Depuis le milieu du XXème siècle, les techniques d’imagerie fonctionnelles ont un rôle grandissant dans notre compréhension sur les fonctions du cerveau en conditions physiologique et pathologique. Bien que l’IRMf fasse partie des techniques les plus communément utilisées pour l’imagerie du cerveau complet lors d’études préclinique et clinique, cette modalité souffre de sa résolution spatiotemporelle et sa sensibilité pour enregistrer finement les fonctions et activités cérébrales. Récemment l’imagerie fonctionnelle par ultrason (ifUS) a subi des développements permettant d’être complémentaires à l’IRMf ainsi qu’aux autres techniques d’imagerie cérébrales classiquement employées. Contrairement aux ultrasons focalisés conventionnels, l’imagerie hémodynamique proposé par l’ifUS repose sur une illumination ultrasonore plane permettant la détection des globules rouges en mouvement et la mesure de leur vitesse dans les micro-vaisseaux cérébraux. De ce fait, l’ifUS est indirectement lié à l’activité cérébrale d’où l’importance d’une meilleure compréhension des mécanismes du couplage neuro-vasculaire liant l’activité neuronale et les variations cérébrales d’apport en sang. De plus, cette technique a le potentiel pour fournir des informations précises sur les processus de certaines pathologies à la fois sur des modèles précliniques et chez l’homme. Dans un premier temps, j’exposerais mes travaux sur les récents développements techniques permettant l’ifUS in vivo (i) en condition chronique, (ii) sur l’animal éveillé, libre de mouvement et effectuant une tache comportementale et (iii) des vaisseaux capillaires chez le rongeur et l’homme. Dans un second temps, je démontrerais que l’ifUS in vivo peut fournir des informations nouvelles sur des pathologies telles que l’accident vasculaire cérébrale. / Since the middle of the 20th century, functional imaging technologies are making an increasing impact on our understanding on brain functions in both physiological and pathological conditions. Even if fMRI is nowadays one of the most used tool for whole brain imaging in pre-clinical and clinical studies, it lacks sufficient spatiotemporal resolution and sensitivity to assess fine brain function and activity. Functional ultrasound imaging (fUSi) has been recently developed and presents a potential to complement fMRI and other existing brain imaging modalities. Contrary to conventional ultrasound using focus beams, fUSi relies on hemodynamic imaging based on ultrasound plane-wave illumination to detect red blood cells movement and velocity in brain micro-vessels. Consequently, the fUSi signal is indirectly related to brain activity and it is therefore important to better understand the mechanisms of the neurovascular coupling linking neural activity and cerebral blood changes. Here again, fUSi may provide relevant information about disease processes in preclinical models but also in humans. First, I will present recent technical developments allowing in vivo fUSi (i) in chronic condition, (ii) in freely moving and behaving rats and (iii) in rodents and human brain capillaries. Second, I will demonstrate how fUSi could provide new insights in brain pathologies such as stroke.

Shi, Lin. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 171-185). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese.

Image segmentation is a process to identify regions of interest from digital images. Image segmentation plays an important role in medical image processing which enables a variety of clinical applications. It is also a tool to facilitate the detection of abnormalities such as cancerous lesions in the brain. Although numerous efforts in recent years have advanced this technique, no single approach solves the problem of segmentation for the large variety of image modalities existing today. Consequently, brain MRI segmentation remains a challenging task. The purpose of this thesis is to demonstrate brain MRI segmentation for delineation of tumors, ventricles and other anatomical structures using Insight Segmentation and Registration Toolkit (ITK) routines as the foundation. ITK is an open-source software system to support the Visible Human Project. Visible Human Project is the creation of complete, anatomically detailed, three-dimensional representations of the normal male and female human bodies. Currently under active development, ITK employs leading-edge segmentation and registration algorithms in two, three, and more dimensions. A goal of this thesis is to implement those algorithms to facilitate brain segmentation for a brain cancer research scientist.

