No description available.

No description available.

Entrainment is the rhythmic coordination of movement with a signal or other person. Most studies on entrainment have looked at synchronization with auditory or visual signals, whereas much less is known about how entrainment emerges mutually between individuals, especially when they are in physical contact with one another. In this dissertation, I empirically explored dance partnering as an ecological model for understanding interpersonal entrainment through haptic interaction. I began by performing a statistical meta-analysis of functional neuroimaging articles devoted to the most common experimental paradigm for entrainment, namely externally-paced finger tapping to an acoustic rhythmic stimulus (Chapter 2). The results showed that the cerebellar vermis was a strong neural marker of entrainment, as it was more activated by externally-paced tapping than by self-paced tapping, whereas the basal ganglia was activated by both types of rhythmic movements. Next, I used functional magnetic resonance imaging (fMRI) with a group of participants trained at couple dancing in order to explore the neural basis of haptic mutual entrainment, with a focus on the dynamics of leading and following (Chapter 3). While mutual interaction overall engaged brain networks involved in somatosensation, internal-body sensation and social cognition, leading showed enhanced activity principally in areas for motor control and self-initiated action, whereas following showed enhanced activity mainly in sensory and social-cognition areas. Finally, I used 3D motion capture to explore multisensory coupling for mutual entrainment at the group level during folk dancing (Chapter 4). The results showed that dancers relied most extensively on haptic coupling to synchronize as a group, whereas auditory and visual coupling were dependent on the spatiotemporal context. These studies advance our understanding of the neural and behavioural mechanisms underlying joint actions in which entrainment emerges mutually through haptic interaction. / Thesis / Doctor of Philosophy (PhD) / Entrainment is the rhythmic coordination of movement with a signal or other person. Most studies on entrainment have looked at synchronization with auditory or visual signals, whereas much less is known about how entrainment emerges mutually between individuals, especially when they are in physical contact with one another. I began my research by performing a statistical analysis of the literature examining the brain basis of synchronization with auditory signals, identifying a key brain area for entrainment. Next, using a group of participants trained at couple dancing, I explored the brain areas engaged when two individuals in physical contact improvised movement together, focusing on who is leading or following the interaction. Finally, I explored how folk dancers use multiple sensory signals (auditory, visual and tactile) to synchronize as a group. These studies advance our understanding of the neural and behavioural mechanisms by which people mutually entrain through physical interaction.

Increasing demands, shrinking financial and human resources, and increased infrastructure deterioration have made the task of maintaining the infrastructure systems more challenging than ever before. Life-cycle cost analysis (LCCA) is an important tool for transportation infrastructure management, which is used extensively to support project level decisions, and is increasingly being applied to enhance network level analysis. However, traditional LCCA tools cannot practically and effectively utilize expert knowledge and handle ambiguous uncertainties. The main objective of this dissertation was to develop enhanced LCCA models using soft computing (mainly fuzzy logic) techniques. The proposed models use available "real-world" information to forecast life-cycle costs of competing maintenance and rehabilitation strategies and support infrastructure management decisions. A critical review of available soft computing techniques and their applications in infrastructure management suggested that these techniques provide appealing alternatives for supporting many of the infrastructure management functions. In particular, LCCA often utilizes information that is uncertain, ambiguous and incomplete, which is obtained from both existing databases and expert opinion. Consequently, fuzzy logic techniques were selected to enhance life-cycle cost analysis of transportation infrastructure investments because they provide a formal approach for the effective treatment of these types of information. The dissertation first proposes a fuzzy-logic-based decision-support model, whose inference rules can be customized according to agency's management policies and expert opinion. The feasibility and practicality of the proposed model is illustrated by its implementation in a life-cycle cost analysis algorithm for comparing and selecting pavement maintenance, rehabilitation and reconstruction (MR&R) policies. To enhance the traditional probabilistic LCCA model, the fuzzy-logic-based model is then incorporated into the risk analysis process. A fuzzy logic approach for determining the timing of pavement MR&R treatments in a probabilistic LCCA model for selecting pavement MR&R strategies is proposed. The proposed approach uses performance curves and fuzzy-logic triggering models to determine the most effective timing of pavement MR&R activities. The application of the approach in a case study demonstrates that the fuzzy-logic-based risk analysis model for LCCA can effectively produce results that are at least comparable to those of the benchmark methods while effectively considering some of the ambiguous uncertainty inherent to the process. Finally, the research establishes a systematic method to calibrate the fuzzy-logic based rehabilitation decision model using real cases extracted from the Long Term Pavement Performance (LTPP) database. By reinterpreting the model in the form of a neuro-fuzzy system, the calibration algorithm takes advantage of the learning capabilities of artificial neural networks for tuning the fuzzy membership functions and rules. The practicality of the method is demonstrated by successfully tuning the treatment selection model to distinguish between rehabilitation (light overlay) and do-nothing cases. / Ph. D.

