392 research outputs found
Analytical validation of innovative magneto-inertial outcomes: a controlled environment study.
peer reviewe
Data ethics : building trust : how digital technologies can serve humanity
Data is the magic word of the 21st century. As oil in the 20th century and electricity in the 19th century:
For citizens, data means support in daily life in almost all activities, from watch to laptop, from kitchen to car,
from mobile phone to politics. For business and politics, data means power, dominance, winning the race. Data can be used for good and bad,
for services and hacking, for medicine and arms race. How can we build trust in this complex and ambiguous data world?
How can digital technologies serve humanity? The 45 articles in this book represent a broad range of ethical reflections and recommendations
in eight sections: a) Values, Trust and Law, b) AI, Robots and Humans, c) Health and Neuroscience, d) Religions for Digital Justice, e) Farming, Business, Finance, f) Security, War, Peace, g) Data Governance, Geopolitics, h) Media, Education, Communication.
The authors and institutions come from all continents.
The book serves as reading material for teachers, students, policy makers, politicians, business, hospitals, NGOs and religious organisations alike. It is an invitation for dialogue, debate and building trust!
The book is a continuation of the volume “Cyber Ethics 4.0” published in 2018 by the same editors
Complexity Science in Human Change
This reprint encompasses fourteen contributions that offer avenues towards a better understanding of complex systems in human behavior. The phenomena studied here are generally pattern formation processes that originate in social interaction and psychotherapy. Several accounts are also given of the coordination in body movements and in physiological, neuronal and linguistic processes. A common denominator of such pattern formation is that complexity and entropy of the respective systems become reduced spontaneously, which is the hallmark of self-organization. The various methodological approaches of how to model such processes are presented in some detail. Results from the various methods are systematically compared and discussed. Among these approaches are algorithms for the quantification of synchrony by cross-correlational statistics, surrogate control procedures, recurrence mapping and network models.This volume offers an informative and sophisticated resource for scholars of human change, and as well for students at advanced levels, from graduate to post-doctoral. The reprint is multidisciplinary in nature, binding together the fields of medicine, psychology, physics, and neuroscience
Mismatch responses: Probing probabilistic inference in the brain
Sensory signals are governed by statistical regularities and carry valuable information about the unfolding of environmental events. The brain is thought to capitalize on the probabilistic nature of sequential inputs to infer on the underlying (hidden) dynamics driving sensory stimulation. Mis-match responses (MMRs) such as the mismatch negativity (MMN) and the P3 constitute prominent neuronal signatures which are increasingly interpreted as reflecting a mismatch between the current sensory input and the brain’s generative model of incoming stimuli. As such, MMRs might be viewed as signatures of probabilistic inference in the brain and their response dynamics can provide insights into the underlying computational principles. However, given the dominance of the auditory modality in MMR research, the specifics of brain responses to probabilistic sequences across sensory modalities and especially in the somatosensory domain are not well characterized.
