Quantitative analysis of axonal fiber activation evoked by deep brain stimulation via activation density heat maps

Dissecting Psychiatric Heterogeneity and Comorbidity with Core Region-Based Machine Learning

Machine learning approaches are increasingly being applied to neuroimaging data from patients with psychiatric disorders to extract brain-based features for diagnosis and prognosis. The goal of this review is to discuss recent practices for evaluating machine learning applications to obsessive-compulsive and related disorders and to advance a novel strategy of building machine learning models based on a set of core brain regions for better performance, interpretability, and generalizability. Specifically, we argue that a core set of co-altered brain regions (namely 'core regions') comprising areas central to the underlying psychopathology enables the efficient construction of a predictive model to identify distinct symptom dimensions/clusters in individual patients. Hypothesis-driven and data-driven approaches are further introduced showing how core regions are identified from the entire brain. We demonstrate a broadly applicable roadmap for leveraging this core set-based strategy to accelerate the pursuit of neuroimaging-based markers for diagnosis and prognosis in a variety of psychiatric disorders.

The current state, challenges, and future directions of deep brain stimulation for obsessive compulsive disorder

INTRODUCTION

Obsessive-compulsive disorder (OCD) is clinically and pathologically heterogenous, with symptoms often refractory to first-line treatments. Deep brain stimulation (DBS) for the treatment of refractory OCD provides an opportunity to adjust and individualize neuromodulation targeting aberrant circuitry underlying OCD. The tailoring of DBS therapy may allow precision in symptom control based on patient-specific pathology. Progress has been made in understanding the potential targets for DBS intervention; however, a consensus on an optimal target has not been agreed upon.

AREAS COVERED

A literature review of DBS for OCD was performed by querying the PubMed database. The following topics were covered: the evolution of DBS targeting in OCD, the concept of an underlying unified connectomic network, current DBS targets, challenges facing the field, and future directions which could advance personalized DBS in this challenging population.

EXPERT OPINION

To continue the increasing efficacy of DBS for OCD, we must further explore the optimal DBS response across clinical profiles and neuropsychiatric domains of OCD as well as how interventions targeting multiple points in an aberrant circuit, multiple aberrant circuits, or a connectivity hub impact clinical response. Additionally, biomarkers would be invaluable in programming adjustments and creating a closed-loop paradigm to address symptom fluctuation in daily life.

Strategic alterations of posture are delayed in Parkinson's disease patients during deep brain stimulation

Parkinson’s disease (PD) is characterized by rigidity, akinesia, postural instability and tremor. Deep brain stimulation (DBS) of the subthalamic nucleus (STN) reduces tremor but the effects on postural instability are inconsistent. Another component of postural control is the postural strategy, traditionally referred to as the ankle or hip strategy, which is determined by the coupling between the joint motions of the body. We aimed to determine whether DBS STN and vision (eyes open vs. eyes closed) affect the postural strategy in PD in quiet stance or during balance perturbations. Linear motion was recorded from the knee, hip, shoulder and head in 10 patients with idiopathic PD with DBS STN (after withdrawal of other anti-PD medication), 25 younger adult controls and 17 older adult controls. Correlation analyses were performed on anterior–posterior linear motion data to determine the coupling between the four positions measured. All participants were asked to stand for a 30 s period of quiet stance and a 200 s period of calf vibration. The 200 s vibration period was subdivided into four 50 s periods to study adaptation between the first vibration period (30–80 s) and the last vibration period (180–230 s). Movement was recorded in patients with PD with DBS ON and DBS OFF, and all participants were investigated with eyes closed and eyes open. DBS settings were randomized and double-blindly programmed. Patients with PD had greater coupling of the body compared to old and young controls during balance perturbations (p ≤ 0.046). Controls adopted a strategy with greater flexibility, particularly using the knee as a point of pivot, whereas patients with PD adopted an ankle strategy, i.e., they used the ankle as the point of pivot. There was higher flexibility in patients with PD with DBS ON and eyes open compared to DBS OFF and eyes closed (p ≤ 0.011). During balance perturbations, controls quickly adopted a new strategy that they retained throughout the test, but patients with PD were slower to adapt. Patients with PD further increased the coupling between segmental movement during balance perturbations with DBS ON but retained a high level of coupling with DBS OFF throughout balance perturbations. The ankle strategy during balance perturbations in patients with PD was most evident with DBS OFF and eyes closed. The increased coupling with balance perturbations implies a mechanism to reduce complexity at a cost of exerting more energy. Strategic alterations of posture were altered by DBS in patients with PD and were delayed. Our findings therefore show that DBS does not fully compensate for disease-related effects on posture.

