Multimodality Neuromonitoring

The monitoring of systemic and central nervous system physiology is central to the management of patients with neurologic disease in the perioperative and critical care settings. There exists a range of invasive and noninvasive and global and regional monitors of cerebral hemodynamics, oxygenation, metabolism, and electrophysiology that can be used to guide treatment decisions after acute brain injury. With mounting evidence that a single neuromonitor cannot comprehensively detect all instances of cerebral compromise, multimodal neuromonitoring allows an individualized approach to patient management based on monitored physiologic variables rather than a generic one-size-fits-all approach targeting predetermined and often empirical thresholds

Robotically Steered Needles: A Survey of Neurosurgical Applications and Technical Innovations

This paper surveys both the clinical applications and main technical innovations related to steered needles, with an emphasis on neurosurgery. Technical innovations generally center on curvilinear robots that can adopt a complex path that circumvents critical structures and eloquent brain tissue. These advances include several needle-steering approaches, which consist of tip-based, lengthwise, base motion-driven, and tissue-centered steering strategies. This paper also describes foundational mathematical models for steering, where potential fields, nonholonomic bicycle-like models, spring models, and stochastic approaches are cited. In addition, practical path planning systems are also addressed, where we cite uncertainty modeling in path planning, intraoperative soft tissue shift estimation through imaging scans acquired during the procedure, and simulation-based prediction. Neurosurgical scenarios tend to emphasize straight needles so far, and span deep-brain stimulation (DBS), stereoelectroencephalography (SEEG), intracerebral drug delivery (IDD), stereotactic brain biopsy (SBB), stereotactic needle aspiration for hematoma, cysts and abscesses, and brachytherapy as well as thermal ablation of brain tumors and seizure-generating regions. We emphasize therapeutic considerations and complications that have been documented in conjunction with these applications

Characterizing and revealing biomarkers on patients with Cerebral Amyloid Angiopathy using artificial intelligence

Dissertação de mestrado em BioinformáticaCerebral Amyloid Angiopathy is a cerebrovascular disorder resulting from the deposition of an amyloidogenic protein in small and medium sized cortical and leptomeningeal vessels. A primary cause of spontaneous intracerebral haemorrhages, it manifests predominantly in the elder population. Although CAA is a common neuropathological finding on itself, it is also known to frequently occur in conjunction with Alzheimer’s disease, being sometimes misdiagnosed. Currently, CAA diagnosis is generally conducted by post-mortem examination or, in live patients by the examination of an evacuated hematoma or brain biopsy samples, which are typically unavailable. Therefore, a reliable and non-invasive method for diagnosing CAA would facilitate the clinical decision making and accelerate the clinical intervention. The main goal of this dissertation is to study the application of Machine Learning (ML) to reveal possible biomarkers to aid the diagnosis and early medical intervention, and better understand the disease. Therefore, three scenarios were tested: Classification of four neurodegenerative diseases with annotation data obtained from visual rating scores, age and gender; Classification of the diseases with radiomic data derived from the patient’s MRI; and a combination of the previous experiments. The results show that the application of Artificial intelligence in the medical field brings advantages to support the physicians in the decision making process and, at some point, make a correct prediction of the disease label. Although the results are satisfactory, there are still improvements to be done. For instance, image segmentation of cerebral lesions or brain regions and additional clinical information of the patients would be of value.Angiopatia Amiloide Cerebral (AAC) é uma doença vascular cerebral resultante da deposição de matéria amiloide. Principal causa de hemorragias cerebral espontâneas, a AAC manifesta se predominantemente na população idosa. Embora a AAC seja uma doença que por si só tem um grande impacto no grupo etário referido, ocorre em simultâneo com inúmeras outras doenças neurodegenerativas, como a doença de Alzheimer. Atualmente, o diagnóstico de AAC realiza-se quer em post-mortem, quer em pacientes vivos. No entanto, o diagnóstico em vida é conseguido por meio de biópsias de tecidos cerebrais, sendo um método invasivo, o que dificulta a intervenção clínica. Deste modo, torna-se imperativa a procura de alternativas fiáveis e não invasivas em vida para auxiliar o diagnóstico da doença e permitir a melhoria da qualidade de vida do paciente. Perante os progressos na área da tecnologia e medicina, esta dissertação propõe o estudo da aplicação de algoritmos de Machine Learning (ML) para revelar possíveis biomarcadores para auxiliar o diagnóstico e permitir uma intervenção precoce. Deste modo, foram testados três cenários distintos: a classificação de quatro doenças neurodegenerativas com dados anotados obtidos a partir de métricas visuais de avaliação da atrofia, idade e sexo; a classificação das doenças com dados gerados a partir de métodos radiómicos; e uma combinação das duas abordagens anteriores. Neste documento apresenta-se e discute-se os resultados obtidos com a aplicação de quatro diferentes algoritmos de ML que visam a deteção automática da doença associada à imagem testada. Adicionalmente, é feita uma análise crítica de quais as características mais relevantes que levaram à tomada de decisão por parte do algoritmo. Os resultados demonstram que através de aplicação de metodologias automáticas é possível o auxílio ao diagnostico médico por especialistas e, no limite, a realização de diagnostico automático com elevada precisão. Finalmente, são apresentadas possíveis alternativas de trabalho futuro para que os resultados possam ser aperfeiçoados, como por exemplo, a segmentação das regiões de interesse, i.e., identificação das lesões, aquando da anotação por especialistas. Mediante a inclusão dessa segmentação, uma vez que será mais especifica, os resultados serão, por sua vez, aprimorados