The work presented here investigates MMRs across the auditory, visual and somatosensory modality by means of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). We designed probabilistic stimulus sequences to elicit and characterize MMRs and employed computational modeling of response dynamics to inspect different aspects of the brain’s generative model of the sensory environment. In the first study, we used a volatile roving stimulus paradigm to elicit somatosensory MMRs and performed single-trial modeling of EEG signals in sensor and source space. Model comparison suggested that responses reflect Bayesian inference based on the estimation of transition probability and limited information integration of the recent past in order to adapt to a changing environment. The results indicated that somatosensory MMRs reflect an initial mismatch between sensory input and model beliefs represented by confidence-corrected surprise (CS) followed by model adjustment dynamics represented by Bayesian surprise (BS). For the second and third study we designed a tri-modal roving stimulus paradigm to delineate modality specific and modality general features of mismatch processing. Computational modeling of EEG signals in study 2 suggested that single-trial dynamics reflect Bayesian inference based on estimation of uni-modal transition probabilities as well as cross-modal conditional dependencies. While early mismatch processing around the MMN tended to reflect CS, later MMRs around the P3 rather reflect BS, in correspondence to the somatosensory study. Finally, the fMRI results of study 3 showed that MMRs are generated by an interaction of modality specific regions in higher order sensory cortices and a modality general fronto-parietal network. Inferior parietal regions in particular were sensitive to expectation violations with respect to the cross-modal contingencies in the stimulus sequences. Overall, our results indicate that MMRs across the senses reflect processes of probabilistic inference in a complex and inherently multi-modal environment.Sensorische Signale sind durch statistische Regularitäten bestimmt und beinhalten wertvolle Informationen über die Entwicklung von Umweltereignissen. Es wird angenommen, dass das Gehirn die Wahrscheinlichkeitseigenschaften sequenzieller Reize nutzt um auf die zugrundeliegenden (verborgenen) Dynamiken zu schließen, welche sensorische Stimulation verursachen. Diskrepanz-Reaktionen ("Mismatch responses"; MMRs) wie die "mismatch negativity" (MMN) und die P3 sind bekannte neuronale Signaturen die vermehrt als Signale einer Diskrepanz zwischen der momentanen sensorischen Einspeisung und dem generativen Modell, welches das Gehirn von den eingehenden Reizen erstellt angesehen werden. Als solche können MMRs als Signaturen von wahrscheinlichkeitsbasierter Inferenz im Gehirn betrachtet werden und ihre Reaktionsdynamiken können Einblicke in die zugrundeliegenden komputationalen Prinzipien geben. Angesichts der Dominanz der auditorischen Modalität in der MMR-Forschung, sind allerdings die spezifischen Eigenschaften von Hirn-Reaktionen auf Wahrscheinlichkeitssequenzen über sensorische Modalitäten hinweg und vor allem in der somatosensorischen Modalität nicht gut charakterisiert.
Die hier vorgestellte Arbeit untersucht MMRs über die auditorische, visuelle und somatosensorische Modalität hinweg anhand von Elektroenzephalographie (EEG) und funktioneller Magnetresonanztomographie (fMRT). Wir gestalteten wahrscheinlichkeitsbasierte Reizsequenzen, um MMRs auszulösen und zu charakterisieren und verwendeten komputationale Modellierung der Reaktionsdynamiken, um verschiedene Aspekte des generativen Modells des Gehirns von der sensorischen Umwelt zu untersuchen. In der ersten Studie verwendeten wir ein volatiles "Roving-Stimulus"-Paradigma, um somatosensorische MMRs auszulösen und modellierten die Einzel-Proben der EEG-Signale im sensorischen und Quell-Raum. Modellvergleiche legten nahe, dass die Reaktionen Bayes’sche Inferenz abbilden, basierend auf der Schätzung von Transitionswahrscheinlichkeiten und limitierter Integration von Information der jüngsten Vergangenheit, welche eine Anpassung an Umweltänderungen ermöglicht. Die Ergebnisse legen nahe, dass somatosen-sorische MMRs eine initiale Diskrepanz zwischen sensorischer Einspeisung und Modellüberzeugung reflektieren welche durch "confidence-corrected surprise" (CS) repräsentiert ist, gefolgt