Functional magnetic resonance imaging (fMRI) as it exists, in its many forms and vari- ants, has revolutionized the fields of neurology and psychology by revealing functional differences non-invasively. Although blood oxygenation level dependent (BOLD) fMRI is used interchangeably with fMRI, it measures one single difference in a phys- iological measurement using a set sequence. As such, there are other established changes in the brain that relate to blood movement and capacity that can also be measured using MRI. One measure, exogenous steady state cerebral blood volume, uses a bolus routine contrast agent administered intravenously alongside a pair of high resolution ‘structural-like’ MRI images to provide detailed information within small cortical and subcortical structures. In this thesis I design a semi-automated algorithm to generate maps of steady state exogenous cerebral blood volume magnetic resonance imaging datasets. To do this I developed an algorithm and tested it on existing MRI scanning protocols. A series of automated pre-processing steps are developed and tested, including automated scan flagging for artifacts and requisite vascular segmentation. Then, a methodology is developed to create cerebral blood volume (CBV) region of interest (ROI) masks that can then be applied on an existing database to test known CBV dysfunction in a group of patients at high risk for psychosis. Finally, we develop an experiment to see if template based cerebral blood alterations co-registered with class segmentation maps have any positive predictive value in determining disease state in a well characterized cohort of five age-matched groups in an Alzheimer’s disease neuroimaging study.

The current thesis addresses some of the unresolved predictions of recent models of the semantic brain system, such as the hub-and-spokes model. In particular, we tackle different aspects of the hypothesis that a widespread network of interacting heteromodal (hub(s)) and unimodal (spokes) cortices underlie semantic cognition. For this purpose, we use connectivity analyses, measures of graph theory and permutation-based statistics with source reconstructed Electro-/MagnetoEncephaloGraphy (EEG/MEG) data in order to track dynamic modulations of activity and connectivity within the semantic networks while a concept unfolds in the brain. Moreover, in order to obtain more accurate connectivity estimates of the semantic networks, we propose novel methods for some of the challenges associated with EEG/MEG connectivity analysis in source space. We utilised data-driven analyses of EEG/MEG recordings of visual word recognition paradigms and found that: 1) Bilateral Anterior Temporal Lobes (ATLs) acted as potential processor hubs for higher-level abstract representation of concepts. This was reflected in modulations of activity by multiple contrasts of semantic variables; 2) ATL and Angular Gyrus (AG) acted as potential integrator hubs for integration of information produced in distributed semantic areas. This was observed using Dynamic Causal Modelling of connectivity among the main left-hemispheric candidate hubs and modulations of functional connectivity of ATL and AG to semantic spokes by word concreteness. Furthermore, examining whole-brain connectomes using measures of graph theory revealed modules in the right ATL and parietal cortex as global hubs; 3) Brain oscillations associated with perception and action in low-level cortices, in particular Alpha and Gamma rhythms, were modulated in response to words with those sensory-motor attributes in the corresponding spokes, shedding light on the mechanism of semantic representations in spokes; 4) Three types of hub-hub, hub-spoke and spoke-spoke connectivity were found to underlie dynamic semantic graphs. Importantly, these results were obtained using novel approaches proposed to address two challenges associated with EEG/MEG connectivity. Firstly, in order to find the most suitable of several connectivity metrics, we utilised principal component analysis (PCA) to find commonalities and differences of those methods when applied to a dataset and identified the most suitable metric based on the maximum explained variance. Secondly, reconstruction of EEG/MEG connectomes using anatomical or fMRI-based parcellations can be significantly contaminated by spurious leakage-induced connections in source space. We, therefore, utilised cross-talk functions in order to optimise the number, size and locations of cortical parcels, obtaining EEG/MEG-adaptive parcellations. In summary, this thesis proposes approaches for optimising EEG/MEG connectivity analyses and applies them to provide the first empirical evidence regarding some of the core predictions of the hub-and-spokes model. The key findings support the general framework of the hub(s)-and-spokes, but also suggest modifications to the model, particularly regarding the definition of semantic hub(s).

