Développement d'outils d’analyse de la motricité fine pour l’investigation de troubles neuromusculaires : théorie, prototype et mise en application dans le contexte des accidents vasculaires cérébraux

RÉSUMÉ Cette thèse examine la possibilité d’évaluer la susceptibilité à un accident vasculaire cérébral (AVC) à partir des attributs des mouvements humains. Ces travaux reposent sur l’hypothèse selon laquelle l’existence d’un état pré-AVC peut, dans certains cas, être détecté par l’évaluation de la santé neuromotrice du patient. À défaut de disposer de données longitudinales permettant d’étudier directement cette conjecture, nos résultats démontrent que le diagnostic des principaux facteurs de risque d’AVC est effectivement réalisable à partir des propriétés des mouvements. Ces conclusions sont tirées à la suite de l’analyse transversale des réponses de 120 sujets à neuf tests neuromoteurs. Par cette étude des liens entre la motricité et la présence de conditions menant potentiellement à l’AVC, on espère stimuler l’intérêt des chercheurs en santé pour l’hypothèse – rapportée de façon anecdotique par plusieurs cliniciens – de l’existence d’un état pré-AVC. Les investigations nécessaires à cette démonstration ont été menées dans le cadre de la Théorie Cinématique et suivent principalement trois axes directeurs, soit l’étude fondamentale du mouvement humain, le développement d’outils d’extraction permettant la modélisation lognormale des mouvements et l’analyse statistique des paramètres lognormaux dans le but du diagnostic des principaux facteurs de risque d’AVC (diabète, obésité, tabagisme, problèmes cardiaques, alcoolisme, hypertension et hypercholestérolémie). En introduction de la première partie de cette thèse sont répertoriés les différents indices disséminés dans la littérature scientifique étayant l’existence de liens entre les mouvements humains et les principaux facteurs de risque d’AVC. Observant que la présence de tels liens est supportée par l’état des connaissances actuelles, le paradigme offert par la Théorie Cinématique ainsi que la modélisation lognormale qui en découle sont adoptés, puis présentés. L’apparition de profils de forme lognormale au niveau des primitives du mouvement est ensuite expliquée d’un point de vue original. Une fois ces bases établies, il a été possible de procéder à l’analyse des données dont nous disposions, ce qui a mis en lumière un certain nombre de phénomènes fondamentaux relatifs à l’étude du contrôle moteur, dont trois sont particulièrement importants. En premier lieu, il a été relevé que la nature des mouvements est intrinsèquement proportionnelle.----------ABSTRACT This Ph. D. thesis investigates the brain stroke susceptibility assessment based on the movement analysis of data acquired using neuromuscular tests. This work is rooted in the hypothesis of the existence of a pre-stroke state which can sometimes be detected by looking at the properties of a patient’s neuromuscular system. As the study of this hypothesis would require longitudinal data that were unavailable, our analysis concentrates on the demonstration that the brain stroke risk factors can be diagnose from a human movement analysis. This conclusion derives from a transversal study of 120 subject’s responses to nine neuromuscular tests. It is hoped that this investigation on the links between fine motor control and brain stroke risk factors can stimulate the interest of the medical community for the anecdotic report, by some clinicians, of the possible existence of a pre-stroke state. The work presented herein was made under the Kinematic Theory and it follows three main axes which are 1) the fundamental study of human movements, 2) the design of extraction algorithms allowing the lognormal modeling of human motion, and 3) the statistical analysis of the kinematic parameters of human movements for the diagnosis of principal brain stroke risk factors. In the first part of this thesis, an overview is presented of the many observations scattered in the scientific literature concerning the link between the human movements and the main brain stroke risk factors (diabetes, obesity, cigarette smoking, cardiac problems, alcoholism, hypertension and hypercholesterolemia). Building on the observation that the existence of such a link is supported, a modeling framework is chosen and the lognormal models forming its foundations are reported from an original point of view. The application of this methodology to our database allowed the investigation of some fundamental phenomena concerning the study of motor control. Notably, the proportional nature of human motion is examined and compared to the serial representation of psychophysical processes. The delta-lognormal modeling of speed-accuracy tradeoffs (Fitts’ task) has also allowed the discovery of some fundamental aspects related to the control of this kind of movements, such as the increase of the coupling between the motor commands as the task becomes more difficult and the enhancement of the temporal coordination of the neuromuscular action as the geometrical properties of the task are scaled up

