Transcranial Focused Ultrasound to the Right Prefrontal Cortex Improves Mood and Alters Functional Connectivity in Humans

Transcranial focused ultrasound (tFUS) is an emerging method for non-invasive neuromodulation akin to transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS). tFUS offers several advantages over electromagnetic methods including high spatial resolution and the ability to reach deep brain targets. Here we describe two experiments assessing whether tFUS could modulate mood in healthy human volunteers by targeting the right inferior frontal gyrus (rIFG), an area implicated in mood and emotional regulation. In a randomized, placebo-controlled, double-blind study, participants received 30 s of 500 kHz tFUS or a placebo control. Visual Analog Mood Scales (VAMS) assessed mood four times within an hour (baseline and three times after tFUS). Participants who received tFUS reported an overall increase in Global Affect (GA), an aggregate score from the VAMS scale, indicating a positive shift in mood. Experiment 2 examined resting-state functional (FC) connectivity using functional magnetic resonance imaging (fMRI) following 2 min of 500 kHz tFUS at the rIFG. As in Experiment 1, tFUS enhanced self-reported mood states and also decreased FC in resting state networks related to emotion and mood regulation. These results suggest that tFUS can be used to modulate mood and emotional regulation networks in the prefrontal cortex

Optimising programming to reduce side effects of subthalamic nucleus deep brain stimulation in Parkinson’s Disease

Subthalamic nucleus deep brain stimulation (STN DBS) is a widely used treatment for Parkinson’s disease patients with motor complications refractory to medical management. However, a significant proportion of treated patients suffer from stimulation induced side effects. Conventional options to address these by modulation of stimulation parameters and programming configurations have been limited. In recent years, technological advances have resulted in the emergence of novel programming features, including the use of short pulse width (PW) and directional steering, that represent further avenues to explore in this regard. In this thesis, I will present data on the utility of these programming techniques in alleviating stimulation induced side effects, and explore mechanisms that may mediate any observed effects. The data presented here is derived from four studies. Study 1 quantified the therapeutic window using short PW stimulation at 30μs relative to conventional 60μs settings. Study 2 represents a randomised controlled trial on short PW in chronic STN DBS patients with dysarthria. Study 3 evaluated the utility of directional steering, short PW, and the combination of these features in reversing stimulation induced dysarthria, dyskinesia, and pyramidal side effects. The findings of these studies suggest that short PW significantly increases the therapeutic window in terms of amplitude and charge, and that while it may not benefit chronic dysarthric patients collectively, directional steering and short PW can each significantly improve reversible stimulation induced side effects early in the course of STN DBS therapy. These novel techniques represent effective additional tools to conventional methods for optimising stimulation. In study 4, imaging and visualisation software are used to model and explore shifts in volume of tissue activated based on clinical data from study 3, and quantitatively compare charge per pulse, in order to explore potential mechanisms underlying the changes seen with these techniques

