Quantitative Assessment of Hypoglycaemia-Induced EEG changes in Type 1 Diabetes Subjects

Scopo di questa tesi è valutare quantitativamente come l’ipoglicemia influenza le caratteristiche dell’EEG, analizzando 19 tracciati EEG e i corrispettivi dati BG registrati in parallelo in soggetti T1D durante un esperimento di ipoglicemia indotta.Tre periodi di un’ora, riferiti a iper eu e ipo-glicemia, selezionati dai dati BG, sono elaborati con metodi basati sull’analisi spettrale per scoprire se specifiche caratteristiche dell'EEG cambiano durante il passaggio da eu a ipoglicemiaopenEmbargo per motivi di segretezza e/o di proprietà dei risultati e informazioni di enti esterni o aziende private che hanno partecipato alla realizzazione del lavoro di ricerca relativo alla tes

Quantitative analysis of hypoglycemia-induced EEG alterations in type 1 diabetes

The main risk for patients affected by type 1 diabetes (T1D) is to fall in hypoglycemia, an event which leads to both short and long-terms automatic failure and can be life-threatening especially when occurs at night without subject awareness. Moreover, T1D patients can develop asymptomatic hypoglycemia, reducing the prompt response of the counterregulatory system triggered by the fall in blood glucose. Avoiding hypoglycemia is important in children and adolescents because hypoglycemia episodes may have clinically relevant effects on cognition. Also in adults, cognitive tests assessed that hypoglycemia results in altered cerebral activity, most likely due to the complete dependence of the brain for glucose supply. The first organ influenced by this fall of glucose in the blood is the brain. Indeed, a lot of studies proved the mirroring of cognitive dysfunction due to hypoglycemia in the spectral power of the electroencephalogram (EEG) signal. In particular, the increase of the power in low frequency EEG bands is a well-known effect during hypoglycemia that seems more pronounced in the EEG recording in the posterior areas of the brain. Pilot studies about the real-time processing of the EEG signal to detect hypoglycemia have indicated that it might be possible to alert the patients by means of EEG analysis. The main advantages in exploiting EEG analysis is that the blood glucose threshold to enter in hypoglycemia has large inter-subjects variations, on the contrary the EEG onset in general occurs before the state of hypoglycemia is critical, i.e., the brain starts to experience neuroglycopoenia and its functions completely fail. The main aim of this work is to broaden out the quantitative analysis on the altered EEG activity due to hypoglycemia in T1D patients to identify potential margins of improvement in EEG processing and further features sensitive to hypoglycemia. In particular, the analyses are extended to different domains, i.e., time and frequency domains, to deepen the knowledge on the effects of hypoglycemia in the brain. So far, studies in the literature have mainly evaluated these changes only on a single EEG channel level on the frequency domain, but limited information is available on the hypoglycemia influence on brain network dynamics and on connection between different brain areas. To do so, this dissertation is structured in 7 chapters, briefly presented below. Chapter 1 will start with a brief overview about the impact of T1D and its main effects on daily life. Moreover, the main consequences of hypoglycemia in human brain will be described by reporting the main findings in the literature. Chapter 2 will present the database where EEG data and blood glucose samples were collected in parallel for about 8 h in 31 T1D hospitalized patients during an hyperinsulinemic - hypoglycemic clamp experiment. Chapter 3 will address on the main effects of hypoglycemia in the frequency domain. After testing the well-known changes in the spectral power of the EEG signal during hypoglycemia, a multivariate analysis based on the concept of Information Partial Directed Coherence will be presented. In particular, we will confirm the general slowing in the frequency domain and we will show how hypoglycemia affects the EEG functional connectivity. Chapter 4 will consider the effects of hypoglycemia on EEG complexity. Fractal dimension features, describing both amplitude and frequency properties, will be computed and compared with the results based on Sample Entropy. We will reveal a decrease of EEG signal complexity in the hypoglycemic condition. Chapter 5 will focus on the consequences of hypoglycemia in the so-called microstates or "athoms of thought". We will hypothesize that the changes in the frequency domain and the decrease of the EEG signal complexity in hypoglycemia have in common the same resting EEG electric potential amplitude map. Chapter 6 will describe how hypoglycemia influences the results of cognitive tests, and the relationship between the drop in the tests performance and the EEG quantitative measures presented in the previous chapters. We will find a direct correlation among the changes in the power spectra, the cognitive tests performance and the changes of one resting EEG electric potential amplitude map. Eventually, Chapter 7 will close the dissertation by interpreting the ensemble of the results from both the medical and engineering point of view, and presenting the possible future developments of this work

