Intention Tremor and Deficits of Sensory Feedback Control in Multiple Sclerosis: a Pilot Study

Background Intention tremor and dysmetria are leading causes of upper extremity disability in Multiple Sclerosis (MS). The development of effective therapies to reduce tremor and dysmetria is hampered by insufficient understanding of how the distributed, multi-focal lesions associated with MS impact sensorimotor control in the brain. Here we describe a systems-level approach to characterizing sensorimotor control and use this approach to examine how sensory and motor processes are differentially impacted by MS. Methods Eight subjects with MS and eight age- and gender-matched healthy control subjects performed visually-guided flexion/extension tasks about the elbow to characterize a sensory feedback control model that includes three sensory feedback pathways (one for vision, another for proprioception and a third providing an internal prediction of the sensory consequences of action). The model allows us to characterize impairments in sensory feedback control that contributed to each MS subject’s tremor. Results Models derived from MS subject performance differed from those obtained for control subjects in two ways. First, subjects with MS exhibited markedly increased visual feedback delays, which were uncompensated by internal adaptive mechanisms; stabilization performance in individuals with the longest delays differed most from control subject performance. Second, subjects with MS exhibited misestimates of arm dynamics in a way that was correlated with tremor power. Subject-specific models accurately predicted kinematic performance in a reach and hold task for neurologically-intact control subjects while simulated performance of MS patients had shorter movement intervals and larger endpoint errors than actual subject responses. This difference between simulated and actual performance is consistent with a strategic compensatory trade-off of movement speed for endpoint accuracy. Conclusions Our results suggest that tremor and dysmetria may be caused by limitations in the brain’s ability to adapt sensory feedback mechanisms to compensate for increases in visual information processing time, as well as by errors in compensatory adaptations of internal estimates of arm dynamics

The role of ball backspin alignment and variability in basketball shooting accuracy

Interaction between the shooting hand and ball at the moment a basketball is released generates a three dimensional backspin of the ball. This study is the first to investigate how characteristics of the backspin alignment and variability contribute to lateral shooting accuracy. Spin axis (SA) direction and backspin magnitude were measured on 25 shot attempts for 26 collegiate basketball players (male: n = 16, female: n = 10). The mean SA alignment, as viewed from the shooting hand side, was found to be tipped down and towards the target (p \u3c 0.001). Standard deviations (SD) in the SA alignment were strong predictors of lateral accuracy (vertical SD: r = 0.80, p \u3c 0.001, forward-backward SD: r = 0.51, p = 0.01), with variation in the vertical alignment being the best predictor. No significant correlation between mean SA misalignment and lateral accuracy was observed. However, intra-individual relationships between SA misalignment and lateral error revealed that individuals tended to have 0.17 degrees more misalignment for each cm of lateral error (p \u3c 0.001, 95% CI: 0.24–0.09). These indicate that while an individual’s mean alignment may not predict lateral accuracy, improving one’s SA alignment and reducing alignment variability may increase lateral accuracy

Semiconducting Polymers for Electronic Biosensors and Biological Interfaces

Bioeletronics aims at the direct coupling of biomolecular function units with standard electronic devices. The main limitations of this field are the material needed to interface soft living entities with hard inorganic devices. Conducting polymers enabled the bridging between these two separate worlds, owing to their biocompatibility, soft nature and the ability to be tailored according to the required application. In particular, the intrinsically conductive poly(3,4-ethylenedioxythiophene):poly(styrenesulfonic acid) (PEDOT:PSS) is one of the most promising polymers, having an excellent chemical and thermal stability, reversible doping state and high conductivity. This thesis relies on the use of PEDOT:PSS as semiconducting material for biological interfaces and biosensors. In detail, OECTs were demonstrated to be able to real-time monitor growth and detachment of both strong-barrier and no-barrier cells, according to the patterning of the device active area and the selected geometry. Thus, these devices were employed to assess silver nanoparticles (AgNPs) toxicity effects on cell lines, allowing further insights on citrate-coated AgNPs uptake by the cells and their toxic action, while demonstrating no cytotoxic activity of EG6OH-coated AgNPs. Moreover, PEDOT:PSS OECTs were proved to be capable of detecting oxygen dissolved in KCl or even cell culture medium, in the oxygen partial pressure range of 0-5%. Furthermore, PEDOT:PSS OECTs were biofunctionalized to impart specificity on the device sensing capabilities, through a biochemical functionalization strategy, electrically characterized. The resulting devices showed a proof of concept detection of a fundamental cytokine for cells undergoing osteogenic differentiation. Finally, PEDOT:PSS thickness-controlled films were employed as biocompatible, low-impedance and soft interfaces between the animal nerve and a gold electrode. The introduction of the plasticizer polyethylene glycol (PEG) enhanced the elasticity of the polymer, while keeping good conductivity and low-impedance properties. An in-vivo, chronic recording of the renal sympathetic nerve activity in rats demonstrated the efficiency of the device