von Modelanpassungsdynamiken repräsentiert von "Bayesian surprise" (BS). Für die zweite und dritte Studie haben wir ein Tri-Modales "Roving-Stimulus"-Paradigma gestaltet, um modalitätsspezifische und modalitätsübergreifende Eigenschaften von Diskrepanzprozessierung zu umreißen. Komputationale Modellierung von EEG-Signalen in Studie 2 legte nahe, dass Einzel-Proben Dynamiken Bayes’sche Inferenz abbilden, basierend auf der Schätzung von unimodalen Transitionswahrscheinlichkeiten sowie modalitätsübergreifenden bedingten Abhängigkeiten. Während frühe Diskrepanzprozessierung um die MMN dazu tendierten CS zu reflektieren, so reflektierten spätere MMRs um die P3 eher BS, in Übereinstimmung mit der somatosensorischen Studie. Abschließend zeigten die fMRT-Ergebnisse der Studie 3 dass MMRs durch eine Interaktion von modalitätsspezifischen Regionen in sensorischen Kortizes höherer Ordnung mit einem modalitätsübergreifenden fronto-parietalen Netzwerk generiert werden. Inferior parietale Regionen im Speziellen waren sensitiv gegenüber Erwartungsverstoß in Bezug auf die modalitätsübergreifenden Wahrscheinlichkeiten in den Reizsequenzen. Insgesamt weisen unsere Ergebnisse darauf hin, dass MMRs über die Sinne hinweg Prozesse von wahrscheinlichkeitsbasierter Inferenz in einer komplexen und inhärent multi-modalen Umwelt darstellen
Impact of Mild Traumatic Brain Injury and Orbitofrontal Cortex Lesion on Affective and Cognitive Brain Functions
Lievää aivovammaa kutsutaan joskus näkymättömäksi vammaksi. Lievän aivovamman saaneet toipuvat yleensä hyvin, mutta yllättävän suuri osa kärsii pitkittyneistä oireista, vaikka tavanomaiset kuvantamistutkimukset ja neuropsykologiset testitulokset näyttäytyvät normaaleina. Myös orbitofrontaalialueen vammaan liittyy ristiriita koettujen kognitiivisten ja tunne- elämän oireiden sekä objektiivisten neuropsykologisten löydösten välillä, vaikka vamma itsessään näkyy selkeästi jo tietokonetomografiakuvissa. Orbitofrontaalinen aivokuori integroi tunnepitoista tietoa toimintaamme ja alueen vaurio heikentää tätä prosessia. Oireet ja haasteet tunteiden ohjaamassa käytöksessä ja tunnesäätelyssä sekä tarkkaavuudessa ja toiminnanohjauksessa ovat yleisiä lievän aivovamman ja orbitofrontaalialueen vaurion jälkeen, mutta niille altistavat hermostolliset mekanismit tunnetaan huonosti.
Tämän väitöskirjan tavoitteena oli ymmärtää paremmin hermostollisia mekanismeja, jotka altistavat lievän aivovamman ja orbitofrontaalialueen vaurion saaneet kognitiivisille ja tunne-elämän oireille. Tavoitteena oli myös valottaa orbitofrontaalialueen roolia tunteiden ja tarkkaavuuden vuorovaikutuksessa ja aivovamman vaikutusta tähän. Tämän lisäksi pyrittiin kehittämään aivosähkökäyrään perustuva biomarkkeri lievälle aivovammalle.
Tutkimme lievän aivovamman saaneita (n = 27), orbitofrontaalialueen vaurion saaneita (n = 16) sekä terveitä verrokkeja. Vamman aiheuttamia muutoksia tarkasteltiin aivojen fysiologisten ilmiöiden, herätevasteiden (ERP) ja frontaalisen alfa-asymmetrian (FAA) avulla. Aivosähkökäyrää (EEG) mitattiin kognitiivisen tehtävän (Executive RT Test) suorituksen aikana. Tehtävässä piti joko reagoida (Go) tai olla reagoimatta (NoGo) ärsykkeisiin sääntöjen mukaan ja samanaikaisesti esitettiin uhkaavia tai neutraaleja ärsykkeitä, jotka olivat joko tehtävän kannalta oleellisia ärsykkeitä tai epäoleellisia häiriöärsykkeitä. Tarkkaavuutta ja kognitiivista kontrollia kuvaavia herätevasteita (N2, P3 ja N2P3) sekä tehtäväsuoritusta vertailtiin ryhmien välillä. Tutkimme myös heijastaisiko FAA ja kehittämämme uusi indeksi, uhkaärsykkeen muovaama FAA (eFAA), lievän aivovamman jälkeisiä oireita.
Lievän aivovamman saaneet raportoivat enemmän tunne-elämän oireita kuin verrokit. He myös reagoivat nopeammin uhkaärsykkeeseen sen ollessa vastaamisen kannalta oleellinen Go-tilanteessa ja tässä yhteydessä nähtiin suuremmat N2P3- amplitudit. NoGo-tilanteessa suuremmat N2P3-amplitudit nähtiin uhkaärsykkeen ollessa sekä tehtävän kannalta oleellinen ärsyke että epäoleellinen häiriöärsyke. Lievän aivovamman saaneilla havaittiin oikeavoittoinen FAA, joka heijastaa suhteellisesti suurempaa aktiviteettia oikealla otsalohkoalueella verrattuna vasempaan, ja tällainen aktivaation epäsuhta on liitetty masennusalttiuteen. eFAA kykeni erottamaan lievän aivovamman saaneista ne, joilla oli aivovamman jälkeisiä oireita ja korreloi subjektiivisten vamman jälkioireiden ja masennusoireiden kanssa.
Orbitofrontaalialueen vaurion saaneilla oli suuremmat N2P3-amplitudit tehtävän kannalta oleellisen uhkaärsykkeen yhteydessä. Uhkaärsykkeen ollessa epäoleellinen häiriöärsyke, orbitofrontaalisen vaurion saaneet eivät kyenneet suuntaamaan tarkkaavuusresursseja siihen nopeasti mutta tunneärsykkeen prosessointiin liittyvät myöhäiset herätevasteaallot olivat suurentuneet. Orbitofrontaalisen vaurion saaneilla oli enemmän vaikeuksia kognitiivisessa tehtävässä, jossa tunneärsyke oli aina epäoleellinen häiriöärsyke, mutta he jopa paransivat suoritustaan uhkaärsykkeen ollessa tehtävän kannalta oleellinen. He raportoivat myös verrokkeja enemmän toiminnanohjauksen vaikeuksia sekä aivovamman jälkeisiä oireita.
Tässä väitöskirjassa havaittiin muutoksia tunteiden, tarkkaavuuden ja toiminnanohjauksen vuorovaikutuksessa orbitofrontaalivaurioon ja lievään aivovammaan liittyen. Lievän aivovamman saaneet suuntasivat verrokkeja enemmän tarkkaavuutta sekä oleellisiin että epäoleellisiin uhkaärsykkeisiin kun taas orbitofrontaalisen vaurion saaneet kohdistivat verrokkeja enemmän tarkkaavuutta vain tehtävän kannalta oleellisiin uhkaärsykkeisiin. Ylikorostunut tarkkaavuuden suuntaaminen uhkaan voi altistaa tunne-elämän oireille ja masennukselle. On mahdollista, että havaitut muutokset heijastavat vaurioita etuotsalohkojen ja sen alaisten alueiden välisissä verkostoissa, jotka ovat tärkeitä tunnesäätelyssä. Orbitofrontaalialue osallistuu tarkkaavuuden tasapainottamiseen tehtävän kannalta oleellisten ja epäoleellisten ärsykkeiden välillä, ja vaurion myötä tarkkaavuuden suuntaaminen epäoleellisiin uhkaärsykkeisiin voi heikentyä. Kyvyttömyys integroida tunnepitoista informaatiota toimintaan voi aiheuttaa ongelmia jokapäiväisessä elämässä, ja voi selittää orbitofrontaalivaurioon liitettyjä haasteita.
Tämän väitöskirjan tulokset tuovat uutta tietoa orbitofrontaalialueen roolista tunteiden ja tarkkaavuuden vuorovaikutuksessa ja mahdollisista mekanismeista lievän aivovamman jälkeisten oireiden pitkittymisessä. Lopuksi esitellään myös uusi biomarkkeri eFAA, joka heijastaa aivovamman jälkeisiä muutoksia kognitiivisissa ja emotionaalisissa aivoverkostoissa.Mild traumatic brain injury (MTBI) is sometimes called an invisible injury. While normally subjects with MTBI recover well, remarkably many suffer from prolonged symptoms despite intact imaging and neuropsychological test results. A similar discrepancy between subjective cognitive and affective complaints and challenges in daily life, but intact neuropsychological test results are frequently encountered in patients with orbitofrontal cortex (OFC) lesion. The OFC has an important role in integrating emotional information into appropriate actions, with lesion to the OFC causing deficits in these processes. Symptoms and challenges related to alterations in emotion guided behaviors, emotion regulation, attention and executive functions (EFs) are common after MTBI and OFC injury, however, the underlying neural mechanisms predisposing to them remain unclear.
In this thesis, we aimed to better understand the neural mechanisms underlying affective and cognitive symptoms in MTBI and OFC injury. We also aimed at unraveling the role of intact OFC in the interplay between emotion and attention and the impact of OFC lesion and MTBI on it. In addition, we aimed to develop a novel electroencephalography (EEG) based biomarker of MTBI.
Subjects with MTBI (n = 27) and OFC lesion (n = 16) were included in the studies along with healthy control subjects. We used event-related potentials (ERPs) and frontal alpha asymmetry (FAA) to study injury-related alterations in brain physiology underlying emotion-attention interaction and cognitive control. EEG was recorded during a cognitive task (Executive RT Test), where threatening and neutral stimuli (task-relevant or irrelevant) were embedded into a Go-NoGo task. We assessed whether attention and cognitive control -related ERP components (N2, P3, N2P3) and task-performance differed between subjects with MTBI or OFC lesion and healthy controls. We further evaluated whether task-induced FAA and a novel index, emotional modulation of FAA (eFAA), reflect post-concussion symptoms (PCS) after MTBI. Self-report questionnaires of PCS, depression symptoms and EFs in daily life were collected to study correlations between symptom scores and objective physiological measures.
The MTBI group reported more emotional symptoms than control subjects. They also had larger N2P3 ERP amplitudes in context of task-relevant threat in the Go-condition coupled with faster reaction times. In the NoGo-condition, larger N2P3 amplitudes were detected both in context of task-relevant and task-irrelevant threat in the MTBI group, and more errors were committed with task-irrelevant threat. Further, rightward FAA i.e., relatively more activity on the right compared to the left frontal regions, a pattern associated with vulnerability to depression was observed in subjects with MTBI. eFAA distinguished subjects with MTBI and prolonged PCS from those without symptoms and from controls. Moreover, the eFAA obtained during the Executive RT Test was negatively correlated with subjective reports of depressive and post-concussion symptoms.
The OFC group exhibited larger N2P3 amplitudes in context with task-relevant threat in both Go- and NoGo-conditions. In contrast, when confronted with threat stimuli that were presented only as task-irrelevant distractors, they didn’t display an initial N2P3 increase but demonstrated pronounced late positive waves, indicative of emotional processing. The OFC group had generally worse task-performance when the task included emotional stimuli only as task-irrelevant distractors, whereas their task-performance seemed to slightly benefit from task-relevant threat. The OFC group also reported more challenges in EFs and more PCS than controls.
To summarize, we detected alterations in interactions of affective, attentional and EF processing after MTBI and OFC lesion. Subjects with MTBI allocated more attention to both task-irrelevant and task-relevant threat than controls while subjects with OFC lesion allocated more attention particularly to task-relevant threat. Alterations in attention to threat may contribute to emotional symptoms and vulnerability to depression in MTBI. We suggest the observed alterations reflect disruption of frontal-subcortical circuits subserving emotion-cognition interactions. The OFC is important in balancing voluntary and involuntary attention, especially in gearing attention to task-irrelevant emotion. Lesion to the OFC leads to challenges in swift allocation of attention to task-irrelevant but emotionally relevant stimuli. This may underlie some of the daily challenges experienced after OFC lesion, where timely evaluation of emotional stimuli and its appropriate integration into actions is crucial.
