18 research outputs found

    Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology

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    The great behavioral heterogeneity observed between individuals with the same psychiatric disorder and even within one individual over time complicates both clinical practice and biomedical research. However, modern technologies are an exciting opportunity to improve behavioral characterization. Existing psychiatry methods that are qualitative or unscalable, such as patient surveys or clinical interviews, can now be collected at a greater capacity and analyzed to produce new quantitative measures. Furthermore, recent capabilities for continuous collection of passive sensor streams, such as phone GPS or smartwatch accelerometer, open avenues of novel questioning that were previously entirely unrealistic. Their temporally dense nature enables a cohesive study of real-time neural and behavioral signals. To develop comprehensive neurobiological models of psychiatric disease, it will be critical to first develop strong methods for behavioral quantification. There is huge potential in what can theoretically be captured by current technologies, but this in itself presents a large computational challenge -- one that will necessitate new data processing tools, new machine learning techniques, and ultimately a shift in how interdisciplinary work is conducted. In my thesis, I detail research projects that take different perspectives on digital psychiatry, subsequently tying ideas together with a concluding discussion on the future of the field. I also provide software infrastructure where relevant, with extensive documentation. Major contributions include scientific arguments and proof of concept results for daily free-form audio journals as an underappreciated psychiatry research datatype, as well as novel stability theorems and pilot empirical success for a proposed multi-area recurrent neural network architecture.Comment: PhD thesis cop

    Recent Advances in Signal Processing

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    The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

    Advancements and Breakthroughs in Ultrasound Imaging

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    Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world

    Astrocyte calcium activity mapping in behaving mice using anterograde axo-astrocytic AAV transfer

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    Astrocytes are considered active partners to neurons in information processing. Heterogeneous, bidirectional interaction between neurons and astrocytes alludes to circuit specific communication. However, the role of astrocytes in information processing, primarily established through ex vivo experiments, has been challenged by a series of controversies that highlighted the importance of studying astrocytes under fully physiological conditions in behaving mice. Astrocytes extend highly ramified processes that cradle synapses. They form functionally independent microdomains where they exhibit a rich repertoire of localized calcium signals. How astrocyte Ca²+ microdomain signals relate to neuronal activity and behaviour in vivo is still unclear. My objective was to investigate circuit specific, single-astrocyte Ca²+ microdomain activity in mice during behavioural states and sensory stimuli. I found that adeno-associated viruses (AAVs) can transfer anterogradely along thalamocortical projections to transduce cortical astrocytes and neurons. This axo-astrocytic AAV transfer enables the study of astrocytes and neurons embedded in specific neuronal circuits. Intersectional approaches, using anterograde axo-astrocytic AAV transfer in combination with membrane tagged genetically encoded calcium indicators (GECIs), enabled sparse, high contrast labelling of cortical astrocytes embedded in the somatosensory system of mice. Continuous imaging with two-photon microscopy of single astrocytes for about ≈1 hour combined with automatic, unbiased extraction of Ca²+ activity revealed a rich repertoire of subsecond, µm scale, localized Ca²+ signals. The number, size and duration of astrocytic Ca²+ signals were modulated with locomotion but not with intermittent whisker-touch stimulation. Locomotion and whisker stimuli evoked rapid [Ca²+]i elevation in thalamocortical axon boutons, whose activity was not correlated with nearby astrocyte Ca²+ microdomain signalling. Astrocyte fine processes exhibit heterogeneous, non-random, Ca²+ signalling patterns giving rise to hotspots of higher activity that are stable over time. Hotspot patterns allude to subcellular specialization. Our study: a) provides a new toolkit for studying neuron-astrocyte interactions within brain circuits, b) extends our understanding of astrocyte Ca²+ microdomain signalling and relationship to neuronal activity in behaving animals and c) suggests that there are astrocyte Ca²+ activity maps in the brain.Okinawa Institute of Science and Technology Graduate Universit