The aim of this doctoral thesis was to study the influence of memory, pain, age and education on the regional cerebral blood flow (rCBF), i.e. brain function, in early Alzheimer's disease (AD) and in chronic neck pain patients in comparison to healthy controls and in healthy elderly per se. This was done by optimizing single photon emission computed tomography (SPECT) as a method to study rCBF with the tracer Technetium-99m (99mTc) hexamethylpropyleneamine oxime (HMPAO) and by matching all image data to a brain atlas before evaluation. The rCBF-SPECT was evaluated and developed to obtain higher diagnostic accuracy in AD and in chronic neck pain patients it was used to study basic pain related cerebral processes in chronic pain of different origin. A new semimanual registration method, based on fiducial marker, suitable for investigations with low spatial resolution was developed. The method was used to reconstruct images with an improved attenuation and scatter correction by using an attenuation-map calculated from the patients' previously acquired CT images. The influence of age and education on rCBF was evaluated with statistical parametric mapping, SPM in healthy elderly. The main findings were age related changes in rCBF in regions close to interlobar and interhemispheric space but not in regions typically affected in early AD, except for the medial temporal lobe. The theory of a 'cognitive reserve' in individuals with a longer education was supported with findings in the lateral temporal lobe, a region related to semantic memory, and in the frontal lobe. A cross-sectional study of chronic neck pain patients showed extensive rCBF changes in coping related regions in a non-traumatic pain patients compared to both healthy and a pain group with a traumatic origin, i.e. whiplash syndrome. The whiplash group displayed no significant differences in rCBF in comparison with the healthy controls. This suggests different pain mechanisms in these groups. The AD-patients showed a significantly lower rCBF in temporoparietal regions including left hippocampus. These changes were associated to episodic memory performance, and especially to face recognition. The diagnostic sensitivity for AD was high. The face recognition test (episodic memory) was used in AD patients to improve the sensitivity of method, i.e. memory-provoked rCBF-SPECT (MP-SPECT). The results were compared to healthy controls and the reductions of rCBF in temporoparietal regions were more pronounced in mild AD during provocation. Memory provocation increased the sensitivity of AD-related rCBF changes at group level. If a higher sensitivity for AD at the individual level is verified in future studies, a single MP-SPECT study might then be of help to set diagnosis earlier. In conclusion rCBF in temporoparietal regions are associated to an impaired episodic memory in early AD. Changes in these regions do not have a strong connection to chronological age. The diagnostic sensitivity of rCBF-SPECT in AD is high and there is a potentially higher sensitivity if memory provoked investigations are used. The findings in this thesis have given an increased knowledge of underlying cerebral pain processing in non-traumatic and traumatic (whiplash) neck pain. Preliminary results supporting the theory of 'cognitive reserve' by showing a correlation between long education and preserved rCBF was found in healthy elderly.

A combined view of the human brain and computer-generated virtual realities is motivated by recent developments in cognitive neuroscience and human-computer interaction (HCI). The emergence of new theories of human brain function, together with an increasing use of realistic human-computer interaction, give reason to believe that a better understanding of the relationship between human brains and virtual realities is both possible and valuable. The concept of “presence”, described as the subjective feeling of being in a place that feels real, can serve as a cornerstone concept in the development of such an understanding, as computer-generated presence is tightly related to how human brains work in virtual realities. In this thesis, presence is related both to theoretical discussions rooted in theories of human brain function, and to measurements of brain activity during realistic interaction. The practical implications of such results are further developed by considering potential applications. This includes the development and evaluation of a prototype application, motivated by presented principles. The theoretical conception of presence in this thesis relies on general principles of brain function, and describes presence as a general cognitive function, not specifically related to virtual realities. Virtual reality (VR) is an excellent technology for investigating and taking advantage of all aspects of presence, but a more general interpretation allows the same principles to be applied to a wide range of applications. Functional magnetic resonance imaging (fMRI) was used to study the working human brain in VR. Such data can inform and constrain further discussion about presence. Using two different experimental designs we have investigated both the effect of basic aspects of VR interaction, as well as the neural correlates of disrupted presence in a naturalistic environment. Reality-based brain-computer interaction (RBBCI) is suggested as a concept for summarizing the motivations for, and the context of, applications building on an understanding of human brains in virtual realities. The RBBCI prototype application we developed did not achieve the set goals, but much remains to be investigated and lessons from our evaluation point to possible ways forward. A developed use of methods and techniques from computer gaming is of particular interest. / Ett kombinerat perspektiv på den mänskliga hjärnan och datorgenererade virtuella verkligheter motiveras av den senaste utvecklingen inom kognitiv neurovetenskap och människa-datorinteraktion (MDI). Framväxten av nya teorier om den mänskliga hjärnan, tillsammans med en ökande användning av realistisk människa-datorinteraktion, gör det troligt att en bättre förståelse för relationen mellan mänskliga hjärnor och virtuella verkligheter är både möjlig och värdefull. Begreppet "närvaro", som i detta sammanhang beskrivs som den subjektiva känslan av att vara på en plats som känns verklig, kan fungera som en hörnsten i utvecklingen av en sådan förståelse, då datorgenererad närvaro är tätt kopplat till hur mänskliga hjärnor fungerar i virtuella verkligheter. I denna avhandling kopplas närvaro både till teoretiska diskussioner grundade i teorier om den mänskliga hjärnan, och till mätningar av hjärnans aktivitet under realistisk interaktion. De praktiska konsekvenserna av sådana resultat utvecklas vidare med en närmare titt på potentiella tillämpningar. Detta inkluderar utveckling och utvärdering av en prototypapplikation, motiverad av de presenterade principerna. Den teoretiska diskussionen av närvaro i denna avhandling bygger på allmänna principer för hjärnans funktion, och beskriver känslan av närvaro som en generell kognitiv funktion, inte specifikt relaterad till virtuella verkligheter. Virtuell verklighet (virtual reality, VR) är en utmärkt teknik för att undersöka och dra nytta av alla aspekter av närvaro, men en mer allmän tolkning gör att samma principer kan tillämpas på ett brett spektrum av applikationer. Funktionell hjärnavbildning (fMRI) användes för att studera den arbetande mänskliga hjärnan i VR. Sådant data kan informera och begränsa en vidare diskussion av närvaro. Med hjälp av två olika försöksdesigner har vi har undersökt både effekten av grundläggande aspekter av VR-interaktion, och neurala korrelat av störd närvaro i en naturalistisk miljö. Verklighets-baserad hjärna-dator interaktion (reality-based brain-computer interaction, RBBCI) föreslås som ett begrepp för att sammanfatta motiv och kontext för applikationer som bygger på en förståelse av den mänskliga hjärnan i virtuella verkligheter. Den prototypapplikation vi utvecklade uppnådde inte de uppsatta målen, men mycket återstår att utforska och lärdomar från vår utvärdering pekar på möjliga vägar framåt. En vidare användning av metoder och tekniker från dataspel är speciellt intressant.