Four Deep Brain Stimulation Targets for Obsessive-Compulsive Disorder: Are They Different?

Deep brain stimulation is a promising therapeutic approach for patients with treatment-resistant obsessive-compulsive disorder, a condition linked to abnormalities in corticobasal ganglia networks. Effective targets are placed in one of four subcortical areas with the goal of capturing prefrontal, anterior cingulate, and basal ganglia connections linked to the limbic system. These include the anterior limb of the internal capsule, the ventral striatum, the subthalamic nucleus, and a midbrain target. The goal of this review is to examine these 4 targets with respect to the similarities and differences of their connections. Following a review of the connections for each target based on anatomic studies in nonhuman primates, we examine the accuracy of diffusion magnetic resonance imaging tractography to replicate those connections in nonhuman primates, before evaluating the connections in the human brain based on diffusion magnetic resonance imaging tractography. Results demonstrate that the four targets generally involve similar connections, all of which are part of the internal capsule. Nonetheless, some connections are unique to each site. Delineating the similarities and differences across targets is a critical step for evaluating and comparing the effectiveness of each and how circuits contribute to the therapeutic outcome. It also underscores the importance that the terminology used for each target accurately reflects its position and its anatomic connections, so as to enable comparisons across clinical studies and for basic scientists to probe mechanisms underlying deep brain stimulation.

Chemogenetics: drug-controlled gene therapies for neural circuit disorders

Many patients with nervous system disorders have considerable unmet clinical needs or suffer debilitating drug side effects. A major limitation of exiting treatment approaches is that traditional small molecule pharmacotherapy lacks sufficient specificity to effectively treat many neurological diseases. Chemogenetics is a new gene therapy technology that targets an engineered receptor to cell types involved in nervous system dysfunction, enabling highly selective drug-controlled neuromodulation. Here, we discuss chemogenetic platforms and considerations for their potential application as human nervous system therapies.

Probabilistic Mapping of Deep Brain Stimulation: Insights from 15 Years of Therapy

Deep brain stimulation (DBS) depends on precise delivery of electrical current to target tissues. However, the specific brain structures responsible for best outcome are still debated. We applied probabilistic stimulation mapping to a retrospective, multidisorder DBS dataset assembled over 15 years at our institution (n_total = 482 patients; n_{Parkinson disease} = 303; n_dystonia = 64; n_tremor = 39; n_{treatment-resistant depression/anorexia nervosa} = 76) to identify the neuroanatomical substrates of optimal clinical response. Using high-resolution structural magnetic resonance imaging and activation volume modeling, probabilistic stimulation maps (PSMs) that delineated areas of above-mean and below-mean response for each patient cohort were generated and defined in terms of their relationships with surrounding anatomical structures. Our results show that overlap between PSMs and individual patients' activation volumes can serve as a guide to predict clinical outcomes, but that this is not the sole determinant of response. In the future, individualized models that incorporate advancements in mapping techniques with patient-specific clinical variables will likely contribute to the optimization of DBS target selection and improved outcomes for patients. ANN NEUROL 2021;89:426-443.

Activation robustness with directional leads and multi-lead configurations in deep brain stimulation

OBJECTIVE

Clinical outcomes from deep brain stimulation (DBS) can be highly variable, and two critical factors underlying this variability are the location and type of stimulation. In this study we quantified how robustly DBS activates a target region when taking into account a range of different lead designs and realistic variations in placement. The objective of the study is to assess the likelihood of achieving target activation.

APPROACH

We performed finite element computational modeling and established a metric of performance robustness to evaluate the ability of directional and multi-lead configurations to activate target fiber pathways while taking into account location variability. A more robust lead configuration produces less variability in activation across all stimulation locations around the target.

