Longitudinal neuroimaging measures of volumetric change across the frontotemporal dementia spectrum

Frontotemporal dementia (FTD) is a common cause of young onset dementia, encompassing several clinical, genetic and pathological subgroups. Currently there are no treatments, but there are promising candidates in development. However, proven biomarkers of disease progression in FTD are lacking and urgently needed to facilitate these trials. Investigating large sporadic and genetic FTD cohorts, this thesis provides a comprehensive comparison of longitudinal neuroimaging measures of structural change within the clinical, genetic and pathological FTD subgroups. Effect size and sample size estimates are computed to explore the feasibility of these brain measures as surrogate markers of disease progression in order to detect disease-modifying treatment effects. The first project compares 17 automated techniques for extracting whole-brain atrophy measures. Many of the techniques showed great promise, producing sample sizes of substantially less than 100 patients required to detect a disease-modifying effect. Significant differences in performance were found between both techniques and patient subgroups, highlighting the importance of informed biomarker choice in matching the optimal marker to the patient group to be enrolled in a trial. In the following chapters, I explored lobar and subcortical change across the disease spectrum. The different patient subgroups presented with unique profiles of change but, interestingly, automated measures of temporal lobe, caudate and thalamic atrophy proved to be particularly sensitive markers of change, producing low sample size estimates across the FTD subgroups. Importantly, I found significantly increased rates of amygdala, hippocampus, caudate and thalamic atrophy in differing patterns across presymptomatic mutation carriers, providing the first comprehensive assessment of the utility of such markers for early therapeutic intervention at this ideal stage before symptoms develop. In summary, this work expands current knowledge and builds on the limited longitudinal investigations currently available in FTD, as well as providing valuable information about the potential of non-invasive biomarkers for sporadic and genetic FTD trials

Micro-, Meso- and Macro-Connectomics of the Brain

Neurosciences, Neurolog

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

Automated morphometry for mouse brain MRI through structural parcellation and thickness estimation

Quantitative morphometric analysis is an important tool in neuroimaging for the study of understanding the physiology of development, normal aging, disease pathology and treatment effect. However, compared to clinical study, image analysis methods specific to preclinical neuroimaging are still lacking. The aim of this PhD thesis is to achieve automatic quantitative structural analysis of mouse brain MRI. This thesis focuses on two quantitative methods which have been widely accepted as quantitative imaging biomarkers: brain structure segmentation and cortical thickness estimation. Firstly, a multi-atlas based structural parcellation framework has been constructed, which incorporates preprocessing steps such as intensity non-uniformity correction and multi-atlas based brain extraction, followed by non-rigid registration and local weighted multi-atlas label fusion. Validation of the framework demonstrated improved performance compared to single-atlas-based structural parcellation, as well as to global weighted multi-atlas label fusion methods. The framework has been further applied to in vivo and ex vivo data acquired from the same cohort so that the respective volumetric analysis can be compared. The results reveal a non-uniform distribution of volume changes from the in vivo to the post-mortem brain. In addition, volumetric analysis based on the segmented structures showed similar statistical power on in vivo or ex vivo data within the same cohort. Secondly, a framework to segment the mouse cerebellar cortex sublayers from brain MRI data and estimate the thickness of the corresponding layers has been developed. Application of the framework on the experimental data demonstrated its ability to distinguish sublayer thickness variation between transgenic strains and their wild-type littermate, which cannot be detected using full cortical thickness measurements alone. In conclusion, two quantitative morphometric analysis frameworks have been pre-sented in this thesis. This demonstrated the successful application of translational quantitative methods to preclinical mouse brain MRI

Toward a Common Terminology for the Thalamus

The wealth of competing parcellations with limited cross-correspondence between atlases of the human thalamus raises problems in a time when the usefulness of neuroanatomical methods is increasingly appreciated for modern computational analyses of the brain. An unequivocal nomenclature is, however, compulsory for the understanding of the organization of the thalamus. This situation cannot be improved by renewed discussion but with implementation of neuroinformatics tools. We adopted a new volumetric approach to characterize the significant subdivisions and determined the relationships between the parcellation schemes of nine most influential atlases of the human thalamus. The volumes of each atlas were 3d-reconstructed and spatially registered to the standard MNI/ICBM2009b reference volume of the Human Brain Atlas in the MNI (Montreal Neurological Institute) space (Mai and Majtanik, 2017). This normalization of the individual thalamus shapes allowed for the comparison of the nuclear regions delineated by the different authors. Quantitative cross-comparisons revealed the extent of predictability of territorial borders for 11 area clusters. In case of discordant parcellations we re-analyzed the underlying histological features and the original descriptions. The final scheme of the spatial organization provided the frame for the selected terms for the subdivisions of the human thalamus using on the (modified) terminology of the Federative International Programme for Anatomical Terminology (FIPAT). Waiving of exact individual definition of regional boundaries in favor of the statistical representation within the open MNI platform provides the common and objective (standardized) ground to achieve concordance between results from different sources (microscopy, imaging etc.)

