Terahertz data processing for standoff detection of improvised explosive devices

Improvised Explosive Devices (IEDs) are homemade, non-conventional explosive devices, which are used to destruct and incapacitate individuals and property. IEDs are becoming a popular weapon of attack among terrorists and insurgents due to their easy of making and capability to cause major damage. Hence, is has become necessary to develop efficient systems for detecting and disarming these devices. The Terahertz technology which uses electromagnetic radiations between 0.3 THz to 10 THz for imaging is one of the most recently developed detection techniques and is ideally suitable for detection of IEDs and similar devices. Although a lot of work has been done for developing a standoff detection system for detecting IEDs using Terahertz imaging, it is still needed to develop advanced techniques for processing of the THz data. In this thesis, efficient signal processing techniques are developed for standoff, real time and wide area detection of IEDs. The signal processing algorithm is a two stage algorithm where the first stage is a preprocessing stage. In this stage, THz data from a large field is given to the correlation filters which detect hotspots in the field where an IED could be present. This stage avoids the computational burden of processing data from the entire field in the second stage. In the second stage, THz data from the hotspots of stage one are unmixed to find the individual explosive materials in each data point/pixel. The unmixing is done using a variant of the Independent Component Analysis algorithm which separates only the required component. Once the components are separated, they are analyzed to see if any of them matches an explosive. Thus, the presence of an IED or explosive can be accurately determined within the field --Abstract, page iii

Identifying Network Biomarkers for Each Breast Cancer Subtypes Along with Their Effective Single and Paired Repurposed Drugs Using Network-Based Machine Learning Techniques

Breast cancer is a complex disease that can be classified into at least 10 different molecular subtypes. Appropriate diagnosis of specific subtypes is critical for ensuring the best possible patient treatment and response to therapy. Current computational methods for determining the subtypes are based on identifying differentially expressed genes (i.e., biomarkers) that can best discriminate the subtypes. Such approaches, however, are known to be unreliable since they yield different biomarker sets when applied to data sets from different studies. Gathering knowledge about the functional relationship among genes will identify “network biomarkers” that will enrich the criteria for biomarker selection. Cancer network biomarkers are subnetworks of functionally related genes that “work in concert” to perform functions associated with a tumorigenic. We propose a machine learning framework that can be used to identify network biomarkers and driver genes for each specific breast cancer subtype. Our results show that the resulting network biomarkers can separate onesubtype from the others with very high accuracy. We also propose an integrated approach that can best capture knowledge (and complex relationships) contained within and between drugs, genes and disease data. A network-based machine learning approach is applied thereafter by using the extracted knowledge and relationships in order to identify single and pair of approved or experimental drugs with potential therapeutic effects on different breast cancer subtypes