MAIN RESULTS

Directional leads demonstrated higher overall performance robustness compared to axisymmetric leads, primarily 1-2 mm outside of the target. Multi-lead configurations demonstrated higher levels of robustness compared to any single lead due to distribution of electrodes in a broader region around the target.

SIGNIFICANCE

Robustness measures can be used to evaluate the performance of existing DBS lead designs and aid in the development of novel lead designs to better accommodate known variability in lead location and orientation. This type of analysis may also be useful to understand how DBS clinical outcome variability is influenced by lead location among groups of patients.

Accounting for Deformation in Deep Brain Stimulation Surgery With Models: Comparison to Interventional Magnetic Resonance Imaging

The efficacy of deep brain stimulation (DBS) depends on electrode placement accuracy, which can be jeopardized by brain shift due to burr hole and dura opening during surgery. Brain shift violates assumed rigid alignment between preoperative image and intraoperative anatomy, negatively impacting therapy.

OBJECTIVE

This study presents a deformation-atlas biomechanical model-based approach to address shift.

METHODS

Six patients, who underwent interventional magnetic resonance (iMR) image-guided DBS burr hole surgery, were studied. A patient-specific model was employed under varying surgical conditions, generating a collection of possible intraoperative shift estimations or a 'deformation atlas.' An inverse problem was driven by sparse measurements derived from iMR to determine an optimal fit of solutions of the atlas. This fit was then used to obtain a volumetric deformation field, which was utilized to update preoperative MR and estimate shift at surgical target region localized on iMR. Model performance was examined by quantitatively comparing intraoperative subsurface measurements to their model-predicted counterparts, and qualitatively comparing iMR, preoperative MR, and model updated MR. A nonrigid image registration was introduced as a comparator.

RESULTS

Model-based approach reduced general parenchyma shift from 8.2 ± 2.2 to 2.7 ± 1.1 mm (∼66.8% correction), and produced updated MR with better agreement to iMR than that of preoperative MR. The average model estimated shift at target region was 1.2 mm.

CONCLUSIONS

This study demonstrates the feasibility of a model-based shift correction strategy in DBS surgery with only sparse data.

SIGNIFICANCE

The developed strategy has the potential to complement and/or enhance current clinical approaches in addressing shift.

Deep brain stimulation for Tourette's syndrome

Tourette syndrome (TS) is a childhood-onset neuropsychiatric disorder characterized by the presence of multiple motor and vocal tics. TS usually co-occurs with one or multiple psychiatric disorders. Although behavioral and pharmacological treatments for TS are available, some patients do not respond to the available treatments. For these patients, TS is a severe, chronic, and disabling disorder. In recent years, deep brain stimulation (DBS) of basal ganglia-thalamocortical networks has emerged as a promising intervention for refractory TS with or without psychiatric comorbidities. Three major challenges need to be addressed to move the field of DBS treatment for TS forward: (1) patient and DBS target selection, (2) ethical concerns with treating pediatric patients, and (3) DBS treatment optimization and improvement of individual patient outcomes (motor and phonic tics, as well as functioning and quality of life). The Tourette Association of America and the American Academy of Neurology have recently released their recommendations regarding surgical treatment for refractory TS. Here, we describe the challenges, advancements, and promises of the use of DBS in the treatment of TS. We summarize the results of clinical studies and discuss the ethical issues involved in treating pediatric patients. Our aim is to provide a better understanding of the feasibility, safety, selection process, and clinical effectiveness of DBS treatment for select cases of severe and medically intractable TS.

An update on best practice of deep brain stimulation in Parkinson's disease

During the last 30 years, deep brain stimulation (DBS) has evolved into the clinical standard of care as a highly effective treatment for advanced Parkinson’s disease. Careful patient selection, an individualized anatomical target localization and meticulous evaluation of stimulation parameters for chronic DBS are crucial requirements to achieve optimal results. Current hardware-related advances allow for a more focused, individualized stimulation and hence may help to achieve optimal clinical results. However, current advances also increase the degrees of freedom for DBS programming and therefore challenge the skills of healthcare providers. This review gives an overview of the clinical effects of DBS, the criteria for patient, target, and device selection, and finally, offers strategies for a structured programming approach.

