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The cholinergic system is one of the most influential and essential signalling systems in the body. In the brain, cholinergic neurons innervate many brain regions where they influence a wide variety of behaviours. However, the precise role of each cholinergic region on distinct types of behaviour is not well known. Furthermore, in recent years there has been evidence that many cholinergic neurons in the brain have a capacity for co-transmission. Yet the functional significance of secreting two classical neurotransmitters from the same neuron is still largely unidentified. In this thesis, we investigated how different cholinergic nuclei modulate behavioural functions. To do that we selectively eliminated acetylcholine (ACh) release from cholinergic neurons of the striatum, brainstem and basal forebrain in mice. We then evaluated cognitive and non-cognitive behaviours using classical behavioural tests as well as sophisticated automated touchscreens tasks. In the striatum cholinergic interneurons are known to co-release ACh and glutamate (Glu), so we focused our investigation on how the individual neurotransmitters modulate striatal-dependent behaviours. We demonstrated that ACh modulates cognitive behaviours such as cognitive flexibility, extinction and cue detection. Glu released from striatal cholinergic interneurons also affects striatal-dependent behaviours but usually in an opposing manner to ACh, so, a balance between ACh and Glu is critical to regulating behaviours. As dopaminergic signalling in the striatum is widely influenced by ACh and Glu released by cholinergic interneurons, we also investigated how dopaminergic signalling changes while animals are performing a striatal-dependent cognitive task. In the brainstem, we showed that ACh influences motor functions and stress but does not have a major impact on cognition. However, stress induced by brainstem ACh-deficiency can interfere with results from cognitive tasks. In the forebrain, we find that ACh signalling is essential for maintaining social memory. Decreased cholinergic signalling in the hippocampus and cortex lead to deficits in social recognition. In conclusion, we demonstrate the complexity that ACh brings into behavioural regulation and how changes in its release can contribute to the pathophysiology of diseases such as Parkinson’s disease and Alzheimer’s disease. Ultimately, this data helps define novel pharmacological mechanisms tailored to improve specific cholinergic-mediated symptoms.

Dynamic processes, such as intracellular calcium signaling, are hallmark of cellular biology. As real-time imaging modalities become widespread, a need for analytical tools to reliably characterize time-series data without prior knowledge of the nature of the recordings becomes more pressing. The goal of this study is to develop a signal-processing algorithm for MATLAB that autonomously computes the parameters characterizing prominent single transient responses (TR) and/or multi-peaks responses (MPR). The algorithm corrects for signal contamination and decomposes experimental recordings into contributions from drift, TRs, and MPRs. It subsequently provides numerical estimates for the following parameters: time of onset after stimulus application, activation time (time for signal to increase from 10 to 90% of peak), and amplitude of response. It also provides characterization of the (i) TRs by quantifying their area under the curve (AUC), response duration (time between 1/2 amplitude on ascent and descent of the transient), and decay constant of the exponential decay region of the deactivation phase of the response, and (ii) MPRs by quantifying the number of peaks, mean peak magnitude, mean periodicity, standard deviation of periodicity, oscillatory persistence (time between first and last discernable peak), and duty cycle (fraction of period during which system is active) for all the peaks in the signal, as well as coherent oscillations (i.e., deterministic spikes). We demonstrate that the signal detection performance of this algorithm is in agreement with user-mediated detection and that parameter estimates obtained manually and algorithmically are correlated. We then apply this algorithm to study how metabolic acidosis affects purinergic (P2) receptor-mediated calcium signaling in osteoclast precursor cells. Our results reveal that acidosis significantly attenuates the amplitude and AUC calcium responses at high ATP concentrations. Collectively, our data validated this algorithm as a general framework for comprehensively analyzing dynamic time-series.