<p>Mice have become the preferred model system for studying brain function and disease. With the powerful genetic tools available, mouse models can be created to study the underlying molecular basis of neurobiology in vivo. Just as magnetic resonance imaging is the dominant tool for evaluating the human brain, high-resolution MRI--magnetic resonance microscopy (MRM)--is a useful tool for studying the brain of mouse models. However, the need for high spatial resolution limits the signal-to-noise ratio (SNR) of the MRM images. To address this problem, T1-shortening contrast agents can be used, which not only improve the tissue contrast-to-noise ratio (CNR) but also increase SNR by allowing the MR signal to recover faster between pulses. By "actively staining" the tissue with these T1-shortening agents, MRM can be performed with higher resolution, greater contrast, and shorter scan times. In this work, active staining with T1-shortening agents was used to enhance three types of in vivo mouse brain MRM: (1) angiographic imaging of the neurovasculature, (2) anatomical imaging of the brain parenchyma, and (3) functional imaging of neuronal activity.</p> <p></p> <p>For magnetic resonance angiography (MRA) of the mouse, typical contrast agents are not useful because they are quickly cleared by the body and/or extravasate from the blood pool before a high-resolution image can be acquired. To address these limitations, a novel contrast agent--SC-Gd liposomes--has been developed, which is cleared slowly by the body and is too large to extravasate from the blood pool. In this work, MRA protocols were optimized for both the standard technique (time-of-flight contrast) and SC-Gd liposomes. When the blood was stained with SC-Gd liposomes, small vessel CNR improved to 250% that of time-of-flight. The SC-Gd liposomes could also be used to reduce scan time by 75% while still improving CNR by 32%.</p> <p>For MRM of the mouse brain parenchyma, active staining has been used to make dramatic improvements in the imaging of ex vivo specimens. However for in vivo imaging, the blood-brain barrier (BBB) prevents T1-shortening agents from entering the brain parenchyma. In this work, a noninvasive technique was developed for BBB opening with microbubbles and ultrasound (BOMUS). Using BOMUS, the parenchyma of the brain could be actively stained with the T1-shortening contrast agent, Gd-DTPA, and MRM images could be acquired in vivo with unprecedented resolution (52 x 52 x 100 micrometers3) in less than 1 hour.</p> <p>Functional MRI (fMRI), which uses blood oxygen level dependant (BOLD) contrast to detect neuronal activity, has been a revolutionary technique for studying brain function in humans. However, in mice, BOLD contrast has been difficult to detect and thus routine fMRI in mice has not been feasible. An alternative approach for detecting neuronal activity uses manganese (Mn2+). Mn2+ is a T1-shortening agent that can enter depolarized neurons via calcium channels. Thus, Mn2+ is a functional contrast agent with affinity for active neurons. In this work, Mn2+ (administered with the BOMUS technique) was used to map the neuronal response to stimulation of the vibrissae. The resultant activation map showed close agreement to published maps of the posterior-lateral and anterior-medial barrel field of the primary sensory cortex.</p> <p>The use of T1-shortening agents to actively stain tissues of interest--blood, brain parenchyma, or active neurons--will facilitate the use of MRM for studying mouse models of brain development, function, and disease.</p> / Dissertation