Deep brain stimulation for severe treatment-resistant obsessive-compulsive disorder: An open-label case series

OBJECTIVE

Deep brain stimulation can be of benefit in carefully selected patients with severe intractable obsessive-compulsive disorder. The aim of this paper is to describe the outcomes of the first seven deep brain stimulation procedures for obsessive-compulsive disorder undertaken at the Neuropsychiatry Unit, Royal Melbourne Hospital. The primary objective was to assess the response to deep brain stimulation treatment utilising the Yale-Brown Obsessive Compulsive Scale as a measure of symptom severity. Secondary objectives include assessment of depression and anxiety, as well as socio-occupational functioning.

METHODS

Patients with severe obsessive-compulsive disorder were referred by their treating psychiatrist for assessment of their suitability for deep brain stimulation. Following successful application to the Psychosurgery Review Board, patients proceeded to have deep brain stimulation electrodes implanted in either bilateral nucleus accumbens or bed nucleus of stria terminalis. Clinical assessment and symptom rating scales were undertaken pre- and post-operatively at 6- to 8-week intervals. Rating scales used included the Yale-Brown Obsessive Compulsive Scale, Obsessive Compulsive Inventory, Depression Anxiety Stress Scale and Social and Occupational Functioning Assessment Scale.

RESULTS

Seven patients referred from four states across Australia underwent deep brain stimulation surgery and were followed for a mean of 31 months (range, 8-54 months). The sample included four females and three males, with a mean age of 46 years (range, 37-59 years) and mean duration of obsessive-compulsive disorder of 25 years (range, 15-38 years) at the time of surgery. The time from first assessment to surgery was on average 18 months. All patients showed improvement on symptom severity rating scales. Three patients showed a full response, defined as greater than 35% improvement in Yale-Brown Obsessive Compulsive Scale score, with the remaining showing responses between 7% and 20%.

CONCLUSION

Deep brain stimulation was an effective treatment for obsessive-compulsive disorder in these highly selected patients. The extent of the response to deep brain stimulation varied between patients, as well as during the course of treatment for each patient. The results of this series are comparable with the literature, as well as having similar efficacy to ablative psychosurgery techniques such as capsulotomy and cingulotomy. Deep brain stimulation provides advantages over lesional psychosurgery but is more expensive and requires significant multidisciplinary input at all stages, pre- and post-operatively, ideally within a specialised tertiary clinical and/or academic centre. Ongoing research is required to better understand the neurobiological basis for obsessive-compulsive disorder and how this can be manipulated with deep brain stimulation to further improve the efficacy of this emerging treatment.

Detecting a Cortical Fingerprint of Parkinson's Disease for Closed-Loop Neuromodulation

Recent evidence suggests that deep brain stimulation (DBS) of the subthalamic nucleus (STN) in Parkinson's disease (PD) mediates its clinical effects by modulating cortical oscillatory activity, presumably via a direct cortico-subthalamic connection. This observation might pave the way for novel closed-loop approaches comprising a cortical sensor. Enhanced beta oscillations (13-35 Hz) have been linked to the pathophysiology of PD and may serve as such a candidate marker to localize a cortical area reliably modulated by DBS. However, beta-oscillations are widely distributed over the cortical surface, necessitating an additional signal source for spotting the cortical area linked to the pathologically synchronized cortico-subcortical motor network. In this context, both cortico-subthalamic coherence and cortico-muscular coherence (CMC) have been studied in PD patients. Whereas, the former requires invasive recordings, the latter allows for non-invasive detection, but displays a rather distributed cortical synchronization pattern in motor tasks. This distributed cortical representation may conflict with the goal of detecting a cortical localization with robust biomarker properties which is detectable on a single subject basis. We propose that this limitation could be overcome when recording CMC at rest. We hypothesized that-unlike healthy subjects-PD would show CMC at rest owing to the enhanced beta oscillations observed in PD. By performing source space analysis of beta CMC recorded during resting-state magnetoencephalography, we provide preliminary evidence in one patient for a cortical hot spot that is modulated most strongly by subthalamic DBS. Such a spot would provide a prominent target region either for direct neuromodulation or for placing a potential sensor in closed-loop DBS approaches, a proposal that requires investigation in a larger cohort of PD patients.

