202 research outputs found
Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring
Artificially intelligent perception is increasingly present in the lives of
every one of us. Vehicles are no exception, (...) In the near future, pattern
recognition will have an even stronger role in vehicles, as self-driving cars
will require automated ways to understand what is happening around (and within)
them and act accordingly. (...) This doctoral work focused on advancing
in-vehicle sensing through the research of novel computer vision and pattern
recognition methodologies for both biometrics and wellbeing monitoring. The
main focus has been on electrocardiogram (ECG) biometrics, a trait well-known
for its potential for seamless driver monitoring. Major efforts were devoted to
achieving improved performance in identification and identity verification in
off-the-person scenarios, well-known for increased noise and variability. Here,
end-to-end deep learning ECG biometric solutions were proposed and important
topics were addressed such as cross-database and long-term performance,
waveform relevance through explainability, and interlead conversion. Face
biometrics, a natural complement to the ECG in seamless unconstrained
scenarios, was also studied in this work. The open challenges of masked face
recognition and interpretability in biometrics were tackled in an effort to
evolve towards algorithms that are more transparent, trustworthy, and robust to
significant occlusions. Within the topic of wellbeing monitoring, improved
solutions to multimodal emotion recognition in groups of people and
activity/violence recognition in in-vehicle scenarios were proposed. At last,
we also proposed a novel way to learn template security within end-to-end
models, dismissing additional separate encryption processes, and a
self-supervised learning approach tailored to sequential data, in order to
ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022
to the University of Port
The Cognitive and Neural Correlates of Rich and Vivid Memory for Real World Events
Episodic memories are composed of rich, perceptual details, and are re-experienced from a specific visual perspective. The aim of this thesis was to investigate the processes which allow us to remember in a rich and vivid way and the neural underpinnings of rich, successful retrieval. The behavioural studies conducted in Chapters 2 and 3 used a newly created video stimulus set, depicting real-world events. In Chapter 2, these stimuli were used to investigate retrieval differences following the encoding of unisensory (audio, visual), compared to multisensory (audio-visual) versions of the videos. Accuracy, vividness and amount of descriptive details retrieved were not positively affected by the presentation of multisensory stimuli. Chapter 3 compared the effects of encoding the videos from a field or an observer perspective on subsequent retrieval performance. No performance differences were observed when comparing the two perspectives, but observer memories contained a greater amount of sensory details, compared to field ones. Chapter 4 reviewed existing literature on the role of the angular gyrus in episodic memory retrieval and proposed that the angular gyrus is sensitive to the richness of recollected information and amount of details retrieved. This hypothesis was tested in an fMRI study in Chapter 5, focusing on the role of the angular gyrus in the retrieval of autobiographical memories. Results indeed demonstrated a positive relationship between angular gyrus activity and amount of details remembered. This association was seen for the retrieval of both episodic (specific) and semantic (categoric) events. This study also illustrated differential involvement of angular gyrus subregions, PGa and PGp in the retrieval of episodic and semantic memories. Taken together, these chapters outline behavioural processes and neural correlates that support our ability to retrieve memories in a rich and vivid manner, giving us a sense of re-living an event
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
Cortical and Brainstem Circuits Responsible for Pain Modulatory Responses in Healthy Humans
The human ability to regulate our own pain is governed by specific sites and circuits within the brain which can powerfully inhibit or enhance nociception. Placebo analgesia and nocebo hyperalgesia are the modulatory phenomena which leverage these circuits in the presence of a pharmacologically inert treatment to cause perceived changes in pain. The principal aim of this thesis was to utilize recent advancements in high field human brain imaging to assess the responsibility of descending pain-modulatory circuits within the brainstem, as well as the cortical connections which recruit these circuits in the generation of placebo analgesia and nocebo hyperalgesia.