No / In this paper we propose a unified approach for integrating implicit and explicit knowledge in neurosymbolic systems as a combination of neural and neuro-fuzzy modules. In the developed hybrid system, training data set is used for building neuro-fuzzy modules, and represents implicit domain knowledge. The explicit domain knowledge on the other hand is represented by fuzzy rules, which are directly mapped into equivalent neural structures. The aim of this approach is to improve the abilities of modular neural structures, which are based on incomplete learning data sets, since the knowledge acquired from human experts is taken into account for adapting the general neural architecture. Three methods to combine the explicit and implicit knowledge modules are proposed. The techniques used to extract fuzzy rules from neural implicit knowledge modules are described. These techniques improve the structure and the behavior of the entire system. The proposed methodology has been applied in the field of air quality prediction with very encouraging results. These experiments show that the method is worth further investigation.

Individuals that are diagnosed with a neurodiverse condition (including, among other things, ADHD, autism, dyslexia, and Tourette syndrome) are not feeling included in organizations in today’s society. The feeling of not being included stems from experiencing discrimination, from bullying and harassment, and not given the same opportunities regarding succeeding at work. The discrimination experienced by neurodiverse individuals in work environments is partly a result of the managers' limited knowledge about the various neurodiverse conditions and how to include them in organizations. In Sweden between 10-15% have a neurodiverse condition, 3% of adults have ADHD, 1-2% have autism, 0,5% have Tourette’s syndrome and 5-8% have dyslexia which indicates that the problem with including neurodiverse individuals is affecting a portion of the population as well as the organizations themselves. This study’s purpose is to examine what managers’ ideas are about how to promote inclusion for neurodivergent employees in Swedish organizations. To conduct this research a literature review was carried out to gather knowledge about neurodiversity and the conditions the term entails, inclusion and the problems of employment for individuals with neurodiversity and it guided the research to a gap in previously written studies. An interview guide was created to use during the semi-structured interviews, a total of five interviews were performed both through online video chats and in person to gather relevant, in-depth information to answer the research question. The results indicated that managers are on the right track in progressing towards promoting inclusion of neurodivergent individuals during the stages of attracting, orientation and familiarization, and in training. However, the interviews also indicated that managers could devote more efforts in adapting towards neurodivergent individuals in the stages of recruitment process, performance tracking, progression plans, and in evaluating processes. Interestingly, as seen in the findings regarding the stages of creating awareness and keeping employees, the managers understand the importance of educating themselves and other employees regarding neurodiversity within their organization. Although, the overall findings indicates that their knowledge regarding neurodiversity is insufficient, even though they are advocating for the significance of including neurodivergent individuals in organizations. A possible reason for the lacking adjustment in inclusion practices towards neurodivergent individuals is that organizations need further resources in terms of human resources as well as the economic aspects of it. Finally, the study shows that the majority of the managers had not reflected anything regarding potentially negative outcomes from the signals that inclusion practices can produce and be perceived by other employees within the organization, which can be crucial in understanding how to successfully implement inclusion practices for neurodivergent individuals

When adults with neurological conditions can access occupational therapy (OT) services, they have better outcomes. Unfortunately, many people who are living in the community with these conditions do not participate in OT services. This can compound deficits, lead to re-hospitalization, and negatively impact independence and quality of life. The issues with access for this population, specifically adults aged 18-65, can largely be attributed to lack of awareness of the role and scope of OT, lack of availability of specialized services, and issues with affordability. Evolve Occupational Therapy (Evolve OT) is a program designed specifically for this group, adults with neurological conditions aged 18-65 who have difficulty accessing OT services. This innovative approach to service delivery provides an alternative access pathway, treatment, and payment models that are intentionally designed to increase participation in and utilization of OT services. With this program, it is expected that patients will report increased satisfaction with participation, increased quality of life, and outcomes will hopefully effect change amongst policymakers.