Seasonal plumage condition variation and the thermal value of the feather coats of house sparrows (Passer domesticus)

Feathers are critical to how birds thermoregulate, and thus their total energy balance. The feather coat insulates birds by trapping air next to the skin and acting as a physical barrier to heat loss. Despite previous work studying thermal balance in birds, relatively few studies have focused on the thermal contribution of the feather coat alone; most studies have focused on physiological and behavioral responses. Moreover, to our knowledge, no studies have directly measured the effect of feather wear through the annual cycle on the thermal performance of the feather coat. To address this, we used a thermal camera to measure the temperature at the surface of the feather coat of live house sparrows (Passer domesticus) in winter (post-molt, unworn feathers) and summer (pre-molt, worn feathers), as well as flat-skins of the same specimens in order to isolate the thermal effect of the feather coat alone. We predicted that worn feather coats would lose more heat than unworn feather coats in both live birds and flat skins. Surprisingly, we found that feather wear had no effect on the thermal performance of the feather coat across seasons. The thermal balance of birds will be better understood when the thermal contribution of the feather coat is directly measured across more species and conditions

Algorithm and software to automatically identify latency and amplitude features of local field potentials recorded in electrophysiological investigation

A function that is called by main_script.m to compute the onset and the maximum latencies and amplitudes from the signal time-derivative. Also the functions that guarantee the correct running of main_script.m. To test the algorithm, invoking only main_script.m is necessary (all the other functions must be contained in the same folder). (M 1 kb

A formação da mão-de-obra para a indústria química: uma retrospectiva histórica

This paper presents a brief review of the socio-historical evolution of the chemical technician and his/her productive praxis, as well as his/her High-School level technical education as situated in the social and political environment in the realm of science and technology. Such an individual is seen as the subject of transforming action, so the approach taken here is not restricted to an immediatic ideology of professional training, but rather attempts to eliminate that reproductive perspective which considers social relations as gears in the capitalist system.Este artigo apresenta uma breve retrospectiva da evolução sociohistórica do técnico em Química e da sua praxis produtiva, voltando-se para uma educação técnica de nível médio, no âmbito social, político e científico-tecnológico. Visa um sujeito de ação transformadora, não se restringindo a uma ideologia imediatista de formação profissional; busca eliminar a perspectiva reprodutivista das relações sociais como engrenagem do sistema capitalista.15116

Brain oscillatory activity in adolescent idiopathic scoliosis

Pathophysiology of Adolescent Idiopathic Scoliosis (AIS) is not yet completely understood. This exploratory study aims to investigate two aspects neglected in clinical practice: a defective postural central nervous system control in AIS, and alterations of body schema due to scoliosis spinal deformities. We recorded EEG data and balance data in four different standing positions in 14 adolescents with AIS and in 14 controls. A re-adaptation of the Image Marking Procedure (IMP) assessed body schema alterations on the horizontal (Body Perception Indices (BPIs)) and vertical direction (interacromial and bisiliac axes inclinations). Our results revealed no differences in balance control between groups; higher EEG alpha relative power over sensorimotor areas ipsilateral to the side of the curve and a significant increase of theta relative power localized over the central areas in adolescents with AIS. The difference in BPI shoulder and BPI waist significantly differed between the two groups. The inclinations of the perceived interacromial axes in adolescents with AIS was opposite to the real inclination. Increased theta activity and alpha lateralization observed may be a compensatory strategy to overcome sensorimotor dysfunction mirrored by altered body schema. Scoliosis onset might be preceded by sensorimotor control impairments that last during curve progression

Editorial: Chasing brain dynamics at their speed: what can time-varying functional connectivity tell us about brain function?