Computer-Assisted Planning and Robotics in Epilepsy Surgery

Epilepsy is a severe and devastating condition that affects ~1% of the population. Around 30% of these patients are drug-refractory. Epilepsy surgery may provide a cure in selected individuals with drug-resistant focal epilepsy if the epileptogenic zone can be identified and safely resected or ablated. Stereoelectroencephalography (SEEG) is a diagnostic procedure that is performed to aid in the delineation of the seizure onset zone when non-invasive investigations are not sufficiently informative or discordant. Utilizing a multi-modal imaging platform, a novel computer-assisted planning (CAP) algorithm was adapted, applied and clinically validated for optimizing safe SEEG trajectory planning. In an initial retrospective validation study, 13 patients with 116 electrodes were enrolled and safety parameters between automated CAP trajectories and expert manual plans were compared. The automated CAP trajectories returned statistically significant improvements in all of the compared clinical metrics including overall risk score (CAP 0.57 +/- 0.39 (mean +/- SD) and manual 1.00 +/- 0.60, p < 0.001). Assessment of the inter-rater variability revealed there was no difference in external expert surgeon ratings. Both manual and CAP electrodes were rated as feasible in 42.8% (42/98) of cases. CAP was able to provide feasible electrodes in 19.4% (19/98), whereas manual planning was able to generate a feasible electrode in 26.5% (26/98) when the alternative generation method was not feasible. Based on the encouraging results from the retrospective analysis a prospective validation study including an additional 125 electrodes in 13 patients was then undertaken to compare CAP to expert manual plans from two neurosurgeons. The manual plans were performed separately and blindly from the CAP. Computer-generated trajectories were found to carry lower risks scores (absolute difference of 0.04 mm (95% CI = -0.42-0.01), p = 0.04) and were subsequently implanted in all cases without complication. The pipeline has been fully integrated into the clinical service and has now replaced manual SEEG planning at our institution. Further efforts were then focused on the distillation of optimal entry and target points for common SEEG trajectories and applying machine learning methods to develop an active learning algorithm to adapt to individual surgeon preferences. Thirty-two patients were prospectively enrolled in the study. The first 12 patients underwent prospective CAP planning and implantation following the pipeline outlined in the previous study. These patients were used as a training set and all of the 108 electrodes after successful implantation were normalized to atlas space to generate ‘spatial priors’, using a K-Nearest Neighbour (K-NN) classifier. A subsequent test set of 20 patients (210 electrodes) were then used to prospectively validate the spatial priors. From the test set, 78% (123/157) of the implanted trajectories passed through both the entry and target spatial priors defined from the training set. To improve the generalizability of the spatial priors to other neurosurgical centres undertaking SEEG and to take into account the potential for changing institutional practices, an active learning algorithm was implemented. The K-NN classifier was shown to dynamically learn and refine the spatial priors. The progressive refinement of CAP SEEG planning outlined in this and previous studies has culminated in an algorithm that not only optimizes the surgical heuristics and risk scores related to SEEG planning but can also learn from previous experience. Overall, safe and feasible trajectory schema were returning in 30% of the time required for manual SEEG planning. Computer-assisted planning was then applied to optimize laser interstitial thermal therapy (LITT) trajectory planning, which is a minimally invasive alternative to open mesial temporal resections, focal lesion ablation and anterior 2/3 corpus callosotomy. We describe and validate the first CAP algorithm for mesial temporal LITT ablations for epilepsy treatment. Twenty-five patients that had previously undergone LITT ablations at a single institution and with a median follow up of 2 years were included. Trajectory parameters for the CAP algorithm were derived from expert consensus to maximize distance from vasculature and ablation of the amygdalohippocampal complex, minimize collateral damage to adjacent brain structures whilst avoiding transgression of the ventricles and sulci. Trajectory parameters were also optimized to reduce the drilling angle to the skull and overall catheter length. Simulated cavities attributable to the CAP trajectories were calculated using a 5-15 mm ablation diameter. In comparison to manually planned and implemented LITT trajectories,CAP resulted in a significant increase in the percentage ablation of the amygdalohippocampal complex (manual 57.82 +/- 15.05% (mean +/- S.D.) and unablated medial hippocampal head depth (manual 4.45 +/- 1.58 mm (mean +/- S.D.), CAP 1.19 +/- 1.37 (mean +/- S.D.), p = 0.0001). As LITT ablation of the mesial temporal structures is a novel procedure there are no established standards for trajectory planning. A data-driven machine learning approach was, therefore, applied to identify hitherto unknown CAP trajectory parameter combinations. All possible combinations of planning parameters were calculated culminating in 720 unique combinations per patient. Linear regression and random forest machine learning algorithms were trained on half of the data set (3800 trajectories) and tested on the remaining unseen trajectories (3800 trajectories). The linear regression and random forest methods returned good predictive accuracies with both returning Pearson correlations of ρ = 0.7 and root mean squared errors of 0.13 and 0.12 respectively. The machine learning algorithm revealed that the optimal entry points were centred over the junction of the inferior occipital, middle temporal and middle occipital gyri. The optimal target points were anterior and medial translations of the centre of the amygdala. A large multicenter external validation study of 95 patients was then undertaken comparing the manually planned and implemented trajectories, CAP trajectories targeting the centre of the amygdala, the CAP parameters derived from expert consensus and the CAP trajectories utilizing the machine learning derived parameters. Three external blinded expert surgeons