Magnetic Resonance Imaging Studies of Angiogenesis and Stem Cell Implantations in Rodent Models of Cerebral Lesions

Molecular biology and stem cell research have had an immense impact on our understanding of neurological diseases, for which little or no therapeutic options exist today. Manipulation of the underlying disease-specific molecular and cellular events promises more efficient therapy. Angiogenesis, i.e. the regrowth of new vessels from an existing vascular network, has been identified as a key contributor for the progression of tumor and, more recently, for regeneration after stroke. Donation of stem cells has proved beneficial to treat cerebral lesions. However, before angiogenesis-targeted and stem cell therapies can safely be used in patients, underlying biological processes need to be better understood in animal models. Noninvasive imaging is essential in order to follow biological processes or stem cell fate in both space and time. We optimized steady state contrast enhanced magnetic resonance imaging (SSCE MRI) to monitor vascular changes in rodent models of tumor and stroke. A modification of mathematical modeling of MR signal from the vascular network allowed for the first time simultaneous measurements of relaxation time T2 and SSCE MRI derived blood volume, vessel size, and vessel density. Limitations of SSCE MRI in tissues with high blood volume and non-cylindrically shaped vessels were explored. SSCE MRI detected angiogenesis and response to anti-angiogenic treatment in two rodent tumor models. In both tumor models, reduction of blood volume in small vessels and a shift towards larger vessels was observed upon treatment. After stroke, decreased vessel density and increased vessel size was found, which was most pronounced one week after the infarct. This is in agreement with two initial, recently published clinical studies. Overall, very little signs of angiogenesis were found. Furthermore, superparamagnetic iron oxide (SPIO) labels were used to study neural stem cells (NSCs) in vivo with MRI. SPIO labeling revealed a decrease in volume of intracerebral grafts over 4 months, assessed by T2* weighted MRI. Since SPIO labels are challenging to quantify and their MR contrast can easily be confounded, we explored the potential of in vivo 19F MRI of 19F labeled NSCs. Hardware was developed for in vitro and in vivo 19F MRI. NSCs were labeled with little effect on cell function and in vivo detection limits were determined at ~10,000 cells within 1 h imaging time. A correction for the inhomogeneous magnetic field profile of surface coils was validated in vitro and applied for both sensitive and quantitative in vivo cell imaging. As external MRI labels do not provide information on NSC function we combined 19F MRI with bioluminescence imaging (BLI). The BLI signal allowed quantification of viable cells whereas 19F MRI provided graft location and density in 3D over 4 weeks both in the healthy and stroke brain. A massive decrease in number of viable cells was detected independent of the microenvironment. This indicates that functional recovery reported in many studies of NSC implantation after stroke, is rather due to release of factors by NSCs than direct tissue replacement. In light of these indirect effects, combination of the imaging methods developed in this dissertation with other functional and structural imaging methods is suggested in order to further elucidate interactions of NSCs with the vasculature

Development and application of inhibitory luminopsins for the treatment of epilepsy

Optogenetics has shown great promise as a direct neuromodulatory tool for halting seizure activity in various animal models of epilepsy. However, light delivery into the brain is still a major practical challenge that needs to be addressed before future clinical translation is feasible. Not only does light delivery into the brain require surgically implanted hardware that can be both invasive and restrictive, but it is also difficult to illuminate large or complicated structures in the brain due to light scatter and attenuation. We have bypassed the challenges of external light delivery by directly coupling a bioluminescent light source (a genetically encoded Renilla luciferase) to an inhibitory opsin (Natronomonas halorhodopsin) as a single fusion protein, which we term an inhibitory luminopsin (iLMO). iLMOs were developed and characterized in vitro and in vivo using intracellular recordings, multielectrode arrays, and behavioral testing. iLMO2 was shown to generate hyperpolarizing outward currents in response to both external light and luciferase substrate, which was sufficient to suppress action potential firing and synchronous bursting activity in vitro. iLMO2 was further shown to suppress single-unit firing rate and local field potentials in the hippocampus of anesthetized and awake animals. Finally, expression of iLMO was scaled up to multiple structures of the basal ganglia to modulate rotational behavior of freely moving animals in a hardware-independent fashion. iLMO2 was further utilized to acutely suppress focal epileptic discharges induced by intracerebral injection of bicuculline and generalized seizures resulting from systemic administration of pentylenetetrazol. Inhibitory luminopsins have enabled the possibility of optogenetic inhibition of neural activity in a non-invasive and hardware-independent fashion. This work increases the versatility, scalability, and practicality of utilizing optogenetic approaches for halting seizure activity in vivo.Ph.D

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

Role of diffusion tensor imaging as an imaging biomarker and theranostic tool in structural imaging of traumatic brain injury

Neuroimaging technology is at a "newborn" stage in the evaluation of TBI. While additional literature are obviously required to decide whether these modalities and progress in knowledge with noninvasive monitors will allow early and consistent recognition of revocable secondary brain damages, the final query is whether these new modalities will help in treatment plans that will absolutely mark result. DTI is an influential instrument for assessing white matter anatomy and related anomalies. DTI was formerly an investigation tool, but is using clinical practice. Accepting the terms and basic ideas of this method can aid in the clinical implementation and interpretation of this blend of structural and physiologic white matter evaluation. In conclusion, although DTI is as a diagnostic tool for severity of TBI and as an outcome predictor, but severe preclinical and clinical validation of each imaging method should be a top importance