La mémoire épisodique permet de se souvenir d’évènements dans leur contexte spatio-temporel. Les données issues de la neuroimagerie fonctionnelle ont mis en évidence que les régions préfrontales gauche et droite sont activées lors de l’encodage et de la récupération en mémoire épisodique, et ceci même lors de tâches mnésiques relativement simples. Sur la base de ces données, il est attendu que des patients présentant une atteinte frontale devraient présenter des déficits mnésiques. Or, de tels patients ont des performances déficitaires spécifiquement lors de tâches mnésiques complexes. Cette divergence quant à l’implication du cortex préfrontal pourrait s’expliquer par le fait que les activations obtenues à partir des techniques de neuroimagerie fonctionnelle sont le reflet de changements hémodynamiques : elles ne signifient pas que les régions activées soient essentielles au fonctionnement mnésique. La technique des Stimulations Magnétiques Transcrâniennes (SMT) permet de contourner certaines de ces limites et permet d’étudier, d’une manière sécuritaire, l’implication d’une région cérébrale donnée dans une fonction cognitive. Selon les paramètres de stimulations appliquées, il est possible d’interférer ou de faciliter les processus neuronaux d’une région donnée. Après une revue de la question et des principaux enjeux théoriques (Chapitre 1), l’objectif principal de la thèse, détaillé au Chapitre 2, était d’étudier le rôle des régions préfrontales dorsolatérales (CPFDL) en encodage et en récupération épisodique selon la nature verbale ou non-verbale du matériel à traiter et ce, à l’aide des effets inhibiteurs et facilitateurs que peuvent induire les SMT. Les bases neuronales de la mémoire épisodique ont donc été étudiées chez de jeunes adultes en santé dans le cadre de deux études originales, présentées aux Chapitres 3 et 4 de la thèse. En induisant des effets d’interférence avec les SMT, la première étude de la thèse a permis de démontrer le rôle essentiel des CPFDL gauche et droit lors de la mise en jeu des processus d’encodage et de récupération de matériel verbal et non-verbal. Les données d’une seconde étude, tablant sur les effets de facilitation des SMT, ont indiqué qu’il est possible d’améliorer le fonctionnement de la mémoire épisodique lorsque des SMT sont appliquées au niveau des CPFDL gauche et droit durant l’encodage ou la récupération. Les résultats des deux études de la thèse sont analysés à la lumière des données issues d’approches complémentaires lors du Chapitre 5. / Data from neuroimaging studies show right and left prefrontal cortex (PFC) activations during episodic encoding and retrieval. However, because functional neuroimaging findings rely on metabolic/hemodynamic indices, even if activations suggest that the activated areas are involved in a cognitive task; this does not necessarily mean that the activated regions are functionally crucial to episodic memory. Transcranial magnetic stimulation (TMS) is a brain stimulation technique that can transiently and safely interfere with ongoing neuronal activity in a targeted region. Depending on the stimulation parameters, it is possible to interfere with, or facilitate, neuronal activity. The first chapter of this thesis reviews current and past literature about on the prefrontal regions and its relation to episodic memory and different technique. This review lays the ground for the main objective (detailed in Chapter 2): to study the critical role of the dorsolateral PFC (DLPFC) in episodic encoding and retrieval processes, according to the nature of the material (verbal or non-verbal). Two original empirical investigations are included in this thesis. The first article (Chapter 3) highlights how the left and right DLPFC are essential for the encoding and retrieval of verbal and non-verbal information. The second article (Chapter 4) uses the potential facilitation effect of TMS for augmenting memory efficiency in young and healthy adults and actually showed that it is possible to improve the efficiency in episodic memory with TMS. These studies contribute to a better understanding of the role of the DLPFCs in episodic memory and promote TMS as a safe and efficient way to study human memory. Results of both studies are discussed in Chapter 5 in the light of data from other approaches.