Photoacoustic tomography (PAT), as well as thermoacoustic tomography (TAT), utilize electromagnetic radiation in its visible, near infrared, microwave, and radiofrequency forms, respectively, to induce acoustic waves in biological tissues for imaging purposes. Combining the advantages of both the high image contrast that results from electromagnetic absorption and the high resolution of ultrasound imaging, these new imaging modalities could be the next successful imaging techniques in biomedical applications. Basic research on PAT and TAT, and the relevant physics, is presented in Chapter I. In Chapter II, we investigate the imaging mechanisms of TAT in terms of signal generation, propagation and detection. We present a theoretical analysis as well as simulations of such imaging characteristics as contrast and resolution, accompanied by experimental results from phantom and tissue samples. In Chapter III, we discuss the further application of TAT to the imaging of biological tissues. The microwave absorption difference in normal and cancerous breast tissues, as well as its influence on thermoacoustic wave generation and the resulting transducer response, is investigated over a wide range of electromagnetic frequencies and depths of tumor locations. In Chapter IV, we describe the mechanism of PAT and the algorithm used for image reconstruction. Because of the broad bandwidth of the laser-induced ultrasonic waves and the limited bandwidth of the single transducer, multiple ultrasonic transducers, each with a different central frequency, are employed for simultaneous detection. Chapter V further demonstrates PAT’s ability to image vascular structures in biological tissue based on blood’s strong light absorption capability. The photoacoustic images of rat brain tumors in this study clearly reveal the angiogenesis that is associated with tumors. In Chapter VI, we report on further developing PAT to image deeply embedded optical heterogeneity in biological tissues. The improved imaging ability is attributed to better penetration by NIR light, the use of the optical contrast agent ICG (indocyanine green) and a new detection scheme of a circular scanning configuration. Deep penetrating PAT, which is based on a tissue’s intrinsic contrast using laser light of 532 nm green light and 1.06 µm near infrared light, is also presented.

Includes vita. / Title from PDF of title page. / Document formatted into pages; contains 222 pages. / Includes bibliographical references. / Text (Electronic thesis) in PDF format. / ABSTRACT: This dissertation presents a knowledge-guided expert system that is capable of applying routinesfor multispectral analysis, (un)supervised clustering, and basic image processing to automatically detect and segment brain tissue abnormalities, and then label glioblastoma-multiforme brain tumors in magnetic resonance volumes of the human brain. The magnetic resonance images used here consist of three feature images (T1-weighted, proton density, T2-weighted) and the system is designed to be independent of a particular scanning protocol. Separate, but contiguous 2D slices in the transaxial plane form a brain volume. This allows complete tumor volumes to be measured and if repeat scans are taken over time, the system may be used to monitor tumor response to past treatments and aid in the planning of future treatment. Furthermore, once processing begins, the system is completely unsupervised, thus avoiding the problems of human variability found in supervised segmentation efforts.Each slice is initially segmented by an unsupervised fuzzy c-means algorithm. The segmented image, along with its respective cluster centers, is then analyzed by a rule-based expert system which iteratively locates tissues of interest based on the hierarchy of cluster centers in feature space. Model-based recognition techniques analyze tissues of interest by searching for expected characteristics and comparing those found with previously defined qualitative models. Normal/abnormal classification is performed through a default reasoning method: if a significant model deviation is found, the slice is considered abnormal. Otherwise, the slice is considered normal. Tumor segmentation in abnormal slices begins with multispectral histogram analysis and thresholding to separate suspected tumor from the rest of the intra-cranial region. The tumor is then refined with a variant of seed growing, followed by spatial component analysis and a final thresholding step to remove non-tumor pixels.The knowledge used in this system was extracted from general principles of magnetic resonance imaging, the distributions of individual voxels and cluster centers in feature space, and anatomical information. Knowledge is used both for single slice processing and information propagation between slices. A standard rule-based expert system shell (CLIPS) was modified to include the multispectral analysis, clustering, and image processing tools.A total of sixty-three volume data sets from eight patients and seventeen volunteers (four with and thirteen without gadolinium enhancement) were acquired from a single magnetic resonance imaging system with slightly varying scanning protocols were available for processing. All volumes were processed for normal/abnormal classification. Tumor segmentation was performed on the abnormal slices and the results were compared with a radiologist-labeled ground truth' tumor volume and tumor segmentations created by applying supervised k-nearest neighbors, a partially supervised variant of the fuzzy c-means clustering algorithm, and a commercially available seed growing package. The results of the developed automatic system generally correspond well to ground truth, both on a per slice basis and more importantly in tracking total tumor volume during treatment over time. / System requirements: World Wide Web browser and PDF reader. / Mode of access: World Wide Web.