14 research outputs found

    Hardware-efficient data compression in wireless intracortical brain-machine interfaces

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    Brain-Machine Interfaces (BMI) have emerged as a promising technology for restoring lost motor function in patients with neurological disorders and/or motor impairments, e.g. paraplegia, amputation, stroke, spinal cord injury, amyotrophic lateral sclerosis, etc. The past 2 decades have seen significant advances in BMI performance. This has largely been driven by the invention and uptake of intracortical microelectrode arrays that can isolate the activity of individual neurons. However, the current paradigm involves the use of percutaneous connections, i.e. wires. These wires carry the information from the intracortical array implanted in the brain to outside of the body, where the information is used for neural decoding. These wires carry significant long-term risks ranging from infection, to mechanical injury, to impaired mobility and quality of life for the individual. Therefore, there is a desire to make intracortical BMIs (iBMI) wireless, where the data is communicated out wirelessly, either with the use of electromagnetic or acoustic waves. Unfortunately, this consumes a significant amount of power, which is dissipated from the implant in the form of heat. Heating tissue can cause irreparable damage, and so there are strict limits on heat flux from implants to cortical tissue. Given the ever-increasing number of channels per implant, the required communication power is now exceeding the acceptable cortical heat transfer limits. This cortical heating issue is hampering widespread clinical use. As such, effective data compression would bring Wireless iBMIs (WI-BMI) into alignment with heat transfer limits, enabling large channel counts and small implant sizes without risking tissue damage via heating. This thesis addresses the aforementioned communication power problem from a signal processing and data compression perspective, and is composed of two parts. In the first part, we investigate hardware-efficient ways to compress the Multi-Unit Activity (MUA) signal, which is the most common signal in modern iBMIs. In the second and final part, we look at efficient ways to extract and compress the high-bandwidth Entire Spiking Activity signal, which, while underexplored as a signal, has been the subject of significant interest given its ability to outperform the MUA signal in neural decoding. Overall, this thesis introduces hardware-efficient methods of extracting high-performing neural features, and compressing them by an order of magnitude or more beyond the state-of-the-art in ultra-low power ways. This enables many more recording channels to be fit onto intracortical implants, while remaining within cortical heat transfer safety and channel capacity limits.Open Acces

    The Retina in Health and Disease

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    Vision is the most important sense in higher mammals. The retina is the first step in visual processing and the window to the brain. It is not surprising that problems arising in the retina lead to moderate to severe visual impairments. We offer here a collection of reviews as well as original papers dealing with various aspects of retinal function as well as dysfunction. New approaches in retinal research are described, such as the expression and localization of the endocannabinoid system in the normal retina and the role of cannabinoid receptors that could offer new avenues of research in the development of potential treatments for retinal diseases. Moreover, new insights are offered in advancing knowledge towards the prevention and cure of visual pathologies, mainly AMD, RP, and diabetic retinopathy

    Self-Organized Criticality as a Neurodynamical Correlate of Consciousness: A neurophysiological approach to measure states of consciousness based on EEG-complexity features

