Corticostriatal connectivity fingerprints:Probability maps based on resting-state functional connectivity

Over the last decade, structure-function relationships have begun to encompass networks of brain areas rather than individual structures. For example, corticostriatal circuits have been associated with sensorimotor, limbic, and cognitive information processing, and damage to these circuits has been shown to produce unique behavioral outcomes in Autism, Parkinson's Disease, Schizophrenia and healthy ageing. However, it remains an open question how abnormal or absent connectivity can be detected at the individual level. Here, we provide a method for clustering gross morphological structures into subregions with unique functional connectivity fingerprints, and generate network probability maps usable as a baseline to compare individual cases against. We used connectivity metrics derived from resting-state fMRI (N = 100), in conjunction with hierarchical clustering methods, to parcellate the striatum into functionally distinct clusters. We identified three highly reproducible striatal subregions, across both hemispheres and in an independent replication dataset (N = 100) (dice-similarity values 0.40-1.00). Each striatal seed region resulted in a highly reproducible distinct connectivity fingerprint: the putamen showed predominant connectivity with cortical and cerebellar sensorimotor and language processing areas; the ventromedial striatum cluster had a distinct limbic connectivity pattern; the caudate showed predominant connectivity with the thalamus, frontal and occipital areas, and the cerebellum. Our corticostriatal probability maps agree with existing connectivity data in humans and non-human primates, and showed a high degree of replication. We believe that these maps offer an efficient tool to further advance hypothesis driven research and provide important guidance when investigating deviant connectivity in neurological patient populations suffering from e.g., stroke or cerebral palsy. Hum Brain Mapp 38:1478-1491, 2017. © 2016 Wiley Periodicals, Inc.status: publishe

A study of the effects of ageing on the characteristics of handwriting and signatures

The work presented in this thesis is focused on the understanding of factors that are unique to the elderly and their use of biometric systems. In particular, an investigation is carried out with a focus on the handwritten signature as the biometric modality of choice. This followed on from an in-depth analysis of various biometric modalities such as voice, fingerprint and face. This analysis aimed at investigating the inclusivity of and the policy guiding the use of biometrics by the elderly. Knowledge gained from extracted features of the handwritten signatures of the elderly shed more light on and exposed the uniqueness of some of these features in their ability to separate the elderly from the young. Consideration is also given to a comparative analysis of another handwriting task, that of copying text both in cursive and block capitals. It was discovered that there are features that are unique to each task. Insight into the human perceptual capability in inspecting signatures, in assessing complexity and in judging imitations was gained by analysing responses to practical scenarios that applied human perceptual judgement. Features extracted from a newly created database containing handwritten signatures donated by elderly subjects allowed the possibility of analysing the intra-class variations that exist within the elderly population

Metagenomic trajectory of gut microbiome in the human lifespan

Co-evolving with the human host, gut microbiota establishes configurations, which vary under the pressure of inflammation, disease, ageing, diet and lifestyle. In order to describe the multi-stability of the microbiome-host relationship, we studied specific tracts of the bacterial trajectory during the human lifespan and we characterized peculiar deviations from the hypothetical development, caused by disease, using molecular techniques, such as phylogenetic microarray and next-generation sequencing. Firstly, we characterized the enterocyte-associated microbiota in breast-fed infants and adults, describing remarkable differences between the two groups of subjects. Successively, we investigated the impact of atopy on the development of the microbiome in Italian childrens, highlithing conspicuous deviations from the child-type microbiota of the Italian controls. To explore variation in the gut microbiota depending on geographical origins, which reflect different lifestyles, we compared the phylogenetic diversity of the intestinal microbiota of the Hadza hunter-gatherers of Tanzania and Italian adults. Additionally, we characterized the aged-type microbiome, describing the changes occurred in the metabolic potential of the gut microbiota of centenarians with respect to younger individuals, as a part of the pathophysiolology of the ageing process. Finally, we evaluated the impact of a probiotics intervention on the intestinal microbiota of elderly people, showing the repair of some age-related dysbioses. These studies contribute to elucidate several aspects of the intestinal microbiome development during the human lifespan, depicting the microbiota as an extremely plastic entity, capable of being reconfigured in response to different environmental factors and/or stressors of endogenous origin

