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HIGHLIGHTS AxonPacking: Open-source software for simulating white matter microstructure.Validation on a theoretical disk packing problem.Reproducible and stable for various densities and diameter distributions.Can be used to study interplay between myelin/fiber density and restricted fraction.Quantitative Magnetic Resonance Imaging (MRI) can provide parameters that describe white matter microstructure, such as the fiber volume fraction (FVF), the myelin volume fraction (MVF) or the axon volume fraction (AVF) via the fraction of restricted water (fr). While already being used for clinical application, the complex interplay between these parameters requires thorough validation via simulations. These simulations required a realistic, controlled and adaptable model of the white matter axons with the surrounding myelin sheath. While there already exist useful algorithms to perform this task, none of them combine optimisation of axon packing, presence of myelin sheath and availability as free and open source software. Here, we introduce a novel disk packing algorithm that addresses these issues. The performance of the algorithm is tested in term of reproducibility over 50 runs, resulting density, and stability over iterations. This tool was then used to derive multiple values of FVF and to study the impact of this parameter on fr and MVF in light of the known microstructure based on histology sample. The standard deviation of the axon density over runs was lower than 10−3 and the expected hexagonal packing for monodisperse disks was obtained with a density close to the optimal density (obtained: 0.892, theoretical: 0.907). Using an FVF ranging within [0.58, 0.82] and a mean inter-axon gap ranging within [0.1, 1.1] μm, MVF ranged within [0.32, 0.44] and fr ranged within [0.39, 0.71], which is consistent with the histology. The proposed algorithm is implemented in the open-source software AxonPacking (https://github.com/neuropoly/axonpacking) and can be useful for validating diffusion models as well as for enabling researchers to study the interplay between microstructure parameters when evaluating qMRI methods.

Chronic systemic inflammatory conditions, such as atherosclerosis, diabetes and obesity are associated with increased risk of stroke, which suggests that systemic inflammation may contribute to the development of stroke in humans. The hypothesis that systemic inflammation may induce brain pathology can be tested in animals, and this was the key objective of the present study. First, we assessed inflammatory changes in the brain in rodent models of chronic, systemic inflammation. PET imaging revealed increased microglia activation in the brain of JCR-LA (corpulent) rats, which develop atherosclerosis and obesity, compared to the control lean strain. Immunostaining against Iba1 confirmed reactive microgliosis in these animals. An atherogenic diet in apolipoprotein E knock-out (ApoE−/−) mice induced microglial activation in the brain parenchyma within 8 weeks and increased expression of vascular adhesion molecules. Focal lipid deposition and neuroinflammation in periventricular and cortical areas and profound recruitment of activated myeloid phagocytes, T cells and granulocytes into the choroid plexus were also observed. In a small, preliminary study, patients at risk of stroke (multiple risk factors for stroke, with chronically elevated C-reactive protein, but negative MRI for brain pathology) exhibited increased inflammation in the brain, as indicated by PET imaging. These findings show that brain inflammation occurs in animals, and tentatively in humans, harbouring risk factors for stroke associated with elevated systemic inflammation. Thus a “primed” inflammatory environment in the brain may exist in individuals at risk of stroke and this can be adequately recapitulated in appropriate co-morbid animal models.

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.

This Neuroimaging data management plan (DMP) template is designed to be completed in two phases: Phase 1 questions probe at a high-level, seeking information about the general direction of the study. Normally, researchers will be able to respond to phase 1 questions at the outset of a project. Phase 2 questions seek greater detail. It is understood that these answers will often depend on the outcome of several steps in the research project, such as: a literature review, imaging protocol design and experimental design, or running multiple pilot subjects and interpreting the outcome. As these details become known, the DMP can and should be revisited. This approach underscores that DMPs are living documents that evolve throughout a research project.

{"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.