    Role of chondrolectin in motor axon development in zebrafish

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    Spinal Muscular Atrophy (SMA) is a childhood form of motor neuron disease (MND). It is monogenic, caused by loss of Smn1. Although Smn is ubiquitously expressed in all cells, and acts in a housekeeping function to correctly assemble the spliceosome, motor neurons are specifically vulnerable to loss of Smn. This leads to degeneration of the motor neurons. Mounting evidence shows that this degeneration starts at the neuromuscular junction and then proceeds to the motor axon. Studies have identified chondrolectin as a downstream gene adversely affected by the Smn deficiency. Chondrolectin is mis-spliced pre-symptomatically in mouse models of SMA. The protein is required for motor axon outgrowth in zebrafish, and over-expression of chondrolectin partially rescues smn knockdown. In this project, I generated a CRISPR/Cas9 knockout of chondrolectin in the zebrafish. The mutant is homozygous viable and has no gross morphological defects, but the primary motor axons phenocopy previously published morpholino knockdown. The axons are stalled at a choice point called the horizontal myoseptum, which is known to be rich in ECM proteins such as collagens. Using both acute manipulation and a stable transgenic line, I demonstrate that only full-length chondrolectin is able to rescue the axon length, and loss of any of its protein domains leaves the protein non-functional. From this, we hypothesise a mechanism of action for this protein where it binds an ECM molecule on the axon surface and transduces a signal via phosphorylation of its intracellular domain. We suggest the binding partner for chondrolectin to be collagenXIXa1. SMA has been linked to synaptic defects, therefore, I analysed the synaptic compartments of the axons in the chondrolectin mutant at embryonic and larval stages. At embryonic stages, the pre-synaptic compartment is enlarged around the horizontal myoseptum in the mutant compared to wild-types, and fully rescued in a stable chondrolectin-FLAG line. When the secondary motor neurons have also extended axons to innervate the myotome, there are fewer synaptic puncta in the mutant compared to wild-type. This synapse phenotype is partially rescued in the stable transgenic chondrolectin-FLAG line. Due to the stereotypical phenotype of the chondrolectin mutant, and its relationship with SMA, we developed a drug screening protocol to discover molecules that improve axon growth. We identified two hits after screening a 40-compound library. Developing this protocol allows refinement of potentially useful compounds from larger libraries to use in other models of SMA. Overall, this project offers new insight into the mechanism and function of chondrolectin, a gene linked to SMA. Novel findings include the domain analysis demonstrating that all protein domains are required for its function in zebrafish. We also found that loss of chondrolectin leads to synaptic defects, with embryonic synapse puncta enlarged in the mutant, and a loss of synaptic puncta in the larval stage. Finally, we have demonstrated potential translational uses for this mutant by designing a protocol to identify axon growth-enhancing compounds

    The radiological investigation of musculoskeletal tumours : chairperson's introduction

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    The Electrophysiology of Resting State fMRI Networks

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    Traditional research in neuroscience has studied the topography of specific brain functions largely by presenting stimuli or imposing tasks and measuring evoked brain activity. This paradigm has dominated neuroscience for 50 years. Recently, investigations of brain activity in the resting state, most frequently using functional magnetic resonance imaging (fMRI), have revealed spontaneous correlations within widely distributed brain regions known as resting state networks (RSNs). Variability in RSNs across individuals has found to systematically relate to numerous diseases as well as differences in cognitive performance within specific domains. However, the relationship between spontaneous fMRI activity and the underlying neurophysiology is not well understood. This thesis aims to combine invasive electrophysiology and resting state fMRI in human subjects to better understand the nature of spontaneous brain activity. First, we establish an approach to precisely coregister intra-cranial electrodes to fMRI data (Chapter 2). We then created a novel machine learning approach to define resting state networks in individual subjects (Chapter 3). This approach is validated with cortical stimulation in clinical electrocorticography (ECoG) patients (Chapter 4). Spontaneous ECoG data are then analyzed with respect to fMRI time-series and fMRI-defined RSNs in order to illustrate novel ECoG correlates of fMRI for both local field potentials and band-limited power (BLP) envelopes (Chapter 5). In Chapter 6, we show that the spectral specificity of these resting state ECoG correlates link classic brain rhythms with large-scale functional domains. Finally, in Chapter 7 we show that the frequencies and topographies of spontaneous ECoG correlations specifically recapitulate the spectral and spatial structure of task responses within individual subjects

    Infective/inflammatory disorders

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    A multimodal imaging approach for quantitative assessment of epilepsy

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    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
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