During a brain tumour resection, a neurosurgeon is constantly navigating a delicate balance between resecting as much of the tumour as possible, while avoiding any damage to healthy brain tissue. This challenge is particularly difficult when the tumour is located in a critical functional area, involved in for example language or motor function. For these types of tumours, the awake craniotomy was developed. During this surgery the patient wakes up to perform language and motor tasks, to enable the surgeon to localize these functions inside the brain. In this thesis, we investigate and develop a new quantitative method to monitoring motor function that could potentially improve intraoperative decision making and enables neuroscientific and neurosurgical research.

Chapter 1 provides a background about surgical strategies and technologies that have been developed to aid surgeons’ decisions during complex brain tumour resections. We will explain the complexity of robust research in the neurosurgical environment and the need for a dedicated Research Operating Room to create an environment to improve neurosurgical and neuroscientific research.

In Chapter 2 we make an overview of the possible solutions to quantify motor function before, during and after awake craniotomies and discuss the best solution for the Erasmus MC.

In Chapter 3 we present a new frame to create a standardized environment inside the operating room for good quality data collection of patient functionality. To design this frame, we identified and interviewed all the important stakeholders and designed three prototypes. The two most promising prototypes were developed. The final prototype was implemented during three awake craniotomies.

This newly developed frame was used in Chapter 4 to explore video tracking as a new tool to quantify hand motor function. Three patients were followed one day prior to the surgery, during the awake craniotomy, and one day postoperatively. During these three cases, we identified several prerequisites for a reliable recording set-up and explored the potential to detect clinically relevant events during fingertapping and direct electrical stimulation (DES). This showed promising results and underscores the potential for video tracking to be further investigated for quantification of hand motor function.

In Chapter 5 we put the discussed work into context, discussing it’s clinical and scientific relevance and future perspectives. In this thesis, we have demonstrated that it is possible to implement a new quantitative measurement method to monitor hand function in the challenging environment of an operating room. Quantification of visual observations has shown to be low-cost, easily available and implementable in clinical context, because of the fast technological advancements in this field. Video tracking can be used for future research to investigate the relation between intraoperative findings and long-term outcomes, and has the potential to add valuable information for neurosurgical and neuroscientific research.

Background context: Intraoperative neuromonitoring (IONM) has proven effective in reducing postoperative neurological complications. However, current understanding of IONM is limited and its precise meaning in relation to neurological outcomes remains unclear. Machine learning (ML) is a promising solution to analyze the excessive amount of IONM data quickly, objectively and in real-time.
Purpose: The goal is to develop a ML algorithm that can effectively predict neurological outcomes after spinal surgery using IONM data that include both motor evoked potentials (MEPs) and somatosensory evoked potentials (SSEPs), and analyze its key predicting features. To more effectively determine the specific independent contribution of both separate modalities, a separate ML model will be created for both MEP and SSEP in addition to a combined MEP-SSEP model.
Study setting: Retrospective study.
Patient sample: A total of 67 patients were analyzed.
Outcome measures: The neurological status three months postoperatively compared to the preoperative status, categorized into three classes: 'Neurological stable deficits', ‘Neurologically intact’ and 'Neurological improvement'.
Methods: 260 features were obtained from patients who underwent spinal surgery monitored by IONM. During nested cross-validation, the data was split into five folds, for both the inner and the outer loop. The four ML classifiers developed were support vector machine, K-nearest neighbors, random forest and extreme gradient boosting, and tested along the three modalities MEP, SSEP, and MEP-SSEP combination.
Results: Extreme gradient boosting outperformed the other classifiers on all performance metrics. The combined MEP-SSEP model exhibited the highest scores for sensitivity: 70.4%, specificity: 88.3% and accuracy: 87.1%, while the MEP model exhibited the highest performance for precision: 75.6%. Highest predicting scores per individual class were also obtained by this XGBoost classifier on the combined MEP-SSEP model. Key predicting features were the presence or absence of preoperative neurological deficits and last measured signal latency compared to baseline, with a contribution of 29% and 13.5% in the best performing model, respectively.
Conclusion: A reliable prediction of neurological outcomes three months postoperatively can be made combining MEP and SSEP IONM features, provided that the patient's preoperative status is accurately documented and included in the prediction. Though either MEP or SSEP features alone offer predictive value, MEP features show superior predictive values compared to SSEP features when both modalities are accessible, with latency emerging as a prominent predictive IONM feature.

The brain stands as the most powerful processor in the known universe. It generates a continuous stream of electrical and chemical signals that underpin every thought, sensation, and action. Our past efforts in decoding these signals have made it possible to diagnose and treat many neurological disorders, helped us gain a deeper understanding of cognitive processes and consciousness, and paved the way for brain-computer interfaces. To take another step forward in our long but rewarding journey of discovering the brain's complex organization, we rely on advances in imaging technologies and signal processing.

Functional ultrasound is a neuroimaging technique that has emerged in the last decade, and has gained remarkable attention since then. The popularity of this technique stems from its portability, high resolution and affordability. Functional ultrasound can detect subtle fluctuations in local blood dynamics, which serve as delayed indicators of the underlying changes in neuronal activity. The goal of this thesis is to develop novel signal models and processing algorithms that can reveal the spatial and temporal characteristics of hemodynamic activity induced by external stimuli using functional ultrasound.

