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The ACLeeve is a portable and easy to use electromyography (EMG) device to be used during ACL injury recovery to aid in the rehabilitation process. The ACLeeve is wrapped around the user's leg using a knee sleeve and electrodes, and then wirelessly transfers data to the user's phone or laptop. To achieve this, the ACLeeve must be small and lightweight, with electronics and software capable of detecting EMG readings and then transmitting them over a secure wireless connection. As well, the ACLeeve must be safe for the user and conform to all relevant standards. ACLeeve will monitor the user's movements of both quadriceps using surface EMGs (SEMGs) and Strain Sensors. These devices will be actively transmitting the collected data to a microprocessor, which will then process and send the data to an external device for further software analysis. The ACLeeve will provide real-time feedback to the user (audio or visual) regarding the performance of their movements, which will allow the user to physically adjust in order to achieve better long-term results.Our software will perform long-term analyses which will evaluate the progress of obtaining the goal of 80-90% asymmetry between the user's ACL-injured quadricep and their other healthy quadricep.

L’objectif de cette thèse était la validation de l’existence ainsi que la découverte de nouveaux sous-types au sein de la maladie d’Alzheimer, première cause de démence au monde. Afin d’explorer son hétérogénéité, nous avons employé des méthodes d’apprentissage profond appliquées à une modalité de neuroimagerie, l’imagerie par résonance magnétique structurelle.Cependant, la découverte de biais méthodologiques importants dans de nombreuses études de notre domaine, ainsi que l’absence de consensus de la communauté sur la manière d’interpréter les résultats des méthodes d’apprentissage profond a fait en partie dévier la thèse de son objectif principal pour s’orienter d’avantage vers des problématiques de validation, de robustesse et d’interprétabilité de l’apprentissage profond. Ainsi, trois études expérimentales ont été menées pour s’assurer de la capacité des réseaux profonds de correctement détecter la maladie. La première est une étude expérimentale de méthodes d’apprentissage profond pour la classification de la maladie d’Alzheimer et a permis d’établir une juste comparaison des méthodes. La seconde étude a permis de constater un manque de robustesse de la classification avec l’apprentissage profond en termes de motifs d’atrophie découverts à l’aide de méthodes d’interprétabilité. Enfin, la dernière étude propose une méthode de découverte de sous-types aidée par l’augmentation de données. Bien que fonctionnant sur des données synthétiques, celle-ci ne généralise pas aux données réelles.Une contribution majeure de la thèse est la librairie ClinicaDL, grâce à laquelle les résultats expérimentaux de la thèse ont été produits de manière à être reproductibles. The goal of this PhD was the validation of the existence and the discovery of new subtypes of Alzheimer’s disease, the first cause of dementia worldwide. Indeed, despite its discovery more than a century ago, this disease is still not well defined and existing treatments are only weakly effective, possibly because several phenotypes exist within the disease. In order to explore its heterogeneity, we employed deep learning methods applied to a neuroimaging modality, structural magnetic resonance imaging.However, the discovery of important methodological biases in many studies in our field, as well as the lack of consensus regarding deep learning interpretability, partly changed the main objective of the PhD to focus more on issues of validation, robustness and interpretability of deep learning. Then, to correctly assess the ability of deep learning to detect Alzheimer’s disease, three experimental studies were conducted. The first one is a study of deep learning methods for Alzheimer’s classification and allowed a fair comparison of the methods. The second study found a lack of robustness of classification with deep learning in terms of atrophy patterns discovered using interpretability methods. Finally, the last study proposed a subtype discovery method aided by data augmentation. Although it works on synthetic data, it does not generalize to real data.Experimental results of this PhD were obtained thanks to ClinicaDL, one major contribution of this PhD. It is an open source Python library that was used to improve the reproducibility of deep learning experiments.

Although a number of cytoskeletal derangements have been described in the setting of traumatic axonal injury (TAI), little is known of early structural changes that may serve to initiate a cascade of further axonal degeneration. Recent work by the authors has examined conformational changes in cytoskeletal constituents of neuronal axons undergoing traumatic axonal injury (TAI) following focal compression through confocal imaging data taken in vitro and in situ. The present study uses electron microscopy to understand and quantify in vitro alterations in the ultrastructural composition of microtubules and neurofilaments within neuronal axons of rats following focal compression. Standard transmission electron microscopy processing methods are used to identify microtubules, while neurofilament identification is performed using antibody labeling through gold nanoparticles. The number, density, and spacing of microtubules and neurofilaments are quantified for specimens in sham Control and Crushed groups with fixation at <1min following load. Our results indicate that the axon caliber dependency known to exist for microtubule and neurofilament metrics extends to axons undergoing TAI, with the exception of neurofilament spacing, which appears to remain constant across all Crushed axon diameters. Confidence interval comparisons between Control and Crushed cytoskeletal measures suggests early changes in the neurofilament spatial distributions within axons undergoing TAI may precede microtubule changes in response to applied loads. This may serve as a trigger for further secondary damage to the axon, representing a key insight into the temporal aspects of cytoskeletal degeneration at the component level, and suggests the rapid removal of neurofilament sidearms as one possible mechanism.