were then selected to undertake feasibility ratings and preference rankings of the trajectories. CAP generated trajectories result in a significant improvement in many of the planning metrics, notably the risk score (manual 1.3 +/- 0.1 (mean +/- S.D.), CAP 1.1 +/- 0.2 (mean +/- S.D.), p<0.000) and overall ablation of the amygdala (manual 45.3 +/- 22.2 % (mean +/- S.D.), CAP 64.2 +/- 20 % (mean +/- S.D.), p<0.000). Blinded external feasibility ratings revealed that manual trajectories were less preferable than CAP planned trajectories with an estimated probability of being ranked 4th (lowest) of 0.62. Traditional open corpus callosotomy requires a midline craniotomy, interhemispheric dissection and disconnection of the rostrum, genu and body of the corpus callosum. In cases where drop attacks persist a completion corpus callosotomy to disrupt the remaining fibres in the splenium is then performed. The emergence of LITT technology has raised the possibility of being able to undertake this procedure in a minimally invasive fashion and without the need for a craniotomy using two or three individual trajectories. Early case series have shown LITT anterior two-thirds corpus callosotomy to be safe and efficacious. Whole-brain probabilistic tractography connectomes were generated utilizing 3-Tesla multi-shell imaging data and constrained spherical deconvolution (CSD). Two independent blinded expert neurosurgeons with experience of performing the procedure using LITT then planned the trajectories in each patient following their current clinical practice. Automated trajectories returned a significant reduction in the risk score (manual 1.3 +/- 0.1 (mean +/- S.D.), CAP 1.1 +/- 0.1 (mean +/- S.D.), p<0.000). Finally, we investigate the different methods of surgical implantation for SEEG electrodes. As an initial study, a systematic review and meta-analysis of the literature to date were performed. This revealed a wide variety of implantation methods including traditional frame-based, frameless, robotic and custom-3D printed jigs were being used in clinical practice. Of concern, all comparative reports from institutions that had changed from one implantation method to another, such as following the introduction of robotic systems, did not undertake parallel-group comparisons. This suggests that patients may have been exposed to risks associated with learning curves and potential harms related to the new device until the efficacy was known. A pragmatic randomized control trial of a novel non-CE marked robotic trajectory guidance system (iSYS1) was then devised. Before clinical implantations began a series of pre-clinical investigations utilizing 3D printed phantom heads from previously implanted patients was performed to provide pilot data and also assess the surgical learning curve. The surgeons had comparatively little clinical experience with the new robotic device which replicates the introduction of such novel technologies to clinical practice. The study confirmed that the learning curve with the iSYS1 devices was minimal and the accuracies and workflow were similar to the conventional manual method. The randomized control trial represents the first of its kind for stereotactic neurosurgical procedures. Thirty-two patients were enrolled with 16 patients randomized to the iSYS1 intervention arm and 16 patients to the manual implantation arm. The intervention allocation was concealed from the patients. The surgical and research team could be not blinded. Trial management, independent data monitoring and trial steering committees were convened at four points doing the trial (after every 8 patients implanted). Based on the high level of accuracy required for both methods, the main distinguishing factor would be the time to achieve the alignment to the prespecified trajectory. The primary outcome for comparison, therefore, was the time for individual SEEG electrode implantation. Secondary outcomes included the implantation accuracy derived from the post-operative CT scan, infection, intracranial haemorrhage and neurological deficit rates. Overall, 32 patients (328 electrodes) completed the trial (16 in each intervention arm) and the baseline demographics were broadly similar between the two groups. The time for individual electrode implantation was significantly less with the iSYS1 device (median of 3.36 (95% CI 5.72 to 7.07) than for the PAD group (median of 9.06 minutes (95% CI 8.16 to 10.06), p=0.0001). Target point accuracy was significantly greater with the PAD (median of 1.58 mm (95% CI 1.38 to 1.82) compared to the iSYS1 (median of 1.16 mm (95% CI 1.01 to 1.33), p=0.004). The difference between the target point accuracies are not clinically significant for SEEG but may have implications for procedures such as deep brain stimulation that require higher placement accuracy. All of the electrodes achieved their respective intended anatomical targets. In 12 of 16 patients following robotic implantations, and 10 of 16 following manual PAD implantations a seizure onset zone was identified and resection recommended. The aforementioned systematic review and meta-analysis were updated to include additional studies published during the trial duration. In this context, the iSYS1 device entry and target point accuracies were similar to those reported in other published studies of robotic devices including the ROSA, Neuromate and iSYS1. The PAD accuracies, however, outperformed the previously published results for other frameless stereotaxy methods. In conclusion, the presented studies report the integration and validation of a complex clinical decision support software into the clinical neurosurgical workflow for SEEG planning. The stereotactic planning platform was further refined by integrating machine learning techniques and also extended towards optimisation of LITT trajectories for ablation of mesial temporal structures and corpus callosotomy. The platform was then used to seamlessly integrate with a novel trajectory planning software to effectively and safely guide the implantation of the SEEG electrodes. Through a single-blinded randomised control trial, the ISYS1 device was shown to reduce the time taken for individual electrode insertion. Taken together, this work presents and validates the first fully integrated stereotactic trajectory planning platform that can be used for both SEEG and LITT trajectory planning followed by surgical implantation through the use of a novel trajectory guidance system