Existing techniques that explore how the brain reacts in response to stimuli model the design variables using a linear time-invariant system with binarized input representations, marking when a stimulus is on or off. However, experimental evidence suggests that the brain reacts in a more intricate manner. While some regions exhibit consistent responses to repeated stimuli, others can show substantial variation even when exposed to the same stimulus seconds apart. In our in-vivo experiments, we particularly focus on key regions within the mouse visual processing pathway, which are analogous to those in the human brain. We track how visual information flows across these areas, and propose methods that can incorporate the spatiotemporal variability of brain responses when identifying evoked activity. Using these methods, we show that functional ultrasound can capture the dynamic nature of brain responses with high spatial and temporal resolution, and provide us with further insights into the functional organization of the brain. Future directions of this dissertation include multimodal processing of the functional ultrasound signal together with neuronal activity, aiming to enhance our understanding of neurovascular coupling.

Ultrasound images are typically generated using the Delay-And-Sum (DAS) method, which assumes a homogeneous propagation medium. When an aberrating layer is situated between the sensor array and the imaging target, this assumption does not hold, and DAS is replaced with model-based methods. These methods are computationally expensive and require to accurately model the aberrations caused by the layer. This thesis investigates novel methods for image formation and aberration estimation. The effect of the layer is described using a set of transfer functions from the sensor array to a virtual array placed after the layer. In the first part, we assume the transfer functions are known, and we propose a new method for image formation. The transfer functions allow to map the signal from the sensor array to the virtual array, and the DAS method is used on the virtual array signal. This technique is equivalent to model-based matched filtering in terms of image quality, without requiring expensive matrix computations. In the second part, the transfer functions are unknown, and a novel technique is introduced for their estimation. Using pulse-echo data, a focus-quality metric is computed to quantify the accuracy of the transfer function estimate. The transfer functions are modeled using a dictionary and the dictionary coefficients are iteratively updated to increase the defined metric. The optimization leads to improved focus quality and sharper images. In the case the layer model requires a limited dictionary, the proposed algorithm generates an accurate estimate of the transfer functions.

Functional ultrasound (fUS) is a neuroimaging modality that offers high spatial and temporal resolution while also providing portability. In this thesis, neuroimaging data acquired with fUS at Center for Ultrasound and Brain imaging at Erasmus MC (CUBE) is processed. Due to the fact that fUS data is inherently multidimensional, we propose using tensor decompositions, tensors here referring to generalizations of matrices, for processing of fUS data.

We define two main research questions regarding fUS data analysis. First, for compressing the large-scale raw beamformed fUS data, we apply sequentially truncated multilinear singular value decomposition. This compression method is compared against ensemble averaging used in the conventional pipeline, and shown to provide a higher compression rate while preserving more temporal resolution for specific ranks. Furthermore, it is observed to denoise the data, resulting in a more precise extraction of the active region of Superior Colliculus using correlation maps.

Secondly, in order to investigate the advantage of multi-slice processing that incorporates 3-D informa- tion, blind-source separation methods are applied to single slice and two-slice fUS recordings. After applying independent component analysis (ICA) to the matricized data as a benchmark method, block term decompo- sition (BTD) is used as a way of processing the data as it is, in its natural 3-D structure without vectorization. Through a simulation study, it is shown that the method is able to separate two images even when using a rank that is lower than the true rank, as well as in noisy conditions. Subsequently, BTD is applied to real 4-D fUS data formed by concatenation of slices in a new dimension. However, this method is seen to perform worse than single slice ICA in terms of extracting the active regions. In order to amplify common information between slices, a new 3-D data structure is then formed by summing the fUS data of two slices. For extraction of this common information, a BTD is then applied to the aggregate 3-D data. The findings of this decomposition reveal that both taking a longer portion of single slice data and incorporating the second slice helps to achieve better results.

In this thesis, we investigate a sparse basis for ultrasound images, so that we can use sparse regularization in imaging. Actually, there are few previous researches explicitly demonstrating that medical ultrasound images can be sparsified for some dictionary. We consider various orthogonal transforms such as wavelet transforms, cosine transforms and wave atom transforms. Then, we perform those transforms on various ultrasound images and analyzes their sparsity. These ultrasound images include the images of two computer ultrasound phantoms and beamformed ultrasound images with good quality from real people. We looked at sparsity of the true pre-beamformed images, as well as beamformed images. We also consider constructing a specific ultrasound image dictionary using the K-SVD algorithm. We observed that, the pre-beamformed images hardly haVe no sparse basis, and the sparsity of beamformed images will only increase slightly if we use different 1D-DWT in each direction. We also found that the wide overdetermined dictionary generated by K-SVD significantly increases sparsity. After this, we simulate the ultrasound image reconstruction from the ultrasound RF measurements, and we analyze the effects of the different sparse spaces on the reconstruction performance. We observed that, the L1-regularization can work for ultrasound imaging better than L2-regularization, but the orthogonal transforms as well as the dictionary do not improve the reconstruction image quality much.