Imaging epileptogenic tubers in children with tuberous sclerosis complex usingΑ-[ 11 C]Methyl- L -tryptophan positron emission tomography

Several reports have indicated that cortical resection is effective in alleviating intractable epilepsy in children with tuberous sclerosis complex (TSC). Because of the multitude of cortical lesions, however, identifying the epileptogenic tuber(s) is difficult and often requires invaise intracranial electroencephalographic (EEG) monitoring. As increased concentrations of serotonin and serotonin-immunoreactive processes have been reported in resected human epileptic cortex, we used Α-[ 11 C]methyl-L-tryptophan ([ 11 C]AMT) position emission tomography (PET) to test the hypothesis that serotonin synthesis is increased interictally in epileptogenic tubers in patients with TSC. Nine children with TSC and epilepsy, aged 1 to 9 years (mean, 4 years 1 month), were studied. All children underwent scalp video-EEG monitoring, PET scans of glucose metabolism and serotonin synthesis, and EEG monitoring during both PET studies. [ 11 C]AMT scans were coregistred with magnetic resonance imaging and with glucose metabolism scans. Whereas glucose metabolism PET showed multifocal cortical hypometabolism corresponding to the locations of tubers in all 9 children, [ 11 C]AMT uptake was increased in one tuber (n = 3), two tubers (n = 3), three tubers (n = 1), and four tubers (n = 1) in 8 of the 9 children. All other tubers showed decreased [ 11 C]AMT uptake. Ictal EEG data available in 8 children showed seizure onset corresponding to foci of increased [ 11 C]AMT uptake in 4 children (including 2 with intracranial EEG recordings). In 2 children, ictal EEG was nonlocalizing, and in 1 child there was discordance between the region of increased [ 11 C]AMT uptake and the region of ictal onset on EEG. The only child whose [ 11 C]AMT scan showed to no regions of increased uptake had a left frontal seizure focus on EEG; however, at the time of his [ 11 C]AMT PET scan, his seizures had come under control. [ 11 C]AMT PET may be a powerful tool in differentiating between epileptogenic and nonepileptogenic tubers in patients with TSC.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/50368/1/410440603_ftp.pd

Gamma oscillations correlate with working memory load in humans

Functional imaging of human cortex implicates a diverse network of brain regions supporting working memory—the capacity to hold and manipulate information for short periods of time. Although we are beginning to map out the brain networks supporting working memory, little is known about its physiological basis. We analyzed intracranial recordings from two epileptic patients as they performed a working memory task. Spectral analyses revealed that, in both patients, gamma (30-60 Hz) oscillations increased approximately linearly with memory load, tracking closely with memory load over the course of the trial. This constitutes the first evidence that gamma oscillations, widely implicated in perceptual processes, support the maintenance of multiple items in working memory

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

On the Volume Conduction Model Validation with Stereo EEG Data

Volume conduction can be defined as the transmission of electric potential and magnetic field generated by a primary current source of brain activation in the surrounding medium, i.e., the human head. Volume conduction simulations are based on sophisticated models whose construction represents a current challenge within the neuroscientific community. Volume conduction models are used in various applications such as electroencephalography (EEG) or magnetoencephalography (MEG) source reconstruction, or in the optimization of the electrode placement in a transcranial electrical stimulation session. Clinical applications based on volume conduction models are, for example, the localization of the epileptogenic zone, i.e., the brain area responsible for the generation of seizures, in the presurgical assessment of focal drug-resistant epilepsy patients, and the antidepressant effects given by transcranial electrical stimulation. Increasing the accuracy of volume conduction simulations is therefore crucial. To the best of our knowledge, the accuracy of volume conduction models have never been validated directly with actual measurements in human patients. The main goal of this thesis is to describe a first attempt to validate volume conduction modeling using electric stimulation stereo-encephalografic (sEEG) data. This work therefore is focused on the research, investigation and test of tools and methods which can be used to describe the accuracy of volume conduction models used in both clinical and basic research. Given a dataset of one pharmaco-resistant epilepsy patient, composed by the anatomical T1 weighted magnetic resonance image (MRI), the electrophysiological signal recorded during electric brain stimulation sessions with sEEG technique and sEEG contact positions extracted by post-implantation CT image, the analysis conducted in this work can be split into three main steps. First, we built volume conduction head models and we simulated the electric potentials during the electric brain stimulations. In this step, we solved the so-called (s)EEG forward problem by means of the finite element method in its classical formulation, and we considered three different conductivity profile to assign to the computational domain, individually extracted by the T1-w MRI. Moreover we computed the solution in meshes with two different resolution, i.e., 1 mm and 2 mm, with three different ways to model the source term, i.e., the partial integration approach, the subtraction approach and Venant\u2019s approach. Second, we extracted the responses to the electric brain stimulations from the actual sEEG measurements. Particular emphasis in this step was given to the optimal referencing systems of sEEG electrodes. Third, we compared the simulated and measured potentials for each of the three volume conduction head models, both in a single shaft and global comparison. The comparison results in overall high relative differences, with only slight modulations given by the distance from the stimulation site, the underlying volume conduction head model used and the compartment where the dipolar source is located. Simulation results show that the computation of sEEG forward problem solution is feasible with the same scheme adopted for scalp EEG in the duneuro software (http:// duneuro.org/), and it is stable for different mesh resolutions and source models also for intracranial electrodes, i.e., for electrodes close to the source positions. From this first validation attempt, we can conclude that the distance contact-source modulates the relative error between measured and simulated potential; for the contacts in the white matter compartment we observed the most accurate results, and the results relative to the three and four compartment results were more accurate than the ones relative to the five compartment results. While we achieved topographical errors within 10% for most of the shafts, the amplitude of simulated and measured potentials notably differs