The purpose of this thesis was to design, build and test a prototype artifact suppressing electroencephalogram data acquisition system (AS-EEG-DAQ-S) to collect electroencephalogram (EEG) evoked potential (EP) data during repetitive transcranial magnetic stimulation (rTMS) without the EEG signal being masked by transcranial magnetic stimulation (TMS) artifact. A functional AS-EEG-DAQ-S capable of blocking TMS artifact would provide for the first time a quantitative measurement system to assist in optimal TMS coil positioning during the rTMS treatment of depression, an alternative to electroconvulsive therapy (ECT). This thesis provides the details for an AS-EEGDAQ-S. Preliminary TMS EP results on a human subject were collected. Results showed transcallosal conduction times of 12ms to 31ms, which are consistent with those predicted and collected by other researchers in the TMS field. The first portion of this work provides electrode heating data for modem rTMS Paradigms for the recording ofEEG during rTMS. The concern is that during rTMS EEG electrodes can heat to an unsafe temperature. Seven electrode types were tested: silver/silver chloride, silver cup, gold cup, notched gold cup, notched silver cup, notched gold-plated silver cup, and carbon. All electrodes tested are commercially available, including the carbon electrodes designed for MRI use. The three notched electrodes tested were standard electrodes notched using metal clippers to reduce induced currents. Induced currents are responsible for electrode heating during rTMS and can cause burns to the skin. The results ,of this study show that electrode heating is a concern when collecting BEG during rTMS. However, a number of standard electrodes or slightly modified standard electrodes are suitable for recording BEG during rTMS if certain stimulating parameters are adhered to. The second portion of this work provides the detailed development and design of the AS-EEG-DAQ-S. Four different approaches were tested and their ability to withstand a TMS pulse compared. Short circuiting the input pins of a commercially available EEG amplifier was the first approach tried and yielded only marginal results due to the switches used being designed for digital logic, transistor built, and creating an undesirable offset between input pins. The second approach tested involved continuing to work with a commercially available EEG amplifier and implementing a sample-and-hold circuit between the patient and the EEG machine inputs. This approach had the drawback of requiring that the BEG signal be attenuated back to EEG signal levels, which are near noise amplitude levels. The third approach involved using a high bandwidth amplification circuit to recover quicker from the baseline voltage offset created by the TMS artifact. However, increasing the bandwidth also allows the artifact to saturate the input amplifiers, which then require on the order of 500ms to recover fully. The fourth approach involved combining the second and third approaches to create a high bandwidth amplifier that incorporates a sample-and-hold circuit to prevent amplifier saturation when gain is increased. The fourth approach provide the high bandwidth and artifact blocking behavior desired. Thesis Master of Applied Science (MASc)

Unhealthy and stress induced lifestyles have resulted in an increase in demand for sleep associated technology. Medeor is a smart headband that acts as a health tracker by monitoring an individual's sleep cycle, pulse, and snoring. The hardware system includes the pulse sensor, EEG, microphone (to detect snoring), and the microcontroller system. The software requirements involve signal processing and application that allows the user to receive their health data. The application will display important information, suggestions, and recommended actions and safety measures, such as calling a caretaker when irregular data is recorded.

In healthy individuals, behavioral outcomes are highly associated with the variability on brain regional structure or neurochemical phenotypes. Similarly, in the context of neurodegenerative conditions, neuroimaging reveals that cognitive decline is linked to the magnitude of atrophy, neurochemical declines, or concentrations of abnormal protein aggregates across brain regions. However, modeling the effects of multiple regional abnormalities as determinants of cognitive decline at the voxel level remains largely unexplored by multimodal imaging research, given the high computational cost of estimating regression models for every single voxel from various imaging modalities. VoxelStats is a voxel-wise computational framework to overcome these computational limitations and to perform statistical operations on multiple scalar variables and imaging modalities at the voxel level. VoxelStats package has been developed in Matlab® and supports imaging formats such as Nifti-1, ANALYZE, and MINC v2. Prebuilt functions in VoxelStats enable the user to perform voxel-wise general and generalized linear models and mixed effect models with multiple volumetric covariates. Importantly, VoxelStats can recognize scalar values or image volumes as response variables and can accommodate volumetric statistical covariates as well as their interaction effects with other variables. Furthermore, this package includes built-in functionality to perform voxel-wise receiver operating characteristic analysis and paired and unpaired group contrast analysis. Validation of VoxelStats was conducted by comparing the linear regression functionality with existing toolboxes such as glim_image and RMINC. The validation results were identical to existing methods and the additional functionality was demonstrated by generating feature case assessments (t-statistics, odds ratio, and true positive rate maps). In summary, VoxelStats expands the current methods for multimodal imaging analysis by allowing the estimation of advanced regional association metrics at the voxel level.