Dynamic Thermal Imaging for Intraoperative Monitoring of Neuronal Activity and Cortical Perfusion

Neurosurgery is a demanding medical discipline that requires a complex interplay of several neuroimaging techniques. This allows structural as well as functional information to be recovered and then visualized to the surgeon. In the case of tumor resections this approach allows more fine-grained differentiation of healthy and pathological tissue which positively influences the postoperative outcome as well as the patient's quality of life. In this work, we will discuss several approaches to establish thermal imaging as a novel neuroimaging technique to primarily visualize neural activity and perfusion state in case of ischaemic stroke. Both applications require novel methods for data-preprocessing, visualization, pattern recognition as well as regression analysis of intraoperative thermal imaging. Online multimodal integration of preoperative and intraoperative data is accomplished by a 2D-3D image registration and image fusion framework with an average accuracy of 2.46 mm. In navigated surgeries, the proposed framework generally provides all necessary tools to project intraoperative 2D imaging data onto preoperative 3D volumetric datasets like 3D MR or CT imaging. Additionally, a fast machine learning framework for the recognition of cortical NaCl rinsings will be discussed throughout this thesis. Hereby, the standardized quantification of tissue perfusion by means of an approximated heating model can be achieved. Classifying the parameters of these models yields a map of connected areas, for which we have shown that these areas correlate with the demarcation caused by an ischaemic stroke segmented in postoperative CT datasets. Finally, a semiparametric regression model has been developed for intraoperative neural activity monitoring of the somatosensory cortex by somatosensory evoked potentials. These results were correlated with neural activity of optical imaging. We found that thermal imaging yields comparable results, yet doesn't share the limitations of optical imaging. In this thesis we would like to emphasize that thermal imaging depicts a novel and valid tool for both intraoperative functional and structural neuroimaging

Combined brain language connectivity and intraoperative neurophysiologic techniques in awake craniotomy for eloquent-area brain tumor resection

Speech processing can be disturbed by primary brain tumors (PBT). Improvement of presurgical planning techniques decrease neurological morbidity associated to tumor resection during awake craniotomy. The aims of this work were: 1. To perform Diffusion Kurtosis Imaging based tractography (DKI-tract) in the detection of brain tracts involved in language; 2. To investigate which factors contribute to functional magnetic resonance imaging (fMRI) maps in predicting eloquent language regional reorganization; 3. To determine the technical aspects of accelerometric (ACC) recording of speech during surgery. DKI-tracts were streamlined using a 1.5T magnetic resonance scanner. Number of tracts and fiber pathways were compared between DKI and standard Diffusion Tensor Imaging (DTI) in healthy subjects (HS) and PBT patients. fMRI data were acquired using task-specific and resting-state paradigms during language and motor tasks. After testing intraoperative fMRI’s influence on direct cortical stimulation (DCS) number of stimuli, graph-theory measures were extracted and analyzed. Regarding speech recording, ACC signals were recorded after evaluating neck positions and filter bandwidths. To test this method, language disturbances were recorded in patients with dysphonia and after applying DCS in the inferior frontal gyrus. In contrast, HS reaction time was recorded during speech execution. DKI-tract showed increased number of arcuate fascicle tracts in PBT patients. Lower spurious tracts were identified with DKI-tract. Intraoperative fMRI and DCS showed similar stimuli in comparison with DCS alone. Increased local centrality accompanied language ipsilateral and contralateral reorganization. ACC recordings showed minor artifact contamination when placed at the suprasternal notch using a 20-200 Hz filter bandwidth. Patients with dysphonia showed decreased amplitude and frequency in comparison with HS. ACC detected an additional 11% disturbances after DCS, and a shortening of latency within the presence of a loud stimuli during speech execution. This work improved current knowledge on presurgical planning techniques based on brain structural and functional neuroimaging connectivity, and speech recordingA função linguística do ser humano pode ser afetada pela presença de tumores cerebrais (TC) A melhoria de técnicas de planeamento pré-cirurgico diminui a morbilidade neurológica iatrogénica associada ao seu tratamento cirúrgico. O objetivo deste trabalho é: 1. Testar a fiabilidade da tractografia estimada por difusor de kurtose (tract-DKI), dos feixes cerebrais envolvidos na linguagem 2. Identificar os fatores que contribuem para o mapeamento linguagem por ressonância magnética funcional (RMf) na predição da neuroplasticidade. 3. Identificar aspetos técnicos do registo da linguagem por accelerometria (ACC). A DKI-tract foi estimada após realização de RM cerebral com 1.5T. O número e percurso das fibras foi avaliado. A RMf foi adquirida durante realização de tarefas linguísticas, motoras, e em repouso. Foi testada influência dos mapas de ativação calculados por RMf, no número de estímulos realizados durante a estimulação direta cortical (EDC) intraoperatória. Medidas de conectividade foram extraídas de regiões cerebrais. A posição e filtragem de sinal ACC foram estudadas após vocalização de palavras. O sinal ACC obtido em voluntários foi comparado com doentes disfónicos, após estimulação do giro inferior frontal, e após a adição de um estímulo sonoro perturbador durante vocalização. A tract-DKI estimou um elevado número de fascículos do feixe arcuato com menos falsos negativos. Os mapas linguísticos de RMf intraoperatória, não influenciou a EDC. Medidas de centralidade aumentaram após neuroplasticidade ipsilateral e contralateral. A posição supraesternal e a filtragem de sinal ACC entre 20-200Hz demonstrou menor ruido de contaminação. Este método identificou diminuição de frequência e amplitude em doentes com disfonia, 11% de erros linguísticos adicionais após estimulação e diminuição do tempo de latência quando presente o sinal sonoro perturbador. Este trabalho promoveu a utilização de novas técnicas no planeamento pré-cirúrgico do doente com tumor cerebral e alterações da linguagem através do estudo de conectividade estrutural, funcional e registo da linguagem

