Liam P. Maguire

Liam P. Maguire
Ulster University · School of Computing and Intelligent Systems

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244
Publications
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Publications

Publications (244)
Article
Full-text available
Introduction: Hearing aid usage has been linked to improvements in cognition, communication, and socialization, but the extent to which it can affect the incidence and progression of dementia is unknown. Such research is vital given the high prevalence of dementia and hearing impairment in older adults, and the fact that both conditions often coex...
Article
Full-text available
Background: Systems Medicine is a novel approach to medicine, i.e. an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral...
Preprint
Full-text available
INTRODUCTION: Hearing aid usage has been linked to improvements in cognition, communication, and socialization, but the extent to which it can affect the incidence and progression of dementia is unknown. Such research is vital given the high prevalence of dementia and hearing impairment in older adults, and the fact that both conditions often coexi...
Chapter
Dementia is a collection of symptoms associated with impaired cognition and impedes everyday normal functioning. Dementia, with Alzheimer’s disease constituting its most common type, is highly complex in terms of etiology and pathophysiology. A more quantitative or computational attitude towards dementia research, or more generally in neurology, is...
Preprint
Full-text available
Dementia is a collection of symptoms associated with impaired cognition and impedes everyday normal functioning. Dementia, with Alzheimer's disease constituting its most common type, is highly complex in terms of etiology and pathophysiology. A more quantitative or computational attitude towards dementia research, or more generally in neurology, is...
Article
Full-text available
Despite resting state networks being associated with a variety of cognitive abilities, it remains unclear how these local areas act in concert to express particular cognitive operations. Theoretical and empirical accounts indicate that large-scale resting state networks reconcile dual tendencies towards integration and segregation by operating in a...
Article
Full-text available
Despite resting state networks being associated with a variety of cognitive abilities, it remains unclear how these local areas act in concert to express particular cognitive operations. Theoretical and empirical accounts indicate that large‐scale resting state networks reconcile dual tendencies towards integration and segregation by operating in a...
Preprint
Full-text available
Background Hearing aid usage has been linked to improvements in cognition, communication, and socialization, but the extent to which it can affect the onset and progression of dementia is unknown. This study leveraged the National Alzheimer's Coordinating Center Uniform Data Set to longitudinally examine the association between the use of hearing a...
Article
The bullwhip effect (BWE) is a phenomenon, which is caused by ineffective inventory decisions made by supply chain members. In addition to known inefficiencies caused by the bullwhip effect within a supply chain product flow, such as excessive inventory, it can also lead to inefficiencies in cash flow such as the cash flow bullwhip (CFB). The CFB r...
Article
Full-text available
Rapid serial visual presentation (RSVP) based brain-computer interfaces (BCIs) can detect target images among a continuous stream of rapidly presented images, by classifying a viewer’s event related potentials (ERPs) associated with the target and non-targets images. Whilst the majority of RSVP-BCI studies to date have concentrated on the identific...
Article
Artificial neural networks have been used as a powerful processing tool in various areas such as pattern recognition, control, robotics, and bioinformatics. Their wide applicability has encouraged researchers to improve artificial neural networks by investigating the biological brain. Neurological research has significantly progressed in recent yea...
Article
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Computerized clinical decision support systems can help to provide objective, standardized, and timely dementia diagnosis. However, current computerized systems are mainly based on group analysis, discrete classification of disease stages, or expensive and not readily accessible biomarkers, while current clinical practice relies relatively heavily...
Article
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Objectives: To describe the laboratory test ordering patterns by general practitioners (GPs) in Northern Ireland Western Health and Social Care Trust (WHSCT) and explore demographic and socioeconomic associations with test requesting. Design: Cross-sectional study. Setting: WHSCT, Northern Ireland. Participants: 55 WHSCT primary care medical practi...
Preprint
Full-text available
Despite resting state networks being associated with a variety of cognitive abilities, it remains unclear how these local areas act in concert to express particular cognitive operations. Theoretical and empirical accounts indicate that large-scale resting state networks reconcile dual tendencies toward integration and segregation by operating in a...
Preprint
Computerized clinical decision support systems can help to provide objective, standardized, and timely dementia diagnosis. However, current computerized systems are mainly based on the group analysis, discrete classification of disease stages, or expensive and not readily accessible biomarkers, while current clinical practice relies relatively heav...