Fetal life is a significant period of human development because organ systems that sustain life outside the uterus begin to develop during this time. A necessary process that begins during fetal life is myelination, which is the process by which myelin, a lipid-rich substance, is wrapped around neurons in the brain to increase the speed of action potential transmission. Since myelination is critical for the normal function of the central nervous system, fetal myelin assessment is important for understanding neurodevelopment and neurodegeneration, such as intrauterine growth restriction (IUGR). Magnetic resonance imaging (MRI) is an excellent tool for visualizing fetal anatomy and identifying pathology. Relaxometry quantifies T1, T2, and T2* relaxation times, which are MR parameters that reflect fundamental tissue properties sensitive to the tissue microenvironment, providing an interpretation of images in absolute units. For the first time, fetal tissue T1 and T2* relaxation times were successfully quantified in uncomplicated pregnancies as a function of gestational age (GA) in the third trimester. A tissue microenvironment that can be investigated by MR relaxometry is myelin water. Myelin water imaging (MWI) uses MR relaxometry to visualize the aqueous components associated with myelin sheath to quantify myelin water fraction (MWF), a validated myelin marker. Moving to a guinea pig model of pregnancy, MWI was successfully conducted in the fetal environment as MWF was quantified in various fetal brain regions late in gestation. To investigate the effects of IUGR on myelination in utero, MWI was applied in a guinea pig model of natural IUGR late in gestation. MWF was significantly reduced in different brain regions of guinea pigs with IUGR compared to those without IUGR. Furthermore, the study highlighted the utility of MWF as a functional marker for IUGR. In conclusion, this dissertation demonstrates using MR relaxometry to quantify T1 and T2* relaxation times of fetal tissues throughout pregnancy and assess fetal brain myelin content in both a normal and IUGR environment. The findings demonstrate MR relaxometry's utility in assessing fetal tissue development in utero.

Magnetic Resonance Imaging (MRI) is a non-invasive medical imaging modality that provides excellent soft tissue contrast and resolution. Objects with high magnetic susceptibility distort the magnetic field, leading to severe artifacts in conventional MRI. It is very challenging to image around metal implants. Novel strategies may exploit the field distortion for spatial encoding. The magnetic field map is required in the development of these methods. A robust field map can also be employed to quantify high susceptibility particles that play a major role in cell tracking studies and hyperthermic treatment of cancers. Pure phase encoding (PPE) techniques with short encoding times are largely immune to magnetic field inhomogeneity artifacts. Artifact-free MR images around titanium were acquired with PPE techniques, from which the magnetic field distribution was derived. The approach was extended to quantify iron microparticles and was compared with conventional MRI to demonstrate its superiority.

We introduce Sleep, a new Python open-source graphical user interface (GUI) dedicated to visualization, scoring and analyses of sleep data. Among its most prominent features are: (1) Dynamic display of polysomnographic data, spectrogram, hypnogram and topographic maps with several customizable parameters, (2) Implementation of several automatic detection of sleep features such as spindles, K-complexes, slow waves, and rapid eye movements (REM), (3) Implementation of practical signal processing tools such as re-referencing or filtering, and (4) Display of main descriptive statistics including publication-ready tables and figures. The software package supports loading and reading raw EEG data from standard file formats such as European Data Format, in addition to a range of commercial data formats. Most importantly, Sleep is built on top of the VisPy library, which provides GPU-based fast and high-level visualization. As a result, it is capable of efficiently handling and displaying large sleep datasets. Sleep is freely available (http://visbrain.org/sleep) and comes with sample datasets and an extensive documentation. Novel functionalities will continue to be added and open-science community efforts are expected to enhance the capacities of this module.

Example code for the analysis pipeline used to create the structural template and quantitative myelin water imaging atlases for An atlas for human brain myelin content throughout the adult life span https://www.nature.com/articles/s41598-020-79540-3