The results of this thesis shed new light on the role of OFC in attention to emotion and on the possible mechanisms of prolonged symptoms after MTBI. Finally, we introduce a novel biomarker for MTBI, known as eFAA, reflecting alterations in affective and cognitive brain circuits and functions due to brain injury
Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends
Understanding how different areas of the human brain communicate with each other is a crucial issue in neuroscience. The concepts of structural, functional and effective connectivity have been widely exploited to describe the human connectome, consisting of brain networks, their structural connections and functional interactions. Despite high-spatial-resolution imaging techniques such as functional magnetic resonance imaging (fMRI) being widely used to map this complex network of multiple interactions, electroencephalographic (EEG) recordings claim high temporal resolution and are thus perfectly suitable to describe either spatially distributed and temporally dynamic patterns of neural activation and connectivity. In this work, we provide a technical account and a categorization of the most-used data-driven approaches to assess brain-functional connectivity, intended as the study of the statistical dependencies between the recorded EEG signals. Different pairwise and multivariate, as well as directed and non-directed connectivity metrics are discussed with a pros-cons approach, in the time, frequency, and information-theoretic domains. The establishment of conceptual and mathematical relationships between metrics from these three frameworks, and the discussion of novel methodological approaches, will allow the reader to go deep into the problem of inferring functional connectivity in complex networks. Furthermore, emerging trends for the description of extended forms of connectivity (e.g., high-order interactions) are also discussed, along with graph-theory tools exploring the topological properties of the network of connections provided by the proposed metrics. Applications to EEG data are reviewed. In addition, the importance of source localization, and the impacts of signal acquisition and pre-processing techniques (e.g., filtering, source localization, and artifact rejection) on the connectivity estimates are recognized and discussed. By going through this review, the reader could delve deeply into the entire process of EEG pre-processing and analysis for the study of brain functional connectivity and learning, thereby exploiting novel methodologies and approaches to the problem of inferring connectivity within complex networks
Multimodal connectivity of the human basal forebrain
The cholinergic innervation of the cortex originates from neurons in the basal forebrain (BF) and plays a crucial role in cognitive processing. However, it is unclear how the organization of BF cholinergic neurons in the human brain is related to their functional and structural integration with the cortex. To address this, we have used high-resolution 7 Tesla diffusion and resting-state functional MRI to examine multimodal forebrain cholinergic connectivity with the neocortex in humans. Discrete parcellation analyses revealed that structural and functional parcellation broadly differentiated the anteromedial from posterolateral nuclei of BF. Next, we used gradient estimation to capture more fine-grained connectivity profile of the BF-cortical projectome and found moving from anteromedial to posterolateral BF, structural and functional gradients became progressively detethered, with the most pronounced dissimilarity localized in the nucleus basalis of Meynert (NbM). Additionally, functional but not structural connectivity with the BF grew stronger at shorter geodesic distances, with weakly myelinated transmodal cortical areas most strongly expressing this divergence. Moreover, [18F] FEOBV PET imaging was used to demonstrate that these transmodal cortical areas are also among the most densely innervated regions. This intrinsic BF cholinergic connectivity map of cortex was compared with meta-analytic connectivity map of cholinergic modulation on attention, demonstrating that patterns of brain activity evoked by directed attention are altered by pharmacological activation of acetylcholine (ACh) compared to placebo and these patterns spatially overlap with the intrinsic BF cholinergic connectivity map. Altogether, our findings provide new insights into how cholinergic signaling is organized in the human brain
Distinctive properties of biological neural networks and recent advances in bottom-up approaches toward a better biologically plausible neural network
Although it may appear infeasible and impractical, building artificial intelligence (AI) using a bottom-up approach based on the understanding of neuroscience is straightforward. The lack of a generalized governing principle for biological neural networks (BNNs) forces us to address this problem by converting piecemeal information on the diverse features of neurons, synapses, and neural circuits into AI. In this review, we described recent attempts to build a biologically plausible neural network by following neuroscientifically similar strategies of neural network optimization or by implanting the outcome of the optimization, such as the properties of single computational units and the characteristics of the network architecture. In addition, we proposed a formalism of the relationship between the set of objectives that neural networks attempt to achieve, and neural network classes categorized by how closely their architectural features resemble those of BNN. This formalism is expected to define the potential roles of top-down and bottom-up approaches for building a biologically plausible neural network and offer a map helping the navigation of the gap between neuroscience and AI engineering
Brain Computations and Connectivity [2nd edition]
This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read on the Oxford Academic platform and offered as a free PDF download from OUP and selected open access locations.
Brain Computations and Connectivity is about how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed.
The aim of this book is to elucidate what is computed in different brain systems; and to describe current biologically plausible computational approaches and models of how each of these brain systems computes.
Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions.
This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed, and updates by much new evidence including the connectivity of the human brain the earlier book: Rolls (2021) Brain Computations: What and How, Oxford University Press.
Brain Computations and Connectivity will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics
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