Presentation
Aims & objectives: Despite increased efforts to develop prognostic models for dementia progression, current frameworks are limited mainly to methodologies that involve either single time point models or are heavily dependent on biomarkers. Our research project aims to develop a prognostic multiple time-point model for assessing the likelihood of de...
Presentation
Background: The growing public health threat posed by dementia raises the urgency to develop reliable prognostic frameworks for early detection of the disease. Moreover, recent direction for dementia drug discovery and development is pointing towards focusing on the pre-symptomatic stage, and hence identifying dementia risk becomes crucial. Despite...
Article
Full-text available
There is currently a lack of an efficient, objective and systemic approach towards the classification of Alzheimer's disease (AD), due to its complex etiology and pathogenesis. As AD is inherently dynamic, it is also not clear how the relationships among AD indicators vary over time. To address these issues, we propose a hybrid computational approa...
Presentation
Introduction: Dementia, with Alzheimer’s disease (AD) being its most common form, is one of the most important contributors to dependence and disability of older people and the focus of growing clinical research interest. Along with the intensive search for interventions that can modify progression of dementia symptoms, researchers investigate vari...
Article
There is a biological evidence to prove information is coded through precise timing of spikes in the brain. However, training a population of spiking neurons in a multilayer network to fire at multiple precise times remains a challenging task. Delay learning and the effect of a delay on weight learning in a spiking neural network (SNN) have not bee...
Article
Full-text available
Rapid serial visual presentation (RSVP) combined with the detection of event related brain responses facilitates the selection of relevant information contained in a stream of images presented rapidly to a human. Event related potentials (ERPs), measured non-invasively with electroencephalography (EEG), can be associated with infrequent target stim...
Article
Full-text available
Alzheimer’s disease (AD) and its prodromal state amnestic mild cognitive impairment (aMCI) are characterized by widespread abnormalities in inter-areal white matter fiber pathways and parallel disruption of default mode network (DMN) resting state functional and effective connectivity. In healthy subjects, DMN and task positive network interaction...
Article
Background: Breast cancer is the most prevalent cancer in women in most countries of the world. Many computer-aided diagnostic methods have been proposed, but there are few studies on quantitative discovery of probabilistic dependencies among breast cancer data features and identification of the contribution of each feature to breast cancer diagno...
Presentation
Background: With the implementation of computer technology in cognitive testing, there is a great potential to transform our approach to conduct psychological and functional assessments for detection of dementia. While the diagnosis of dementia in primary care is often difficult due to a lack of the necessary specialized knowledge of GPs and their...
Article
Full-text available
Magnetic resonance imaging (MRI) and positron emission tomography (PET) are neuroimaging modalities typically used for evaluating brain changes in Alzheimer’s disease (AD). Due to their complementary nature, their combination can provide more accurate AD diagnosis or prognosis. In this work, we apply a multi-modal imaging machine-learning framework...
Article
Full-text available
Existing energy monitoring tools do not offer to monitor the basic 3 C's of energy, i.e. cost ,carbon emission and more importantly direct energy consumption with its associated value added and auxiliary components in a single envelope during the entire hierarchy of a manufacturing process. The visualisation of these hierarchical KPIs, however, pos...
Article
Recent research has shown that a glial cell of astrocyte underpins a self-repair mechanism in the human brain, where spiking neurons provide direct and indirect feedbacks to presynaptic terminals. These feedbacks modulate the synaptic transmission probability of release (PR). When synaptic faults occur, the neuron becomes silent or near silent due...
Article
Spiking astrocyte-neuron networks (ANNs) have the potential to emulate the self-repair capability in the mammalian brain. Recent research has explored the mimicking of this capability in hardware with the aim to make electronic circuits autonomous with self-detection and repair. The provision of hardware architectures and interconnectivity between...
Article
This paper presents an enhanced rank-order-based learning algorithm, called SpikeTemp, for spiking neural networks (SNNs) with a dynamically adaptive structure. The trained feed-forward SNN consists of two layers of spiking neurons: 1) an encoding layer which temporally encodes real-valued features into spatio-temporal spike patterns and 2) an outp...