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    Background and Objectives This thesis was based on the hypothesis that the physics-derived theoretical framework of self-organized criticality can be applied to the neuronal dynamics of the human brain. From a consciousness science perspective, this is especially appealing as critical brain dynamics imply a vicinity a phase transition, which is associated with optimized information processing functions as well as the largest repertoire of configurations that a system explores throughout its temporal evolution. Hence, self-organised criticality could serve as a neurodynamical correlate for consciousness, which provides the possibility of deriving empirically testable neurophysiological indices suitable to characterise and quantify states of consciousness. The purpose of this work was to experimentally examine the feasibility of the self-organized criticality theory as a correlate for states of consciousness. Therefore, it was aimed at answering the following research questions based on the analysis of three 64 channel EEG datasets: (i) Can signatures of self-organized criticality be found on the level of the EEG in terms of scale-free distribution of neuronal avalanches and the presence of long-range temporal correlations (LRTC) in neuronal oscillations? (ii) Are criticality features suitable to differentiate state of consciousness in the spectrum of wakefulness? (iii) Can the neuronal dynamics be shifted towards the critical point of a phase transition associated with optimized information processing function by mind-body interventions? (iv) Can an explicit relationship to other nonlinear complexity features and power spectral density parameter be identified? (v) Do EEG-based criticality features reflect individual temperament traits? Material and Methods (1): Re-analysis: Thirty participants highly proficient in meditation (mean age 47 years, 11 females/19 males, meditation experience of at least 5 years practice or more than 1000 h of total meditation time) were measured with 64-channel EEG during one session consisting of a task-free baseline resting, a reading condition and three meditation conditions, namely thoughtless emptiness, presence monitoring and focused attention. (2): 64-channel EEG was recorded from 34 participants (mean age 36.0 ±13.4 years, 24 females/ 10 males) before, during and after a professional singing bowl massage. Further, psychometric data was assessed including absorption capacity defined as the individual’s capacity for engaging attentional resources in sensory and imaginative experiences measured by the Tellegen-Absorption Scale (TAS-D), subjective changes in in body sensation, emotional state, and mental state (CSP-14) as well as the phenomenology of consciousness (PCI-K). (3): Electrophysiological data (64 channels of EEG, EOG, ECG, skin conductance, and respiration) was recorded from 116 participants (mean age 40.0 ±13.4 years, 83 females/ 33 males) – in collaboration with the Institute of Psychology, Bundeswehr University Munich - during a task-free baseline resting state. The individual level of sensory processing sensitivity was assessed using the High Sensitive Person Scale (HSPS-G). The datasets were analysed applying analytical tools from self-organized criticality theory (detrended fluctuation analysis, neuronal avalanche analysis), nonlinear complexity algorithms (multiscale entropy, Higuchi’s fractal dimension) and power spectral density. In study 1 and 2, task conditions were contrasted, and effect sizes were compared using a paired two-tailed t-test calculated across participants, and features. T-values were corrected for multiple testing using false discovery rate. To calculate correlations between the EEG features, Spearman’s rank correlation was applied after determining that the distribution was not appropriate for parametric testing by the Shapiro-Wilk test. In addition, in study 1, a discrimination analysis was carried out to determine the classification performance of the EEG features. Here, partial least squares regression and receiver operating characteristics analysis was applied. To determine whether the EEG features reflect individual temperament traits, the individual level of absorption capacity (study 2) and sensory processing sensitivity (study 3) was correlated with the EEG features using Spearman’s rank correlation. Results Signatures of self-organized criticality in the form of scale-free distribution of neuronal avalanches and long-range temporal correlations (LRTCs) in the amplitude of neural oscillations were observed in three distinct EEG-datasets. EEG criticality as well as complexity features were suitable to characterise distinct states of consciousness. In study 1, compared to the task-free resting condition, all three meditative states revealed significantly reduced long-range temporal correlation with moderate effect sizes (presence monitoring: d= -0.49, p<.001; thoughtless emptiness: d= -0.37, p<.001; and focused attention: d= -0.28, p=.003). The critical exponent was suitable to differentiate between focused attention and presence monitoring (d= -0.32, p=.02). Further, in study 2, the criticality features significantly changed during the course of the experiment, whereby values indicated a shift towards the critical regime during the sound condition. Both analyses of the first and second dataset revealed that the critical exponent was significantly negatively correlated with the sample entropy, the scaling exponent resulting from the DFA denoting the amount of long-range temporal correlations as well as Higuchi’s fractal dimension in each condition, respectively. In addition, the critical scaling exponent was found to be significantly negatively correlated with the trait absorption (Spearman's ρ= -0.39, p= .007), whereas an association between critical dynamics and the level of sensory processing sensitivity could not be verified (study 3). Conclusion The findings of this thesis suggest that neuronal dynamics are governed by the phenomena of self-organized criticality. EEG-based criticality features were shown to be sensitive to detect experimentally induced alterations in the state of consciousness. Further, an explicit relationship with nonlinear measures determining the degree of neuronal complexity was identified. Thus, self-organized criticality seems feasible as a neurodynamical correlate for consciousness with the potential to quantify and characterize states of consciousness. Its agreement with the current most influencing theories in the field of consciousness research is discussed

    Integrated information theory in complex neural systems

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    This thesis concerns Integrated Information Theory (IIT), a branch of information theory aimed at providing a fundamental theory of consciousness. At its core, lie two powerful intuitions: • That a system that is somehow more than the sum of its parts has non-zero integrated information, Φ; and • That a system with non-zero integrated information is conscious. The audacity of IIT’s claims about consciousness has (understandably) sparked vigorous criticism, and experimental evidence for IIT as a theory of consciousness remains scarce and indirect. Nevertheless, I argue that IIT still has merits as a theory of informational complexity within complexity science, leaving aside all claims about consciousness. In my work I follow this broad line of reasoning: showcasing applications where IIT yields rich analyses of complex systems, while critically examining its merits and limitations as a theory of consciousness. This thesis is divided in three parts. First, I describe three example applications of IIT to complex systems from the computational neuroscience literature (coupled oscillators, spiking neurons, and cellular automata), and develop novel Φ estimators to extend IIT’s range of applicability. Second, I show two important limitations of current IIT: that its axiomatic foundation is not specific enough to determine a unique measure of integrated information; and that available measures do not behave as predicted by the theory when applied to neurophysiological data. Finally, I present new theoretical developments aimed at alleviating some of IIT’s flaws. These are based on the concepts of partial information decomposition and lead to a unification of both theories, Integrated Information Decomposition, or ΦID. The thesis concludes with two experimental studies on M/EEG data, showing that a much simpler informational theory of consciousness – the entropic brain hypothesis – can yield valuable insight without the mathematical challenges brought by IIT.Open Acces