Development and evaluation of a test battery for the assessment of brain stroke susceptibility from human movement analysis

This technical report presents the design and the evaluation of a battery of neuromuscular tests to be used for assessing the brain stroke susceptibility from the analysis of human movements. The test battery has been evaluated using a sample of 120 subjects. Preliminary results show the advantages and limitations of the different tests. Suggestions for improvements are discussed. The proposed battery of neuromuscular tests should be of interest for many experimenters working in the field of human movement science. It should also be valuable for engineers, psychologists, and researchers using human movements for the development of diagnostic and neuromuscular assessment tools. For an easier reuse, the guiding sheets for the original battery are included in appendix

Système de synthèse de l'écriture manuscrite par l'utilisation du modèle Sigma-lognormal : bilan de la conception et documentation de l'application SIMSCRIPT

An application was built to simulate complex movements with the use of the sigmalognormal model. The theory used to conduct simulation is shortly described, the built application interface and the applications functionalities are shown and discussed. Finally some simulations results are compared with typical handwritten data acquired from a subject with the use of a digitizer

A Framework for Collaborative Curation of Neuroscientific Literature

Large models of complex neuronal circuits require specifying numerous parameters, with values that often need to be extracted from the literature, a tedious and error-prone process. To help establishing shareable curated corpora of annotations, we have developed a literature curation framework comprising an annotation format, a Python API (NeuroAnnotation Toolbox; NAT), and a user-friendly graphical interface (NeuroCurator). This framework allows the systematic annotation of relevant statements and model parameters. The context of the annotated content is made explicit in a standard way by associating it with ontological terms (e.g., species, cell types, brain regions). The exact position of the annotated content within a document is specified by the starting character of the annotated text, or the number of the figure, the equation, or the table, depending on the context. Alternatively, the provenance of parameters can also be specified by bounding boxes. Parameter types are linked to curated experimental values so that they can be systematically integrated into models. We demonstrate the use of this approach by releasing a corpus describing different modeling parameters associated with thalamo-cortical circuitry. The proposed framework supports a rigorous management of large sets of parameters, solving common difficulties in their traceability. Further, it allows easier classification of literature information and more efficient and systematic integration of such information into models and analyses

Is functional brain connectivity atypical in autism? A systematic review of EEG and MEG studies

Background Although it is well recognized that autism is associated with altered patterns of over-and under-connectivity, specifics are still a matter of debate. Little has been done so far to synthesize available literature using whole-brain electroencephalography (EEG) and magnetoencephalography (MEG) recordings. Objectives 1) To systematically review the literature on EEG/MEG functional and effective connectivity in autism spectrum disorder (ASD), 2) to synthesize and critically appraise findings related with the hypothesis that ASD is characterized by long-range underconnectivity and local overconnectivity, and 3) to provide, based on the literature, an analysis of tentative factors that are likely to mediate association between ASD and atypical connectivity (e.g., development, topography, lateralization). Methods Literature reviews were done using PubMed and PsychInfo databases. Abstracts were screened, and only relevant articles were analyzed based on the objectives of this paper. Special attention was paid to the methodological characteristics that could have created variability in outcomes reported between studies. Results Our synthesis provides relatively strong support for long-range underconnectivity in ASD, whereas the status of local connectivity remains unclear. This observation was also mirrored by a similar relationship with lower frequencies being often associated with underconnectivity and higher frequencies being associated with both under-and over-connectivity. Putting together these observations, we propose that ASD is characterized by a general trend toward an under-expression of lower-band wide-spread integrative processes compensated by more focal, higher-frequency, locally specialized, and segregated processes. Further investigation is, however, needed to corroborate the conclusion and its generalizability across different tasks. Of note, abnormal lateralization in ASD, specifically an elevated left-over-right EEG and MEG functional connectivity ratio, has been also reported consistently across studies. Conclusions The large variability in study samples and methodology makes a systematic quantitative analysis (i.e. meta-analysis) of this body of research impossible. Nevertheless, a general trend supporting the hypothesis of long-range functional underconnectivity can be observed. Further research is necessary to more confidently determine the status of the hypothesis of short-range overconnectivity. Frequency-band specific patterns and their relationships with known symptoms of autism also need to be further clarified

Linking brain stroke risk factors to human movement features for the development of preventive tools