Conference Paper
Due to the increase of physical defects in advanced manufacturing processes, Networks-on-Chip (NoC) system reliability is a critical challenge as faults often occur post manufacturing. Therefore it is important to add fault tolerance to the NoC system. In this paper, a novel routing algorithm for 2D mesh NoCs is proposed which aims to enhance the f...
Conference Paper
This paper presents an Extended Delay Learning based Remote Supervised Method, called EDL, which extends the existing DL-ReSuMe learning method previously proposed by the authors for mapping spatio-temporal input spiking patterns into desired spike trains. EDL merges the weight adjustment property of STDP and anti-STDP with a delay shift method sim...
Conference Paper
This paper presents a new supervised learning algorithm (SpikeComp) with an adaptive compact structure for Spiking Neural Networks (SNNs). SpikeComp consists of two layers of spiking neurons: an encoding layer which temporally encodes real valued features into spatio-temporal spike patterns, and an output layer of dynamically grown neurons which pe...
Chapter
Confirming that synaptic loss is directly related to cognitive deficit in Alzheimer’s disease (AD) has been the focus of many studies. Compensation mechanisms counteract synaptic loss and prevent the catastrophic amnesia induced by synaptic loss via maintaining the activity levels of neural circuits. In this chapter we investigate the interplay bet...
Article
Full-text available
A novel adaptive routing algorithm – Efficient Dynamic Adaptive Routing (EDAR) is proposed to provide a fault-tolerant capability for Networks-on-Chip (NoC) via an efficient routing path selection mechanism. It is based on a weighted path selection strategy, which exploits the status of real-time NoC traffic made available via monitor modules. The...
Article
Robotic ecologies are systems made out of several robotic devices, including mobile robots, wireless sensors and e�ectors embedded in everyday environments, where they cooperate to achieve complex tasks. This paper demonstrates how endowing robotic ecologies with information processing algorithms such as perception, learning, planning, and novelty...
Article
Fault tolerance and adaptive capabilities are challenges for modern Networks-on-Chip (NoC) due to the increase in physical defects in advanced manufacturing processes. Two novel adaptive routing algorithms, namely coarse and fine-grained look-ahead algorithms, are proposed in this paper to enhance 2D mesh/torus NoC system fault-tolerant capabilitie...
Conference Paper
Spikes are an important part of information transmission between neurons in the biological brain. Biological evidence shows that information is carried in the timing of individual action potentials, rather than only the firing rate. Spiking neural networks are devised to capture more biological characteristics of the brain to construct more powerfu...
Article
Full-text available
Current approaches to networked robot systems (or ecology of robots and sensors) in ambient assisted living applications (AAL) rely on pre-programmed models of the environment and do not evolve to address novel states of the environment. Envisaged as part of a robotic ecology in an AAL environment to provide different services based on the events a...
Article
Recent research has shown the potential capability of spiking neural networks (SNNs) to model complex information processing in the brain. There is biological evidence to prove the use of the precise timing of spikes for information coding. However, the exact learning mechanism in which the neuron is trained to fire at precise times remains an open...
Article
In order to evaluate noise in a sound environment, it is necessary to estimate the sound levels at evaluation points based on the observations at a reference point. In this study, a method is derived based on the observations contaminated by a background noise to estimate system parameters reflecting several orders of correlation information betwee...
Article
The chapter is organised in two parts: In the first part, the focus is on a combined power spectral and non-linear behavioural analysis of a neural mass model of the thalamocortical circuitry. The objective is to study the effectiveness of such multi-modal analytical techniques in model-based studies investigating the neural correlates of abnormal...
Conference Paper
Modern Networks-on-Chip (NoC) have the capability to tolerate and adapt to the faults and failures in the hardware. Monitoring and debugging is a real challenge due to the NoC system complexity and large scale size. A key requirement is an evaluation and benchmarking mechanism to quantitatively analyse a NoC system's fault tolerant capability. A no...
Conference Paper
A key requirement for modern Networks-on-Chip (NoC) is the ability to detect and diagnose faults and failures. A novel approach is proposed which addresses the challenge of fault detection using an online mechanism. The approach minimises online intrusion by employing dynamic rates of testing to maximize NoC throughput while still ensuring sufficie...
