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

Developing a Brain‐Based, Non‐Invasive Treatment for Pain

Chronic pain cost society more than $500 billion each year and contributes to the ongoing opioid overdose crisis. Substantial risks and low efficacy are associated with opiate usage for chronic pain. This dissertation seeks to fill the urgent need for a new pain treatment using a neural-circuit based approach in healthy controls and chronic pain patients. First, we performed a single-blind study examining the causal effects of transcranial magnetic stimulation (TMS), compared to a well-matched control condition. Using interleaved TMS/fMRI we explored brain activation in response to dorsolateral prefrontal cortex (DLPFC) stimulation in 20 healthy controls. This study tested the hypothesis that the TMS evoked responses would be in frontostriatal locations. Consistent with this hypothesis active TMS, compared to the control, led to significantly greater activity in the caudate, thalamus and anterior cingulate cortex (ACC). Building on these findings, we developed a single-blind, sham-controlled study examining two TMS strategies for analgesia in 45 healthy controls. We completed an fMRI thermal pain paradigm before and after modulatory repetitive TMS at either the DLPFC or the medial prefrontal cortex (MPFC). Despite a role in pain processing, the MPFC has not yet been explored as a target for analgesia. Only MPFC stimulation significantly improved behavioral pain measures. These effects were associated with increased motor and parietal cortex activity during the pain task. We then supplement these findings by testing the hypothesis that chronic pain patients who use opioids (n=14) would have elevated brain responses to thermal pain relative to healthy controls (n=14). Despite indistinguishable self-report measures, we found increased brain activity in the ACC and sensory areas in patients which were positively correlated with opioid dose. We conclude by evaluating the feasibility of these approaches in chronic pain patients, reporting preliminary findings from a pilot study examining the two treatment strategies tested previously in controls. Collectively, our findings support a circuits-first approach to pain treatment. Though MPFC stimulation was effective in reducing pain in healthy controls, further work is required to confirm these results in a chronic pain population, as chronic pain and opioid usage alter how the brain processes the pain experience

A computational approach to motivated behaviour and apathy

The loss of motivation and goal-directed behaviour is characteristic of apathy. Across a wide range of neuropsychiatric disorders, including Huntington’s disease (HD), apathy is poorly understood, associated with significant morbidity, and is hard to treat. One of the challenges in understanding the neural basis of apathy is moving from phenomenology and behavioural dysfunction to neural circuits in a principled manner. The computational framework offers one such approach. I adopt this framework to better understand motivated behaviour and apathy in four complementary projects. At the heart of many apathy formulations is impaired self-initiation of goal-directed behaviour. An influential computational theory proposes that “opportunity cost”, the amount of reward we stand to lose by not taking actions per unit time, is a key variable in governing the timing of self-initiated behaviour. Using a novel task, I found that free-operant behaviour in healthy participants both in laboratory conditions and in online testing, conforms to predictions of this computational model. Furthermore, in both studies I found that in younger adults sensitivity to opportunity cost predicted behavioural apathy scores. Similar pilot results were found in a cohort of patients with HD. These data suggest that opportunity cost may be an important computational variable relevant for understanding a core feature of apathy – the timing of self-initiated behaviour. In my second project, I used a reinforcement learning paradigm to probe for early dysfunction in a cohort of HD gene carriers approximately 25 years from clinical onset. Based on empirical data and computational models of basal ganglia function I predicted that asymmetry in learning from gains and losses may be an early feature of carrying the HD gene. As predicted, in this task fMRI study, HD gene carriers demonstrated an exaggerated neural response to gains as compared to losses. Gene carriers also differed in the neural response to expected value suggesting that carrying the HD gene is associated with altered processing of valence and value decades from onset. Finally, based on neurocomputational models of basal ganglia pathway function, I tested the hypothesis that apathy in HD would be associated with the involvement of the direct pathway. Support for this hypothesis was found in two related projects. Firstly, using data from a large international HD cohort study, I found that apathy was associated with motor features of the disease thought to represent direct pathway involvement. Secondly, I tested this hypothesis in vivo using resting state fMRI data and a model of basal ganglia connectivity in a large peri-manifest HD cohort. In keeping with my predictions, whilst emerging motor signs were associated with changes in the indirect pathway, apathy scores were associated with connectivity changes in the direct pathway connectivity within my model. For patients with apathy across neuropsychiatry there is an urgent need to understand the neural basis of motivated behaviour in order to develop novel therapies. In this thesis, I have used a computational framework to develop and test a range of hypotheses to advance this understanding. In particular, I have focussed on the computational factors which drive us to self-initiate, their potential neural underpinnings and the relevance of these models for apathy in patients with HD. The data I present supports the hypothesis that opportunity cost and basal ganglia pathway connectivity may be two important components necessary to generate motivated behaviour and contribute to the development of apathy in HD