Computational Face Recognition Using Machine Learning Models

Faces are among the most complex stimuli that the human visual system processes. Growing commercial interest in face recognition is encouraging, but it also turns out to be a challenging endeavour. These challenges arise when the situations are complex and cause varied facial appearance due to e.g., occlusion, low-resolution, and ageing. The problem of computer-based face recognition using partial facial data is still largely an unexplored area of research and how does computer interpret various parts of the face. Another challenge is age progression and regression, which is considered to be the most revealing topic for understanding the human face changes during life. In this research, the various computational face recognition models are investigated to overcome the challenges posed by ageing and occlusions/partial faces. For partial face-based face recognition, a pre-trained VGGF model is employed for feature extraction and then followed by popular classifiers such as SVMs and Cosine Similarity CS for classification. In this framework, parts of faces such as eyes, nose, forehead, are used individually for training and testing. The results showing that there is an improvement in recognition in small parts, such as recognition rate in forehead enhanced form about 0% to nearly 35%, eyes from about 22% to approximately 65%. In the second framework, five sub-models were built based on Convolutional Neural Networks (CNNs) and those models are named Eyes-CNNs, Nose-CNNs, Mouth-CNNs, Forehead-CNNs, and combined EyesNose-CNNs. The experimental results illustrate a high recognition rate when it comes to small parts, for example, eyes increased up to about 90.83% and forehead reached about 44.5%. Furthermore, the challenge of face ageing is also approached by proposing an age-template based framework, generating an age-based face template for enhanced face generation and recognition. The results showing that generated new aged faces are more reliable comparing with state-of-the-art

Privacy-aware Biometric Blockchain based e-Passport System for Automatic Border Control

In the middle of 1990s, World Wide Web technology initially steps into our life. Now, 30 years after that, widespread internet access and established computing technology bring embodied real life into Metaverse by digital twin. Internet is not only blurring the concept of physical distance, but also blurring the edge between the real and virtual world. Another breakthrough in computing is the blockchain, which shifts the root of trust attached to a system administrator to the computational power of the system. Furthermore, its favourable properties such as immutable time-stamped transaction history and atomic smart contracts trigger the development of decentralized autonomous organizations (DAOs). Combining above two, this thesis presents a privacy-aware biometric Blockchain based e-passport system for automatic border control(ABC), which aims for improving the efficiency of existing ABC system. Specifically, through constructing a border control Metaverse DAO, border control workload can be autonomously self-executed by atomic smart contracts as transaction and then immutably recorded on Blockchain. What is more, to digitize border crossing documentation, biometric Blockchain based e-passport system(BBCVID) is created to generate an immutable real-world identity digital twin in the border control Metaverse DAO through Blockchain and biometric identity authentication. That is to say, by digitizing border crossing documentation and automatizing both biometric identity authentication and border crossing documentation verification, our proposal is able to significantly improve existing border control efficiency. Through system simulation and performance evaluation by Hyperledger Caliper, the proposed system turns out to be able to improve existing border control efficiency by 3.5 times more on average, which is remarkable. What is more, the dynamic digital twin constructed by BBCVID enables computing techniques such as machine learning and big data analysis applicable to real-world entity, which has a huge potential to create more value by constructing smarter ABC systems

Fingerprint Recognition for Children

This document is the final report of the Fingerprint Recognition Study of Children below the Age of 12 Years . The key findings of the study are: • Growth has limited influence on fingerprint recognition • Size (in terms of the dimensions of the relevant fingerprint characteristics) does not constitute any theoretical barrier for automated fingerprint recognition. • Image quality (in terms of low contrast and distortion effects) is the ultimate problem for child fingerprints, and image quality is strongly influenced by size. • Relevant quality metrics for fingerprints need revision with regard to the children case. • Isotropic growth model may serve as a good approximation to cover changes over time. • Alternative acquisition devices for fingerprints should be seriously considered in the future. Apart from these major findings, the report provides a number of recommendations to improve the process of acquiring fingerprints from children.JRC.G.7-Digital Citizen Securit

