• 2015-2024close
• Open Access close
• Closed Access close
• Restricted close
• Open Source close
• Embargo close
• Publications close
• Research software close
• Other research products close
• Natural Sciences and Engineering Re... close
• CA close
• English close
• Neuroinformatics close

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.

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.

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.

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.

In recent years, both Virtual Reality (VR) and Augmented Reality (AR) technology have shown great promise for the instruction of individuals with autism spectrum disorder (ASD) by simulating real-world experiences in a safe and controlled environment. However, there are many reports of the failure of such research to include individuals with both ASD and Intellectual Disability (ID). The present scoping review consists of 20 studies which utilized VR/AR to teach various skills to children and youth with comorbid ASD and ID. Findings show that within the small number of eligible studies, a great deal of variation exists in essentially every intervention element (e.g., identification of ID, VR/AR equipment, target skills). Beyond increasing the quantity of VR/AR intervention research conducted on this population, the current review suggests the need for greater uniformity and consistency to improve research, practice, and the lives of those with ASD and ID who may benefit from such interventions.

Gas-exchange is the primary function of the lungs and involves removing carbon dioxide from the body and exchanging it within the alveoli for inhaled oxygen. Several different pulmonary, cardiac and cardiovascular abnormalities have negative effects on pulmonary gas-exchange. Unfortunately, clinical tests do not always pinpoint the problem; sensitive and specific measurements are needed to probe the individual components participating in gas-exchange for a better understanding of pathophysiology, disease progression and response to therapy. In vivo Xenon-129 gas-exchange magnetic resonance imaging (129Xe gas-exchange MRI) has the potential to overcome these challenges. When participants inhale hyperpolarized 129Xe gas, it has different MR spectral properties as a gas, as it diffuses through the alveolar membrane and as it binds to red-blood-cells. 129Xe MR spectroscopy and imaging provides a way to tease out the different anatomic components of gas-exchange simultaneously and provides spatial information about where abnormalities may occur. In this thesis, I developed and applied 129Xe MR spectroscopy and imaging to measure gas-exchange in the lungs alongside other clinical and imaging measurements. I measured 129Xe gas-exchange in asymptomatic congenital heart disease and in prospective, controlled studies of long-COVID. I also developed mathematical tools to model 129Xe MR signals during acquisition and reconstruction. The insights gained from my work underscore the potential for 129Xe gas-exchange MRI biomarkers towards a better understanding of cardiopulmonary disease. My work also provides a way to generate a deeper imaging and physiologic understanding of gas-exchange in vivo in healthy participants and patients with chronic lung and heart disease.

Short-ranged connectivity comprise the majority of connections throughout the brain, joining together nearby regions and contributing to important networks that facilitate complex function and cognition. Despite constituting the majority of white matter in the brain and their importance, studies examining short-ranged connections have thus far been limited in part due to the challenges associated with identifying and validating them. Tractography, a computational technique for reconstructing axon trajectories from diffusion magnetic resonance imaging, has been commonly used to identify and study major white connections (e.g. corticospinal tract), which are easier to identify relative to the short-ranged connections. The use of additional constraints (e.g. geometry, regions of interest) together with tractography has enabled the ability to identify short-ranged connections of interest, such as the ”U”-shaped tracts residing just below the cortical surface, and the subcortical connectome tracts found in the deep brain. In this thesis, we aimed to quantify the reliability of such techniques for studying the short- ranged connections and applied them to examine changes to short-ranged connectivity in patients with first episode schizophrenia. First, the reliability of identifying short-ranged, ”U”- shaped tracts is examined in Chapter 1, leveraging geometric constraints for identifying the ”U”-shaped geometry together with clustering techniques to establish distinct tracts. Here, we two different clustering techniques, applying them to two datasets to study both the reliability of identifying short-ranged, ”U”-shaped tracts across different subjects and in a single individual (across different sessions). In Chapter 2, the reliability for identifying the subcortical connectome (short-ranged connections between subcortical structures) is evaluated. Connectivity of the deep brain is often hard to recapitulate due to the multiple orientations contributing to com- plex diffusion signals. Thus, we leveraged regions of interests determined through histological data to aid identification of the short-ranged connections in the compact region. Finally, Chapter 4, uses the techniques from chapter 2 in combination with quantitative measures sensitive to microstructural changes to study changes to short-ranged, ”U”-shaped tracts in the frontal lobes of patients with first-episode schizophrenia (FES). By studying the short-ranged connections in patients with FES, biomarkers associated with clinical presentation may be elucidated and may aid the current understanding to improve future treatment. Overall, the projects presented here quantify the reliability of current techniques for investigating short-ranged connectivity and provides a framework for evaluating of future techniques. Additionally, the techniques evaluated here can be used to elucidate new findings and improve treatment in clinical popula- tions.

Brain nonapeptides have been suggested to regulate social behaviours. However, the contribution of Arginine-Vasotocin (AVT) and Isotocin (IT) to social behaviour in fishes is not well-characterized. Using the guppy (Poecilia reticulata), I first measured association preference for conspecifics in individuals injected with either AVT, an AVT-antagonist, or saline. The time spent associating with conspecifics did not differ significantly among the injection treatments. However, individuals injected with AVT performed more movement among areas of the tank than individuals injected with either the AVT-antagonist or saline, consistent with an effect of AVT on anxiety-related behaviours (i.e. hyperactivity). Second, I measured association preference for conspecifics in individuals injected with IT and an IT-antagonist. Individuals injected with IT spent more time associating with conspecifics than individuals injected with an IT-antagonist, consistent with a positive relationship between IT on shoaling. Third, I compared shoaling behaviour between a high- and a low-predation population and between sexes. Individuals from a high-predation population spent more time associating with conspecifics than individuals from a low-predation population, and females spent more time than males associating with conspecifics. Movement did not differ significantly between populations and sexes. Brain AVT and IT immunoreactivity measurements showed that AVT intensity in the gigantocellular neurons in the preoptic area was higher in individuals from the high-predation population. I found no difference in IT intensity between the two populations and no difference between the sexes in AVT and IT intensity. Finally, I examined the distribution of AVT receptors in the brain of individuals of mixed populations and sexes showing potential sites of action for AVT in the telencephalon, diencephalon, mesencephalon, and rhombencephalon. Overall, my study suggests a role of IT in shoaling behaviour, albeit IT intensity in the preoptic area was not associated with shoaling differences across populations and sexes. I did not observe an effect of AVT on shoaling, but instead showed a positive relationship between AVT and an anxiety-related behaviour, as well as greater AVT intensity in a population with high predation, which suggests the potential of AVT-associated fear as an important response to differences in predation.

{"references": ["Harel et al., (2023). Open design and validation of a reproducible videogame controller for MRI and MEG."]} Full documentation and files required to build the CNeuromod controller.