Automated Classification for Electrophysiological Data: Machine Learning Approaches for Disease Detection and Emotion Recognition

Smart healthcare is a health service system that utilizes technologies, e.g., artificial intelligence and big data, to alleviate the pressures on healthcare systems. Much recent research has focused on the automatic disease diagnosis and recognition and, typically, our research pays attention on automatic classifications for electrophysiological signals, which are measurements of the electrical activity. Specifically, for electrocardiogram (ECG) and electroencephalogram (EEG) data, we develop a series of algorithms for automatic cardiovascular disease (CVD) classification, emotion recognition and seizure detection. With the ECG signals obtained from wearable devices, the candidate developed novel signal processing and machine learning method for continuous monitoring of heart conditions. Compared to the traditional methods based on the devices at clinical settings, the developed method in this thesis is much more convenient to use. To identify arrhythmia patterns from the noisy ECG signals obtained through the wearable devices, CNN and LSTM are used, and a wavelet-based CNN is proposed to enhance the performance. An emotion recognition method with a single channel ECG is developed, where a novel exploitative and explorative GWO-SVM algorithm is proposed to achieve high performance emotion classification. The attractive part is that the proposed algorithm has the capability to learn the SVM hyperparameters automatically, and it can prevent the algorithm from falling into local solutions, thereby achieving better performance than existing algorithms. A novel EEG-signal based seizure detector is developed, where the EEG signals are transformed to the spectral-temporal domain, so that the dimension of the input features to the CNN can be significantly reduced, while the detector can still achieve superior detection performance

Assistive Technology and Biomechatronics Engineering

This Special Issue will focus on assistive technology (AT) to address biomechanical and control of movement issues in individuals with impaired health, whether as a result of disability, disease, or injury. All over the world, technologies are developed that make human life richer and more comfortable. However, there are people who are not able to benefit from these technologies. Research can include development of new assistive technology to promote more effective movement, the use of existing technology to assess and treat movement disorders, the use and effectiveness of virtual rehabilitation, or theoretical issues, such as modeling, which underlie the biomechanics or motor control of movement disorders. This Special Issue will also cover Internet of Things (IoT) sensing technology and nursing care robot applications that can be applied to new assistive technologies. IoT includes data, more specifically gathering them efficiently and using them to enable intelligence, control, and new applications

Magnetic Hyperthermia for the Treatment of Glioblastoma

Introduction: Glioblastoma, the most common primary adult brain malignancy, is an aggressive tumour with median survival of around one year. Despite extensive research there has been minimal improvement in prognosis and innovative new treatments are urgently required. The research within this thesis focussed on designing a novel therapeutic approach using nanotechnology to achieve in-situ immune stimulation mediated by localised hyperthermia and characterising the effects of hyperthermia within the tumour microenvironment (TME). Methods: In-situ heating was generated using superparamagnetic iron-oxide nanoparticles (SPIONs) stimulated by an alternating magnetic field (AMF); a combined process known as magnetic hyperthermia. Candidate SPIONs were first tested for biocompatibility and favourable heating properties. In-vivo experiments utilised the immunocompetent GL261 glioblastoma model and included: (i) Testing reticuloendothelial system blocking, and direct intratumoural injection to obtain sufficient intratumoural SPION concentrations; (ii) Utilising 89Zr-labelled SPIONs to evaluate in-vivo fate using PET-CT Imaging; (iii) Evaluation of SPION in-vivo heating ability using thermal imaging; (iv) Tumour growth and timed immunohistochemical (IHC) response analysis; (v) Flow cytometry analysis of the tumour infiltrating lymphocyte (TIL) populations following treatment and (vi) testing a combination therapeutic approach combining magnetic hyperthermia with immune checkpoint inhibition. Results: Perimag-COOH was identified as the lead candidate SPION, and intratumoural injection chosen as the optimal method to obtain sufficient intratumoural SPION concentrations. Perimag-COOH remained within the tumour following injection and retained ability to generate AMF-induced heat for at least 72 h post injection. Digital image analysis of IHC demonstrated a specific, localised, heat-shock protein response following hyperthermia. Tumour growth inhibition was observed up to one week following treatment and tumour flow cytometry analysis revealed changes in TIL populations suggestive of an immune response, providing a rational for a combination approach with immune checkpoint inhibition. Conclusions: SPION mediated hyperthermia is achievable in-vivo and can generate TME changes suggestive of an anti-tumour immune response