Central auditory detection and pre-attentive discrimination in children

Central auditory processes to detection and pre-attentive discrimination in children were studied using auditory event related potentials (AERP). Discrimination potentials were elicited by infrequent deviant stimuli embedded pseudo-randomly in a sequence of frequent standard stimuli. The major obligatory components of the AERP, P85-120, N1 and N2 were recorded to stimuli that varied in complexity (pure tones to words). A later component evoked by deviant stimuli, termed the mismatch negativity (MMN), thought to reflect pre-attentive auditory discrimination processes that occur within the duration of echoic memory, was also noticed.Five groups of children were studied. 1. Experiments in normal adult and children controls were carried out to validate the methodology. MMN to duration and frequency deviance was dissociated temporally but not spatially. 2. Intracranial recordings revealed cortical activation in the peri-sylvian and frontal regions that was dependent on the complexity and context of the stimuli. 3. Scalp recordings in children who had undergone hemispherectomy provided a model of the scalp distribution of AERPs arising from one hemisphere and a comparison to the intracranial recordings. Significant differences in AERP components to pure tones and syllables suggested optimal processing by the intact left hemisphere. 4. Recordings in awake children with benign rolandic epilepsy show an alteration in the topography of the P85-120 component of the AERP contralateral to the hemisphere generating spikes during sleep. As there is no structural lesion these findings suggest long term effects of epileptic spikes. 5. In a previously poorly described group of children with normal peripheral hearing who have difficulties in challenging acoustic environments, the AERPs were sensitive to deficits in a behavioural test of central auditory processing. Other findings included the increase in latencies of AERP components with more complex stimuli and differing morphology/topography of the obligatory and mismatch components both to each other and between adults and children

Autonomic function in epilepsy

Autonomic function may help to localize and manage the epilepsies. It is likely that the mechanisms of Sudden Unexpected death in Epilepsy (SUDEP) involve autonomic disturbance and a better understanding of these might lead to measures that would help reduce the mortality in patients afflicted with epilepsy. In this thesis, I first provide a comprehensive literature review of the association between epilepsy and the autonomic nervous system. I then evaluate heart rate variability and other cardiac and endocrine parameters as indices of cardiac autonomic function to test three hypothesis; 1) Changes in heart rate variability (HRV), can occur in the peri-ictal period during both (a) subclinical electrographic seizures and (b) clinically overt partial seizures, and can help to localise and lateralise the ictal discharge. 2) Intractable epilepsy can disrupt the heart rate variability and its circadian rhythm. 3) Epileptic seizures affect the serum concentration of the catecholamines and the electrolytes and that these changes could impact on the corrected QT interval. Subjects (n=207) with intractable epilepsy who were being evaluated with video-EEG telemetry for epilepsy surgery were recruited for this study. I found that subclinical seizures have no effect on the HRV. However, in overt partial seizures, HRV decreases, corrected QT is prolonged and plasma catecholamines increases. The reduction in HRV during seizures is not affected by the hemispheric or lobar location of the epileptic focus. However, in the interictal period, reduced HRV differs in left vs. right hemisphere, and in temporal vs. extratemporal areas. The diurnal pattern of HRV is not altered in epilepsy and the mean day HRV were significantly different from mean night HRV. The reduction in HRV is also associated with the following clinical factors: prolonged medical history of epilepsy, the cortical pathology itself, the nature of the seizures, higher seizure frequency and the antiepileptic drug treatment. The plasma electrolytes: Na, K+, Ca2+ and cardiac troponin are not affected after a seizure. However, plasma Mg2+ was seen to increase after a seizure. These abnormalities in autonomic control, particularly the reduction in HRV might be one contributory mechanism of Sudden Unexpected Death in Epilepsy (SUDEP)

The Electrophysiology of Resting State fMRI Networks

Traditional research in neuroscience has studied the topography of specific brain functions largely by presenting stimuli or imposing tasks and measuring evoked brain activity. This paradigm has dominated neuroscience for 50 years. Recently, investigations of brain activity in the resting state, most frequently using functional magnetic resonance imaging (fMRI), have revealed spontaneous correlations within widely distributed brain regions known as resting state networks (RSNs). Variability in RSNs across individuals has found to systematically relate to numerous diseases as well as differences in cognitive performance within specific domains. However, the relationship between spontaneous fMRI activity and the underlying neurophysiology is not well understood. This thesis aims to combine invasive electrophysiology and resting state fMRI in human subjects to better understand the nature of spontaneous brain activity. First, we establish an approach to precisely coregister intra-cranial electrodes to fMRI data (Chapter 2). We then created a novel machine learning approach to define resting state networks in individual subjects (Chapter 3). This approach is validated with cortical stimulation in clinical electrocorticography (ECoG) patients (Chapter 4). Spontaneous ECoG data are then analyzed with respect to fMRI time-series and fMRI-defined RSNs in order to illustrate novel ECoG correlates of fMRI for both local field potentials and band-limited power (BLP) envelopes (Chapter 5). In Chapter 6, we show that the spectral specificity of these resting state ECoG correlates link classic brain rhythms with large-scale functional domains. Finally, in Chapter 7 we show that the frequencies and topographies of spontaneous ECoG correlations specifically recapitulate the spectral and spatial structure of task responses within individual subjects

The biophysical basis and clinical applications of rheoencephalography

Rheoencephalography methods for determining aviators potentially subject to cerebrovascular diseas