Predictability of epileptic seizures by fusion of scalp EEG and fMRI

The systems for prediction of epileptic seizure investigated in recent years mainly rely on the traditional nonlinear analysis of the brain signals from intracranial electroencephalograph (EEG) recordings. The overall objective of this work focuses on investigation of the predictability of seizure from the scalp signals by applying effective blind source separation (BSS) techniques to scalp EEGs, in which the epileptic seizures are considered as independent components of the scalp EEGs. The ultimate goal of the work is to pave the way for epileptic seizure prediction from the scalp EEG. The main contributions of this research are summarized as follows. Firstly, a novel constrained topographic independent component analysis (CTICA) algorithm is developed for the improved separation of the epileptic seizure signals. The related CTICA model is more suitable for brain signal separation due to the relaxation of the independence assumption, as the source signals geometrically close to each other are assumed to have some dependencies. By incorporating the spatial and frequency information of seizure signals as the constraint, CTICA achieves a better performance in separating the seizure signals in comparison with other conventional ICA methods. Secondly, the predictability of seizure is investigated. The traditional method for quantification of the nonlinear dynamics of time series is employed to quantify the level of chaos of the estimated sources. The simultaneously recorded intracranial and scalp EEGs are used for the comparison of the results. The experiment results demonstrate that the separated seizure sources have a similar transition trend as those achieved from the intracranial EEGs. Thirdly, simultaneously recorded EEG and functional Magnetic Resonance Imaging (fMRI) is studied in order to validate the activated area of the brain related to the seizure sources. An effective method to remove the fMRI scanner artifacts from the scalp EEG is established by applying the blind source extraction (BSE) algorithm. The results show that the effect of fMRI scanner artifacts has been reduced in scalp EEG recordings. Finally, a data driven model, spatial ICA (SICA) subject to EEG as the temporal constraint is proposed in order to detect the Blood Oxygen-Level Dependence (BOLD) from the seizure fMRI. In contrast to the popular model driven method General Linear Model (GLM), SICA does not rely on any predefined hemodynamic response function. It is based on the fact that brain areas executing different tasks are spatially independent. Therefore SICA works perfectly for non-event-related fMRI analysis such as seizure fMRI. By incorporating the temporal information existing within the EEG as the constraint, the superiority of the proposed constrained SICA is validated in terms of better algorithm convergence and a higher correlation between the time courses of the component and the seizure EEG signals as compared to SICA