Neurosurgery in Obsessive Compulsive Disorder:From targets to treatment to tracts and back again

People with obsessive compulsive disorder (OCD) suffer from obsessive thoughts and/or behavior, with a constant presence that can hardly be ignored. A range of interventions is effective in the management of OCD including behavioral therapy, cognitive therapy and cognitive behavioral therapy (CBT). In addition, a large body of evidence advocate on the use of selective serotonin reuptake inhibitors (SSRIs) and clomipramine, a tricyclic antidepressant, in the treatment of OCD, often used in combination with CBT. However, 40-60% of patients remain treatment-refractory, defined as a less than 25% reduction in Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) score. This scale is used to determine the severity of the disorder. The resistance of such a big amount of patients to therapy may urge the need for alternative treatment strategies, such as deep brain stimulation (DBS) of subcortical structures or gamma knife ventral capsulotomy (GVC), a noninvasive procedure using gamma rays to destroy certain brain tissues. The first part of this thesis aimed at identifying fiber bundles associated with clinical response to DBS or GVC. OCD patients consistently underperform across multiple cognitive domains. The second part of this thesis was focused on the neuropsychological outcome of OCD DBS in order to identify a cognitive pattern associated with a good outcome or that would (in part) help explain the functional mechanism of OCD-DBS. The third part focused on several postoperative aspects of (OCD)-DBS patients including surgical and hardware related adverse events of DBS and reviewing the effectiveness, timing and procedural aspects of CBT after DBS with the aim to provide clinical recommendations

Interictal Network Dynamics in Paediatric Epilepsy Surgery

Epilepsy is an archetypal brain network disorder. Despite two decades of research elucidating network mechanisms of disease and correlating these with outcomes, the clinical management of children with epilepsy does not readily integrate network concepts. For example, network measures are not used in presurgical evaluation to guide decision making or surgical management plans. The aim of this thesis was to investigate novel network frameworks from the perspective of a clinician, with the explicit aim of finding measures that may be clinically useful and translatable to directly benefit patient care. We examined networks at three different scales, namely macro (whole brain diffusion MRI), meso (subnetworks from SEEG recordings) and micro (single unit networks) scales, consistently finding network abnormalities in children being evaluated for or undergoing epilepsy surgery. This work also provides a path to clinical translation, using frameworks such as IDEAL to robustly assess the impact of these new technologies on management and outcomes. The thesis sets up a platform from which promising computational technology, that utilises brain network analyses, can be readily translated to benefit patient care

Investigation of intraoperative accelerometer data recording for safer and improved target selection for deep brain stimulation