Restoring Behavior via Inverse Neurocontroller in a Lesioned Cortical Spiking Model Driving a Virtual Arm

Neural stimulation can be used as a tool to elicit natural sensations or behaviors by modulating neural activity. This can be potentially used to mitigate the damage of brain lesions or neural disorders. However, in order to obtain the optimal stimulation sequences, it is necessary to develop neural control methods, for example by constructing an inverse model of the target system. For real brains, this can be very challenging, and often unfeasible, as it requires repeatedly stimulating the neural system to obtain enough probing data, and depends on an unwarranted assumption of stationarity. By contrast, detailed brain simulations may provide an alternative testbed for understanding the interactions between ongoing neural activity and external stimulation. Unlike real brains, the artificial system can be probed extensively and precisely, and detailed output information is readily available. Here we employed a spiking network model of sensorimotor cortex trained to drive a realistic virtual musculoskeletal arm to reach a target. The network was then perturbed, in order to simulate a lesion, by either silencing neurons or removing synaptic connections. All lesions led to significant behvaioral impairments during the reaching task. The remaining cells were then systematically probed with a set of single and multiple-cell stimulations, and results were used to build an inverse model of the neural system. The inverse model was constructed using a kernel adaptive filtering method, and was used to predict the neural stimulation pattern required to recover the pre-lesion neural activity. Applying the derived neurostimulation to the lesioned network improved the reaching behavior performance. This work proposes a novel neurocontrol method, and provides theoretical groundwork on the use biomimetic brain models to develop and evaluate neurocontrollers that restore the function of damaged brain regions and the corresponding motor behaviors.

Tractography Activation Patterns in Dorsolateral Prefrontal Cortex Suggest Better Clinical Responses in OCD DBS

Background: Medication resistant obsessive-compulsive disorder (OCD) patients can be successfully treated with Deep Brain Stimulation (DBS) which targets the anterior limb of the internal capsule (ALIC) and the nucleus accumbens (NA). Growing evidence suggests that in patients who respond to DBS, axonal fiber bundles surrounding the electrode are activated, but it is currently unknown which discrete pathways are critical for optimal benefit. Our aim was to identify axonal pathways mediating clinical effects of ALIC-NA DBS.

Methods: We created computational models of ALIC-NA DBS to simulate the activation of fiber tracts and to identify connected cerebral regions. The pattern of activated axons and their cortical targets was investigated in six OCD patients who underwent ALIC-NA DBS.

Results: Modulation of the right anterior middle frontal gyrus (dorsolateral prefrontal cortex) was associated with an excellent response. In contrast, non-responders showed high activation in the orbital part of the right inferior frontal gyrus (lateral orbitofrontal cortex/anterior ventrolateral prefrontal cortex). Factor analysis followed by step-wise linear regression indicated that YBOCS improvement was inversely associated with factors that were predominantly determined by gray matter activation results.

Discussion: Our findings support the hypothesis that optimal therapeutic results are associated with the activation of distinct fiber pathways. This suggests that in DBS for OCD, focused stimulation of specific fiber pathways, which would allow for stimulation with lower amplitudes, may be superior to activation of a wide array of pathways, typically associated with higher stimulation amplitudes.

Modeling sequence and quasi-uniform assumption in computational neurostimulation

Computational neurostimulation aims to develop mathematical constructs that link the application of neuromodulation with changes in behavior and cognition. This process is critical but daunting for technical challenges and scientific unknowns. The overarching goal of this review is to address how this complex task can be made tractable. We describe a framework of sequential modeling steps to achieve this: (1) current flow models, (2) cell polarization models, (3) network and information processing models, and (4) models of the neuroscientific correlates of behavior. Each step is explained with a specific emphasis on the assumptions underpinning underlying sequential implementation. We explain the further implementation of the quasi-uniform assumption to overcome technical limitations and unknowns. We specifically focus on examples in electrical stimulation, such as transcranial direct current stimulation. Our approach and conclusions are broadly applied to immediate and ongoing efforts to deploy computational neurostimulation.