Low health literacy (HL) increases the risk of adverse stroke-related health outcomes. The aim of this review was to identify 1. what the quality and what the limitations to educational materials used to improve HL in stroke patients are 2. what the levels of HL among stroke patients and stroke survivors are, and 3. how HL and stroke literacy levels affect health-related behaviours and outcomes of stroke patients. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. 6 computerized databases and gray literature sources were searched: MEDLINE, OVID, EMBSE, CINAHL, Cochrane library, Web of Science, and Health and Psychosocial Instruments, and Google Scholar. Papers published in English between January 01, 2000 and August 01, 2020 were included. Five themes were identified across the 26 studies regarding the education and measurement of stroke with relevance to HL. This review concludes that current instruments used to improve HL in stroke are inadequate as they fail to provide a holistic assessment of health literacy, especially concerning stroke patients and stroke literacy. This review identified a paucity of literature on HL in relation to stroke management and outcomes. Therefore, the authors are in strong favour of future research prioritizing the development of effective tools to assess HL and develop best-practice guidelines for stroke education materials.

Example code for the multivariate template creation process used for Myelin imaging in the central nervous system: Comparison of multi-echo T2 relaxation and steady-state approaches

Release Notes Minor release, incorporate major bug fixes and recommendations from OHBM meeting. CHANGES [FIX] unique already creates an index, unique made a repetitive index (#600) @jdkent [ENH] add base_study attribute (#599) @jdkent [FIX] ignore string if doi/pmid/name string is empty (#598) @jdkent [ENH] add conditions and weights to specification (#597) @jdkent [ENH] add studyset-references endpoint (#596) @jdkent chore(deps): bump @cypress/request and cypress in /compose/neurosynth-frontend (#595) @dependabot 197 display user icon name (#591) @nicoalee [ENH] add username and neurostore studyset script (#590) @jdkent [FIX] nick feedback (#589) @jdkent [ENH] add usernames to compose (#588) @jdkent [MAINT] update openapi to main branch (#586) @jdkent [ENH] bulk upload (#585) @jdkent 565 update the advanced search input (#583) @nicoalee [ENH] add username as resource attribute (#584) @jdkent 561 switch api from studies to abstract studies (#582) @nicoalee [FIX] id not showing up for info=true (#581) @jdkent [ENH] using an iterator over a list comprehension should be faster (#578) @jdkent [FIX] add data_type to base_study (#577) @jdkent [FIX] info query param behavior (#576) @jdkent [ENH] make it quicker to filter studies by whether they have images/points. (#574) @jdkent chore(deps): bump tough-cookie and @cypress/request in /compose/neurosynth-frontend (#569) @dependabot

Release Notes Update licensing for zenodo integration Changes [FIX] Update license and license fields (#117)

The main organ of the human central nervous system, the human brain, is one of the most complex organs in the human body. The causes of many brain diseases and disorders, such as Alzheimer’s disease, and their ideal treatments are still not fully understood with the current medical technology. With medical imaging techniques such as magnetic resonance imaging (MRI), magnetoencephalography (MEG), and electroencephalography (EEG), the data obtained from these techniques can be used to study and examine brain diseases and disorders. This project focuses on utilizing a surface registration method on multiple brain surfaces to obtain various geometric transformations for brain studies, and the implementation of the analysis pipeline on a high performance computing (HPC) environment. Due to the infeasibility on runtime for performing surface registration between one template brain surface and multiple target brain surfaces, an approach to perform sub-surface extraction on each brain surface and computation on a HPC environment has been employed. This has allowed a significant reduction in runtime and has also allowed the results to be obtained within reasonable time.

bidspm is a set of pipelines and tools for Octave / MATLAB to process and analyze BIDS data sets using SPM12. Please check the code at: https://github.com/cpp-lln-lab/bidspm