A Performance Assessment Framework for Mobile Biometrics

This project aims to develop and explore a robust framework for assessing biometric systems on mobile platforms, where data is often collected in non-constrained, potentially challenging environments. The framework enables the performance assessment given a particular platform, biometric modality, usage environment, user base and required security level. The ubiquity of mobile devices such as smartphones and tablets has increased access to Internet-based services across various scenarios and environments. Citizens use mobile platforms for an ever-expanding set of services and interactions, often transferring personal information, and conducting financial transactions. Accurate identity authentication for physical access to the device and service is, therefore, critical to ensure the security of the individual, information, and transaction. Biometrics provides an established alternative to conventional authentication methods. Mobile devices offer considerable opportunities to utilise biometric data from an enhanced range of sensors alongside temporal information on the use of the device itself. For example, cameras and dedicated fingerprint devices can capture front-line physiological biometric samples (already used for device log-on applications and payment authorisation schemes such as Apple Pay) alongside voice capture using conventional microphones. Understanding the performance of these biometric modalities is critical to assessing suitability for deployment. Providing a robust performance and security assessment given a set of deployment variables is critical to ensure appropriate security and accuracy. Conventional biometrics testing is typically performed in controlled, constrained environments that fail to encapsulate mobile systems' daily (and developing) use. This thesis aims to develop an understanding of biometric performance on mobile devices. The impact of different mobile platforms, and the range of environmental conditions in use, on biometrics' accuracy, usability, security, and utility is poorly understood. This project will also examine the application and performance of mobile biometrics when in motion

The Molecular-enriched Functional Circuits Underlying Consciousness and Cognition

Homo Sapiens consist of trillions of atoms, each inanimate, yet somehow collectively constituting a conscious being. The fundamental question of how organisms are organised to beget consciousness and cognition has largely been approached through independent examination of the structure and function of the nervous system at varying levels of granularity. As neuroscience progresses, it has thus increasingly fragmented into separate streams of research which study the brain at these different scales. This has resulted in the field becoming “data rich, but theory poor”, which is largely attributable to the paucity of methods which bridge these levels of analysis to provide novel trans-hierarchical insights and inform unified theories. The research in this doctoral thesis therefore aims to explore how a specific type of multimodal analysis - Receptor-Enriched Analysis of functional Connectivity by Targets (REACT) – can begin to bridge the theoretic void between molecular level mechanisms and systems levels dynamics to provide novel perspectives on the function and dysfunction of the brain. First, I provide a narrative synthesis of the challenges precluding a meaningful understanding of the human brain utilising conventional functional neuroimaging and outlining how incorporation of molecular information may help overcome these limitations. Specifically, by embedding functional dynamics in the molecular landscape of the brain, we can begin to move from the simple characterisation of “where” cognitive phenomena may be within the brain towards mechanistic accounts of “how” they are produced. Additionally, this offers enticing opportunities to link pharmacological treatments to novel molecular-network based biomarkers. Second, I explore how networks enriched with the spatial configurations of serotonergic and dopaminergic receptor subtypes are modulated by lysergic acid diethylamide (LSD) as compared to placebo in healthy participants. The results highlight the challenges of disentangling pharmacodynamics of drugs exhibiting rich pharmacology as well as identifying differential relationship between serotonergic and dopaminergic networks and phenomenological sub- components of psychedelic state. Third, I expand the remit of molecular-enriched network analyses beyond pure psychopharmacology to examine the direct and indirect actions of propofol anaesthesia on inhibitory and modulatory neurotransmission at both rest as well as during a naturalistic listening task. This work demonstrates for the first time that these molecular-networks can capture broader perceptual and cognitive-driven network reconfigurations as well as indirect pharmacological actions on neuromodulatory systems. Moreover, it provides evidence that the effects of propofol on consciousness are enacted through both direct inhibitory as well as indirect neuromodulatory mechanisms.Finally, I produce normative models of networks enriched with the principal neuromodulatory, excitatory, and inhibitory transmitter systems, testing their capacity to characterise neural dysfunction within and across several neuropsychiatric disorders. This work provides a computational foundation for large scale integration of molecular mechanisms and functional imaging to provide novel individualised biomarkers for neuropsychiatric disorders. Collectively, this thesis offers methodological and theoretical progress towards a trans-hierarchical characterisation of the human brain, providing insights into the neural correlates of both conscious contents and level as well as the perturbations underlying key neuropsychiatric conditions

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition