specific level. RESULTS: Anatomical characterization suggested that the identified hypointensity constituted the terminal part of the dentatorubrothalamic tract. Overlap between DBS stimulation volumes and the hypointensity in standard space significantly correlated with tremor improvement (R2  = 0.16, p = 0.017) and distance to hotspots previously reported in the literature (R2  = 0.49, p = 7.9e-4). In contrast, the amount of variance explained by other anatomical atlas structures was reduced. When accounting for interindividual neuroanatomical variability, the predictive power of the hypointensity increased further (R2  = 0.37, p = 0.002). INTERPRETATION: Our findings introduce and validate a novel imaging-based marker attainable from FGATIR sequences that has the potential to personalize and inform targeting and programming in DBS for essential tremor. ANN NEUROL 2022;91:613-628.

A neural network for tics: insights from causal brain lesions and deep brain stimulation.

Brain lesions are a rare cause of tic disorders. However, they can provide uniquely causal insights into tic pathophysiology and can also inform on possible neuromodulatory therapeutic targets. Based on a systematic literature review, we identified 22 cases of tics causally attributed to brain lesions and employed 'lesion network mapping' to interrogate whether tic-inducing lesions would be associated with a common network in the average human brain. We probed this using a normative functional connectome acquired in 1000 healthy participants. We then examined the specificity of the identified network by contrasting tic-lesion connectivity maps to those seeding from 717 lesions associated with a wide array of neurological and/or psychiatric symptoms within the Harvard Lesion Repository. Finally, we determined the predictive utility of the tic-inducing lesion network as a therapeutic target for neuromodulation. Specifically, we collected retrospective data of 30 individuals with Tourette disorder, who underwent either thalamic (n = 15; centromedian/ventrooralis internus) or pallidal (n = 15; anterior segment of globus pallidus internus) deep brain stimulation and calculated whether connectivity between deep brain stimulation sites and the lesion network map could predict clinical improvements. Despite spatial heterogeneity, tic-inducing lesions mapped to a common network map, which comprised the insular cortices, cingulate gyrus, striatum, globus pallidus internus, thalami and cerebellum. Connectivity to a region within the anterior striatum (putamen) was specific to tic-inducing lesions when compared with control lesions. Connectivity between deep brain stimulation electrodes and the lesion network map was predictive of tic improvement, regardless of the deep brain stimulation target. Taken together, our results reveal a common brain network involved in tic generation, which shows potential as a therapeutic target for neuromodulation.

Subthalamic and pallidal deep brain stimulation: are we modulating the same network?

The subthalamic nucleus and internal pallidum are main target sites for deep brain stimulation in Parkinson's disease. Multiple trials that investigated subthalamic versus pallidal stimulation were unable to settle on a definitive optimal target between the two. One reason could be that the effect is mediated via a common functional network. To test this hypothesis, we calculated connectivity profiles seeding from deep brain stimulation electrodes in 94 patients that underwent subthalamic and 28 patients with pallidal treatment based on a normative connectome atlas calculated from 1000 healthy subjects. In each cohort, we calculated connectivity profiles that were associated with optimal clinical improvements. The two maps showed striking similarity and were able to cross-predict outcomes in the respective other cohort (R = 0.37 at P < 0.001; R = 0.34 at P = 0.032). Next, we calculated an agreement map, which retained regions common to both target sites. Crucially, this map was able to explain an additional amount of variance in clinical improvements of either cohort when compared to the maps calculated on each cohort alone. Finally, we tested profiles and predictive utility of connectivity maps calculated from different motor symptom subscores with a specific focus on bradykinesia and rigidity. While our study is based on retrospective data and indirect connectivity metrics, it may deliver empirical data to support the hypothesis of a largely overlapping network associated with effective deep brain stimulation in Parkinson's disease irrespective of the specific target.

Structuro-functional surrogates of response to subcallosal cingulate deep brain stimulation for depression.

Subcallosal cingulate deep brain stimulation produces long-term clinical improvement in approximately half of patients with severe treatment-resistant depression. We hypothesized that both structural and functional brain attributes may be important in determining responsiveness to this therapy. In a treatment-resistant depression subcallosal cingulate deep brain stimulation cohort, we retrospectively examined baseline and longitudinal differences in MRI-derived brain volume (n = 65) and 18F-fluorodeoxyglucose-PET glucose metabolism (n = 21) between responders and non-responders. Support vector machines were subsequently trained to classify patients' response status based on extracted baseline imaging features. A machine learning model incorporating preoperative frontopolar, precentral/frontal opercular and orbitofrontal local volume values classified binary response status (12 months) with 83% accuracy [leave-one-out cross-validation (LOOCV): 80% accuracy] and explained 32% of the variance in continuous clinical improvement. It was also predictive in an out-of-sample subcallosal cingulate deep brain stimulation cohort (n = 21) with differing primary indications (bipolar disorder/anorexia nervosa; 76% accuracy). Adding preoperative glucose metabolism information from rostral anterior cingulate cortex and temporal pole improved model performance, enabling it to predict response status in the treatment-resistant depression cohort with 86% accuracy (LOOCV: 81% accuracy) and explain 67% of clinical variance. Response-related patterns of metabolic and structural post-deep brain stimulation change were also observed, especially in anterior cingulate cortex and neighbouring white matter. Areas where responders differed from non-responders-both at baseline and longitudinally-largely overlapped with depression-implicated white matter tracts, namely uncinate fasciculus, cingulum bundle and forceps minor/rostrum of corpus callosum. The extent of patient-specific engagement of these same tracts (according to electrode location and stimulation parameters) also served as an independent predictor of treatment-resistant depression response status (72% accuracy; LOOCV: 70% accuracy) and augmented performance of the volume-based (88% accuracy; LOOCV: 82% accuracy) and combined volume/metabolism-based support vector machines (100% accuracy; LOOCV: 94% accuracy). Taken together, these results indicate that responders and non-responders to subcallosal cingulate deep brain stimulation exhibit differences in brain volume and metabolism, both pre- and post-surgery. Moreover, baseline imaging features predict response to treatment (particularly when combined with information about local tract engagement) and could inform future patient selection and other clinical decisions.