Markerless Human Motion Analysis

Measuring and understanding human motion is crucial in several domains, ranging from neuroscience, to rehabilitation and sports biomechanics. Quantitative information about human motion is fundamental to study how our Central Nervous System controls and organizes movements to functionally evaluate motor performance and deficits. In the last decades, the research in this field has made considerable progress. State-of-the-art technologies that provide useful and accurate quantitative measures rely on marker-based systems. Unfortunately, markers are intrusive and their number and location must be determined a priori. Also, marker-based systems require expensive laboratory settings with several infrared cameras. This could modify the naturalness of a subject\u2019s movements and induce discomfort. Last, but not less important, they are computationally expensive in time and space. Recent advances on markerless pose estimation based on computer vision and deep neural networks are opening the possibility of adopting efficient video-based methods for extracting movement information from RGB video data. In this contest, this thesis presents original contributions to the following objectives: (i) the implementation of a video-based markerless pipeline to quantitatively characterize human motion; (ii) the assessment of its accuracy if compared with a gold standard marker-based system; (iii) the application of the pipeline to different domains in order to verify its versatility, with a special focus on the characterization of the motion of preterm infants and on gait analysis. With the proposed approach we highlight that, starting only from RGB videos and leveraging computer vision and machine learning techniques, it is possible to extract reliable information characterizing human motion comparable to that obtained with gold standard marker-based systems

Impedance modulation: a means to cope with neuromuscular noise

Dieen, J.H. [Promotor]van Beek, P.J. [Promotor

Robot-assisted fMRI assessment of early brain development

Preterm birth can interfere with the intra-uterine mechanisms driving cerebral development during the third trimester of gestation and often results in severe neuro-developmental impairments. Characterizing normal/abnormal patterns of early brain maturation could be fundamental in devising and guiding early therapeutic strategies aimed at improving clinical outcome by exploiting the enhanced early neuroplasticity. Over the last decade the morphology and structure of the developing human brain has been vastly characterized; however the concurrent maturation of brain function is still poorly understood. Task-dependent fMRI studies of the preterm brain can directly probe the emergence of fundamental neuroscientific notions and also provide clinicians with much needed early diagnostic and prognostic information. To date, task-fMRI studies of the preterm population have however been hampered by methodological challenges leading to inconsistent and contradictory results. In this thesis I present a modular and flexible system to provide clinicians and researchers with a simple and reliable solution to deliver computer-controlled stimulation patterns to preterm infants during task-fMRI experiments. The system is primarily aimed at studying the developing sensori-motor system as preterm infants are often affected by neuro-motor dysfunctions such as cerebral palsy. Wrist and ankle robotic stimulators were developed and firstly used to study the emerging somatosensory “homunculus”. The wrist robotic stimulator was then used to characterize the development of the sensori-motor system throughout the mid-to-late preterm period. An instrumented pacifier system was also developed to explore the potential sensorimotor modulation of early sucking activity; however, despite its clear potential to be employed in future fMRI studies, results have not yet been obtained on preterm infants. Functional difficulties associated with prematurity are likely to extend to multi-sensory integration, and the olfactory system currently remains under-investigated despite its clear developmental importance. A custom olfactometer was developed and used to assess its early functionality.Open Acces

Models and Analysis of Vocal Emissions for Biomedical Applications

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies

AutoGraff: towards a computational understanding of graffiti writing and related art forms.

The aim of this thesis is to develop a system that generates letters and pictures with a style that is immediately recognizable as graffiti art or calligraphy. The proposed system can be used similarly to, and in tight integration with, conventional computer-aided geometric design tools and can be used to generate synthetic graffiti content for urban environments in games and in movies, and to guide robotic or fabrication systems that can materialise the output of the system with physical drawing media. The thesis is divided into two main parts. The first part describes a set of stroke primitives, building blocks that can be combined to generate different designs that resemble graffiti or calligraphy. These primitives mimic the process typically used to design graffiti letters and exploit well known principles of motor control to model the way in which an artist moves when incrementally tracing stylised letter forms. The second part demonstrates how these stroke primitives can be automatically recovered from input geometry defined in vector form, such as the digitised traces of writing made by a user, or the glyph outlines in a font. This procedure converts the input geometry into a seed that can be transformed into a variety of calligraphic and graffiti stylisations, which depend on parametric variations of the strokes

Computational Approaches to Explainable Artificial Intelligence:Advances in Theory, Applications and Trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications

Index to 1986 NASA Tech Briefs, volume 11, numbers 1-4

Short announcements of new technology derived from the R&D activities of NASA are presented. These briefs emphasize information considered likely to be transferrable across industrial, regional, or disciplinary lines and are issued to encourage commercial application. This index for 1986 Tech Briefs contains abstracts and four indexes: subject, personal author, originating center, and Tech Brief Number. The following areas are covered: electronic components and circuits, electronic systems, physical sciences, materials, life sciences, mechanics, machinery, fabrication technology, and mathematics and information sciences