Background: Deep Brain Stimulation (DBS) is a well established surgical treatment for Parkinson’s Disease (PD) and Essential Tremor (ET). Electrical leads are surgically implanted in the deeply seated structures in the brain and chronically stimulated. The location of the lead with respect to the anatomy is very important for optimal treatment. Therefore, clinicians carefully plan the surgery, record electrophysiological signals from the region of interest and perform stimulation tests to identify the best location to permanently place the leads. Nevertheless, there are certain aspects of the surgery that can still be improved. Firstly, therapeutic effects of stimulation are estimated by visually evaluating changes in tremor or passively moving patient's limb to evaluate changes in rigidity. These methods are subjective and depend heavily on the experience of the evaluator. Secondly, a significant amount of patient data is collected before and during the surgery like various CT and MR images, surgical planning information, electrophysiological recordings and results of stimulation tests. These are not fully utilized at the time of choosing the position for lead placement as they are either not available or acquired on separate systems or in the form of paper notes only. Thirdly, studies have shown that the current target structures to implant the leads (Subthalamic Nucleus (STN) for PD and Ventral Intermediate Nucleus (VIM) for ET) may not be the only ones responsible for the therapeutic effects. The objective of this doctoral work is to develop new methods that help clinicians subdue the above limitations which could in the long term improve the DBS therapy. Method: After a thorough review of the existing literature, specifically customized solutions were designed for the shortcomings described above. A new method to quantitatively evaluate tremor during DBS surgery using acceleration sensor was developed. The method was then adapted to measure acceleration of passive movements and to evaluate changes in rigidity through it. Data from 30 DBS surgeries was collected by applying these methods in two clinical studies: one in Centre Hospitalier Universitaire, Clermont-Ferrand, France and another multi-center study in Universitäspital Basel and Inselspital Bern in Switzerland. To study the role of different anatomical structures in the therapeutic and adverse effects of stimulation, the data collected during the study was analysed using two methods. The first classical approach was to classify the data based on the anatomical structure in which the stimulating contact of the electrode was located. The second advanced approach was to use patient-specific Finite Element Method (FEM) simulations of the Electric Field (EF) to estimate the spatial distribution of stimulation in the structures surrounding the electrode. Such simulations of the adverse effect inducing stimulation current amplitudes are used to visualize the boundaries of safe stimulation and identify structures that could be responsible for these effects. In addition, the patient-specific simulations are also used to develop a new method called "Improvement Maps" to generate 2D and 3D visualization of intraoperative stimulation test results with the patient images and surgical planning. This visualization summarized the stimulation test results by dividing the explored area into multiple regions based on the improvement in symptoms as measured by the accelerometric methods. Results: The accelerometric method successfully measured changes in tremor and rigidity. Standard deviation, signal energy and spectral amplitude of dominant frequency correlated with changes in the symptoms. Symptom suppressing stimulation current amplitudes identified through quantitative methods were lower than those identified through the subjective methods. Comparison of anatomical targets using the accelerometric data showed that to suppress rigidity in PD patients, stimulation current needed was marginally higher for Fields of Forel (FF) and Zona Incerta (ZI) compared to STN. On the other hand, the adverse effect occurrence rate was significantly lower in ZI and FF, indicating them to be better targets compared to STN. Similarly, for ET patients, other thalamic nuclei like the Intermediolateral (InL) and Ventro-Oral (VO) as well as the Pre-Lemniscal Radiations (PLR) are as efficient in suppressing tremor as the VIM but have lower occurrence of adverse effects. Volumetric analysis of spatial distribution of stimulation agreed with these results suggesting that the structures other than the VIM could also play a role in therapeutic effects of stimulation. The visualization of the adverse effect simulations clearly show the structures which could be responsible for such effects e.g. stimulation in the internal capsula induced pyramidal effects. These findings concur with the published literature. With regard to the improvement maps, the clinicians found them intuitive and easy to use to identify the optimal position for lead placement. If the maps were available during the surgery, the clinicians' choice of lead placement would have been different. Conclusion: This doctoral work has shown that modern techniques like quantitative symptom evaluation and electric field simulations can suppress the existing drawbacks of the DBS surgery. Furthermore, these methods along with 3D visualization of data can simplify tasks for clinicians of optimizing lead placement. Better placement of the DBS lead can potentially reduce adverse effects and increase battery life of implanted pulse generator, resulting in better therapy for patients