This paper uses human movement analyses to assess the susceptibility of brain stroke, one of the most important causes of disability in elders. To that end, a computerized battery of nine neuromuscular tests has been designed and evaluated with a sample of 120 subjects with or without stoke risk factors. The kinematics of the movements produced was analyzed using a computational neuromuscular model and predictive characteristics were extracted. Logistic regression and linear discriminant analysis with leave-one-out cross-validation was used to infer the probability of presence of brain stroke risk factors. The clinical potential value of movement information for stroke prevention was assessed by computing area under the receiver operating characteristic curve (AUC) for the diagnostic of risk factors based on motion analysis. AUC mostly varying between 0.6 and 0.9 were obtained, depending on the neuromuscular test and the risk factor investigated (obesity, diabetes, hypertension, hypercholesterolemia, cigarette smoking, and cardiac disease). Our results support the feasibility of the proposed methodology and its potential application for the development of brain stroke prevention tools. Although further research is needed to improve this methodology and its outcome, results are promising and the proposed approach should be of great interest for many experimenters open to novel approaches in preventive medicine and in gerontology. It should also be valuable for engineers, psychologists, and researchers using human movements for the development of diagnostic and neuromuscular assessment tools

Linking the main modifiable brain stroke risk factors with human movements features

This paper investigates the assessment of brain stroke susceptibility using human movement analyses. It is supported by the knowledge that 1) many stroke risk factors are correlated with human movement characteristics and 2) the anecdotal reports of motor control disturbance (e.g. in handwritten signatures and handwriting) prior to some cerebrovascular accidents. Thus, to investigate the potential plus-value of human movement information for the development of tools dedicated to stroke prevention, we analyzed the relationship between human motor control and brain stroke risk factors. Hundred and twenty subjects with or without stoke risk factors have performed a neuromuscular test battery. The kinematic of the movements was analyzed using a computational neuromuscular model and predictive characteristics were extracted. Logistic regression and linear discriminant analysis with leave-one-out cross-validation was used to infer, from movements characteristics, the probability of presence of brain stroke risk factors. The clinical potential value of movement information for stroke prevention was assessed by computing area under the receiver operating characteristic curve (AUC) for the diagnostic of risk factors based on motion analysis. AUC mostly varying between 0.6-0.9 are obtained, depending on the neuromuscular test and the risk factor investigated (obesity, diabetes, hypertension, hypercholesterolemia, cigarette smoking, and cardiac disease). Our results support the feasibility of the proposed methodology and its potential application for the development of brain stroke prevention tools. Considering the novelty of this topic, these results are promising. Further research is needed to improve this methodology and its outcome

Piezo-force and vibration analysis of ZnO nanowire arrays for sensor application

To estimate the potential of ZnO nanostructures for force sensing applications, arrays of single nanowires and arrays of nanowire bundles have been fabricated by wet chemical growth method. The piezoelectrical and electrical properties of the single nanowires have been investigated by atomic force microscopy based techniques. The piezoelectric constant d(33) = 15 pm/V has been determined from vibration analyses. The electrical response in the range up to 400 fA upon applying force between 40 nN and 1 mu N has been recorded. The nanowire bundles were studied by electro-mechanical macro probing technique within the force range 1 - 10 mN, where a reproducible response in pA range has been measured

Protein complexes in cells by AI-assisted structural proteomics

Abstract Accurately modeling the structures of proteins and their complexes using artificial intelligence is revolutionizing molecular biology. Experimental data enable a candidate‐based approach to systematically model novel protein assemblies. Here, we use a combination of in‐cell crosslinking mass spectrometry and co‐fractionation mass spectrometry (CoFrac‐MS) to identify protein–protein interactions in the model Gram‐positive bacterium Bacillus subtilis. We show that crosslinking interactions prior to cell lysis reveals protein interactions that are often lost upon cell lysis. We predict the structures of these protein interactions and others in the SubtiWiki database with AlphaFold‐Multimer and, after controlling for the false‐positive rate of the predictions, we propose novel structural models of 153 dimeric and 14 trimeric protein assemblies. Crosslinking MS data independently validates the AlphaFold predictions and scoring. We report and validate novel interactors of central cellular machineries that include the ribosome, RNA polymerase, and pyruvate dehydrogenase, assigning function to several uncharacterized proteins. Our approach uncovers protein–protein interactions inside intact cells, provides structural insight into their interaction interfaces, and is applicable to genetically intractable organisms, including pathogenic bacteria