Ce mémoire rend compte de l'expertise développée par l'auteur au Centre de recherchede l'Institut universitaire en santé mentale de Québec (CRIUSMQ) en endoscopie fibrée. Il décrit la construction d'un nouveau type de microscope optique, le MicroscopeInterstitiel Panoramique (PIM). Par la juxtaposition d'un court morceau de fibre à gradientd'indice et d'un prisme à l'extrémité d'une fibre monomode, la lumière laser estfocalisée sur le côté de la sonde. Pour former une image, cette dernière est rapidementtournée autour de son axe pendant qu'elle est tirée verticalement par un actuateurpiézo-électrique. Ce design de système rotatif d'imagerie interstitielle peu invasif est uneffort pour limiter les dégâts causés par la sonde tout en imageant la plus grande régionpossible en imagerie optique cérébrale profonde. / This thesis documents the expertise developed by the author at the Centre de recherchede l'Institut universitaire en santé mentale de Québec (CRIUSMQ) in fibered endoscopy, particularly the design and construction of a new kind of optical microscope: ThePanoramic Interstitial Microscope (PIM). Through the juxtaposition of a short piece ofGraded-Index fibre and a prism at the end of a single-mode fibre, laser light is focussedon the side of the probe. To form an image, the latter is quickly spun around its axiswhile it is being pulled vertically by a piezoelectric actuator. This minimally invasivefluorescence rotary interstitial imaging system is an endeavor to limit the damage causedby the probe while imaging enough tissue to provide good context to the user in deep brain optical imaging.

L’étude expérimentale de la résilience d’un réseau complexe repose sur la capacité à reproduire l’organisation structurelle et fonctionnelle du réseau à l’étude. Ayant choisi le réseau neuronal du poisson-zèbre larvaire comme modèle animal pour sa transparence, on peut utiliser des techniques telles que l’imagerie calcique par feuillet de lumière pour imager son cerveau complet plus de deux fois par seconde à une résolution spatiale cellulaire. De par les bonnes résolutions spatiale et temporelle, les données à segmenter représentent par le fait même un gros volume de données qui ne peuvent être traitées manuellement. On doit donc avoir recours à des techniques numériques pour segmenter les neurones et extraire leur activité.Trois techniques de segmentation ont été comparées, soit le seuil adaptatif (AT), la forêtd’arbres décisionnels (ML), ainsi qu’un réseau de neurones à convolution (CNN) déjà entrainé. Alors que la technique du seuil adaptatif permet l’identification rapide et presque sans erreurdes neurones les plus actifs, elle génère beaucoup plus de faux négatifs que les deux autres méthodes. Au contraire, la méthode de réseaux de neurones à convolution identifie plus deneurones, mais en effectuant plus de faux positifs qui pourront, dans tous les cas, être filtrés parla suite. En utilisant le score F1 comme métrique de comparaison, les performances moyennes de la technique de réseau de neurones (F1= 59,2%) surpassent celles du seuil adaptatif (F1= 25,4%) et de forêt d’arbres de décisions (F1= 48,8%). Bien que les performances semblent faibles comparativement aux performances généralement présentées pour les réseauxde neurones profonds, il s’agit ici d’une performance similaire à celle de la meilleure techniquede segmentation connue à ce jour, soit celle du 3dCNN, présentée dans le cadre du concours neurofinder (F1= 65.9%). / The experimental study of the resilience of a complex network lies on our capacity to reproduceits structural and functional organization. Having chosen the neuronal network of the larvalzebrafish as our animal model for its transparency, we can use techniques such as light-sheet microscopy combined with calcium imaging to image its whole brain more than twice every second, with a cellular spatial resolution. Having both those spatial and temporal resolutions, we have to process and segment a great quantity of data, which can’t be done manually. Wethus have to resort to numerical techniques to segment the neurons and extract their activity. Three segmentation techniques have been compared : adaptive threshold (AT), random deci-sion forests (ML), and a pretrained deep convolutional neural network. While the adaptive threshold technique allow rapid identification and with almost no error of the more active neurons, it generates many more false negatives than the two other methods. On the contrary, the deep convolutional neural network method identify more neurons, but generates more false positives which can be filtered later in the proces. Using the F1 score as our comparison metrics, the neural network (F1= 59,2%) out performs the adaptive threshold (F1= 25,4%) and random decision forests (F1= 48,8%). Even though the performances seem lower compared to results generally shown for deep neural network, we are competitive with the best technique known to this day for neurons segmentation, which is 3dCNN (F1= 65.9%), an algorithm presented in the neurofinder challenge.