In the past decades, the growing field of network neuroscience has opened new perspectives on the study of the brain and its function. The integration of tools from network analysis and system neuroscience has allowed researchers to explore the properties of brain networks, offering a valuable alternative to traditional methods based on simple subtraction and mass univariate analysis (Sporns, 2010; Behrens and Sporns, 2012). This has led to an exponential growth of connectivity algorithms and methods designed to capture the intrinsic dynamics of human brain networks, both at rest and during active tasks. As a result, a new research direction has emerged. The quantification of spatio-temporal dynamics of functional connectivity (FC) is offering new means to observe a vast repertoire of brain functions. Despite significant advances in this domain, there are still major challenges to address. This is partly due to the rapid and distributed nature of brain interactions, with large-scale networks that constantly evolve and coordinate activity to produce human perception, cognition, and behavior at sub-second timescales. Additionally, brain network activity can vary widely within and across individuals (Finn et al., 2015; Van De Ville et al., 2021), as well as in clinical conditions and brain disorders (see Miao et al.). Thus, modeling whole-brain network dynamics, accounting for the necessary spatial and temporal resolution at both individual and population levels, remains a crucial goal yet to be fully achieved. The present Research Topic contains a collection of methodological and empirical studies that touch upon some of the main challenges in the field, collectively providing insight into the current state of research and the potential solutions for advancing the field of dynamic network neuroscience in the future

Time-varying effective EEG source connectivity: the optimization of model parameters*

Adaptive estimation methods based on general Kalman filter are powerful tools to investigate brain networks dynamics given the non-stationary nature of neural signals. These methods rely on two parameters, the model order p and adaptation constant c, which determine the resolution and smoothness of the time-varying multivariate autoregressive estimates. A sub-optimal filtering may present consistent biases in the frequency domain and temporal distortions, leading to fallacious interpretations. Thus, the performance of these methods heavily depends on the accurate choice of these two parameters in the filter design. In this work, we sought to define an objective criterion for the optimal choice of these parameters. Since residual- and information-based criteria are not guaranteed to reach an absolute minimum, we propose to study the partial derivatives of these functions to guide the choice of p and c. To validate the performance of our method, we used a dataset of human visual evoked potentials during face perception where the generation and propagation of information in the brain is well understood and a set of simulated data where the ground truth is available

Exoskeleton Training Modulates Complexity in Movement Patterns and Cortical Activity in Able-Bodied Volunteers

Robot-aided gait training (RAGT) plays a crucial role in providing high-dose and high-intensity task-oriented physical therapy. The human-robot interaction during RAGT remains technically challenging. To achieve this aim, it is necessary to quantify how RAGT impacts brain activity and motor learning. This work quantifies the neuromuscular effect induced by a single RAGT session in healthy middle-aged individuals. Electromyographic (EMG) and motion (IMU) data were recorded and processed during walking trials before and after RAGT. Electroencephalographic (EEG) data were recorded during rest before and after the entire walking session. Linear and nonlinear analyses detected changes in the walking pattern, paralleled by a modulation of cortical activity in the motor, attentive, and visual cortices immediately after RAGT. Increases in alpha and beta EEG spectral power and pattern regularity of the EEG match the increased regularity of body oscillations in the frontal plane, and the loss of alternating muscle activation during the gait cycle, when walking after a RAGT session. These preliminary results improve the understanding of human-machine interaction mechanisms and motor learning and may contribute to more efficient exoskeleton development for assisted walking