Chapter 2 establishes the brainstem’s role in both phenomena. We utilized a response conditioning model and a brainstem-specific imaging pipeline to reveal how activation within discrete nuclei altered depending on the intensity of placebo and nocebo responses. Building on this work, Chapter 3 presents a dual network model of the human cortical sites which regulate brainstem output in the context of placebo analgesia. Relative to chapter 2, this work included a larger sample size, a higher placebo response rate, and analyses sensitive to how cortical connections to the brainstem change across time. Chapter 4 bridges function and biochemistry, circumventing limitations in functional magnetic resonance imaging by incorporating proton magnetic resonance spectroscopy (1H-MRS) to investigate how metabolite concentrations within the dorsolateral prefrontal cortex (dlPFC) - a primary node in the cortical pain system - play a role in the generation of placebo analgesia. I conclude by discussing the clinical and experimental implications of our three studies, with a focus on how further interrogation of the circuits revealed could aid and assist in the development of new approaches that treat chronic pain, by leveraging the neural mechanisms of placebo analgesia and nocebo hyperalgesia
The Ageing Brain: Exploring Corticocerebellar Network Contributions to Cognition Across the Lifespan
The dynamics of pattern matching in camouflaging cuttlefish
Many cephalopods escape detection using camouflage. This behaviour relies on a visual assessment of the surroundings, on an interpretation of visual-texture statistics and on matching these statistics using millions of skin chromatophores that are controlled by motoneurons located in the brain. Analysis of cuttlefish images proposed that camouflage patterns are low dimensional and categorizable into three pattern classes, built from a small repertoire of components. Behavioural experiments also indicated that, although camouflage requires vision, its execution does not require feedback, suggesting that motion within skin-pattern space is stereotyped and lacks the possibility of correction. Here, using quantitative methods, we studied camouflage in the cuttlefish Sepia officinalis as behavioural motion towards background matching in skin-pattern space. An analysis of hundreds of thousands of images over natural and artificial backgrounds revealed that the space of skin patterns is high-dimensional and that pattern matching is not stereotyped-each search meanders through skin-pattern space, decelerating and accelerating repeatedly before stabilizing. Chromatophores could be grouped into pattern components on the basis of their covariation during camouflaging. These components varied in shapes and sizes, and overlay one another. However, their identities varied even across transitions between identical skin-pattern pairs, indicating flexibility of implementation and absence of stereotypy. Components could also be differentiated by their sensitivity to spatial frequency. Finally, we compared camouflage to blanching, a skin-lightening reaction to threatening stimuli. Pattern motion during blanching was direct and fast, consistent with open-loop motion in low-dimensional pattern space, in contrast to that observed during camouflage.journal articl
Quantifying cognitive and mortality outcomes in older patients following acute illness using epidemiological and machine learning approaches
Introduction:
Cognitive and functional decompensation during acute illness in older people are poorly understood. It remains unclear how delirium, an acute confusional state reflective of cognitive decompensation, is contextualised by baseline premorbid cognition and relates to long-term adverse outcomes. High-dimensional machine learning offers a novel, feasible and enticing approach for stratifying acute illness in older people, improving treatment consistency while optimising future research design.
Methods:
Longitudinal associations were analysed from the Delirium and Population Health Informatics Cohort (DELPHIC) study, a prospective cohort ≥70 years resident in Camden, with cognitive and functional ascertainment at baseline and 2-year follow-up, and daily assessments during incident hospitalisation. Second, using routine clinical data from UCLH, I constructed an extreme gradient-boosted trees predicting 600-day mortality for unselected acute admissions of oldest-old patients with mechanistic inferences. Third, hierarchical agglomerative clustering was performed to demonstrate structure within DELPHIC participants, with predictive implications for survival and length of stay.
Results:
i. Delirium is associated with increased rates of cognitive decline and mortality risk, in a dose-dependent manner, with an interaction between baseline cognition and delirium exposure. Those with highest delirium exposure but also best premorbid cognition have the “most to lose”.
ii. High-dimensional multimodal machine learning models can predict mortality in oldest-old populations with 0.874 accuracy. The anterior cingulate and angular gyri, and extracranial soft tissue, are the highest contributory intracranial and extracranial features respectively.
iii. Clinically useful acute illness subtypes in older people can be described using longitudinal clinical, functional, and biochemical features.
Conclusions:
Interactions between baseline cognition and delirium exposure during acute illness in older patients result in divergent long-term adverse outcomes. Supervised machine learning can robustly predict mortality in in oldest-old patients, producing a valuable prognostication tool using routinely collected data, ready for clinical deployment. Preliminary findings suggest possible discernible subtypes within acute illness in older people
Recent Advances in Single-Particle Tracking: Experiment and Analysis
This Special Issue of Entropy, titled “Recent Advances in Single-Particle Tracking: Experiment and Analysis”, contains a collection of 13 papers concerning different aspects of single-particle tracking, a popular experimental technique that has deeply penetrated molecular biology and statistical and chemical physics. Presenting original research, yet written in an accessible style, this collection will be useful for both newcomers to the field and more experienced researchers looking for some reference. Several papers are written by authorities in the field, and the topics cover aspects of experimental setups, analytical methods of tracking data analysis, a machine learning approach to data and, finally, some more general issues related to diffusion
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