    Graphs behind data: A network-based approach to model different scenarios

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    openAl giorno d’oggi, i contesti che possono beneficiare di tecniche di estrazione della conoscenza a partire dai dati grezzi sono aumentati drasticamente. Di conseguenza, la definizione di modelli capaci di rappresentare e gestire dati altamente eterogenei è un argomento di ricerca molto dibattuto in letteratura. In questa tesi, proponiamo una soluzione per affrontare tale problema. In particolare, riteniamo che la teoria dei grafi, e più nello specifico le reti complesse, insieme ai suoi concetti ed approcci, possano rappresentare una valida soluzione. Infatti, noi crediamo che le reti complesse possano costituire un modello unico ed unificante per rappresentare e gestire dati altamente eterogenei. Sulla base di questa premessa, mostriamo come gli stessi concetti ed approcci abbiano la potenzialità di affrontare con successo molti problemi aperti in diversi contesti. ​Nowadays, the amount and variety of scenarios that can benefit from techniques for extracting and managing knowledge from raw data have dramatically increased. As a result, the search for models capable of ensuring the representation and management of highly heterogeneous data is a hot topic in the data science literature. In this thesis, we aim to propose a solution to address this issue. In particular, we believe that graphs, and more specifically complex networks, as well as the concepts and approaches associated with them, can represent a solution to the problem mentioned above. In fact, we believe that they can be a unique and unifying model to uniformly represent and handle extremely heterogeneous data. Based on this premise, we show how the same concepts and/or approach has the potential to address different open issues in different contexts. ​INGEGNERIA DELL'INFORMAZIONEopenVirgili, Luc

    The effects of DMT and associated psychedelics on the human mind and brain

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    This work presents seven investigations conducted with the aim to determine the effects of DMT (a compound which is able to cause remarkable effects in consciousness) and associated psychedelic drugs on the human brain and mind. Including a variety of neuroimaging (EEG, MEG and fMRI), phenomenological, psychometric and naturalistic research methods, these are the first controlled investigations of the impact of DMT in the human resting brain. Results revealed that DMT disrupted several brain mechanisms associated with top-down control (alpha power, integrity of high-level networks, modularity), increased measures related to entropy, or disorder (Lempel-Ziv complexity, novel pairwise connectivity) and immersive states of consciousness (delta/theta power), with some of these effects following the experiential trajectories of the DMT state. We also observed a significant temporal correlation between some of these effects (alpha power and default-mode network integrity fluctuations), which were supported by LSD effects of reduced feedback connectivity and neural adaption mechanisms. suggesting that the psychedelic brain state is one of reduced modularity, increased integration and functional plasticity. These findings were complemented by psychological studies showing that the DMT state is one of immersive visual imagery, intense somatic experiences and partial disconnection from the environment, which we found shared significant overlap with near- death experiences. DMT administration also resulted in positive mental health outcomes in healthy volunteers providing evidence for the first time that DMT may provide a useful alternative to currently- investigated psychedelic treatments. Finally, results from our last study performed in naturalistic environments revealed that psychedelics are able to have a transformative potential on core beliefs concerning the fundamental nature of reality and consciousness for up to 6 months, with important social and bioethical implications. Collectively these results attest to the strong impact that psychedelics have on varied human domains, which range experience, brain activity, mental health, intersubjectivity and beliefs.Open Acces

    Whole-Brain Models to Explore Altered States of Consciousness from the Bottom Up.

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    The scope of human consciousness includes states departing from what most of us experience as ordinary wakefulness. These altered states of consciousness constitute a prime opportunity to study how global changes in brain activity relate to different varieties of subjective experience. We consider the problem of explaining how global signatures of altered consciousness arise from the interplay between large-scale connectivity and local dynamical rules that can be traced to known properties of neural tissue. For this purpose, we advocate a research program aimed at bridging the gap between bottom-up generative models of whole-brain activity and the top-down signatures proposed by theories of consciousness. Throughout this paper, we define altered states of consciousness, discuss relevant signatures of consciousness observed in brain activity, and introduce whole-brain models to explore the biophysics of altered consciousness from the bottom-up. We discuss the potential of our proposal in view of the current state of the art, give specific examples of how this research agenda might play out, and emphasize how a systematic investigation of altered states of consciousness via bottom-up modeling may help us better understand the biophysical, informational, and dynamical underpinnings of consciousness

    Libro de actas. XXXV Congreso Anual de la Sociedad Española de Ingeniería Biomédica

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    596 p.CASEIB2017 vuelve a ser el foro de referencia a nivel nacional para el intercambio científico de conocimiento, experiencias y promoción de la I D i en Ingeniería Biomédica. Un punto de encuentro de científicos, profesionales de la industria, ingenieros biomédicos y profesionales clínicos interesados en las últimas novedades en investigación, educación y aplicación industrial y clínica de la ingeniería biomédica. En la presente edición, más de 160 trabajos de alto nivel científico serán presentados en áreas relevantes de la ingeniería biomédica, tales como: procesado de señal e imagen, instrumentación biomédica, telemedicina, modelado de sistemas biomédicos, sistemas inteligentes y sensores, robótica, planificación y simulación quirúrgica, biofotónica y biomateriales. Cabe destacar las sesiones dedicadas a la competición por el Premio José María Ferrero Corral, y la sesión de competición de alumnos de Grado en Ingeniería biomédica, que persiguen fomentar la participación de jóvenes estudiantes e investigadores
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