Article
Novelty detection is especially important for monitoring safety-critical systems in which novel conditions rarely occur and knowledge about novelty in that system is often limited or unavailable. There are a large number of studies in the area of novelty detection, but there is a lack of a comprehensive experimental evaluation of existing novelty d...
Conference Paper
This paper proposes a new locally adaptive boundary evolution algorithm for level set methods (LSM)-based novelty detection. The proposed approach consists of level set function construction, boundary evolution, and evolution termination. It utilises the exterior data points lying outside the decision boundary to effect the segments of the boundary...
Article
Full-text available
The study presents a thalamocortical network model which oscillates within the alpha frequency band (8-13 Hz) as recorded in the wakeful relaxed state with closed eyes to study the neural causes of abnormal oscillatory activity in Alzheimer’s disease (AD). Incorporated within the model are various types of cortical excitatory and inhibitory neurons...
Conference Paper
Full-text available
In an ambient assisted living environment, raw data can often be very noisy making is difficulty to interrupt by a decision and reasoning system. To help reduce the effects of noise, we propose a decision and reasoning system which combines an interval fuzzy system and a self-organising fuzzy neural network (SOFNN) is presented in this paper. The m...
Article
This paper presents a level set boundary description (LSBD) approach for novelty detection that treats the nonlinear boundary directly in the input space. The proposed approach consists of level set function (LSF) construction, boundary evolution, and termination of the training process. It employs kernel density estimation to construct the LSF of...
Conference Paper
STDP is believed to play an important role in learning and memory. Additionally, experimental evidence shows that a few strong neural inputs can drive a neuron response and subsequently affect the learning of other inputs. Furthermore, recent studies have shown that local dendritic depolarization can impact STDP induction. This paper integrates the...
Article
A key requirement for modern Networks-on-Chip (NoC) is the ability to detect and diagnose faults and failures. This paper addresses the challenge of fault diagnosis using online testing where the interruption of the runtime operation (performance) under diagnosis is minimised. A novel Monitor Module (MM) is proposed to detect NoC interconnect fault...
Chapter
Functional magnetic resonance imaging (fMRI) is a technique to indirectly measure activity in the brain through the flow of blood. fMRI has been a powerful tool in helping us gain a better understanding of the human brain since it appeared over 20 years ago. However, fMRI poses many challenges for engineers. In particular, to detect and interpret t...
Chapter
Degeneration of cognitive functioning due to dementia is among the most important health problems in the ageing population and society today. Alzheimerʼs disease (AD) is the most common cause of dementia, affecting more than 5 Mio. people in Europe with the global prevalence of AD predicted to quadruple to 106 Mio. by 2050. This chapter is focused...
Article
The trust region method which originated from the Levenberg-Marquardt (LM) algorithm for mixed effect model estimation are considered in the context of second level functional magnetic resonance imaging (fMRI) data analysis. We first present the mathematical and optimization details of the method for the mixed effect model analysis, then we compare...
Conference Paper
This paper proposes a locally adaptive level set boundary description (LALSBD) method for novelty detection. The proposed method adjusts the nonlinear boundary directly in the input space and consists of a number of processes including level set function (LSF) construction, local boundary evolution and termination. It employs kernel density estimat...
Article
A context-aware cognitive system is a prime requirement for a sensor rich smart home environment. In this paper, we discuss the development and evaluation of a self-sustaining cognitive architecture for the RUBICON (Robotic UBIquitous COgnitive Network) system which builds its knowledge as per the environmental situations. The proposed cognitive ar...
Article
Based on the information processing functionalities of spiking neurons, hierarchical spiking neural networks are proposed to simulate visual attention. Using spiking neural networks inspired by the visual system, an image can be decomposed into multiple visual image components. Based on specific visual image components and image features, a visual...
Article
The focus of this paper is to correlate the bifurcation behaviour of a thalamocortical neural mass model with the power spectral alpha (8–13 Hz) oscillatory activity in Electroencephalography (EEG). The aim is to understand the neural correlates of alpha rhythm slowing (decrease in mean frequency of oscillation), a hallmark in the EEG of Alzheimer'...
Conference Paper
The problem of learning from imbalanced data is of critical importance in a large number of application domains and can be a bottleneck in the performance of various conventional learning methods that assume the data distribution to be balanced. The class imbalance problem corresponds to dealing with the situation where one class massively outnumbe...
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