Inter-Subject Correlation Using Movie-Driven fMRI in Drug-Resistant Epilepsy

Treating drug-resistant epilepsy with surgery requires the localization of the epileptic focus. We explored the potential for movie-driven functional magnetic resonance imaging (fMRI) to act as a sensitive, non-invasive, and cost-effective tool to identify functionally disturbed networks. We assessed neural synchronization (inter-subject correlation; ISC) between presurgical epilepsy patients (n = 18) and healthy controls (n = 24) as they watched a suspenseful movie clip in the scanner. To optimize denoising, we compared ISC values with and without an automated Independent Components Analysis-based denoising step (ICA-AROMA). We found that denoising with ICA-AROMA elicited augmented correlation values, supporting its use for denoising naturalistic fMRI data. We identified abnormal overall ISC profiles in five of 18 patients and also observed region- and patient-specific ISC abnormalities. Naturalistic fMRI should be further explored for its utility as a sensitive and reliable complement to standard epilepsy surgical planning tools, potentially leading to improved treatment and outcomes

Cortisol, cognition and the ageing prefrontal cortex

The structural and functional decline of the ageing human brain varies by brain region, cognitive function and individual. The underlying biological mechanisms are poorly understood. One potentially important mechanism is exposure to glucocorticoids (GCs; cortisol in humans); GC production is increasingly varied with age in humans, and chronic exposure to high levels is hypothesised to result in cognitive decline via cerebral remodelling. However, studies of GC exposure in humans are scarce and methodological differences confound cross-study comparison. Furthermore, there has been little focus on the effects of GCs on the frontal lobes and key white matter tracts in the ageing brain. This thesis therefore examines relationships among cortisol levels, structural brain measures and cognitive performance in 90 healthy, elderly community-dwelling males from the Lothian Birth Cohort 1936. Salivary cortisol samples characterised diurnal (morning and evening) and reactive profiles (before and after a cognitive test battery). Structural variables comprised Diffusion Tensor Imaging measures of major brain tracts and a novel manual parcellation method for the frontal lobes. The latter was based on a systematic review of current manual methods in the context of putative function and cytoarchitecture. Manual frontal lobe brain parcellation conferred greater spatial and volumetric accuracy when compared to both single- and multi-atlas parcellation at the lobar level. Cognitive ability was assessed via tests of general cognitive ability, and neuropsychological tests thought to show differential sensitivity to the integrity of frontal lobe sub-regions. The majority of, but not all frontal lobe test scores shared considerable overlap with general cognitive ability, and cognitive scores correlated most consistently with the volumes of the anterior cingulate. This is discussed in light of the diverse connective profile of the cingulate and a need to integrate information over more diffuse cognitive networks according to proposed de-differentiation or compensation in ageing. Individuals with higher morning, evening or pre-test cortisol levels showed consistently negative relationships with specific regional volumes and tract integrity. Participants whose cortisol levels increased between the start and end of cognitive testing showed selectively larger regional volumes and lower tract diffusivity (correlation magnitudes <.44). The significant relationships between cortisol levels and cognition indicated that flatter diurnal slopes or higher pre-test levels related to poorer test performance. In contrast, higher levels in the morning generally correlated with better scores (correlation magnitudes <.25). Interpretation of all findings was moderated by sensitivity to type I error, given the large number of comparisons conducted. Though there were limited candidates for mediation analysis, cortisol-function relationships were partially mediated by tract integrity (but not sub-regional frontal volumes) for memory and post-error slowing. This thesis offers a novel perspective on the complex interplay among glucocorticoids, cognition and the structure of the ageing brain. The findings suggest some role for cortisol exposure in determining age-related decline in complex cognition, mediated via brain structure

Multi-parametric Imaging Using Hybrid PET/MR to Investigate the Epileptogenic Brain

Neuroimaging analysis has led to fundamental discoveries about the healthy and pathological human brain. Different imaging modalities allow garnering complementary information about brain metabolism, structure and function. To ensure that the integration of imaging data from these modalities is robust and reliable, it is fundamental to attain deep knowledge of each modality individually. Epilepsy, a neurological condition characterised by recurrent spontaneous seizures, represents a field in which applications of neuroimaging and multi-parametric imaging are particularly promising to guide diagnosis and treatment. In this PhD thesis, I focused on different imaging modalities and investigated advanced denoising and analysis strategies to improve their application to epilepsy. The first project focused on fluorodeoxyglucose (FDG) positron emission tomography (PET), a well-established imaging modality assessing brain metabolism, and aimed to develop a novel, semi-quantitative pipeline to analyse data in children with epilepsy, thus aiding presurgical planning. As pipelines for FDG-PET analysis in children are currently lacking, I developed age-appropriate templates to provide statistical parametric maps identifying epileptogenic areas on patient scans. The second and third projects focused on two magnetic resonance imaging (MRI) modalities: resting-state functional MRI (rs-fMRI) and arterial spin labelling (ASL), respectively. The aim was to i) probe the efficacy of different fMRI denoising pipelines, and ii) formally compare different ASL data acquisition strategies. In the former case, I compared different pre-processing methods and assessed their impact on fMRI signal quality and related functional connectivity analyses. In the latter case, I compared two ASL sequences to investigate their ability to quantify cerebral blood flow and interregional brain connectivity. The final project addressed the combination of rs-fMRI and ASL, and leveraged graph-theoretical analysis tools to i) compare metrics estimated via these two imaging modalities in healthy subjects and ii) assess topological changes captured by these modalities in a sample of temporal lobe epilepsy patients