A multimodal imaging approach for quantitative assessment of epilepsy

Le tecniche di coregistrazione elettroencefalogramma-risonanza magnetica funzionale (EEG-fMRI) ed EEG ad alta densità (hdEEG) consentono di mappare attivazioni cerebrali anomale evocate da processi epilettici. L’EEG-fMRI è una tecnica di imaging non invasivo che permette la localizzazione delle variazioni del livello di ossigenazione nel sangue presente nelle regioni irritative (segnale BOLD). Diversamente, l’analisi di sorgente stima, a partire da un potenziale elettrico misurato sullo scalpo (EEG), la densità di corrente della sorgente elettrica a livello corticale producendo una plausibile localizzazione del dipolo nelle regioni irritative. Lo scopo di questa tesi è quello di sviluppare un approccio multimodale attraverso l’uso di dati di coregistrazione EEG-fMRI e hdEEG al fine di localizzare l’attività epilettica e verificare l’affidabilità sia dell’attivazione BOLD che della localizzazione della sorgente. Nel Capitolo I si introduce il concetto di approccio multimodale. Il capitolo è suddiviso principalmente in due parti: la prima descrive la tecnica di coregistrazione EEG-fMRI e la seconda la tecnica di localizzazione della sorgente in epilessia. La prima parte consiste in una breve analisi delle basi fisiologiche del dato di coregistrazione EEG-fMRI, nella descrizione di tecniche di registrazione simultanea e nell’introduzione del metodo convenzionale di analisi dei dati. Sono inoltre descritti problemi tecnici, problemi di sicurezza, modalità di scansione e strategie di rimozione degli artefatti EEG. È quindi presentata una panoramica sullo stato dell’arte delle coregistrazioni EEG-fMRI con discussione dei problemi aperti riguardanti l’analisi convenzionale. La seconda parte introduce i principi di base della stima delle sorgenti da dati hdEEG ed i loro limiti. Il primo capitolo fornisce un quadro generale, mentre i due capitoli successivi sono dedicati ad introdurre approcci di tipo diverso. Nell’analisi convenzionale di dati EEG-fMRI, l’apparizione di eventi interictali (IED) guida l’analisi dei dati fMRI. Il neurologo identifica gli intervalli degli eventi IED, che sono rappresentati da un’onda quadra, e successivamente questo protocollo viene convoluto con una risposta emodinamica (HRF) canonica per la costruzione di un modello o regressore da impiegare nell’analisi con modelli lineari generalizzati (GLM). I problemi principali dell’analisi convenzionale consistono nel fatto che essa non è automatica, ossia soffre di soggettività nella classificazione degli IED, e che, se la scelta dell’HRF non è ottimale, l’attivazione può essere sovra o sotto stimata. Il nuovo metodo proposto integra nell’analisi GLM convenzionale due nuove funzioni: il regressore basato sul segnale EEG (Capitolo II), e l’individuazione di una risposta emodinamica individual-based (ibHRF) (Capitolo III). Nel Capitolo IV le prestazioni del nuovo metodo per l’analisi di dati EEG-fMRI sono validate su dati in silico. A questo scopo sono stati creati dati fMRI simulati per testare la scelta dell’HRF ottima tra cinque modelli: quattro standard ed un modello HRF individual-based. Le prestazioni del metodo sono state valutate utilizzando come selezione il criterio di Akaike. Le simulazioni dimostrano la superiorità del nuovo metodo rispetto a quelli convenzionali e mostrano come la variazione del modello HRF influisce sui risultati dell’analisi statistica. Il Capitolo V introduce un criterio automatico volto a separare le componenti del segnale fMRI relative a network interni dal rumore. Dopo il processo di decomposizione probabilistico delle componenti indipendenti (PICA), si seleziona il numero ottimale di componenti applicando un nuovo algoritmo che tiene conto, per ciascuna componente, dei valori medi delle mappe spaziali di attivazione seguito da passaggi di clustering, segmentazione ed analisi spettrale. Confrontando i risultati dell’identificazione visiva dei network neuronali con i risultati di quella automatica, l’algoritmo mostra elevata accuratezza e precisione. In questo modo, il metodo di selezione automatica permette di separare ed individuare i network in stato di riposo, riducendo la soggettività nella valutazione delle componenti indipendenti. Nel Capitolo VI sono descritti il design sperimentale e l’analisi dei dati reali. Il capitolo illustra i risultati di dodici pazienti epilettici, concentrandosi sull’attività BOLD, sulla localizzazione della sorgente e sulla concordanza con il quadro clinico del paziente. Lo scopo è quello di applicare un approccio multimodale che combini tecniche non invasive di acquisizione ed analisi. Sequenze di EEG standard e fMRI sono acquisite nel corso della stessa sessione di scansione. L’analisi dei dati EEG-fMRI è eseguita utilizzando l’approccio GLM tradizionale, il nuovo approccio e l’analisi PICA. La sorgente dell’attività epilettica è stimata a partire da tracciati EEG a 256-canali. L’attivazione BOLD è confrontata con la ricostruzione della sorgente EEG. Questi risultati sono infine confrontati con l’attività epilettica definita da EEG standard ed esiti clinici. La combinazione di tecniche multimodali ed i loro rispettivi metodi di analisi sono strumenti utili per creare un workup prechirurgico completo dell’epilessia, fornendo diversi metodi di localizzazione dello stesso focolaio epilettico. L’approccio non invasivo di integrazione multimodale di dati EEG-fMRI e hdEEG sembra essere uno strumento molto promettente per lo studio delle scariche epilettiche.Electroencephalography-functional magnetic resonance imaging (EEG-fMRI) coregistration and high density EEG (hdEEG) can be combined to noninvasively map abnormal brain activation elicited by epileptic processes. EEG-fMRI can provide information on the pathophysiological processes underlying interictal activity, since the hemodynamic changes are a consequence of the abnormal neural activity generating interictal