IFCN-endorsed practical guidelines for clinical magnetoencephalography (MEG)

Magnetoencephalography (MEG) records weak magnetic fields outside the human head and thereby provides millisecond-accurate information about neuronal currents supporting human brain function. MEG and electroencephalography (EEG) are closely related complementary methods and should be interpreted together whenever possible. This manuscript covers the basic physical and physiological principles of MEG and discusses the main aspects of state-of-the-art MEG data analysis. We provide guidelines for best practices of patient preparation, stimulus presentation, MEG data collection and analysis, as well as for MEG interpretation in routine clinical examinations. In 2017, about 200 whole-scalp MEG devices were in operation worldwide, many of them located in clinical environments. Yet, the established clinical indications for MEG examinations remain few, mainly restricted to the diagnostics of epilepsy and to preoperative functional evaluation of neurosurgical patients. We are confident that the extensive ongoing basic MEG research indicates potential for the evaluation of neurological and psychiatric syndromes, developmental disorders, and the integrity of cortical brain networks after stroke. Basic and clinical research is, thus, paving way for new clinical applications to be identified by an increasing number of practitioners of MEG. (C) 2018 International Federation of Clinical Neurophysiology. Published by Elsevier B.V.Peer reviewe

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

Integrated navigation and visualisation for skull base surgery

Skull base surgery involves the management of tumours located on the underside of the brain and the base of the skull. Skull base tumours are intricately associated with several critical neurovascular structures making surgery challenging and high risk. Vestibular schwannoma (VS) is a benign nerve sheath tumour arising from one of the vestibular nerves and is the commonest pathology encountered in skull base surgery. The goal of modern VS surgery is maximal tumour removal whilst preserving neurological function and maintaining quality of life but despite advanced neurosurgical techniques, facial nerve paralysis remains a potentially devastating complication of this surgery. This thesis describes the development and integration of various advanced navigation and visualisation techniques to increase the precision and accuracy of skull base surgery. A novel Diffusion Magnetic Resonance Imaging (dMRI) acquisition and processing protocol for imaging the facial nerve in patients with VS was developed to improve delineation of facial nerve preoperatively. An automated Artificial Intelligence (AI)-based framework was developed to segment VS from MRI scans. A user-friendly navigation system capable of integrating dMRI and tractography of the facial nerve, 3D tumour segmentation and intraoperative 3D ultrasound was developed and validated using an anatomically-realistic acoustic phantom model of a head including the skull, brain and VS. The optical properties of five types of human brain tumour (meningioma, pituitary adenoma, schwannoma, low- and high-grade glioma) and nine different types of healthy brain tissue were examined across a wavelength spectrum of 400 nm to 800 nm in order to inform the development of an Intraoperative Hypserpectral Imaging (iHSI) system. Finally, functional and technical requirements of an iHSI were established and a prototype system was developed and tested in a first-in-patient study

Deep Brain Stimulation (DBS) Applications

The issue is dedicated to applications of Deep Brain Stimulation and, in this issue, we would like to highlight the new developments that are taking place in the field. These include the application of new technology to existing indications, as well as ‘new’ indications. We would also like to highlight the most recent clinical evidence from international multicentre trials. The issue will include articles relating to movement disorders, pain, psychiatric indications, as well as emerging indications that are not yet accompanied by clinical evidence. We look forward to your expert contribution to this exciting issue

Experimental and Model-based Approaches to Directional Thalamic Deep Brain Stimulation

University of Minnesota Ph.D. dissertation. September 2016. Major: Biomedical Engineering. Advisor: Matthew Johnson. 1 computer file (PDF); xii, 181 pages.Deep brain stimulation (DBS) is an effective surgical procedure for the treatment of several brain disorders. However, the clinical successes of DBS hinges on several factors. Here, we describe the development of tools and methodologies in the context of thalamic DBS for essential tremor (ET) to address three key challenges: 1) accurate localization of nuclei and fiber pathways for stimulation, 2) model-based programming of high-density DBS electrode arrays (DBSA) and 3) in vivo assessment of computational DBS model predictions. We approached the first challenge through a multimodal imaging approach, utilizing high-field (7T) susceptibility-weighted imaging and diffusion-weighted imaging data. A nonlinear image deformation algorithm was used in conjunction with probabilistic fiber tractography to segment individual thalamic sub-nuclei and reconstruct their afferent fiber pathways. We addressed the second challenge by developing subject-specific computational model-based algorithms built on maximizing population activating function values within a target region using convex optimization principles. The algorithms converged within seconds and only required as many finite-element simulations as the number of electrodes on the DBSA being modeled. For the third challenge, we recorded (in two non-human primates) unit-spike data from neurons in the vicinity of chronically implanted thalamic DBSAs before, during and after high-frequency stimulation. A novel entropy-based method was developed to quantify the degree and significance of stimulation-induced changes in neuronal firing pattern. Results indicated that neurons modulated by thalamic DBS were distributed and not confined to the immediate proximity of the active electrode. For those that were modulated by DBS, their responses increasingly shifted from firing rate modulation to firing pattern modulation with increased stimulation amplitude. Additionally, strong low-pass filtering effect was observed where <4% of DBS pulses produced phase-locked spikes in cells exhibiting significant excitatory firing pattern modulation. Finally, we quantified the spatial distribution of neurons modulated by DBS by developing a novel spherical statistical framework for analysis. Together, these tools and methodologies are poised to improve our understanding of DBS mechanisms and improve the efficacy and efficiency of DBS therapy