Advances in prostate cancer (PCa) screening techniques have led to diagnosis of many cases of low-grade and highly localized disease. Conventional whole-gland therapies often result in overtreatment in such cases and debate still surrounds the optimal method of oncologic control. MRI-guided prostate focal laser ablation (FLA) is a minimally invasive treatment option, which has demonstrated potential to destroy localized lesions while sparing healthy prostatic tissue, thereby reducing treatment-related side effects. Many challenges still exist in the development of FLA, including patient selection; tumour localization, visualization, and characterization; needle guidance; and evaluation of treatment efficacy. The objective of this thesis work was to advance and enhance techniques for needle guidance in MRI-guided focal laser ablation (FLA) therapy of PCa. Several steps were taken in achieving this goal. Firstly, we evaluated the overlap between identified lesions and MRI-confirmed ablation regions using conventional needle guidance. Non-rigid thin-plate spline registration of pre-operative and intra-operative images was performed to align lesions with ablation boundaries and quantify the degree of coverage. Complete coverage of the lesion with the ablation zone is a clinically important metric of success for FLA therapy and we found it was not achieved in many cases. Therefore, our next step was to develop an MRI-compatible, remotely actuated mechatronic system for transperineal FLA of prostate cancer. The system allows physicians in the MRI scanner control room to accurately target lesions through 4 degrees of freedom while the patient remains in the scanner bore. To maintain compatibility with the MRI environment, piezoelectric motors were used to actuate the needle guidance templates, the device was constructed from non-ferromagnetic materials, and all cables were shielded from electromagnetic interference. The MR compatibility and needle placement accuracy of the device were evaluated with virtual and phantom targets. The system should next be validated for accuracy and usefulness in a clinical trial where more complex tissue properties and potential patient motion will be encountered. Future advances in modeling the tissue properties and compensating for deformation of the prostate, as well as predicting needle deflection, will further bolster the potential of FLA as option for the management of PCa.

Musculoskeletal (MSK) conditions are a leading cause of pain and disability worldwide. Rehabilitation is critical for recovery from these conditions and for the prevention of long-term disability. Robot-assisted therapy has been demonstrated to provide improvements to stroke rehabilitation in terms of efficiency and patient adherence. However, there are no wearable robot-assisted solutions for patients with MSK injuries. One of the limiting factors is the lack of appropriate models that allow the use of biosignals as an interface input. Furthermore, there are no models to discern the health of MSK patients as they progress through their therapy. This thesis describes the design, data collection, analysis, and validation of a novel muscle health model for elbow trauma patients. Surface electromyography (sEMG) data sets were collected from the injured arms of elbow trauma patients performing 10 upper-limb motions. The data were assessed and compared to sEMG data collected from the patients' contralateral healthy limbs. A statistical analysis was conducted to identify trends relating the sEMG signals to muscle health. sEMG-based classification models for muscle health were developed. Relevant sEMG features were identified and combined into feature sets for the classification models. The classifiers were used to distinguish between two levels of health: healthy and injured (50% baseline accuracy rate). Classification models based on individual motions achieved cross-validation accuracies of 48.2--79.6%. Following feature selection and optimization of the models, cross-validation accuracies of up to 82.1% were achieved. This work suggests that there is a potential for implementing an EMG-based model of muscle health in a rehabilitative elbow brace to assess patients recovering from MSK elbow trauma. However, more research is necessary to improve the accuracy and the specificity of the classification models.

We describe here a simple, cost-effective apparatus for continuous tethered electroencephalographic (EEG) monitoring of spontaneous recurrent seizures in mice. We used a small, low torque slip ring as an EEG commutator, mounted the slip ring onto a standard mouse cage and connected rotary wires of the slip ring directly to animal's implanted headset. Modifications were made in the cage to allow for a convenient installation of the slip ring and accommodation of animal ambient activity. We tested the apparatus for hippocampal EEG recordings in adult C57 black mice. Spontaneous recurrent seizures were induced using extended hippocampal kindling (≥95 daily stimulation). Control animals underwent similar hippocampal electrode implantations but no stimulations were given. Combined EEG and webcam monitoring were performed for 24 h daily for 5–9 consecutive days. During the monitoring periods, the animals moved and accessed water and food freely and showed no apparent restriction in ambient cage activities. Ictal-like hippocampal EEG discharges and concurrent convulsive behaviors that are characteristics of spontaneous recurrent seizures were reliably recorded in a majority of the monitoring experiments in extendedly kindled but not in control animals. However, 1–2 rotary wires were disconnected from the implanted headset in some animals after continuous recordings for ≥5 days. The key features and main limitations of our recording apparatus are discussed.