epileptiform discharges (IEDs). The source analysis estimates the current density of the source that generates a measured electric potential and it yields a plausible dipole localization of irritative regions. The aim of this thesis is to develop a multimodal approach with hdEEG and EEG-fMRI coregistration in order to localize the epileptic activity and to verify the reliability of source localization and BOLD activation. In Chapter I the multimodal approach is introduced. The chapter is divided in two main parts: the first is based on EEG-fMRI coregistration and the second on the source localization in epilepsy. The first part consists of a brief review of the physiologic basis of EEG and fMRI and the technical basics of simultaneous recording, examining the conventional method for EEG-fMRI data. Technical challenges, safety issues, scanning modalities and EEG artifact removal strategies are also described. An overview of the state of EEG-fMRI is presented and the open problems of conventional analysis are discussed. The second part introduces the basic principles of the source estimation from EEG data in epilepsy and their limitations. The first chapter provides a general framework. The next two are devoted to introduce different approaches. Conventional analysis of EEG-fMRI data relies on spike-timing of epileptic activity: the neurologist identifies the intervals of the IEDs events, as represented by a square wave; this protocol is then convolved with a canonical hemodynamic response function (HRF) to construct a model for the general linear model (GLM) analysis. There are limitations to the technique, however. The conventional analysis is not automatic, suffers of subjectivity in IEDs classification, and using a suboptimal HRF to model the BOLD response the activation map may result over or under estimated. The novel method purposed integrates in the conventional GLM two new features: the regressor based on the EEG signal (Chapter II) and the individual-based hemodynamic response function (ibHRF) (Chapter III). In Chapter IV the performance of the novel method of EEG-fMRI data was tested on in silico data. Simulated fMRI datasets were created and used for the choice of the optimal HRF among five models: four standard and an individual-based HRF models. The performance of the method was evaluated using the Akaike information criterion as selection. Simulations would demonstrate the superiority of the novel method compared with the conventional ones and assess how the variations in HRF model affect the results of the statistical analysis. Chapter V introduces an automatic criterion aiming to separate in fMRI data the signal related to an internal network from the noise. After the decomposition process (probabilistic independent component analysis [PICA]), the optimal number of components was selected by applying a novel algorithm which takes into account, for each component, the mean values of the spatial activation maps followed by clustering, segmentation and spectral analysis steps. Comparing visual and automatic identification of the neuronal networks, the algorithm demonstrated high accuracy and precision. Thus, the automatic selection method allows to separate and detect the resting state networks reducing the subjectivity of the independent component assessment. In Chapter VI experimental design and analysis on real data are described. The chapter focuses on BOLD activity, source localization and agreement with the clinical history of twelve epileptic patients. The scope is to apply a multimodal approach combining noninvasive techniques of acquisition and analysis. Standard EEG and fMRI data were acquired during a single scanning session. The analysis of EEG-fMRI data was performed by using both the conventional GLM, the new GLM and the PICA. Source localization of IEDs was performed using 256-channels hdEEG. BOLD localizations were then compared to the EEG source reconstruction and to the expected epileptic activity defined by standard EEG and clinical outcome. The combination of multimodal techniques and their respectively methods of analysis are useful tools in the presurgical workup of epilepsy providing different methods of localization of the same epileptic foci. Furthermore, the combined use of EEG-fMRI and hdEEG offers a new and more complete approach to the study of epilepsy and may play an increasingly important role in the evaluation of patients with refractory focal epilepsy

Information processing for mass spectrometry imaging

Mass Spectrometry Imaging (MSI) is a sensitive analytical tool for detecting and spatially localising thousands of ions generated across intact tissue samples. The datasets produced by MSI are large both in the number of measurements collected and the total data volume, which effectively prohibits manual analysis and interpretation. However, these datasets can provide insights into tissue composition and variation, and can help identify markers of health and disease, so the development of computational methods are required to aid their interpretation. To address the challenges of high dimensional data, randomised methods were explored for making data analysis tractable and were found to provide a powerful set of tools for applying automated analysis to MSI datasets. Random projections provided over 90% dimensionality reduction of MALDI MSI datasets, making them amenable to visualisation by image segmentation. Randomised basis construction was investigated for dimensionality reduction and data compression. Automated data analysis was developed that could be applied data compressed to 1% of its original size, including segmentation and factorisation, providing a direct route to the analysis and interpretation of MSI datasets. Evaluation of these methods alongside established dimensionality reduction pipelines on simulated and real-world datasets showed they could reproducibly extract the chemo-spatial patterns present